Face Expression Recognition: A Review
Authors:- M.Tech. Student Palak Jain, Assistant professor Anamika Pyasi
Abstract:- Recognition of outward appearances assumes a significant job in many mechanized framework applications like mechanical technology, instruction, man-made brainpower, and security. Perceiving outward appearances precisely is testing. Approaches for explaining FERi(Facial Expression Recognition) issue can be ordered into 1) Static single pictures and 2) Image successions. Customarily, various procedures like Multi-layer Perceptron Model, k-Nearest Neighbors, Support Vector Machines were utilized by scientists for fathoming FER. These techniques removed highlights like Local Binary Patterns, Eigenfaces, Face-milestone highlights, and Texture highlights. Among every one of these techniques, Neural Networks have increased especially ubiquity and they are broadly utilized for FER. As of late, CNNsi(Convolutional Neural Networks) have picked up notoriety in field of profound learning in view of their easygoing engineering and capacity to give great outcomes without prerequisite of manual component extraction from crude picture information. This paper centers around review of different face demeanor acknowledgment methods dependent on CNN. It incorporates cutting edge techniques proposed by various scientists. The paper additionally shows steps required for utilization of CNN for FER. This paper additionally incorporates examination of CNN based methodologies and issues requiring consideration while picking CNN for unraveling FER.
Income Analysis of Census Database Using Pyspark
Authors:- Abhishek R., Pavan Dutt, Sahana S. R., Assistant Professor Dr.M.Sujithra M.C.A,M.Phil,Ph.D, Assistant Professor Dr.P.Velvadivu M.C.A.,M.Phil.,Ph.D.
Abstract:-The data set contains information about every individual’s age, education level and various other features from the census along with the income . The income feature is a categorical feature with two classes i.e., less than 50k dollars or greater than 50k dollars. The problem is to build a machine learning model that could effectively predict the income of people given the input features. Considering the size of the data and the dimension of the data, the model is built using Big Data Techniques.
Review of Machine Learning Algorithm on Cancer DataSet
Authors:- M. Tech. Scholar Animesh Urgiriye, Assistant Professor Rupali Bhartiya
Abstract:-Cancer is a basic disease from numerous years. This prompts demise in the event that it isn’t analyzed at beginning phase. It is a subject of concern on the grounds that genuine treatment of this infection isn’t found till date. Patients having this sickness must be spared if and just in the event that it is found in beginning phase (I and II). On the off chance that it is recognized in last stage (III and IV) at that point possibility of endurance is extremely less. AI and information mining strategy is exceptionally useful method to deal with this issue. AI is exhibiting the guarantee of creating reliably precise appraisals. AI framework successfully “realizes” how to assess from preparing dataset of finished activities. There are different procedures accessible in Machine Learning to foresee the Cancer based on gathered standard datasets. The datasets may have been recorded by web-based media, medical care sites and some different vaults. We have to apply a few classifiers of Machine Learning Techniques on these dataset to recognize the disease in a human. The primary point of the audit is to help the exploration on exact assessment, for example to ease different specialists for pertinent right assessment examines utilizing AI procedures. Our survey recommends that these procedures are serious with conventional assessors on datasets and furthermore show that these strategies are touchy to the information on which they are prepared.
Application of the Analytical Hierarchy Process (AHP) for Geo-Hazards Susceptibility Mapping:Urban Settlement Marquez De León, La Paz.,Mexico.
Authors:-Joel Hirales-Rochin
Abstract:- Geology as a tool to identify areas of geological risk is useful to determine the close relationship between thegeological Space and the sustain able urban development ofacity. Atthenational, regional and local level where the study are a is located, there is a growing need to create new urban areas, but these are not linked to an adequate analys is of the geological environment and the knowledge of the main factors that control risk conditions. The methodology was based on a characterization of the geological, hydro geological and geo mechanical conditions of one of the main urban settlements of the city of La Paz, capital of the state of Baja California Sur., Mexico. Therefore, using the Analytic Hierarchy Process (AHP) methodology it was generateda risk susceptibility map-related in local areas to flood events and landslides. The results represent the first stage of a larger scale project and with this; it is possible to contribute new knowledgeto be used in the most precisezoning of geographic risks, which will allow the state capital a sustainable growth of the population of the city. The improvement of current constructionst and ards and the corresponding zoning to anticipate their development in anorderlymanner.Finally, it is considered that this research´s type (urban settlementscale) provides ananalysis of the risk conditions where the citizen can locate in their community and know their conditions of civil protection, all the opposite of most risk studies that offer large- scale results and only offer a broaderview.
Website Development of Mudis Cooling
Authors:-Professor Jadhav P.D, Akshay Gade, Piyush Pipriye, Pranav Bansod,
Soham Badjate, Mohammed Mustafa
Abstract:- A website or web site is a collection of related network web resources, such as web pages, multimedia content, which are typically identified with a common domain name, and published on at least one web server. Notable examples are wikipedia.org, google.com, and amazon.com. Websites can have many functions and can be used in various fashions; a website can be a personal website, a corporate website for a company, a government website, an organization website, etc. A static website is one that has web pages stored on the server in the format that is sent to a client web browser. It is primarily coded in Hypertext Markup Language (HTML); Cascading Style Sheets (CSS) are used to control appearance beyond basic HTML. Images are commonly used to effect the desired appearance and as part of the main content. Audio or video might also be considered “static” content if it plays automatically or is generally non-interactive. A dynamic website is one that changes or customizes itself frequently and automatically. Server-side dynamic pages are generated “on the fly” by computer code that produces the HTML (CSS are responsible for appearance and thus, are static files). There are a wide range of software systems, such as CGI, Java Servlets and Java Server Pages (JSP), Active Server Pages and ColdFusion (CFML) that are available to generate dynamic web systems and dynamic sites The objective of this project is to prepare the static web site for Modi’s cooling, a dealer of cooling electronic appliances such as Air conditioning units, deep freezer, fruit freezer etc. The function of a web site to provide online information to the customer about the product available, the prices address and contact number of the dealer. For design of this web page HTML is used whereas for database and uploading SQL, MYSQL
Seismic Response Study of Multi-Storied Reinforced Concrete Building With Accordion Mass Dampers
Authors:-Mr. P. G. Kadwade, Prof. Zubair Shaikh, Dr G. R. Gandhe
Abstract:- Damping plays important role in design of earthquake resistant structures, which reduces the response of the structure when they are susceptible to lateral loads. There are many different types of dampers in use. In the present study accordion mass dampers (AMD) are used to evaluate the response of RCC buildings. The main task of a structure is to bear the lateral loads and transfer them to the foundation. In order to have earthquake resistant structures, accordion mass dampers (AMD) have been used. The building is modeled in ETAB 2018 and modeled with different location of AMD. After the study results show building having AMD on outer side shows better performance than building having other location of accordion mass damper.
Development of a Healthy Noodles Enriched with the Flavors of Coleus Aromaticus, Mentha, Erythrina Indica
Authors:- Suruthi B., Amutha A.
Abstract:- Noodles have been the staple foods for Asian countries since ancient time. They can be made from wheat, rice and other raw materials such as buckwheat and starches derived from potato, sweet potato and pulses. Normally, wheat noodles is enriched with protein, carbohydrates and fibre content. This study was conducted to add additional nutrients content to the wheat noodles by incorporating the powders of Coleus amboinicus (Omavalli leaves), Mentha (mint leaves) and Erythrina Indica (Kalyana murungai leaves). Omavalli leaves is used in curing cough, cold, stomach problems, indigestion etc. Mint leaves is used for easing queasy stomach, calming stress and anxiety etc. Kalyana murungai leaves is useful for treating cough, cold and is very good for women as it treats many ailments that women regularly face. People use to crush these leaves and eat in the normal mouth or boiled these leaves in the water and from cold, cough or fever. But not everyone like to have this. As we know, noodles is a food, with zero haters. So we have incorporated these flavours in noodles. This noodles is prepared with the composition of 55% wheat flour, 15% omavalli powder, 15% mint powder and 15% kalyana murugai powder.
Upfc Power Compensation with Power Generation Smart Grid
Authors:- Suresh Waskle, Mr. Lavkesh Patidar
Abstract:- The problem of power system to face many problem like as power quality, THD and stabailuty.in modern technology to use of FACT Devices, The FACT Device are many types these paper are present of UPFC power stability device oriented. For recent used many technique to improvement of UPFC performance and reduction of THD. We proposed Machine learning technique like as Fuzzy logic and shunt filter with transformer work as controller to controlling of power transient conditions.
A Review: Air Pollution Analysis System Using IOT
Authors:- KulprakashSingh Avatarsingh Mistry, Balaji Khansole Sir
Abstract:- In this paper we are getting to make an IOT Based pollution Monitoring System during which we’ll monitor the Air Quality over a webserver using internet and can display on webpage when the air quality goes down beyond a particular level, means when there are sufficient amount of harmful gases are present within the air like CO2, smoke, alcohol, benzene and NH3. It will show the air quality on webpage so that we can monitor it very easily. The main reason for increasing of pollution level are crop’s remaining burning, emission from the automobile, open defecation of smoke in atmosphere from the industries and burning of garbage openly. Internet of Things (IoT) based pollution system is employed to detect the present level of hazardous gases in the atmosphere. The IoT based pollution system will help us to fetch the info from any location where device is installed. By using the concept of IoT we can use multiple pollution devices at different locations and fetch the data to the web server.
Design and Analysis Of A Upqc Power Flow Control Using Distribution Transformer
Authors:- Bhavsingh Ajnare, Mr. Deepak Bhataniya
Abstract:- Unified power quality conditioners (UPQCs) allow the mitigation of voltage and current disturbances that could affect sensitive electrical loads while compensating the load reactive power. Diverse control techniques have been proposed to evaluate the instantaneous output voltage of the series active power filter of the UPQC but, in most cases, these controllers only can compensate a kind of voltage disturbance.
Construction of Runways by Soil Stabilisation/Recycling Technology Gmr Airport Hyderabad
Authors:- K.Hima Vamsi, K.V Prabhakar Rao
Abstract:- Rajiv Gandhi international airport servers as a important international Airport in south India.The RITES has done a comprehensive study of the Existing Pavement and identified the weak areas. Stabilization has been taken up in those areas. The total area of stabilization work is 7 Lakh Sqm which includes main runway, secondary runway and taxiway. Out of this 7 Lakhs Sqm Approximately 33(2,30,000 Sqm) Approximately 67% (4.70,000 Sqm) for relaying works.
Credit Card Fraud Detection using Autoencoders
Authors:- Saatwik Bisaria, Aryan Aditya Singh, Sarita Yadav
Abstract:- There are certain characteristics of fraudulent transactions that differentiate them from legitimate ones. Machine Learning algorithms recognize patterns in the data points which allow them to detect fraud transactions from legitimate ones, based on thousands of pieces of information, that sometimes may seem completely unrelated to a human being. In Machine Learning, problems like fraud detection are usually framed as classification problems —predicting a discrete class label output given a data observation. Autoencoders are special type of neural network architectures in which the output is same as the input. Autoencoders are trained in an unsupervised manner in order to learn the extremely low-level representations of the input data. These low-level features are then deformed back to project the actual data. An autoencoder is a regression task where the network is asked to predict its input (in other words, model the identity function). These networks have a tight bottleneck of a few neurons in the middle, forcing them to create effective representations that compress the input into a low-dimensional code that can be used by the decoder to reproduce the original input.
Enhanced MalJPEG: A Novel Approachfor the Detection of Malicious JPEG Images
Authors:- Nasla K, Shabna M
Abstract:- In recent year cyber-attacks are increased.The attackers targeting individuals, businesses and organizations. Such attacks usually result in critical harm to the organization, such as the loss and or leakage of sensitive and confidential information. Some non-executable files allow an attacker to run arbitrary malicious code on the targeted victim machine when the file is opened. Millions of people are used images for daily purpose. In some cases, some types of images can contain a malware codes and perform harmful actions. JPEG images are used by almost everyone, from individuals to large enterprises, and on various platforms;Because cyber criminals misuse JPEG image for malicious purpose. In this paper, we design a new method is named as Enhanced MalJPEG. Our system can to detect malicious JPEG images using CNN and machine learning techniques. This method extracts different features from the JPEG file structure and CNN based features from JPEG file and leverages them with a machine learning classifier, in order to discriminate between benign and malicious JPEG images.
RPL: IPv6 Routing Protocol for Low Power and Lossy Networks
Authors:- Shubham Saini
Abstract:- Today, LLn represent most important (interesting) research areas in wireless sensor networks. In which we study about wireless personal area, networks and wireless sensor network these network use to save energy high performance, support traffic network (Pattern). it run on a routing over link layer with restricted frame size and many other. this paper represent protocol performance in smart grid applications based on it.which is design for overcome routing issues in llns.it implements to reduces of energy consumptions such as dynamics sending rate of control messages and addressing topologies which is send packets.it support not only of traffic pattern but also support traffic following from a gateway node to all other network. this paper focus on Rpl and wireless sensor networks of brief overviews.
A Survey of Adaptive Steganographic Methods
Authors:- Amina S, Mubeena A K
Abstract:- Image steganography is a method used to hide data within an image.The most common image steganographic methods can be divided into three categories namely naive steganography,adaptive and deep learning based embedding.Among these adaptive steganography is the most commonly used practical method naw days.This method not only improves the security of embedding message in an image but also uses efficient steganographic codes.In this paper,we compare various adadptive steganographic method that are currently used and also compare them with the deep learning based methods using various convolutional neural networks.
Study of Hardness and Wear Analysis in AL-SI Alloy (AL-5%SI, AL-11% SI and AL-17%SI) Along With Microstructural Analysis
Authors:- Kajal Shakya, Prof. Krishana Bhushan Patel
Abstract:- In this study, there is an alloying element named as silicon is studied with its effect when varied through Al composition. As silicon is used for making alloy harder and it evident since the last few years there has been a rapid increase in the uses of aluminum-silicon alloys, particularly in the automobile industries, because it has the high strength to weight ratio, high wear resistance along with low density and low coefficient of thermal expansion. Hence, advancements in the field of application make the study of their wear and tensile behavior of utmost importance in this present investigation, Al-based alloys containing5%, 11% and 17% weight of Silicon were synthesized using casting method. Compositional analysis and hardness along with wear analysis performed for different samples of same composition have shown near uniform distribution of Si in the prepared alloys. Study of microstructure has showed the presence of primary silicon. Wear tests were carried out to check whether there will be any change in mechanical property as well as strength ie high hardness with increase in silicon percentage. Wear behavior was studied by using computerized pin on disc wear testing machine. Resistance to wear has increased with increase in silicon amount.
Practical approach for energy audit in Residential building
Authors:-Saurabh Bansal, Asst. Prof. Neeraj Kumar Kumawat
Abstract:- Energy consumption increasing day by day and energy production using conventional sources becomes limited. To reduce this energy consumption and for maximum utilization of energy, energy audit for residential building is playing vital role these days. By adopting this energy audit survey we can minimize the use of energy consumption. The energy audit surveys are mainly done for industries. But now a day’s energy consumption is also increasing in residential building. So it is essential to adopt energy audit surveys for residential buildings too. This paper suggests the ways how we can minimize the losses and reduce the electricity bill for residential building. The paper is also useful for consumers as electrical energy audit survey is explained with procedure.
A Review of Fatigue Detection Techniques
Authors:- Shalu, Nahan Rahman M K
Abstract:- Road accidents are increasing tremendously and accident causing factors include over speeding, violating traffic rules, fatigue driving etc. Fatigue driving is due to inadequate sleep or physical state of driver is not good. Many researches have been taken place for detecting fatigue while driving. This paper describes the methods that already implemented and analyse the best method that is much accurate compared to others. The methods include machine learning, deep learning algorithms. In the algorithms single facial features, multiple facial features, physiological features are used for detection.
Comparative Study Evaluating METEONORM Estimates of Sunshine Duration and air Temperature in Major Cities of Mali
Authors:- Moussa Ibrahim MAIGA, Cheick Oumar SANOGO, Souleymane SANOGO
Abstract:- The successful implementation of renewable energy project relies on the climatic data available in the site. Due to the scarcity of climatic station data, various estimation databases are proposed as an alternative. In this work, we performed an evaluation of METEONORM estimations of sunshine duration and air temperature by comparing them with stations records for some selected cities in the semi-arid zone of Mali, which are Bamako, Nara, Sikasso, Segou and Mopti. The results from the comparative study reveal that the METEONORM estimate of temperature has a very good correlation coefficient with the station record for all the cities. The bias and the root mean square error are very weak and do not exceed 1.5% of the reference values. The results of the sunshine duration show some difference between the two datasets with a maximum bias of 24.73 hours per month and a correlation coefficient that oscillates between 0.76 and 0.98. The semi-arid zone of West Africa being a region with less meteorological data records, it is advisable that METEONORM dataset could be used as an alternative for study energy systems.
LSTM Based Personality Prediction from Social Media
Authors:- Thahira M, Shafna K P
Abstract:- The usage of social media networks are increasing rapidly. Which are used as a platform to share People’s feelings, emotions and experiences etc. This Proposed method predict the person’s behaviour from social media networks. Personality prediction from social media networks are become a challenging task. This method follow the big-five-factor model (OCEAN) namely, openness to experience (O), conscientiousness (C), extraversion (E), agreeableness (A), neuroticism (N) for defining personality. Here the mathematical regular expression operation is used for the data preprocessing. There are three feature selection algorithms such as Pearson correlation coefficient (PCC), information gain(IG) and chi-squared (CHI) method are used for the relevant features. Deep learning neural network such as LSTM is used as a classifier for predicting personality of a person. This Proposed method improves the accuracy of personality prediction compare to other existing system.
Reduction of Iron Oxide using Microwave Irradiation
Authors:- Ravindra Kumar Verma, Prof. Krishana Bhushan Patel
Abstract:- The reduction of iron ore with carbon was investigated using a microwave heating techniques. The heat generated by the reaction mixtures while interacting with microwave, percentage, chemical composition and microstructure of iron produced were studied in detail. The experiments were carried out using 2.45 GHz microwave processor and reaction rates were analyzed at different power ratings: 540, 720 and 900 watts. The maximum reduction in weight obtained was 50.47% at 900 W power supply. The effect of varying the concentration of binders was also studied. Unlike conventional processing the microwave heating was found to be through the absorption of microwaves by reaction mixtures. The microwave processing showed considerable reduction in reaction time increases the yield and also reduces unwanted side reactions. However it showed uneven heating as the heating is due to the distribution of microwave absorbers present in the reaction mixture. Solid state reduction was observed at initial 10-40 minutes and molten iron produced above 40 minutes and the temperature attained was around 8000C. The microwave reduced mixture were characterized using SEM, EDS and XRD technique to find out the reduction of hematite. After the EDS analysis, it was found out that the iron content formed after the reduction process was 46.23 wt %.
An Intelligent Recommendations on Mobile Pose Identification
Authors:- Fahisa Pallikkattil, Asst. Prof. Shabna.M
Abstract:- These days MEMS sensors like accelerometer, gyroscope, and magnetometers are spreading in a wide range of applications due to its smaller size, cheapness and expanding execution. For occasion, smartphones are right now prepared with these kind of sensors. Which may well be utilized to make strides the client involvement of the phone itself or the route functionalities. In this work, these sensor measurements are exploited to provide advanced information about the user bringing the phone. Here proposed an intelligent concept is applying on the mobile web browser. Depending upon the state of the user detected by sensor, the intelligent web browser offer assistance to associate the client and can consequently recognize the textual size estimate and brightness. Light alteration is according to the brightness. These are the applications that we will perform by utilizing our proposed strategy. For the proposed framework a web application is utilized in both server side and user side. The intelligent mobile browser concept offer assistance to associate the recognized information that collected by sensor.
Solvent Extraction Study of Injection Moulded M2 High Speed Steel Using Palm Stearin/Waste Rubber Based Binder
Authors:- M.A.Omar, N. Wahab
Abstract:- Metal Injection Moulding (MIM) has undergone development of various binder systems with the aims of shortening the overall debinding time duration. In the present work, a novel binder system based on waste rubber has been utilised in injection moulding of Molybdenum High Speed Steel (M2 HSS). The feedstock consisted of M2 HSS powder with mean diameter particle size of 16µm and binder which comprised of palm stearin, polyethylene, waste rubber and stearic acid. The moulded part was immersed into n-heptane at 60°C in order to remove the paraffin wax and stearic acid, followed by sintering in a controlled vacuum atmosphere. Results showed that solvent extraction debinding technique allowed complete removal of paraffin wax and stearic acid from the injection moulded part within 2 hours without swelling or distortion of the debound part. Scanning Electron Microscopy observation showed that large pores were formed from the surface to the interior of the debound part during the process. In addition, this technique was found to be suitable to shorten the debinding time, which consequently resulted in a debound part.
Collation: Features Extraction in Text Classification
Authors:- Shireen MT, Nahan Rahaman MK
Abstract:- In the last few years, a difference of deep learning models has been applied to natural language processing (NLP) to improve the ability of computers to understand human speech as it is spoken. NLP helps to analyse, understand, and derive meaning from human language in a smart and useful way. For impressive performance of deep learning methods on challenging NLP processing problems we use some text classification features extraction technique.it means that distribution representation for text or words that allows the words with same meaning to have similar representation as real valued vectors in a predefined vector space. In this paper we are comparing the learning technique like Bag of words (BOW), Term frequency-inverse document frequency(tf-idf), word2vec, Glove in word embedding and also explaining the advantage and disadvantage.
Enhanced QR: Approaches for QR Code Embedding Techniques
Authors:- Swathi Krishna P S, Meera K
Abstract:- In recent years, QR codes have grown more widespread in facilitating digital-based commercial scenarios like product promotion, mobile payment, and product information management. These 2D barcodes are square-shaped matrices of dark or light pixels employed to encode and promptly retrieve data utilizing computer devices. According to the international standards, quick response (QR) codes are considered an advancement from older, unidimensional barcodes. Traditional QR codes are reliable and fast to decode but lack aesthetic appearance to demonstrate customers’ visual information. This paper explains various methods of data to embed data QR codes. In order to minimize processing time, the optimization technique considers the mechanics of a standard binarization method, genetic algorithm, etc. These embeddings are suitable with standard decoding and can be applied to any color images with full area coverage.
Design of College Chatbot using Amazon Web Services
Authors:- Dhanush Pakanati , Gourav Thanner , R. Ravinder Reddy
Abstract:- As the mobile and web-based applications have increased rapidly there is a growing need for chatbots. A chatbot helps in easy navigation through a website, simplifies the process of searching information and helps in understanding the needs of the customer. Over the years, the world has seen multiple uses of chatbots implemented in banking and e-commerce platforms. Inspired by these examples, a chatbot for college website can provide various functionalities to the students, lecturers, and visitors. This chatbot can provide information like announcements, quick links to various subjects in the site, account related information for registered users. Students and lecturers can mostly use this to access their college portals to check numerous things like attendance, examination results, assignments deadlines and timetables etc. This deviation from traditional search capabilities through a website can reduce the search time and can make accessing information easier. It also provides an engaging user interaction and imitate real life conversations for the users. Also, such functionalities implemented via cloud are economical and easy to update.
Review Article Improvement Of Power Flow And Voltagestability Using Upfc With Artificial Neural Network In Matlab
Authors:- Bipul vats Khurana. Dr.L.S.Titare
Abstract:- The increasing pressure on the power system increases the complexity that is becoming a concern for the stability of the power system and mainly for transitory stability. To operate the system in the event of faults, Flexible AC Transmission System (FACTS) devices that provide opportunities to control the power and damping oscillations are used. This paper presents the enhancement transient stability of the Multi-Machine power system (MMPS) with Unified Power Flow Controller (UPFC) by using Artificial Neural Network (ANN) controller. Performance the power system under event of fault is investigating by utilizes the proposed the strategy to simulate the operational characteristics of power system by the UPFC using Artificial Neural Network (ANN) controller. The simulation results show the behavior of power system with and without UPFC, that the proposed (ANN) technicality has enhanced response the system, that since it gives undershoot and over-shoot previously existence minimized in the transitions, it has a ripple lower. The use (MATLAB R2014a) in all simulations carried out.
Online Toxic Speech: Automatic Detection Methods and Techniques
Authors:- Asmi P, Sanaj M S
Abstract:- Nowadays, due to the increase of social media individuals are freely to communicate and also to express their thoughts of viewspublicly.It may be a text including blog post, updated status or comments posted on the social media. Some of them misuse the freedom of speech by harass in got hers. With the exponential growth of online communication, the hate speech behavior also goes unchecked to alarming proportion. Speech may be normal, hate or offensive. Hate speech or toxic speech is an antisocial behavior. Thus, it is important to detect and remove the toxic or hate speech from the social media. Which is also a challenging problem. This paper gives a brief explanation about different toxic speech detection methods.
Review: Methods of Steganalysis Of Digital Images
Authors:- Muhsina Jasmin Parapparambil, Haseena P.V
Abstract:- Nowadays cybercriminals excavate malicious data to your devices in different ways. In such cybercrime, the use of steganography needs an effective technique. Cryptography consists of a scrambled message that is not easily understandable. The main aim of steganography is to hide the data or information, by embedding it into an image, audio, video, or text files. So, unauthorized users cannot access the hidden information. The study of steganography is called steganalysis, which is used to detect hidden messages. So, this paper gives a comparative study between different methods used for the steganalysis process. The comparison network is based on digital images. This method is the best challenge for digital forensic investigation.
Reduction of Iron Oxide using Microwave Irradiation
Authors:- Ravindra Kumar Verma, Prof. Krishana Bhushan Patel
Abstract:- The reduction of iron ore with carbon was investigated using a microwave heating techniques. The heat generated by the reaction mixtures while interacting with microwave, percentage, chemical composition and microstructure of iron produced were studied in detail. The experiments were carried out using 2.45 GHz microwave processor and reaction rates were analyzed at different power ratings: 540, 720 and 900 watts. The maximum reduction in weight obtained was 50.47% at 900 W power supply. The effect of varying the concentration of binders was also studied. Unlike conventional processing the microwave heating was found to be through the absorption of microwaves by reaction mixtures. The microwave processing showed considerable reduction in reaction time increases the yield and also reduces unwanted side reactions. However it showed uneven heating as the heating is due to the distribution of microwave absorbers present in the reaction mixture. Solid state reduction was observed at initial 10-40 minutes and molten iron produced above 40 minutes and the temperature attained was around 8000C. The microwave reduced mixture were characterized using SEM, EDS and XRD technique to find out the reduction of hematite. After the EDS analysis, it was found out that the iron content formed after the reduction process was 46.23 wt %.
Impact of Online learning during Covid-19 – An Empirical Investigation
Authors:- Mateen Yousuf
Abstract:- Online education changes the pedagogy and components of conventional teaching. Research conducted on the issues and challenges faced in online teaching learning process have raised many issues in online education. Covid-19 has enabled access to online education to masses which gave rise to new challenges previously unknown to researchers. Review of literature has identified many issues but the research seems to be incomplete. This research aims at identifying the trends, issues and challenges faced by students and teaching instructors during Covid-19 during their access to online education. Issues faced by students and teaching instructors include technical issues, psychological issues, sociological issues, health issues, time management, lack of training, issues related to content and interface of electronic gadgets and educational applications. To mitigate these problems, schools need to provide training to the instructors, rope in psychologists, doctors, health experts and private players to address various genuine concerns. Technical support to instructors must be provided as well help in content development.
Non Orthogonal Multiple Access: A Survey
Authors:- Reshma Ramachandran , Radhika P
Abstract:- The densification of portable organizations should empower the fifth generation (5G) versatile organizations to adapt to the regularly expanding interest for higher rate traffic, improved reliability and reduced latency. Non-Orthogonal Multiple Access (NOMA) has as of late arose as a potential access conspire for the fifth era of portable frameworks. It comprises in exploiting another domain for power domain, user multiplexing, by exploiting the channel gain difference between paired clients on the equivalent subcarrier. User partition is done at the recipient side, utilizing Successive Interference Cancellation (SIC). Thusly, NOMA can expand normal framework throughput by over 30% compared to orthogonal signalling, while additionally improving cell-edge user experience. Additionally, NOMA verifiably fortifies the reasonableness between clients situated in a similar cell, and evades the underutilization of subcarriers experienced when a cell-edge client is planned alone utilizing OFDM. In this paper we are discussing techniques, features of NOMA.
RPL:IPv6 Routing Protocol for Low Power and Lossy Networks
Authors:- Shubham Saini, Tarun Dhiman
Abstract:- Today, LLn represent most important (interesting) research areas in wireless sensor networks. In which we study about wireless personal area, networks and wireless sensor network these network use to save energy high performance, support traffic network (Pattern). it run on a routing over link layer with restricted frame size and many other. this paper represent protocol performance in smart grid applications based on it.which is design for overcome routing issues in llns.it implements to reduces of energy consumptions such as dynamics sending rate of control messages and addressing topologies which is send packets.it support not only of traffic pattern but also support traffic following from a gateway node to all other network. this paper focus on Rpl and wireless sensor networks of brief overviews.
Analytical Study on Automobile Engine Line Work Stations and Analyze Their Working
Authors:- Anupam Pandey, Prof. Krishana Bhushan Patel
Abstract:- In an automobile assembly line, a series of stations are arranged along a conveyor belt and an automated guided vehicle performs on tasks at each station. Parallel assembly lines can provide improving line balance, productivity and so on. Combining robotic and parallel assembly lines ensure increasing flexibility of system, capacity and decreasing breakdown sensitivity. Although afore mentioned benefits, balancing of robotic parallel assembly lines is lacking – to the best knowledge of the authors- in the literature. Therefore, an observed study is proposed to define/solve the problem of automobile assembly line. The automobile assembly line also tested on the generated benchmark problems for automated guided vehicle/robotic parallel assembly line balancing problem. The superior performances of the proposed algorithms are verified by using a statistical test. The results show that the algorithms are very competitive and promising tool for further researches in the literature.
Use of Open Source Applications in Higher Learning by Prospective Computer Science Teachers in Abubakar Tafawa Balewa University, Bauchi-Nigeria
Authors:- Sadiq Adamu , Jonathan Johnson Kuba
Abstract:- This study investigated the use of open source application in higher learning by prospective computer teachers. Colleges and universities are increasingly aware of the necessity to use technology to meet both the business and academic mission of the institution. Often, open source software (OSS) and Free Source Software (FSS) are seen as viable options for meeting these challenges. Many Institutions are recognizing the importance of the interplay or overlap between FSS and OSS as an important feature for providing high quality teaching and educational experiences. Descriptive survey research design was used, which typically employs the use of questionnaire, sampling random sampling was adopted, where 79 respondents were as the sample size of the study. The finding of the research shows that the prospective teachers of computer education in ATBU are aware of the open source application, but lack of awareness about the uses and benefits of open source software, is a large factor towards less use of open source software. Factors inhibiting the optimal utilization of open source Application by prospective computer science teachers are: Unfamiliarity, Complex licensing situation, backward compatibility issues, Component and architecture incompatibilities and Migration and usage. The Findings of this study serves as the basis for making the following recommendations: Educational institutions should not spend huge money on proprietary software and Universities should replace the proprietary software by open source software for saving the money of license renewal fee, up gradation charges etc. to encourage the use, accessibility and benefits of Open Source Software by student, The school website should be linked to Datamation which highlights a list of open source software related to a particular category such as security, cloud computing, small businesses, big data, games, etc.
Machine Learning Based Defensive Alerting System in a Vehicular Network: A Survey
Authors:- Raseela.KP, Sreevarsha.V
Abstract:- Reckless driving accounts for most of the road accidents, consistent monitoring of reckless maneuver and timely activation of corrective feedback are the best remedial here comes the significance of a defensive alerting system in a vehicular network ,which examines the vehicle throughout whether it showing any reckless behavior in driving ,this timely monitoring duty is reserved for its neighbor vehicle and the neighbor vehicle sends the reckless alert to the road side unit which in turn sends to the cloud server. This cloud server generates warning alerts to approaching neighboring vehicles in that road segment .machine learning algorithms like support vector machine and decision tree are used to classify vehicular datasets .this server client based system implemented in a traffic simulator for evaluation.
Channel Estimation and Beam Squint For Wideband Mmwave Massive Mimo-Ofdm Systems A Survey
Authors:- Vishnu Priya P, Vipin Krishnan C V
Abstract:- Millimeter-wave (mmW) frequencies somewhere in the range of 30 and 300 GHz are another wilderness for cell correspondence that offers the guarantee of significant degrees more noteworthy data transmissions joined with additional increases by means of beamforming and spatial multiplexing from multi component reception. The mix of millimeter-wave interchanges, exhibits with a large number of recieving wires, and little cell calculations is a assembly together of advances that can possibly improve remote access and throughput. The exhibition of multiple input multiple output (MIMO) frameworks is vigorously influenced by pilot contamination. A low-unpredictability channel assessment for mixture millimeter wave (mmWave) frameworks, where the quantity of radio frequency (RF) chains is considerably less than the quantity of reception signal prepared at each handset. The attainable spectral efficiency (SE) of MIMO transmission frameworks with spatial wide band effect structures dependent on discrete fourier transform (DFT) handling, where the base station (BS) has amazing channel state data and baseband preparing is performed by zero-forcing precoding. Transmission plot that abuses the spatial levels of independence from enormous reception apparatus exhibits to amplify the downlink whole pace of user in a heterogeneous organization. With the expanding size of recieving clusters in wideband millimeter-wave (mmWave) interchanges, the actual engendering deferrals of electromagnetic waves bridging the entire exhibit will turn out to be huge and equivalent to the time-domain test period and update channels with a fundamentally limited quantity of preparing and user input in FDD frameworks.
Synthesis, Characterization and Wear Behavior of Co0.5 CrCu0.5 Fe Ni1.5 Al Ti0.4 and Co Cr Fe Ni Al0.25 Ti0.75 High Entropy Alloy (Hea) Prepared by Mechanical Alloying
Authors:- Rajeev Kumar, Prof. Krishana Bhushan Patel
Abstract:- Structure-tribological property relations have been studied for two high entropy alloys (HEAs). Microhardness, room temperature sliding friction coefficients and wear rates were determined for two HEAs: Co0.5CrCu0.5Fe Ni1.5AlTi0.4 and Co Cr Fe Ni Al0.25 Ti0.75. Wear surfaces were characterized with scanning electron microscopy and micro-Raman spectroscopy to determine the wear mechanisms and tribochemical phases, respectively. It was determined that the both HEAs exhibit an excellent balance of high hardness and lower wear rates compared to 440C stainless steel, a currently used bearing steel. This was attributed to their more ductile body centered cubic (BCC) solid solution phase along with the formation of tribochemical Cr oxide and Ti oxide phases, respectively, in the wear surfaces. This study provides guidelines for fabricating novel, low-friction, and wear-resistant HEAs for potential use at room and elevated temperatures, which will help reduce energy and material losses in friction and wear applications.
Study of Microstructure and Hardness Along With Compression Test of Heat Treated Aluminium 2014 Alloy
Authors:- Dharmendra Kumar Kumhar, Prof. Krishana Bhushan Patel
Abstract:- Microstructures, hardness and true stress strain through compression test have been studied for for Aluminium 2014 alloy. Heat treated surfaces were characterized with scanning electron microscopy (SEM) and optical microscopy to determine the mechanisms and phases. It was determined that the heat treated alloy with aging of 3 hours and 6 hours shows an excellent balance of high hardness and lower strain rates compared to non heat treated Al alloy. This was attributed to their more ductile body centered cubic (BCC) solid solution phase along with the formation of Mg2Si phases. The XRD analysis of non heat treated 2014 alloy shows the distribution of Mg2Si in Alloy as a measure element for strengthen the alloy. This study provides guidelines for fabricating novel, high compressive strength, and high hardness Al 2014 alloy for potential use where low weight high strength required.
Online Task Allocation and Flying Control in Fog-Aided Internet of Drones: A Survey
Authors:- Jobin Mathew , Deepa T
Abstract:- Internet of Drones (IoD) networks utilize mist hubs to give processing assets to the postponement touchy assignments offloaded from drones. In IoD networks, drones are dispatched to finish an excursion wherein a few areas of interest are visited. At every area, a robot gathers the ground data, creates figuring assignments and offloads them to the haze hubs for preparing. In our work, we think about both the errand designation (which conveys undertakings to various mist hubs) and the flying control (which changes the robot’s flying velocity) to limit the robot’s excursion fruition time obliged by the robot’s battery limit and assignment consummation cutoff times. We detail this joint streamlining issue as a blended number non-direct programming (MINLP) issue. Regarding the handy situation that the future undertaking data is hard to acquire, we plan an online calculation to give techniques to task assignment and flying control when the robot visits every area without knowing what’s to come. The exhibitions of our proposed online calculation are shown by means of broad recreations.
Electro Chemical Machining Of Micropin Tool by Using Ultrasonic Vibration PolishingAluminium 2014 Alloy
Authors:- Vivek Mishra, Prof. Krishana Bhushan Patel
Abstract:-Micro Electric chemical Machining (MECM) is one of the most efficiently employed non-traditional machining process for cutting hard-to-cut materials & to cut geometrically complex shapes that are difficult to machine by conventional machines. In the present paper reviews is conducted of experimental investigations carried out to study the effect of Electric chemical Machining parameters on material removal rate (MRR), electrode wear (EWR), surface roughness (Ra) and diameteral overcut n corrosion resistant stainless steels. The non-contact machining technique has been continuously evolving from a mere tool and die making process to a microscale application machining alternative attracting a significant amount of research nterests and Electrochemical machining offers several special advantages including higher machining rate, better precision and control, and a wider range of materials that can be machined.
Utilization of Fly Ash in Bitumen And In Flexible Pavements
Authors:- M.Tech. Scholar Asif Ahmad Lone , Assistant Professor Chitranjan Kumar
Abstract:- Flexible pavements with bituminous surfacing are widely used in India. Exponential increase in traffic, overloading of commercial vehicles and significant variations in daily and seasonal temperatures have shown some limitations of conventional bitumen performance.Early developments of distress symptoms like cracking, rutting, raveling, undulations, shoving and potholing of bituminous surfacing have been reported for flexible pavements.A bituminous mixture needs to be flexible enough at low service temperatures to prevent pavement cracking and to be stiff enough at high service temperature to prevent rutting. Bitumen modified with fly ash offers a combination of performance related benefits as the physical properties of the bitumen is improved without changing the chemical nature of it.This paper presents the experimental study carried out conventional bitumen and fly ash modified binder. It has been shown that marshal stability, flow value, ductility, rutting resistance, indirect tensile strength and resilient modulus of the bituminous concrete mix with fly ash modified bitumen is significantly improved.The advantage with fly ash is it is easily available and their cost low and it is a waste material.These additives increase the elasticity, decrease the brittle point and increase the softening point of the bitumen. This, in turn, will alter the properties of the mix in which such modified bitumen is used and these mixes will exhibit greater stiffness at higher temperature and high flexibility at low temperatures.
Product Development Hand Rail Assisting Walking Cane for Physically Aid
Authors:- Bankapalli Vamsi, MD Affan, Kancharana Sohith, Sasubilli Rakesh Rao
Abstract:- One of the basic problems of the user with conventional walking cane is overcoming the balancing problems in congested areas (i.e. Non-spacious regions such as bathrooms, offices, Indian middle class houses, etc.). Even though many research studies have been reported in different fields to increase the independence of users, the question of overcoming these problems always remains a topic of discussion for many researchers. Our project mainly concentrates on the difficulties encountered during walking and to lay for a support to hold on something in non-spacious regions. This idea is truly based on capital involvement to buy different kind of things many a times. Ending up with one tool that can solve our basic needs is better concern. Some time was spent on the project to fix the metrology regarding the dimension, and having a tough sketch the CAD models is done Autodesk fusion 360 and Dassault CATIA software. This structure and mechanism will analyze in Ansys software. All the design parameters of the product were based on the standard design of the walking cane in India. The major part of the project focuses on the proposed design concept and concludes by discussing the physical working model of the proposed design.
A Student Network Model for Image Classification
Authors:- Saleema N P, Sruthy K G
Abstract:- – Nowadays, Convolutional Neural Network achieved remarkable changes in image classification. To obtain the demanding precision of the categorized image we need a high-performance GPU and Huge datasets are necessary for training images. Image classification is computationally very expensive since it requires high-performance hardware devices and memory storage. So to tackle this issue we propose a student network model by utilizing the Knowledge Distillation technique, Let small networks learn from big teacher networks with accuracy compete with teacher networks.There exist many methods for trimming the convolutional neural network and designing a small network. But all these techniques are very expensive and have not achieved great results. The proposed model addresses the aforementioned issues effectively.
Face Expression Recognition : A Review
Authors:- M.Tech. Scholar Vaishali Sharma, Asst. Prof. Vijay Yadav
Abstract:- Recognition of out ward appearances assumes a significant job in many mechanized frame work applications like mechanical technology, instruction, man-made brainpower, and security. Perceiving outward appearances precisely is testing. Approaches for explaining FER (Facial Expression Recognition) issue can be ordered nto 1) Static single pictures and 2) Image successions. Customarily, various procedures like Multi- layer Perceptron Model, k-Nearest Neighbors, Support Vector Machines were utilized by scientists for fathoming FER. These techniques removed highlights like Local Binary Patterns, Eigenfaces, Face-milestone highlights, and Texture highlights. Among every one of these techniques, Neural Networks have increased especially iubiquity and they are broadly utilized ifor FER. As of late, CNNs (Convolutional Neural Networks) have picked up inotoriety in field of profound learning in view of their easygoing engineering and capacity to give great outcomes without prerequisite of manual component extraction from crude ipicture information. This paper centers around review of different face demeanor acknowledgment methods dependent on CNN. It incorporates cutting edge techniques proposed by various scientists. The paper additionally shows steps required for utilization of CNN for FER. This paper additionally incorporates examination of CNN based methodologies and issues requiring consideration while picking CNN for unraveling FER.
Comparative Study on Environmental Pollution through Software Techniques
Authors:-Assistant Professor C.Brintha Malar, Associate Professor K.Siva Sankar
Abstract:- Water pollution is one of the serious threats to the society, as water is the primary need of every organism thriving on earth. It is necessary to control and detect water pollution by assessing the quality of water. However, the production of wastewater is always there and is inevitable. Hence, it is equally important to treat the wastewater in a better way, such that the environment is not affected. The pollution control board has formulated certain standards, which provides the range of values for each pollutant and the feasible discharge locations. Taking these standards as the input for training the system, this work extracts basic statistical features such as mean, standard deviation, entropy and variance for training the classification system. The ensemble classification is incorporated, which includes k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The performance of the proposed approach is evaluated in terms of accuracy, sensitivity and specificity. The results of the proposed approach are found to be satisfactory.
A Review : Cyber Warfare
Authors:- Phd Scholar Col Bhim Sen, Dr. F. R. Khan, Dr. S.M. Salim (HOD)
Abstract:- The paper defines that cyber Warfare s a vast topic with various subtopics receiving attention from the research community. It’s a new type of warfare, merely a new weapon that is merged with the traditional conflict. In this generation of mankind, the arrival and the global expansion of the internet is being proved as the fastest and the most powerful technological revolution. Today we are in the situation where we have to decide which governing laws apply to cyber warfare and how much it really applied. Cyber warfare. During research it seemed critical to understand the current situation and the boundaries that are crossed and cyber warfare.
A Review Article ofAnalysis of Islanding Phenomenon in PV Connected System with Protection of Its Effects
Authors:- Manish Kumar, Arun Pachori
Abstract:- The investiture in Distributed Generations (DGs) technologies have begun to monopolies transmission system’s architecture instantaneously after announcing its liberation and deregulation of the energy market to the public. Such movement has propelled vicious competitions among power generation companies to initiate superior innovations in meeting consumers’ needs, administering high power quality yet economical. With the integration of DG technologies, power grid operations have dynamically benefited from such commendable services which apprehend power failures from transpiring; securing power quality, accommodating capricious demand curve, fault compensatory applications and other auxiliary services. Subsequently, the presence of DGs have re-volutionised power grid management to conceive off-grid and self-sustaining criterion where grid/islanding topology adaptations are commonly affiliated.
Prediction of Colon Cancer Using Region Seed Growing Segmentation and Dnn Classifier
Authors:- Asst. Prof. M.V.Crispin, Asst. Prof. T. Anandhan, Asst. Prof. Kpv Pinkey Roshan
Abstract:- To determine the clinical manifestations and optimal management of patients with colorectal cancer (CRC) metastasis advanced malignancy we propose a new predictive modelling by using region seed growing segmentation and classification for predicting the colon cancer disease through continuous monitoring. Here, the region seed growing is used and which is based on transition region extraction for effective image segmentation. Moreover, region seed growing algorithm is used to categorize the transitional region features from the colon cancer image. Inaddition, We used to establish the deep neural network concept for training the image and testing the image with the help of weight estimating classifier.The experiments have been conducted by using the standard images that are collected from database and the current health data which are collected from patient. The results proved that the performance of the proposed prediction model which is able to achieve the better accuracy when it is compared with other existing prediction model.
IOT Based Disease Monitoring System for Apple Orchardin Himachal Pradesh
Authors:- Karuna Sheel, Dr. Anil Sharma
Abstract:- IoT is the technology which has a huge contribution now a days in the diverse application areas. Apple, a cash crop, is one of the fruits which is high in demand throughout the year. Himachal Pradesh secured second largest placein India for apple production. The economy of this state is predominantly dependent on apple cultivation.Fluctuation in climate and weather conditionshave a huge impact on production and quality of apple and plant as well. The success of apple production highly depends on climatic and environmental factor like temperature and humidity. The present study is focusing to design an IoT based system to predict the various diseases and their impact on the apple during the different whether conditions. For this, some popular IoT boards with sensors, and Wi-Fi transreceiver will be used to monitor the data which can be evaluated for further recommendations.
A Review Article of Improvement of Solid Oxide Fuel Power Plant Efficiency Using Power System Topology
Authors:- Raunak Kumar Jha, Dr.L.S.Titare
Abstract:- Fuel cell technology is a relatively new energy-saving technology that has the potential to compete with the conventional existing generation facilities. Among the various Distributed Generation or onsite generation or localized generation technologies available, fuel cells are being considered as a potential source of electricity because they have no geographic limitations and can be placed anywhere on a distribution system. Fuel cells have numerous benefits which make them superior compared to the other technologies. The integration of the fuel cell system is to provide the continuous power supply to the load as per the demand. In this paper, design and modeling of Solid Oxide Fuel cell (SOFC) is discussed for the distributed generation applications. Modeling and simulations are carried out in MATLAB Simulink platform. Solid oxide fuel cells operate at temperatures near these are highly efficient combined heat and electric power. Modeling of SOFC is done by using by using Nernst equation. In that the output power of the fuel cell can be controlled by controlling the flow rate of the fuels used in the process.
Modeling and Optimization of Self-Organizing Energy- Saving Mechanism for Het Nets
Authors:- Mohit Kumar Soni, Prof. Ratan Singh
Abstract:- Smart and versatile wireless devices brings with them, an ever continuing challenge of finding efficient means for resource usage. Scope and avenues for capacity and coverage improvement in cellular networks are constantly explored. Deployment of small cells such as microcells, Pico cells, hotspots, and relays proved an effective solution to improve network coverage and capacity. However, this increase in performance occurs with the cost of deployment and maintenance of additional base stations. Another interesting solution to improve coverage and network capacity is the use of user equipment with relaying support. Currently, mobile devices are equipped with higher processing power and battery life and hence can act as relay nodes in idle slots for nearby users which have lower coverage. This project explores the possibility of user equipment deployed as relays node in heterogeneous networks and analyses the performance improvement for the same. And also analyses the energy efficiency aspect of such communication and show that using user equipment’s as relay helps improving energy efficiency too. Obtained results are verified using extensive simulations.
The Potentiality Examination of Mixture Imitative Resistive System for Infringement Quested Based On Hybrid Belief Function
Authors:- Rajesh Kumar, Prof. Ratan Singh
Abstract:- – The aim of this analysis is the creation and associate build up the system to forestall an organism against each well-known and new attacks, and functions as an adaptive distributed defense system or adaptive artificial system. Artificial Immune Systems abstract the structure of immune systems to include memory, fault detection and adaptive learning. We tend to propose associate system primarily based real time intrusion detection system exploitation supervised learning algorithmic rule. Basically that system or model consists of two layers.
A probabilistic model primarily based T-cell formula that identifies potential attacks, and
A call tree primarily based B-cell model that uses the output from T-cellsalong with feature info to verify true attacks.
The formula is tested on the KDD 99 information, wherever it achieves an occasionalwarning rate whereas maintaining a high detection rate. This can be true even just in case of novel attacks, that may be an important improvement over alternative algorithms.
A Review on effect of positioning of RCC shear walls of different shapes on seismic performance of building resting on sloping ground using STAAD-Pro
Authors:- M.Tech Scholar Ayush Kumar Agrawal, Prof.Nitesh Kushwaha
Abstract:- Reinforced concrete (RC) buildings often have vertical plate-like RC walls called Shear Walls in addition to slabs, beams and columns. These walls generally start at foundation level and are continuous throughout the building height. Their thickness can be as low as 150mm, or as high as 400mm in high rise buildings. Shear walls are usually provided along both length and width of buildings. Shear walls are like vertically-oriented wide beams that carry earthquake loads downwards to the foundation.Properly designed and detailed buildings with shear walls have shown very good performance in past earthquakes. Shear walls provide large strength and stiffness to buildings in the direction of their orientation, which significantly reduces lateral sway of the building and thereby reduces damage to structure and its contents. Shear walls in high seismic regions require special detailing. However, in past earthquakes, even buildings with sufficient amount of walls that were not specially detailed for seismic performance (but had enough welldistributed reinforcement) were saved from collapse. Shear wall buildings are a popular choice in many earthquake prone countries, like Chile, New Zealand and USA.
Keywords: RCC, shear wall, building, earthquake loads
Review of Structural Diagrid in Tall Building
Authors:- M. Tech. Scholar Naveen Rewapati, Asst. Prof. Priyanka Dubey
Abstract:- In the modern world due to overpopulation and decrease in the landmass in the cities the need to make high rise structure is now more prevalent than ever before and hence many solutions like outrigger, shear wall and diagrid are available in the structural stability of high rise. This paper deals with the previous research of on diagrids as an emerging alternative to control the lateral displacement in high rise.
Review of Structural Diagrid in Tall Building
Authors:- M. Tech. Scholar Deepak Birla, Asst. Prof. Ashwin Hardiya
Abstract:- Fiber-reinforced polymer (FRP) materials are used in different configration and techniques for strengthening of Reinforced concrete (RC) elements to ensure their longer service life. Use of FRP material as Externally bonded surface is one of the popular strengthening techniques that consists of bond in FRP material using primer and saturant.
Review of GRPF Strengthen Column
Authors:- M. Tech. Scholar Farooq Khan, Asst. Prof. Ashwin Hardiya
Abstract:- Multiplication in hardware can be implemented in two ways either by using more hardware for achieving fast execution or by using less hardware and end up with slow execution. The area and speed of the multiplier is an important issue, increment in speed results in large area consumption and vice versa. Multipliers play vital role in most of the high performance systems. Performances of a system depend to a great extent on the performance of multiplier thus multipliers should be fast and consume less area and hardware. This idea forced us to study and review about the Booth’s Algorithm, modified Booth’s algorithm and its radix-2, radix-4, radix-8 forms.
RE Wall: A Review On An Innovation In Construction Industry
Authors:- M. Tech. Scholar Salman Shaikh, Assistant Prof. Priyanka Dubey
Abstract:- Retaining Wall has been a structure that has been used from ancient times in order to provide stability to slope of soil and prevent the downhill or mound from landslide. The current thesis gives an insight about the usage of conventional cantilever retaining wall and cantilever Wall with shelf relief. The comparison and analysis has been done manually and in Standard analysis package STAAD – Pro. The difference in results of staad and manual calculation were less than 10%. The moment and the deflection at top of cantilever were observed and noted. The FEM element used to perform the analysis in Staad Pro is Shell element. All the lateral and vertical loads were calculated and assigned to the model with corresponding grade of concrete and Steel.
Study of Different Types of Mixer Topologies
Authors:- M. Tech. Scholar Rohit Kumar Sonkar, Assistant Professor K. K. Sharma
Abstract:- In this paper, a comparative study of different types of mixer topologies is presented. Gilbert cell is widely used as core of the mixer because it provides high conversion gain, good port-to-port isolation and low even-order distortion. It is found that the linearity of mixer is very good for Multi-Tanh technique by incorporating multiple differential trans-conductance stage but it reaches to very low conversion gain whereas, use of current bleeding technique increase linearity and conversion gain of the mixer by adding current source to increase the bias current at the expense of power consumption. A very low value of noise figure can be achieved with the switched biasing technique by replacing current source with parallel connected nMOS transistors but due to use of the transistor in place of tail current source, linearity is degraded and more power is consumed. Folded Cascode Technique is used to reduce DC supply voltage by folding the LO switching stage with pMOS transistors in switching stage but it degrades the noise figure. Bulk-driven technique can be employed to lower down the power consumption by providing the switching action via the gate of LO (RF) and amplification by the bulk of LO (RF) transistors, however it reduces the linearity. High linearity is obtained by using CCPD (Cross coupled post distortion) technique by cancelling of third order derivatives but it decreases the conversion gain and consume more power due to increase in the number of auxiliary transistors. MGTR enables to achieve high linearity by incorporating auxiliary transistor but it decreases the overall conversion gain and increases noise figure of the mixer. So, it is observed that there is a trade-off among the performance metrics, i.e., conversion gain, noise figure linearity, and power consumption of the mixer.
Review of Shear wall in High Rise
Authors:- M.Tech.Scholar Dilip Varvaniya, Asst. Prof. Ashwin Hardiya
Abstract:- In current world scenario with ongoing research for new methodology and techniques to improve the stability of building against seismic and other lateral forces. This paper reviews the effect of frames with and without the shear wall system of simulation conducted by various researchers. Comparison of response of shear wall and RC framed structure will have to be done in future scope of this research work. In order to minimize the damages due to earthquake, shear wall are efficient in terms of cost and effectiveness. On the other hand bracings can absorb great degree of energy which is exerted by earthquake.
Coconut: A Review on a Plentiful Farming Produce
Authors:- M.Tech. Scholar Sandeep Patil, Asst. Prof.Ashwin Hardiya
Abstract:- As there is an economic boom there is an increasing demand for infrastructure in order to accommodate the requirement. As this will lead to high demand for natural construction material and this will deplete them quickly. This paper has been presented in order to give the developers an insight about the usage of alternate material and how it make the whole project structurally and economically feasible. A number of literatures has been reviewed and ultimately the coconut shell has been highlighted for a variety of function such as landfill, light weight concrete and structural concrete. This paper contains a thorough investigation on the application of coconut shell in structural concrete with the percentage of replacement of coconut shell as partially so that natural aggregates usage can be minimized. The experiment has been done in this project has considered 7 days and 28 compressive strength of concrete with no replacement 5%, 10%, 15%, 20% and 25%. The coconut shells are widely available natural material and can be helpful to contribute to the sustainability of the construction.
Review : Important Element in Building: Shear wall vs Brace
Authors:- M.Tech. Scholar Sameer Khan, Asst. Prof. Priyanka Dubey
Abstract:- As new technology are becoming more versatile and commercially available, their usage is also becoming popular in improving the seismic resistance. This paper reviews the effect of shear wall and brace independently on a building for simulation conducted by various researchers.In order to minimize the damages due to earthquake, shear wall are efficient in terms of cost and effectiveness. On the other hand bracings can absorb great degree of energy which is exerted by earthquake.
Challenges of 5G Wireless Technologies
Authors:- M. Tech. Scholar Garima Jain, Dr. Sudhir Agrawal(Dean academics), Prof. Ankit Shrivastava
Abstract:- With the evaluation and simulation of long-term evolution/4G cellular network and hot discussion about new technologies or network architecture for 5G, the appearance of simulation and evaluation guidelines for 5G is in urgent need. This paper analyzes the challenges of building a simulation platform for 5G considering the emerging new technologies and network architectures. Based on the overview of evaluation methodologies issued for 4G candidates, challenges in 5G evaluation are formulated. Additionally, a cloud-based two-level framework of system-level simulator is proposed to validate the candidate technologies and fulfill the promising technology performance identified for 5G.
Implementation and Design of Xilinx Based Booth multiplier
Authors:-M.Tech.Scholar Vinod Kumar Balhar, Asst. Prof. Shiva Bhatnagar
Abstract:- Multiplication in hardware can be implemented in two ways either by using more hardware for achieving fast execution or by using less hardware and end up with slow execution. The area and speed of the multiplier is an important issue, increment in speed results in large area consumption and vice versa. Multipliers play vital role in most of the high performance systems. Performances of a system depend to a great extent on the performance of multiplier thus multipliers should be fast and consume less area and hardware. This idea forced us to study and review about the Booth’s Algorithm, modified Booth’s algorithm and its radix-2, radix-4, radix-8 forms.
Silicon on Insulator Technology Review
Authors:- M.Tech. Scholar Abhishek Soni, Asst.Prof. Shiva Bhatnagar(HOD)
Abstract:- An effort to reduce the power consumption of the circuit, the supply voltage can be reduced leading to reduction of dynamic and static power consumption. This paper introduces one of the greatest future technologies of this decade and that is SOI technology. Silicon-On-Insulator transistors are fabricated in a small (~100 nm) layer of silicon, located on top of a silicon dioxide layer, called buried oxide. This oxide layer provides full dielectric isolation of the transistor and thus most of the parasitic effects present in bulk silicon transistors are eliminated. The structure of the SOI transistor is depicted and is very similar to that of the bulk transistor. The main difference is the presence of the buried oxide it provides attractive properties to the SOI transistor. Power has become one of the most important paradigms of design convergence for multi gigahertz communication such as optical data links wireless products and microprocessor ASIC/SOC designs. POWER consumption has become a bottleneck in microprocessor design. For more than three decades, scientists have been searching for a way to enhance existing silicon technology to speed up the computer performance. This new success in harnessing SOI technology will result in faster computer chips that also require less power a key requirement for extending the battery life of small, hand-held devices that will be pervasive in the future. SOI is a major breakthrough because it advances chip manufacturing one to two years ahead of conventional bulk silicon. The following provides a step-by-step look at the developments leading up to the development of SOI technology.
SVD Based Gesture Detection using Cultural Algorithm for Spectrum Sensing
Authors:- M.Tech. Scholar Rakesh Parmar, Asst.Prof. Shiva Bhatnagar (HOD)
Abstract:- Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. Recent research studied spectrum sensing using energy detection and network cooperation via modeling and simulations. However, there is a lack of experimental study that shows the feasibility and practical performance limits of this approach under real noise and interference sources in wireless channels. This paper presents the development of efficient and reliable spectrum sensing algorithm for cognitive radio network with the help of soft computing techniques. A Cultural Algorithm (CA) optimized model for conventional SVD based spectrum sensing algorithm has been presented.
Review of Wide Bandwidth Low Power Low NOISE Amplifier
Authors:- M.Tech. Scholar Surendra Ningwal, Asst.Prof. K. K. Sharma
Abstract:- Recently, Low noise amplifier versatile used in modern wireless communication like Wi-Max, WLAN, GSM, Bluetooth and satellite communication. Low Noise amplifier have important feature like amplify the signal with rejection of noise. Low noise amplifier in modern communication used as filter with amplifier. In recent scenario low noise amplifier available in wide band, single band, multi-band frequency of application. In present days low noise amplifier, the also reduces the reflection of signal exist by elements and connecting interface inside the amplifier. Low noise amplifier available with high gain, noise rejection and with less power consumption. In this paper review the work of past decades done in low noise amplifier. Low Noise Amplifier (LNA) is versatile used as a broadband mixer, low noise amplifier, power amplifier and Darlington amplifier, active balunes, multiband amplifier. Today technology required high speed of transmission efficiency with small power consumption and less utilization of elements in proposed amplifier, Low Noise Amplifier (LNA) products full fill all requirement of modern wireless communications, so that review and discussion, future requirement of technology is needed to discuss. In this paper discusses issues of low noise amplifier, its application, issues and recent trends. In this paper review some techniques of Low Noise Amplifier (LNA) to improve perform and surveyed almost all the Possible Work of Past Decades.
An Assessment Of Set Back And Step Back Building
Authors:- M.Tech. Scholar Rohit Karoda , Asst.Prof. Priyanka Dubey
Abstract:- India consists of great arc of mountains which consists of Himalayas in its northern part which was formed by on-going tectonic collision of plates Hill buildings are different from those in plains; they are very irregular and unsymmetrical in horizontal and vertical planes, and torsionally coupled. Hence, they are susceptible to severe damage when affected by earthquake ground motion. Such buildings have mass and stiffness varying along the vertical and horizontal planes, resulting the center of mass and center of rigidity do not coincide on various floors.
Numerical Analysis of Air Cooled Condenser using Cfd
Authors:- Research Scholar Pramod Kumar, Assistant Professor Jagdish Saini
Abstract:- In frameworks including heat move, a condenser is a gadget or unit used to gather a substance from its vaporous to its fluid state, reliably by cooling it. thusly, the lethargic warmth will move to the coolant in the condenser. condensers are regularly heat exchangers which have different structures and come in different sizes running from somewhat little to incredibly gigantic present-day scale units utilized in plant structures. air-cooled condensers are utilized in little units like family refrigerators, huge coolers, water coolers, window obliged air structures, split air control systems, minimal bundled compelled air structures, and so forth these are utilized in plants where the cooling load is almost nothing and the rigid proportion of the refrigerant for the pattern of refrigeration is basically nothing. air-cooled condensers are in like way called hover condensers as they are typically made of copper or aluminum contort. air-cooled condensers eat up a practically more noteworthy space than water-cooled condensers.
Tensorflow Based Automatic Personality Recognition Based On Facial, Tone and Resume Analysis
Authors:- Divya Gharaniye, Tejasvini Kale, Gauri Suryavanshi, Prof. Sanjay Jadhav
Abstract:- With the development of artificial intelligence (AI), the automatic analysis of video interviews to recognize individual personality traits has become an active area of research and has applications in personality computing, human-computer interaction, and psychological assessment. Machine learning techniques have led to the establishment of convolutional neural network (CNN) models that can successfully recognize human their personality traits with the use of a camera. In this an end-to-end AI interviewing system was developed using asynchronous video interview (AVI) processing and a TensorFlow AI engine to perform automatic personality recognition (APR) based on the features extracted from the AVIs and the true personality scores from the facial expressions. The main task of this study is to predict the big-five traits personality dimensions from video images by using machine learning techniques and artificial neural networks. In this study, video images and the emotional states of the person obtained from videos were utilized and an artificial intelligence based system was developed to be able to predict automatically the personality traits of a person from videos.
Face Recognition Using PNN Classifier and SIFT Feature Extraction
Authors:- Research Scholar Chitransh Popli, Asst. Prof. Priyanshu Dhameniya
Abstract:- The main motive to design to recognize Automatic Face Recognition is the ability of person’s and identity based on facial characteristics. One of the ways to do this is by comparing selected facial features from the test image and a facial database. Usually, the face image of a test subject is matched to the gallery data using a one-to-one or one-to-many scheme. The one-to-one and one-to-many matching are called verification and identification, the feature extraction on the other hand is usually applied to obtain the relevant facial features such as face regions, variations, angles or measures etc. from the data. The system proposes new approach in extension with local binary pattern called DRLBP and PNN classifier used for classification . By using these methods, the category recognition system has developed for application to image retrieval. The category recognition is to classify an object into one of several predefined categories. The discriminative robust local binary pattern (DRLBP) is used for different object texture and edge contour feature extraction process. It is robust to illumination and contrast variations as it only considers the signs of the pixel differences. The proposed features retain the contrast information of image patterns. They contain both edge and texture information which is desirable for object recognition, the simulated results will be shown that used discriminative robust local binary pattern has better discriminatory power and recognition accuracy compared with prior approaches.
A Review on “Energy-Efficient Spectrum Access in Cognitive Radio
Authors:- M.Tech. Scholar Anita Pandey, Assistant Professor Dr. Shivangini Saxena
Abstract:- In cognitive radio sensor networks (CRSNs), the sensor devices which are enabled to perform dynamic spectrum access have to frequently sense the licensed channel to find idle channels. The behavior of spectrum sensing will consume a lot of battery power of sensor devices and reduce the network lifetime In the next-generation cognitive radio networks, numerous secondary users will share the spectrum resource with the primary users. As it may not be possible to support all the communication rate requirements, there are many supporting sets for the secondary users as long as the communication rates of the primary users are guaranteed? In this paper, we study the maximum feasible set problem to access as many secondary users as possible, under the constraints of power budgets and communication rates in cognitive radio networks. In this interesting issue, the existing literature generally removes a subset of the secondary users so that the remaining users achieve the thresholds with communication rates and power budgets. However, the removal algorithms cause more interference when there are plenty of unsupported secondary users. In this work leverage the spectral radius of the network characteristic matrix as the admission price to access the new secondary user. Then, will design a hybrid access control algorithm to reduce the interference time and approximate the maximum network capacity. Moreover, different supported sets produce the different energy efficiency, even having the same network capacity, while all users require the high communication rates. Numerical results demonstrate that our algorithms will provide the decent energy efficiency under the communication rate constraint the simulation will be perform on MATLAB simulation.
Intrastate Student Migration and Trends: Impact On Urbanisation And Subsequent Migration Behavior
Authors:- Mateen Yousuf
Abstract:-Capital cities in J&K have emerged as hub of education activities and as such students from all over the state migrate to the places seasonally. Srinagar as well as Jammu have emerged as the hub of education where students and aspirants flock to enroll themselves in the institutes of their choice. The availability of large number of teachers, enhanced investment, safety reasons, choice and competition, easily available supplementary and complementary facilities are some of the main reasons why students preferred the urban areas as their ideal choice of education. The objective of this research is to find out the trends of migration of students and teachers from rural to urban places in Kashmir. Various changes that have taken place in time have been observed. The research process involved setting up of focus groups of various stakeholders and discussing the research objectives. The inferences drawn were compared to the hypothesis and compared with the data which was collected through open ended questionnaire. It was found out that parents prefer to send their wards to urban areas to avail various facilities but over the years efforts have been made to setup the same facilities in native towns and villages which was found to have a significant negative impact of growth of educational infrastructure in the urban areas. The trend of shifting to urban areas was found to be positive over the past 30 years. It is recommended that the stakeholders collaborate with government to develop the twin cities in the state of J&K as the hub for education not only among intra-state but inter-state as well.
A Review Articleanalysis of Distribution Grid, Power Loss Reduction and Fault Detection In 33 Bus System Using Optimization
Authors:- Vinay Kumar Gupta, Arun Pachori
Abstract:- Power flow is nothing but the flow of active and reactive power. Power flow analysis is used to determine the steady state operating condition of a power system. In short it is to find the approximate values of various bus voltages, their phase angle, active and reactive power flows through different branches, generators and loads under steady state condition. Newton Raphson methods are generally referred as experience based techniques for solving problem, learning and discovery. Heuristics are simple and efficient rules coded by evolutionary processes. In this Work, ANN, one of such GS have been used to do the power flow analysis in a simple three bus system.
Design and Implementation Low Power Consumption Level Shifter
Authors:- M.Tech. Scholar Kishan Bandil, Professor K. K. Sharma
Abstract:- A voltage level shifter is a circuit which converts low level input voltages to a desired higher level voltage or vice versa as desired, depending upon the system requirements. The major application of a voltage level shifter is in resolving mixed voltage incompatibility. The integrated circuits which are widely used nowadays may have different parts within, which operate at different voltage levels. Most of these circuits were provided with different supply voltage levels which prove to be difficult when the number of supply voltages increases within a single circuit increasing its complexity. Energy efficiency is a primary concern in modern CMOS circuits. Thus level shifters which are capable of converting a single low level voltage to other voltage levels have become useful in circuits having parts operating in multiple voltage domains. In the proposed design, the major aim is to lower the power dissipation and make the circuit faster.
Deep Semantic Image Segmentation using Convolutional Neural Networks for Multi Modal Data
Authors:-Asst.Prof. P Jaganmohan , Asst. Prof. R Suneel Kumar
Abstract:- In this paper, a deep semantic segmentation of aerial imagery based on multi-modal data is discussed. given multi-modal data composed of true orthophotos and the corresponding digital surface models (dsms), we extract a variety of handcrafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. the latter is represented by a residual shuffling convolutional neural network (rscnn) combining the characteristics of a residual network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. the derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. furthermore, the consideration of data on both modalities leads to an improvement of the classification results. however, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation.
Free Energy Generator Neodium Magnets Repulsion
Authors:- M.Tech. Student Shubham Khatri, Professor Dr. Shailesh Gupta
Abstract:- This thesis is devoted to the design and optimization of magnetic system ,More than 90% world’s power is being generated using electromagnets based on the faraday’s law of electro-magnetic induction. Many new technologies were discovered with time which led a drastic change in the perception of electric energy. But at the same time there is misconception of free energy. Energy becomes free only at a point after which we don’t have to pay for power generation after commissioning the unit. By using the magnetic force of magnets continuous motion (Energy) is generated. We used Neodymium magnets are placed on the outer drum and an inner drum by placing same poles for maximum repultion. Rectangle shaped magnets are placed in such a way that all the north poles or north poles are facing opposite direction.
Artificial Intelligence Based MPPT Algorithm For Grid Connected Solar PV System
Authors:- M. Tech. Student Darshil N Shah, Asst.Prof.Dr. Manisha Shah
Abstract:- On going investigates situated to photovoltaic (pv) frameworks highlight blasting enthusiasm for current decade. for productivity improvement, greatest force point following (mppt) of pv exhibit yield power is obligatory. herein the article, different analytical strategies has been discussed based on ai (artificial intelligence). Conventional mppt techniques provide abridged assembly and execution; also its routine is dishonored after associated through reproduction reasoning techniques are uncertain sense regulator (fl), artificial neuronic grid (ang), flexible nerves- uncertain border structure (fnubs). one of farcical ai based technique which name is flexible nerves uncertain border structure get closer and extra accurate outcome against all the ai based technique and also ordinary systems. likewise, this article demonstrates that the anfis strategy is better in all the ai-based mppt power following plans for low overshoot, high dependability with the higher consistent state condition, and less tedious in yield. as, per this proposed technique to the pv array and applying all kind of conditions to it, effective and enhanced outcome has been arrived.
A Review: Uses of Additives in the Development of Water Treatment Plant Sludge Bricks
Authors:- Nurul Farah Syaheyra Rahmat, Nurul Nadiah Mohd Firdaus Hum
Abstract:- Water treatment plant sludge generation and management has becoming a global problem, leading to increased concern for the environment. Sludge management and recycling into safe building materials have proven as an alternative to waste disposal to reduce environmental and economic pollution. Reused of sludge with additives in brick making industry is a sustainable solution due to increasing demand for bricks in construction works nowadays. This study reviews development of water treatment plant sludge bricks and different additives implemented in brick making process. The study addressed the characteristics of water treatment sludge, the advantages of natural and chemically made additives, as well as the compressive strength and water absorption properties of bricks.
Under Water Imaging by Signal Processing Techniques
Authors:- M.Tech. Nagabathula Ramya, Asst. Prof. Akurathi Gangadhar
Abstract:- SONAR is an acronym for sound Navigation And Ranging. The basic principle of sonar is to use sound to detect or locate objects, typically in the ocean. Sonar technology is similar to other technology such as RADAR (radio Detection And Ranging).Basic radar systems use electromagnetic wave reflections from targets to determine the characteristics of the targets. Synthetic Aperture Radar (SAR) systems use the reflections to produce target images as well. SAR and SAS are an imaging system that produces high resolution images of a scene or target by using motion to synthesize the antenna aperture. While Synthetic Aperture Sonar(SAS) is closely resembles to SAR.In this paper we are demonstrating Synthetic Aperture Sonar (SAS) processing for a point target case. The input raw SAS data is generated and the SAS processing is simulated in Matlab.
Control Strategy for Bidirectional AC-DC Interlinking Converter in AC-DC Hybrid Microgrid Using PV System
Authors:- M.Tech. Scholar Nimesh Upadhyaya, Prof. Madhu Upadhyay
Abstract:- This paper presents optimize energy extraction in photovoltaic (PV) energy systems. The maximum power of the photovoltaic module will change due to changes in temperature, solar radiation and load. To maximize efficiency, the photovoltaic system uses a maximum power point tracker (MPPT) to continuously extract the most power that the solar panel can generate and then pass it on to the load. The overall structure of the MPPT system consists of a DC-DC converter (an electronic device that converts DC energy from one voltage level to another) and a controller. During changes in weather conditions, MPPT uses a tracking algorithm to find and maintain operation at the point of maximum power. Many different algorithms for MPPT have been proposed and discussed in the literature, but most of these methods have disadvantages in terms of efficiency, precision, and flexibility. Due to the non-linear behavior of the current voltage characteristics of the PV module and the non-linearity of the DC-DC converter due to switching, conventional controllers cannot provide an optimal response, especially when dealing with a wide range of shifting line parameters and transients. The purpose of this work is to design and implement a maximum power point tracker using fuzzy logic control algorithms. Fuzzy logic naturally provides an excellent controller for such nonlinear applications. This method also benefits from artificial intelligence methods that can overcome the complexity of modeling nonlinear systems. To make this work a success, Simulink designed and simulated an MPPT system consisting of photovoltaic modules, DC-DC converters, batteries, and fuzzy logic controllers. Perform the characterization of the buck, boost and buck-boost converter to find the most suitable topology for the PV system used. The integrated model of the PV module with the identified converter and battery was simulated in MATLAB to gain the necessary experience to formulate and adjust the fuzzy logic controller. The simulation results show that fuzzy logic.
A Review on Fuzzy Logic Control Based PV Module and Bidirectional Dc-Dc Converter Design
Authors:- M.Tech.Scholar Nimesh Upadhyaya, Prof. Madhu Upadhyaya
Abstract:- This work presents a optimize energy extraction in photovoltaic (PV) energy systems. The maximum power of the photovoltaic module will change due to changes in temperature, solar radiation and load. To maximize efficiency, the photovoltaic system uses a maximum power point tracker (MPPT) to continuously extract the most power that the solar panel can generate and then pass it on to the load. The overall structure of the MPPT system consists of a DC-DC converter (an electronic device that converts DC energy from one voltage level to another) and a controller. During changes in weather conditions, MPPT uses a tracking algorithm to find and maintain operation at the point of maximum power. Many different algorithms for MPPT have been proposed and discussed in the literature, but most of these methods have disadvantages in terms of efficiency, precision, and flexibility. Due to the non-linear behavior of the current voltage characteristics of the PV module and the non-linearity of the DC-DC converter due to switching, conventional controllers cannot provide an optimal response, especially when dealing with a wide range of shifting line parameters and transients. The purpose of this work is to design and implement a maximum power point tracker using fuzzy logic control algorithms. Fuzzy logic naturally provides an excellent controller for such nonlinear applications. This method also benefits from artificial intelligence methods that can overcome the complexity of modeling nonlinear systems. To make this work a success, Simulink designed and simulated an MPPT system consisting of photovoltaic modules, DC-DC converters, batteries, and fuzzy logic controllers. Perform the characterization of the buck, boost and buck-boost converter to find the most suitable topology for the PV system used. The integrated model of the PV module with the identified converter and battery was simulated in MATLAB to gain the necessary experience to formulate and adjust the fuzzy logic controller. The simulation results show that fuzzy logic.
Internet of Thing Based Air Pollution Analysis System Using Sensors
Authors:-ME Student Mistri KulprakashSingh AvatarSingh , Associate Prof. Khansole Balaji
Abstract:- The level of pollution has increased with times by lot of things just like the increase in population, increased vehicle use, industrialization and urbanization which ends up in harmful effects on human wellbeing by directly affecting health of population exposed thereto. so as to watch during this project we are getting to make an IOT Based pollution Monitoring System during which we’ll monitor the Air Quality over an internet server using internet and can trigger a alarm when the air quality goes down beyond a particular level, means when there are sufficient amount of harmful gases are present within air like CO2, smoke, alcohol, benzene and NH3. it’ll show the air quality in PPM and also as on webpage so that we will monitor it very easily. During this IOT project, you can monitor the pollution level from anywhere using your computer or mobile.
Review of Microalgae for Intensified CO2 Fixation & Sustainable Bio-Fuel Production
Authors:- PhD Scholar Manish Sutradhar
Abstract:- Microalgae are the convincing oxygenic photoautotrophic organisms furnished with tremendous potential of performing environmental services and energy recovery to promote carbon neutral bio-economy. There is a major challenge to the global sustainability due to unbalanced production of the CO2. The concentration of CO2 is rising gradually primarily due to human activities such as burning of fossil fuels leads to increase the level of carbon dioxide and has a different ratio of heavy-to-light carbon atoms, so it leaves prominent footprints that instruments can measure. Technologies have thus been developed for enhanced biological carbon fixation. This review briefly examines the current technologies available for enhanced micro algal CO2 fixation and specifically explores eventual production of bio fuels and/or added-value products, with an emphasis on its productivity.
Smart Door Lock Using IOT
Authors:- Student Nirmit Shah, Student Tejas Potekar, Student Shrey Mistry, Asst. Prof. Manish Bhelande
Abstract:- Security has always been a major concern for the households and the office environment. With this consideration, a design and prototype of a keypad-based door lock system has been presented in this paper. This provides tools to enforce reliable logs of system transactions and protect an individual’s right to privacy. Passwords of the authorized users are enrolled and verified to provide access to a facility that is used by multiple users. A user can also be removed and a new user can be enrolled in the system. We have implemented a centralized control system from where we can control who can enter in which rooms and who cannot. It is an Arduino UNO device based flexible working device that provides physical security.
The Application of Weather Forecast using Time Series Analysis
Authors:- Vidhya Vasantrao Sambare, Prof. Arvind Jain
Abstract:- Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.
Review on Microplastics Abundance in Malaysian Marine Environment
Authors:- Nurul Nadiah Binti Mohd Firdaus Hum, Siti Anisah Binti Zainal Abidin
Abstract:- Microplastics in marine environment posed an alarming threat to aquatic lifeforms, and to human consumers. They are characterized according to their physical appearance (size, shape, colour) and its chemical composition through the types of polymers. However, the study on microplastics in Malaysian marine environment still limited due to few studies conducted. Hence, this study aims to visualize, assess and characterize the microplastics abundance in Malaysian marine environment. Ten years’ duration of literature review (2010 until 2020) was used in this study. For water sample study, the highest microplastics abundance is from coastal area of Pulau Pinang and Langkawi meanwhile, in sediment sample study is from coastal zone at Straits of Johor. Tourisms and industrial are huge contributor on microplastics abundance in Malaysia. Fragment and fiber, dark colours (black, grey) and white showed the most dominant physical characterization of microplastics analyzed. The dominant polymer’s composition is originated from the industry and consumer products.
Wireless Sensor Networks for Distributed Access Control using Priccess Method
Authors:- Asst. Prof. A. Rajiv, Asst. Prof. C. Lakshmi
Abstract:- The Distributed access control allows the network to authorize and grant the permission to user to access the data in Wireless Sensor Network. In terms of providing security and authentication to the Wireless sensor network, our project is created. A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical conditions and to cooperatively pass their data through the network to a main location. The more modern networks are bi- directional, also enabling control of sensor activity. Hence the wireless sensor network is especially creating a high secure environment. Our paper mainly focuses on designing such access control modules for WSNs. It focuses on group sharing too. That is a distributed access control will share the data to all the users in that network whereas it will provide security to the WSN by avoiding a new user to unaccess the data. Sometimes a user does not want to associate his identity to data which he request. Only the data he needs. Because it might helps a new user to know his details while sharing the data. To avoid such issues here in our project we are introducing a query for a group to access the data. Here a single person will act as a leader in a group and he can create a query to sign in. So that the identity of the users will not known by anyone and also the whole group can get the data via this process.
Automatic Glaucoma Detection Using Sobel Edge Detection & Svm Classifier
Authors:-M.Tech.Scholar Akansha Sharma, Asst.Prof.Jayshree Boaddh, Asst.Prof.Jashwant Samar
Abstract:- It is hard to diagnose the glaucoma from single routine test that is why it is supposed as one of the most complex disease for diagnosis. Glaucoma may affect the eyes that reach the deficiency towards either partial blindness or complete blindness. It cannot be treated once it occurred but it can be cured by routine examination. Diagnosing glaucoma on time may save the rest vision but it does not improve the eyesight by treating by any medical experts. An OCT scan is an important test in diagnosing and monitoring glaucoma. Here the system is based on Sobel Edge Detection and Support image processing and classification methods to detect glaucoma by comparing and measuring various parameters of the fundus images of glaucoma patients and general patients.Vector Machine (SVM) Classifier. Sobel can highlight the nerves or blood vessels and segment the fundus optic disc that may or may not affect by glaucoma. On the other hand SVM can classify the affected area or impaired cells that can resulted a better diagnostic system. It is an irregular or polynomial data classification that can work for Iris based fundus images. System acquired 94.45 % of accuracy with minimal false alarm rate and obtains less processing time.
Digital Signature Server Using Elliptic Curve Cryptograpic Algorithm
Authors:- S.B.Sangeetha, Jasmine Sait Mohmmad, Praba.M
Abstract:- Application servers are heavily loaded by performing signature computation Instead of using Application server which act as the tenant to more dedicated proxy called Signature servers called GUESSThus to improve the performance of the GUESS comprehensive implementation of the EDSA is tested against network trafficDigital signatures are encrypted and decrypted using Elliptical curve cryptographic algorithm
A Review of Automatic Glaucoma Detection from Fundus Imaging
Authors:- M.Tech.Scholar Akansha Sharma Asst.Prof. Jayshree Boaddh Asst.Prof. Jashwant Samar
Abstract:- To mographical imaging is a technique through which diagnosis and monitoring can happen with an effective manner. If a disease can be diagnosed in an early stage always been a preference in the field of medical science. Glaucoma is one of the chronical disorder of optic discs in which fluid pressure increases and if it is remain untreated; the patients may lose their vision and even they may pertain total blindness. This disease cannot be cured, early recognition and precautions may protect fundus nerve against serious vision loss. It does not affect instantly, the loss of vision may occurs gradually over a long period. Glaucoma detection involves some measurements like shape and size of optic disc. Here this paper reviewed various researches and concluded that most of the researches are intended to classify the blood vessels for broken vessels detection that signifies the Glaucoma in a better way. Accuracy may degrade due to incorrect or inappropriate masking. An IRIS has some sensitive information that has not to be tampered any how.
Matriculation College Students Evaluation of English Program using the Process Component of CIPP Model
Authors:- R.Nirumala Rothinam
Abstract:- This study aimed to evaluate the Matriculation College English Program using Stufflebeam’sCIPP (context, input, process, and product) evaluation model focusing on the process component. Data was collected through a cippsurvey from42 matriculation students and correlated with their Malaysian University English Test (MUET)trial exam. The CIPP survey sent to students, which had a response rate of 100%, helped to identify the needs of a focus group to obtain better results in MUET. Accordingly, teaching methodology was redesigned to include more visual-audio based learning, student-instructor interaction was increased through face-to-face meetings together with more reinforcement exercises .The results indicated thatwith the new methodology based on the student feedback through the survey, students performed well in the MUET.
Study Report on Failure Analysis of TMT Bar Due to Steel Defects
Authors:- Pramod Kumar
Abstract:- Thermo-mechanical treated (TMT) rebar is used as material for reinforcing concrete structures due to its unique Properties of thermal expansion, ability to bond well with concrete and resist the tensile stress acting on the structure and also steel manufacturing industry has successfully developed a corrosion- resistant variety of rebar for the construction industry. By controlling the proper rolling parameters and water quenching box is required to achieve adequate property. Water quenching box & used billets plays an important role for achieving the final structure and property of the rebars as well the bend properties. Steel quality of the rolled billets are for the production of good surface finish quality TMT bar & also passed in bending of bar. The present paper highlights failure investigation of a failed rebar during bending operations. From micro structural analysis & study of fracture surface it is confirmed that the rebar sample failed in ductile manner due to the steel defects like sub surface blow holes inside the rolled billets originating from thecaster.
Analysis of Performance of Mutual Funds of India
Authors:- Research Scholar Saloni Chauhan, Dr. Suresh Kataria, Dr. Rakesh Dhand Retd. Professor and DSW, HOD Professor
Abstract:- – Among various investment alternatives, mutual funds have emerged as an important investment option among the investors. It is with this fact in mind that present study attempts to analyses the growth and performance of 5 Mutual funds from 2005 to 2015. The performance of selected 20 mutual fund schemes has been analyzed on the basis of risk – return relationship using standard performance evaluation measures. These measures were extensively used in various other studies earlier. So, using these measures, the researcher has attempted to analyze the performance of the selected 20 schemes of the selected mutual funds during the period 2005 to March 2015. Out of these 20 schemes 10 are open ended fund schemes and other 10 are oriented equity fund schemes. The performance of the selected schemes was analyzed in terms of risk-return relationship using the leading performance evaluation measures. In order to calculate return of the selected schemes, the monthly adjusted Net Asset Value data were used for the period from April 2005 to March 2015 The scheme returns are compared with the Fund’s return, Market return. The result of the study shows that as compare to the Market return 50% schemes are earned better return, 50% schemes were riskier. In terms of standard deviation 25% schemes are doing better whereas 75% are risky schemes. In performance of the market ration 10 schemes out of 20 schemes performed the market under various measures. However, the study reported underperformance of the majority of the schemes out of the selected twenty (20) schemes. Gold investment is characterized by a high degree of liquidity. You can trade gold anywhere and anytime. Mutual funds also carry a considerable degree of liquidity as you can encase your mutual fund at the present Net Asset Value. However, you can sell them only in particular segments of the market. It is not possible to sell mutual funds to anyone and anywhere. Both mutual funds and fixed deposits are a popular choice among investors. Each of these financial instruments are unique and provide investors with good returns over a period of time. If you want to invest in either of these financial instruments, it is advisable to make a stern comparison between the two. Here is a list of differences between Mutual Funds and Fixed Deposits. Stock market investing means investing directly in the stocks of the company. Here, you are purchasing the companies listed on the stock exchange with an expectation to earn profits when the price of that stock goes up. On the other hand, a mutual fund is a collective investment that pools together the money of a large number of investors to purchase a number of securities like stocks, FDs, bonds, etc. A professional fund manager manages this fund. When you purchase a share in the mutual fund, you have a small stake in all investments included in that fund. Hence, by owning a mutual fund, the investor participates in gains or losses of the fund’s portfolio. When you buy a bond, you’re essentially lending money to an entity. Generally, this is a business or a government entity. Companies issue corporate bonds, whereas local governments issue municipal bonds.
Grid Connected DFIG system Based on STATCOM and PID Controller
Authors:- M.Tech. Scholar Siddhartha Tiwari, Prof. Dwarka Prasad
Abstract:- In this paper, the relation of electricity production to electricity will affect the quality and reliability of the system. The effects of wind turbines on the quality of electricity in the system are high power, active power, volume change, flicker, harmonics, and power consumption in rotation, which all are measured in accordance with national and international standards. The findings of this paper prove the problem of electrical quality resulting from the installation of a turbine with a power supply. Through MATLAB / SIMULINK, control measures have been proposed to alleviate the problem of electrical quality based on the corners that are connected to STATCOM and PID controller.
Age Estimation from Facial Image:A Survey
Authors:- Chandra Prakash Patidar, Pooja Patidar, Ashish Kumar Jain, Jagdish Raikwal, Ritubala Patidar
Abstract:- In recent years, Age estimation approaches are challenging and time consuming. Age estimation has a lot of applications such as minimum driving age, cosmetology, finding lost people, etc. There are different models and algorithms for age estimation techniques. In this survey paper, we review the previous research papers and analyze their age estimation methods. We are shortly analyzing the classification and regression techniques.A comprehensive summary of analyzing methods is introduced with popular datasets.
Problems Formulation and Observation Of Repairing Damaged Floor Laid Expansive Soil
Authors:- Ritu Mewade
Abstract:- Engineering structures constructed on expansive soils detrimental behavior of such soils, leading to their damage and cracking. The structure which can not resist the heave pressure of soil and undergo temporary or permanent deformation is known as light structure. Less lightly loaded structures like, house, canal banks and linings, cross drainage works, have been damaged and cracked due to these soil. The damage occurs, due to the swelling and shrinking behavior of such soils. Since the structures built on such soils get lifted up during rainy season due to the heave of the foundation soil and settle down during summer season due to the shrinkage of the foundation soil, there is a need to adopt remedial measures so as to prevent lifting and sinking of the structures.
Implementing Artificial Intelligence in Thermoelectric Generators: A Review of Data Science Applications in Enhancing Efficiency and Security
Authors:- Arvind Malhotra, Rohit Bedi
Abstract:- The integration of Artificial Intelligence (AI) into thermoelectric generator (TEG) technologies offers a groundbreaking approach to improving both energy efficiency and cybersecurity within the rapidly evolving Internet of Things (IoT) ecosystems. This review delves into the diverse applications of AI-driven methodologies—including machine learning, big data analytics, and predictive modeling—to enhance the operational performance of TEGs, with a particular focus on systems utilizing advanced thermoelectric materials such as bismuth telluride (Bi2Te3) and lead telluride (PbTe). By conducting an extensive examination of the existing literature, this paper identifies and analyzes key AI techniques that have been instrumental in optimizing energy conversion processes, thereby significantly boosting the efficiency of TEG systems. Moreover, it explores how AI can be leveraged to fortify the security of IoT ecosystems, addressing vulnerabilities and safeguarding interconnected devices against potential cyber threats. The review also discusses the synergistic potential of integrating AI with TEGs to create intelligent, adaptive systems capable of responding dynamically to varying conditions and threats. The findings underscore AI’s pivotal role in not only advancing TEG efficiency and IoT security but also in shaping future research trajectories aimed at overcoming persistent challenges. Ultimately, this review highlights the transformative impact of AI on developing resilient and sustainable energy solutions, emphasizing its importance in meeting the growing demands of modern energy systems and securing digital infrastructure in an increasingly interconnected world.
DOI: 10.61137/ijsret.vol.6.issue6.214
Performance of Temperature Over Selected Flow Improvers
Authors:- Igwe Japheth Chidiamara
Abstract:- Wax crystallization and deposition at temperature below wax appearance temperature (WAT) in crude oil stream poses a serious problem, in the production, storage, processing and transport of crude oil. In this attempt, high temperature (heating) which readily comes to mind might not be the best option when its contribution is evaluated side by side with that of triethanolamine(TEA) and ethylvinyleacetate(EVA). This is particularly true when the cost of heat is put into consideration together with its operational difficulties of working at high temperatures. 0%, 0.025%, 0.05%, 0.1%, 0.2%, 0.4% and 1% concentrations (by volume percentage) of TEA and EVA were separately introduced into separate but same quantities of the same crude oil stock at temperatures 30oC, 40oC, 50oC, 60oC, 70oC and viscosity effects monitored with respect to temperatures. Zero concentration of the improvers served as control. TEA and EVA both drastically dropped viscosity of the crude oil very close to what temperature could offer although temperature had an overriding effect. EVA did not appear to be affected by higher temperature/heat as with TEA. Combination of TEA or EVA with higher temperature gave better results as from the lowest viscosity achieved across the temperatures, viz: 0.0944(TEA) and 0.1015 (EVA) which happened at 70oC and concentration of 0.025%. TEA proved to be a better flow improver with lowest viscosity of 0.1124centistokes against EVA having 0.1184 centistokes at 30oC and 0.025% concentration. The same was also observed at the apex temperature of 70oC (TEA = 0.0997, and EVA = 0.1015centistokes at 0.025% concentration). These findings when applied in the industry will minimize the processing cost of crude oil both in material and non-material terms.
Creational Design Patterns for Dynamic Device Management in IoT Biometrics
Authors:- Vinayak Ashok Bharadi
Abstract:- This paper investigates the application of creational design patterns—specifically the Singleton and Factory patterns—in Java-based IoT frameworks for managing biometric devices. The study builds on Ramakrishna Manchana’s 2019 research on design patterns, demonstrating how these patterns can enhance scalability, modularity, and flexibility in IoT systems dealing with multimodal biometric data. By employing a dynamic configuration model, the proposed framework enables efficient device management and secure data processing, ensuring adaptability to evolving IoT requirements.
DOI: 10.61137/ijsret.vol.6.issue6.818
Writing Orality as Resistance: An Exploration of Janice Pariat’s Boats on Land and Its Postcolonial Underpinnings
Authors: Joyshree Saikia. Assistant Professor
Abstract: The representation of North East India has been colonial and ethnographically reductive and there has been a gross misrepresentation of the heterogeneous traits of the region under the faulty homogeneous label of 'Northeast'. It is undeniable that the region is a treasure house of unique traditions, cultures, languages and dialects. However, the only obvious trait of homogeneity among them is reclaiming their indigenous oral tradition/s in present time. Within any colonial discourse, the oral form “is generally identified with the illiterate and even the uncivilized” (Ao 104). Hence, tribal oral culture has oftentimes been misinterpreted by colonial discourse, which leads to identifying the communities as illiterate, ignorant, and backward. The study will examine how the concept of writing orality is accomplished in Boats on Land, a short story collection by contemporary Khasi writer Janice Pariat. The stories in this collection attempt to disrupt the hierarchy of the textual over the spoken, a binary that Pariat believes is a colonial construct. The study aims to show how the valorisation of the Khasi oral tradition in the text challenges the pre-established dominance of written literature.
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Unified BI Strategy for Enterprise Migration: Aligning Qlik, Tableau, and Power BI for Seamless Reporting
Authors: Ajay Kumar Kota
Abstract: In today's complex enterprise environments, organizations often operate multiple business intelligence (BI) platforms such as Qlik, Tableau, and Power BI across various departments. While each tool offers unique capabilities, this fragmented approach can lead to inconsistent reporting, duplicated efforts, and governance challenges. This article explores the strategic importance of developing a unified BI framework that aligns these tools within a centralized governance and data architecture. It discusses the drivers behind BI unification—including digital transformation, compliance, and cost optimization—while addressing the technical and organizational challenges of multi-tool environments. A detailed case study demonstrates how an enterprise successfully integrated multiple BI platforms to reduce costs and improve data trust. The article concludes with best practices and a future outlook, emphasizing the importance of flexibility, interoperability, and user empowerment in driving long-term analytics success. A unified BI strategy is presented not as tool consolidation, but as a cohesive vision that ensures consistent, trusted, and actionable insights across the entire organization.
DOI: https://doi.org/10.5281/zenodo.16023841
Enhancing EHR Security Compliance through Adaptive Unix Server Hardening Models
Authors: Natalia Ivanovna Baranova, Dmitry Alekseevich Tikhonov, Yulia Sergeevna Pankratova, Ivan Mikhailovich Rogozin
Abstract: Electronic Health Records (EHRs) are foundational to modern healthcare systems, but they are also lucrative targets for cyberattacks due to the sensitivity of medical data. Ensuring the confidentiality, integrity, and availability of EHRs requires robust server-level defenses. This study investigates the implementation of adaptive Unix server hardening models tailored for healthcare environments. It outlines a layered approach to security that integrates dynamic configuration baselines, continuous monitoring, and compliance mapping to standards like HIPAA, HITRUST, and NIST. Through adaptive hardening strategies, including automated shell scripts, auditing frameworks, and anomaly detection, we propose a defense-in-depth model that significantly enhances EHR security posture. Real-world use cases and benchmarks validate its practicality in live healthcare infrastructures.
DOI: https://doi.org/10.5281/zenodo.16312367
Compliance-Centric Server Automation for Genomic Data Repositories
Authors: Rahmatulloh Tohirovich Saidov, Mavjuda Gafurovna Khudoerzoda, Akmal Ziyodulloevich Kholov, Shahrukh Nasrulloevich Nazarov, Dilnoza Mahmadaliyevna Qalandarova
Abstract: As genomic data repositories expand rapidly with the growing need for precision medicine and population-scale genomics, managing the integrity, security, and regulatory compliance of these repositories has become a paramount concern. This paper presents a compliance-centric approach to automating server infrastructure specifically tailored for genomic data management. We explore the integration of Unix-based server automation tools with security-first policies and standards such as HIPAA, GDPR, and ISO 27001. The study outlines how configuration management, access control automation, logging, and continuous compliance auditing are implemented to ensure operational resilience and regulatory alignment. By leveraging scripting, cron-based scheduling, and policy-as-code frameworks, genomic data infrastructures can be both scalable and secure. The proposed automation model reduces human error, enhances traceability, and allows for real-time response to compliance deviations, making it a critical foundation for modern biomedical computing environments.
DOI: https://doi.org/10.5281/zenodo.16313840
Comparative Assessment of Server Virtualization Techniques in Biomedical Data Centers
Authors: Sergey Artyomovich Mamedov, Yelena Ramizovna Isayeva, Anar Fikret oglu Mahmudov, Kamilla Rauf qizi Veliyeva
Abstract: Biomedical data centers serve as the backbone of modern healthcare analytics, precision medicine, and hospital informatics. As the volume of healthcare data surges, the need for scalable, secure, and efficient computing infrastructure becomes paramount. Server virtualization has emerged as a critical enabler in this space, offering resource abstraction, fault tolerance, and operational flexibility. This study performs a comparative assessment of leading server virtualization techniques—namely hypervisor-based (e.g., KVM, VMware ESXi), container-based (e.g., Docker, LXC), and hybrid models—based on key parameters such as performance, scalability, resource utilization, latency, and compliance with biomedical data handling norms. Benchmarks using real-world datasets, including EHRs and PACS workloads, reveal that no single approach dominates across all metrics, emphasizing the need for context-driven infrastructure design.
DOI: https://doi.org/10.5281/zenodo.16314536
Biodegradation Of Pharmaceutical Waste Through Environmental Microbial Systems
Authors: Shivendra Singh Thakur, Deepali Pandey
Abstract: Pharmaceutical waste, including antibiotics, hormones, and analgesics, is increasingly contaminating aquatic and terrestrial environments due to inadequate treatment in conventional wastewater systems. These compounds, often persistent and bioactive at low concentrations, pose severe risks to ecosystems and human health. Microbial degradation has emerged as a sustainable and eco-friendly approach to remove such contaminants from the environment. This study explores the potential of environmental microbial systems—both natural and engineered—to biodegrade pharmaceutical residues. We examine the microbial taxa involved, their enzymatic pathways, and the environmental conditions that influence degradation efficiency. The research emphasizes the synergistic interactions among microbial communities in biofilms, activated sludge, and constructed wetlands. Methodologies included sample collection from pharmaceutical effluent sites, microbial isolation, and high-throughput sequencing to analyze community structure and functional gene abundance. Results showed promising degradation rates for several commonly detected pharmaceuticals, especially by bacterial genera such as Pseudomonas, Bacillus, and Sphingomonas. The findings advocate for integrating microbial solutions into existing treatment frameworks to mitigate pharmaceutical pollution. Ultimately, understanding microbial dynamics and optimizing bioremediation strategies can lead to more sustainable and effective waste management systems.
DOI: https://doi.org/10.5281/zenodo.16868961
Engineered Microbial Consortia For Heavy Metal Detoxification In Industrial Sludge
Authors: Rahul Kumar Tiwari, Sonali Chouksey
Abstract: Industrial sludge often contains toxic concentrations of heavy metals such as cadmium, lead, mercury, and chromium, posing significant environmental and public health risks. Conventional remediation techniques are often costly, inefficient, or generate secondary pollutants. In recent years, engineered microbial consortia have emerged as a sustainable and biologically robust solution for the detoxification of heavy metal-laden sludge. These consortia are composed of synergistically interacting microbial strains, each contributing distinct metabolic or binding capabilities that enhance overall detoxification performance. This study explores the role of genetically or selectively assembled microbial communities in metal biotransformation and immobilization processes. The article highlights mechanisms such as biosorption, bioaccumulation, enzymatic transformation, and bioprecipitation as pivotal pathways used by consortia to neutralize toxic metals. Laboratory-scale and pilot-scale applications have demonstrated promising results in reducing metal toxicity, improving sludge quality, and enabling potential reuse. Moreover, the use of multi-omics tools has refined the selection and optimization of functional strains, paving the way for tailor-made bioremediation strategies. This review integrates scientific findings from recent experiments and discusses the challenges, technological limitations, and future potential of engineered microbial consortia in industrial sludge management. Ultimately, such biotechnological interventions hold promise for transforming hazardous sludge into environmentally benign materials.
DOI: https://doi.org/10.5281/zenodo.16869242
Exploring The Synergistic Functions Of Microbiota In Urban Biowaste Conversion
Authors: Sandeep Kumar Mishra, Anjana Jain
Abstract: Industrial sludge often contains toxic concentrations of heavy metals such as cadmium, lead, mercury, and chromium, posing significant environmental and public health risks. Conventional remediation techniques are often costly, inefficient, or generate secondary pollutants. In recent years, engineered microbial consortia have emerged as a sustainable and biologically robust solution for the detoxification of heavy metal-laden sludge. These consortia are composed of synergistically interacting microbial strains, each contributing distinct metabolic or binding capabilities that enhance overall detoxification performance. This study explores the role of genetically or selectively assembled microbial communities in metal biotransformation and immobilization processes. The article highlights mechanisms such as biosorption, bioaccumulation, enzymatic transformation, and bioprecipitation as pivotal pathways used by consortia to neutralize toxic metals. Laboratory-scale and pilot-scale applications have demonstrated promising results in reducing metal toxicity, improving sludge quality, and enabling potential reuse. Moreover, the use of multi-omics tools has refined the selection and optimization of functional strains, paving the way for tailor-made bioremediation strategies. This review integrates scientific findings from recent experiments and discusses the challenges, technological limitations, and future potential of engineered microbial consortia in industrial sludge management. Ultimately, such biotechnological interventions hold promise for transforming hazardous sludge into environmentally benign materials.
DOI: https://doi.org/10.5281/zenodo.16869277
Microbial Signatures Of Climate-Driven Ecosystem Shifts
Authors: Prashant Kumar Sinha, Shalini Gupta
Abstract: Microbial communities are critical yet often overlooked components of ecosystems, acting as sensitive indicators and drivers of environmental change. As climate change intensifies, shifts in temperature, precipitation, and salinity regimes influence microbial diversity, composition, and function across diverse ecosystems, including soil, freshwater, and marine environments. These microbial responses often precede visible ecological transformations, making them powerful early-warning indicators of ecosystem shifts. This study explores how microbial signatures—defined as changes in taxonomic composition, functional gene expression, and metabolic profiles—respond to climate-driven perturbations. By synthesizing recent meta-analyses and case studies from tundra, mangroves, coral reefs, and desert biomes, we demonstrate that microbial indicators reflect stress gradients and adaptation thresholds, often aligning with changes in plant productivity, carbon cycling, and trophic interactions. Our analysis also emphasizes the need for high-resolution temporal monitoring and multi-omic approaches to decode microbial responses to changing climates. The results suggest that integrating microbial data into climate impact models could enhance prediction accuracy for ecosystem resilience and tipping points. This article calls for repositioning microbial research from a peripheral to a central role in climate change ecology. Understanding microbial signatures in relation to environmental stressors is essential to detect, forecast, and potentially mitigate large-scale ecosystem transformations.
DOI: https://doi.org/10.5281/zenodo.16869392
Role Of Halophilic Microbes In Saline Soil Reclamation
Authors: Anurag Shukla, Priyanka Patel
Abstract: Salinity poses a significant threat to agricultural productivity and soil health worldwide, particularly in arid and semi-arid regions where irrigation practices and climate change exacerbate salt accumulation. The role of halophilic and halotolerant microorganisms in reclaiming saline soils has gained prominence due to their ability to survive in high-salt environments and facilitate soil bioremediation. This study investigates the diverse mechanisms through which halophilic microbes contribute to the reclamation of salt-affected soils, including bioaccumulation of salts, production of extracellular polymeric substances (EPS), and enhancement of soil nutrient cycling. By isolating and characterizing microbial consortia from hypersaline environments, this research reveals their potential to promote plant growth, reduce soil electrical conductivity, and improve microbial biomass in degraded lands. Functional attributes such as nitrogen fixation, phosphate solubilization, and synthesis of osmoprotectants were analyzed to evaluate their contribution to ecosystem restoration. The integration of halophilic bioinoculants with sustainable land management practices could offer a biotechnological solution to reclaiming saline soils while enhancing crop resilience. This paper highlights the ecological and agricultural significance of halophilic microbes and proposes a model for their incorporation into soil restoration programs, aligning with broader goals of climate adaptation and food security in salt-affected regions.
DOI: https://doi.org/10.5281/zenodo.16869439
Soil Microbiomes As Catalysts For Sustainable Agriculture
Authors: Ravindra Kumar Baghel, Shraddha Tiwari
Abstract: Soil microbiomes, the complex communities of bacteria, fungi, archaea, and protists residing in soil ecosystems, play a critical role in determining soil fertility, plant productivity, and ecosystem resilience. This study examines the role of soil microbiomes as natural catalysts for sustainable agriculture, emphasizing their function in nutrient cycling, disease suppression, and stress tolerance. By integrating metagenomic analysis, field trials, and literature synthesis, we present evidence that microbial diversity and community structure are key determinants of sustainable crop production. Results show that practices enhancing microbial abundance—such as organic farming, reduced tillage, and biofertilizer application—improve plant health and yield while minimizing reliance on synthetic inputs. Specific microbial taxa, including nitrogen-fixing Rhizobia, phosphate-solubilizing Pseudomonads, and mycorrhizal fungi, emerge as critical agents for plant growth promotion and soil regeneration. Furthermore, microbial interactions influence carbon sequestration and greenhouse gas mitigation, making soil microbiomes vital to climate-smart agriculture. The study proposes a framework for integrating soil microbial indicators into sustainable agriculture policies and recommends precision microbiome management as a frontier in agroecology. By harnessing the biological potential of soil microbiota, we can transition toward more resilient, low-impact farming systems that balance productivity with environmental stewardship.
DOI: https://doi.org/10.5281/zenodo.16869501
Microbial Dynamics In Polluted Ecosystems: Indicators Of Ecological Recovery
Authors: Deepak Chouhan, Vandana Sharma
Abstract: Microbial communities are fundamental to the structure and function of ecosystems, and their responses to pollution provide critical insights into environmental degradation and recovery. This study investigates how microbial dynamics—community structure, diversity, and metabolic functions—can act as sensitive bioindicators of ecological recovery in polluted habitats. Using high-throughput sequencing, functional gene profiling, and ecological modeling, we examined microbial community transitions in heavy metal-contaminated riverbeds, hydrocarbon-polluted soils, and nutrient-enriched wetlands undergoing restoration. The results show that microbial diversity and the re-establishment of functional guilds such as nitrogen-fixers and sulfate-reducers coincide with improvements in physicochemical conditions. Shifts in microbial taxa and functions were predictive of ecosystem resilience and aligned with known ecological recovery benchmarks. We propose a Microbial Recovery Index (MRI) based on taxonomic and functional traits as a tool for ecological monitoring. Our findings demonstrate that microbial indicators can detect early stages of recovery, often before changes in macroscopic biota are observable. This microbial lens provides a cost-effective, high-resolution approach to track restoration progress and inform adaptive management strategies. By placing microbial communities at the core of ecological assessment frameworks, we contribute to a more nuanced understanding of how ecosystems respond to remediation interventions.
DOI: https://doi.org/10.5281/zenodo.16869589
Compost-Derived Microbial Enzymes For Plastic Waste Breakdown
Authors: Manish Kumar Sahu, Swati Dubey
Abstract: The persistence of plastic waste in terrestrial and aquatic ecosystems has become a pressing global environmental concern, exacerbated by the limited degradability of synthetic polymers. In response, biological strategies utilizing microbial enzymes are being explored for sustainable plastic remediation. Composting systems, enriched with diverse thermophilic and mesophilic microbial populations, serve as promising environments for discovering enzymes capable of degrading plastics. This study investigates the enzymatic potential of microbes isolated from municipal compost to break down common plastic polymers such as polyethylene (PE), polyethylene terephthalate (PET), and polystyrene (PS). Through isolation, culturing, and enzymatic assays, microbes exhibiting hydrolytic activity were identified, with particular focus on PETase, cutinase, and laccase enzymes. Analytical techniques including FTIR spectroscopy, SEM imaging, and gravimetric analysis were used to assess the extent of plastic degradation. Results indicated partial breakdown of plastic substrates within several weeks, confirming the activity of compost-derived enzymes. The findings underscore the role of compost microbiota as a reservoir of biocatalysts with potential application in bioremediation and industrial plastic waste management. This research offers insights into developing eco-friendly solutions for plastic pollution through microbial enzyme exploitation, fostering a circular economy and reducing ecological harm.
DOI: https://doi.org/10.5281/zenodo.16869630
Bioelectrochemical Systems: Microbial Innovations In Renewable Energy Generation
Authors: Sanjay Singh Rajput, Anshu Kaurav
Abstract: Bioelectrochemical systems (BES) represent a promising frontier in the nexus of microbiology and renewable energy. These systems harness the metabolic activity of electroactive microbes to convert organic substrates into electricity, biofuels, or valuable chemicals. This paper explores the structural and functional dynamics of BES, focusing on microbial fuel cells (MFCs), microbial electrolysis cells (MECs), and hybrid technologies. Emphasis is placed on the role of microbial consortia, biofilm formation, electron transfer mechanisms, and electrode-material interactions in enhancing system efficiency. The paper reviews recent advancements in BES optimization, including synthetic biology approaches, nanostructured electrodes, and system miniaturization for decentralized applications. Comparative analysis of BES performance in treating wastewater and converting it into energy underscores their dual utility in environmental bioremediation and green energy generation. Challenges such as power density limitations, scale-up issues, and long-term operational stability are discussed. Finally, the paper outlines future research directions in microbial engineering, smart control systems, and integration with smart grids. This work underscores BES as transformative tools in sustainable energy science, combining ecological engineering with renewable innovation to pave the way for low-carbon, microbe-driven energy alternatives
DOI: http://doi.org/10.5281/zenodo.1687152
Adaptive Microbial Pathways In Oil Spill Bioremediation
Authors: Ashok Kumar Barik, Sudha Tripathy
Abstract: Oil spills pose a persistent threat to marine and terrestrial ecosystems, demanding effective and eco-friendly remediation strategies. Microbial bioremediation, particularly through the adaptive pathways of native or introduced microbial populations, offers a sustainable alternative to physicochemical cleanup methods. This article explores the mechanisms by which microbial communities adapt to hydrocarbon contamination, focusing on metabolic flexibility, gene regulation, and community-level interactions. We review recent studies highlighting the role of hydrocarbonoclastic bacteria, including Alcanivorax, Marinobacter, and Pseudomonas, in degrading crude oil components. Special attention is given to horizontal gene transfer, biofilm formation, and enzyme induction in the context of oil degradation. Through comparative analyses of field trials and laboratory microcosms, we assess the resilience and adaptability of microbial consortia to different spill environments. This study further identifies knowledge gaps in current bioremediation models, proposing a framework for integrating omics technologies and biosensors for real-time monitoring and pathway optimization. By delineating adaptive microbial responses at both genetic and ecological scales, this work contributes to developing more efficient and predictive oil spill bioremediation strategies.
DOI: http://doi.org/10.5281/zenodo.16871702
A Comparative Study Of Indigenous Vs. Engineered Microbes In Wastewater Treatment
Authors: Dilip Kumar Malviya, Poonam Khare
Abstract: Microbial wastewater treatment is a cornerstone of modern environmental engineering, with both indigenous and genetically engineered microbes playing pivotal roles. This study explores the comparative efficacy of native microbial communities versus engineered strains in degrading pollutants in municipal and industrial wastewater. Indigenous microbes, naturally adapted to local environmental conditions, exhibit broad resilience and stability, while engineered microbes are tailored for enhanced degradation of specific pollutants such as heavy metals, pharmaceuticals, and nitrogen compounds. Through controlled bioreactor experiments and field studies, this research examines pollutant removal efficiency, microbial survival, system stability, and overall ecological impacts. Our findings reveal that while indigenous microbes are more robust under fluctuating environmental conditions, engineered microbes demonstrate superior performance in targeted degradation tasks when environmental parameters are tightly controlled. However, the integration of both microbial types offers a promising hybrid approach to maximize pollutant removal. This study emphasizes the importance of context in selecting microbial strategies for wastewater treatment, advocating for tailored applications based on pollution load, regulatory needs, and environmental resilience. The results support the broader transition toward biologically intelligent wastewater treatment systems that leverage microbial diversity and synthetic biology. Ultimately, this research informs future developments in sustainable wastewater management practices globally.
DOI: http://doi.org/10.5281/zenodo.16871983
From Waste To Wealth: Microbial Platforms For Organic Resource Recovery
Authors: Gopal Prasad Bhoi, Madhuri Jena
Abstract: The global surge in organic waste production necessitates the development of sustainable and economically viable recovery methods. Microbial platforms have emerged as promising biotechnological tools for converting waste into valuable products, including biofertilizers, bioplastics, biofuels, and organic acids. This study explores the multifaceted roles of microbial consortia in decomposing organic waste and facilitating its transformation into commercially usable outputs. The research highlights key microbial species and their enzymatic capacities that enable efficient bioconversion, as well as system designs such as anaerobic digesters and compost bioreactors. Emphasis is also placed on the environmental and economic benefits of microbial waste valorization, including carbon footprint reduction, resource circularity, and income generation in agricultural and industrial sectors. The paper further discusses comparative efficiencies of indigenous versus genetically modified microbes and evaluates case studies showcasing real-world applications. Results suggest that well-optimized microbial platforms can achieve over 80% recovery efficiency in controlled systems. The study concludes by identifying technological gaps and future research priorities, particularly the need for integration with AI-based process monitoring and decentralized waste recovery systems for rural and urban settings. This research supports the broader vision of transforming the linear waste paradigm into a regenerative bioeconomy through microbial innovation.
DOI: http://doi.org/10.5281/zenodo.16872081
Enhancing Landfill Microbial Activity Using Bio-Stimulants
Authors: Amitesh Kumar Patel, Roshni Verma
Abstract: – Municipal landfills are one of the largest contributors to greenhouse gas emissions and long-term environmental degradation due to the slow degradation of organic waste. Enhancing microbial activity within these landfills is a promising approach for accelerating the decomposition process and reducing environmental burdens. This study explores the application of bio-stimulants—substances that promote microbial activity—within landfill environments to boost the metabolic rate and diversity of native microbial communities. By introducing bioavailable carbon, nitrogen, and trace minerals, microbial consortia involved in anaerobic digestion can be stimulated to more effectively degrade organic matter and stabilize landfill content. This paper examines various classes of bio-stimulants, including humic acids, molasses, compost tea, and amino acid-based formulations, and their impacts on microbial respiration, gas production (e.g., methane and CO₂), and leachate quality. The results suggest that targeted bio-stimulant application can lead to enhanced microbial enzymatic activity and accelerated waste mineralization, thereby promoting more efficient landfill management. This research contributes to the development of sustainable landfill technologies by highlighting the biochemical interactions and ecological benefits of microbial stimulation through natural amendments. The findings serve as a foundation for future bioengineering practices aimed at transforming traditional landfill sites into active bioreactors for organic waste treatment.
DOI: http://doi.org/10.5281/zenodo.16872527
The Salesforce Ecosystem: A Comprehensive Guide To Service Cloud, Experience Cloud, And More
Authors: Vandana Tomar
Abstract: The Salesforce ecosystem stands today as one of the most influential platforms in the global business technology landscape, transforming the way organizations build customer relationships, automate processes, and enhance engagement. At its core, Salesforce extends far beyond the traditional concept of customer relationship management (CRM) by offering an integrated suite of cloud-based solutions that empower enterprises across industries to foster innovation, drive productivity, and scale operations seamlessly. Among its many offerings, Service Cloud and Experience Cloud emerge as two of the most impactful tools designed to elevate customer service operations and provide highly personalized digital experiences. Service Cloud optimizes support workflows, case resolution, and omni-channel communication, while Experience Cloud enables businesses to build branded portals, partner portals, and customer communities that enhance connectivity and collaboration. Together, these two solutions form an integral part of Salesforce's larger value proposition centered around delivering customer-centric excellence. This article intends to provide an in-depth exploration of the Salesforce ecosystem by examining the broad functionalities and strategic value of its interconnected tools. Beginning with a comprehensive overview of the Salesforce platform, the discussion will then move into the specific strengths and applications of Service Cloud and Experience Cloud, while also analyzing other critical innovations within the ecosystem including Sales Cloud, Marketing Cloud, Commerce Cloud, and advanced capabilities such as AI-driven insights and analytics. Furthermore, this discourse evaluates how Salesforce has become an indispensable strategic asset for digital transformation, influencing industries from healthcare to retail to financial services. Special focus is placed on the ways in which organizations integrate Salesforce into their operations to achieve higher levels of personalization, efficiency, and customer loyalty. The article is structured under eight distinct sections, beginning with this abstract, followed by a thorough introduction and six detailed insights into different aspects of Salesforce, concluding with reflections on the ecosystem’s overall significance. Keywords chosen for this study highlight the central themes of this ecosystem and its offerings, presenting a valuable resource for both business leaders and technical professionals who seek to maximize the potential of Salesforce solutions. Ultimately, this work captures not just the technological framework of Salesforce, but also the cultural and strategic paradigms it represents in the era of digital-first, customer-driven business models
DOI: http://doi.org/10.5281/zenodo.17278072
AI In Service Cloud: A Deep Dive Into Intelligent Case Management
Authors: Seema Solanki
Abstract: The integration of Artificial Intelligence (AI) into service cloud platforms has transformed the way organizations approach customer support, issue resolution, and long-term service management. Intelligent case management, driven by AI technologies such as natural language processing, predictive analytics, and machine learning, ushers service operations into an era of proactive, personalized, and efficient solutions. Unlike traditional service management strategies that often rely on manual interventions and reactive measures, AI-powered service clouds provide an end-to-end automated system that places intelligence at the center of customer experiences. By leveraging insights from large datasets, AI enhances decision-making processes, recommends appropriate solutions, and empowers customer service teams to improve both speed and accuracy in their responses. Additionally, advancements in sentiment analysis allow AI systems to not only classify issues but also assess customer emotions, which further enriches the quality of engagement. This convergence of smart technology and cloud capabilities ensures that businesses can scale their operations, promote consistency, and deliver hyper-personalized experiences to diverse customers across industries. Intelligent case management thus becomes more than a process of resolving tickets—it evolves into an ecosystem of predictive support and customer-centric adaptability. As organizations progressively invest in AI-driven service technologies, the service cloud becomes an essential hub of innovation where KPIs such as resolution time, customer satisfaction scores, and retention are consistently optimized. This article delves deeply into the mechanisms, benefits, challenges, and prospects of AI integration within service clouds, examining how intelligent case management reshapes customer service and positions enterprises to thrive in an increasingly digital and experience-driven economy.
DOI: http://doi.org/10.5281/zenodo.17278076
Building AI-Enhanced CRM Pipelines With Salesforce DX Integrated Into Hybrid Unix-Based Cloud Systems With Security Controls
Authors: Gagandeep Hundal
Abstract: The evolution of customer relationship management (CRM) has accelerated with the integration of artificial intelligence (AI), automation pipelines, and hybrid cloud architectures. This review article explores the development of AI-enhanced CRM pipelines built on Salesforce DX and deployed within hybrid Unix-based cloud systems fortified with advanced security controls. Salesforce DX, with its modular architecture and version-controlled development framework, provides enterprises with a foundation for agile release management and collaborative development. When combined with the resilience and security of Unix environments, it enables organizations to manage multi-cloud deployments with greater efficiency and compliance assurance. The integration of AI introduces predictive analytics, anomaly detection, and self-optimizing workflows that transform CRM pipelines into intelligent, adaptive ecosystems. The discussion emphasizes critical enablers such as DevOps-driven workflows, automation frameworks, and proactive monitoring strategies, while also addressing challenges including interoperability across heterogeneous platforms, regulatory compliance, and scalability in large enterprise settings. Future research opportunities are identified in areas such as blockchain-enabled pipeline auditability, AI-native orchestration, and standardized hybrid CRM frameworks. By synthesizing technological advancements with strategic considerations, this review highlights how enterprises can reimagine CRM as a secure, intelligent, and continuously evolving capability. Ultimately, the combination of Salesforce DX, AI-driven enhancements, and Unix-based hybrid cloud systems offers a blueprint for building resilient, compliant, and customer-centric CRM infrastructures that align with modern digital transformation goals.
DOI: https://doi.org/10.5281/zenodo.17366814
AIX And Solaris Resilience Strategies For Salesforce CRM Operations Using Disaster
Authors: Harsimran Aulakh
Abstract: Ensuring resilience and compliance in Salesforce CRM operations is critical for enterprises managing sensitive customer and transactional data. This review examines strategies for integrating Salesforce CRM with AIX and Solaris Unix platforms, emphasizing disaster recovery architectures, high-availability mechanisms, and compliance hardening tools. AIX and Solaris provide robust back-end environments capable of sustaining mission-critical CRM workloads, while advanced monitoring, clustering, and fault-tolerance strategies ensure minimal disruption during hardware failures or cyber incidents. The article evaluates backup solutions, replication methods, and hybrid cloud integration, highlighting how these measures protect CRM data and maintain operational continuity. Compliance hardening, including the use of Unix security baselines, auditing tools, and Salesforce Shield, ensures alignment with regulations such as HIPAA, GDPR, SOX, and PCI-DSS. Challenges in legacy application compatibility, hybrid DR complexity, performance impacts, and skill gaps are also discussed, alongside emerging trends in AI-driven auditing, blockchain-enabled audit trails, and cloud-native disaster recovery. By synthesizing resilience and compliance frameworks, this review provides a roadmap for enterprises seeking to optimize Salesforce CRM operations within hybrid Unix ecosystems, ensuring continuous availability, regulatory adherence, and enhanced operational efficiency.
DOI: https://doi.org/10.5281/zenodo.17366980
Real-Time Healthcare Talent Orchestration Using IoT-Driven Telemetry, Big Data Pipelines, And AI-Based Forecasting Within Enterprise ERP Frameworks
Authors: Victor Petrov, Kenji Nakamura, Thomas Bauer, Elena Garcia, Ananya Kulkarni
Abstract: Healthcare systems operate in highly dynamic environments where patient demand, workforce availability, and clinical resource utilization fluctuate continuously, creating significant challenges for effective talent coordination and resource planning. Traditional workforce management approaches within enterprise resource planning (ERP) systems often rely on historical reporting and static scheduling mechanisms that struggle to respond to real-time operational changes. The growing adoption of Internet of Things (IoT)–enabled medical devices and hospital telemetry infrastructure has created opportunities to capture continuous streams of operational data across healthcare environments. This study proposes a real-time healthcare talent orchestration framework that integrates IoT-driven telemetry, scalable big data pipelines, and artificial intelligence–based forecasting models within enterprise ERP architectures. Telemetry data generated from clinical monitoring systems, hospital infrastructure sensors, and workforce management platforms are processed through distributed big data pipelines capable of handling high-velocity operational information. Machine learning algorithms analyze these data streams to forecast patient inflow, anticipate staffing requirements, and identify potential operational bottlenecks before they impact service delivery. By embedding predictive insights directly into ERP-driven workforce coordination systems, healthcare organizations can dynamically adjust staffing allocations, optimize resource utilization, and support proactive decision-making. The proposed approach demonstrates how combining IoT telemetry, big data engineering, and AI-based forecasting can significantly improve workforce agility, operational efficiency, and service continuity in modern healthcare environments.
DOI: https://doi.org/10.5281/zenodo.18813979
The Role Of Financial Education In Enhancing The Socio-Economic Stability Of International Migrants – A Study Of Global Banking Adaptation
Authors: Dipikaben Solanki
Abstract: In the past few decades, there has been an upsurge in international migration which has led to the creation of new economic opportunities as well as exposing migrants to financial marginalization and institutional obstacles in the host nations. A lot of migrants have problems with the comprehension of new banking systems, financial policies, and credit models that restrict their access to formal financial services and obtaining economic stability in the long term. This paper explores how financial education can help to improve the socio-economic well-being of international migrants and how financial literacy can aid international migrants to adapt to the banking systems of other countries. The study takes the qualitative research design based on secondary data, such as scholarly works and research conducted on migration, financial inclusion, and banking access worldwide. The article examines the connection between financial literacy, bank account ownership, and economic wellbeing of migrants. The results provide that financial literacy enhances the capacity of the migrants to control the level of income, earn savings behaviour, and use formal banking products like bank accounts, remittances, and credit facilities. Financial education can also enable migrants to fit into the institutional financial systems and diminish the reliance on informal financial systems. The paper also notes that financial inclusion can bring to the economies of host countries more comprehensive economic benefits in terms of increased productivity, entrepreneurship, and inclusion in the financial system. The study finds that enhancing the effectiveness of migrant-oriented financial education and enhancing access to non-discriminatory banking services are critical measures to counter social mobility and sustainable economic integration.
DOI: https://doi.org/10.5281/zenodo.19250336
Published by: vikaspatanker
