Review of Improvement and Prevent Bridges from Contributing to the Nation’s Supply Chain Problem
Authors:- Kailash Nath Bhagat, Prof. Shashikant B. Dhobale
Abstract- Successful highway transportation must address three areas of consideration: (1) creative and aesthetic, (2) analytical, and (3) technical and practical considerations. Given that most bridge transportation today are performed by multidisciplinary teams, addressing the first two considerations is fairly easy to achieve. The last is often the most challenging. This WORK discusses the practical challenges associated in the selection of Highway Bridge, the bridge types that are available for use and their range of applicability, the methods of analysis used, the dominant Supply in use today, and, finally, an example based on the AI of a bridge design following the practical considerations given here.
Image Classification for Dogs and Cats Using CNN
Authors:- Arnav Bhargava, Kunal Paliwal, Komarsamy.G
Abstract- Image classification is an important task in computer vision and has a wide range of applications. In this project, we have developed a deep learning model using Convolutional Neural Networks (CNN) to classify images of dogs and cats. The model was trained on the Cats and Dogs dataset available on Kaggle, which consists of 25,000 images of cats and dogs.
Secured Sim Ejection
Authors:- Sahaya Joseph Rohan.A, Sengodan.D, Kavin Kumar.S, Eswanth Raj.S
Abstract- A SIM card, also known as a subscriber identity module, is a smart card that stores identification information that pinpoints a smartphone to a specific mobile network. Data that SIM cards contain include user identity, location and phone number, network authorization data, personal security keys, contact lists and stored text messages. SIM cards allow a mobile user to use this data and the features that come with them. We all use sim card but without the knowledge about the consequences of losing a sim card. Once we lose a sim card, we let it free and move on to another sim card. Have you ever wondered that our sim stores a lot of data but what if it goes to someone’s hand, all our personal details are leaked and there are even chances of selling them in the dark web? By applying this project, it is easy to avoid the circumstances of losing a sim card. We don’t have a proper protection for our sim card. It can be easily inserted and also easily ejected. Understand that you are in a danger if you have lost your sim card. But this is not going to happen again. This project fully focusses on securing the sim card by giving utmost protection to the sim card. This involves a security page to be authenticated if someone needs to eject the sim. Sim Ejection wouldn’t be easier after the implementation of this project.
A Review of Facial Expression Recognition using Machine Learning
Authors:- M.Tech. Scholar Poonam Gujre, Asst. Prof. Sudha Sharma, HOD. Trapti Sharma,Director Durgesh Mishra
Abstract-How a person seems to be feeling In interpersonal communication, one of the most difficult and vital skills to master is the ability to give and receive acknowledgement. It is easy to tell how people feel and what they aim to accomplish by their facial expressions. Nonverbal communication relies heavily on facial expressions. Automated facial expression identification is becoming more dependent on deep neural networks. In part, this is because FER has gone from lab-controlled to real-world situations, where deep learning methods have proven useful in a variety of industries. Two major issues have been addressed in recent deep FER systems: overfitting, which occurs when there isn’t enough training data, and elements that don’t have anything to do with the expression of the subject, such as illumination, head position, and identification bias. This study provides an in-depth examination of deep FER, which includes datasets and approaches that shed light on the issues at hand. To begin, we’ll go through the datasets that the general public has access to. These datasets have been extensively studied in the scientific literature, and a variety of data selection and assessment techniques have been used. This is followed by an explanation of the standard deep FER system pipeline, as well as background information and recommendations for successful implementations at each level. For deep FER, we look at the most cutting-edge deep neural networks and training approaches for FER based on static photographs and dynamic image sequences, as well as advantages and drawbacks. Other commonly used benchmarks are included in this section as well. Then, in order to make our poll even more helpful, we add more topics and purposes to it. Last but not least, we examine the challenges and opportunities that remain in this sector and how to construct robust deep FER systems in the near future.
A Study of Multiserver Retrial Queues with Different Stages of Homogeneous Service
Authors:- M. Renisagayaraj, R.Roja, S.Bhuvaneswari
Abstract- We discuss a queuing system with retrial of customers. Two models are discussed. First, we investigate single server queues in parallel, when the customer going to search and join the shorter of the two queues and in the second model we introduce the multiserver queue to multiserver retrial queue system. Multiserver provides different stages of homogeneous service in succession.
Geolocation
Authors:- Tejas Shinde, Binit Shirsath, Pranali Vekhande, Varun Margam, Professor Priya Gupta
Abstract-As long as they have the necessary device, such a smart phone, users may now locate and track the locations of other people, objects, machines, cars, and resources from the comfort of their own homes. Location-sensitive information requests are often made by a user known as the client or network provider. Today’s most popular applications employ the Global Positioning System (GPS) to give position data.
Geolocation
Authors:- Tejas Shinde, Binit Shirsath, Pranali Vekhande, Varun Margam, Professor Priya Gupta
Abstract-As long as they have the necessary device, such a smart phone, users may now locate and track the locations of other people, objects, machines, cars, and resources from the comfort of their own homes. Location-sensitive information requests are often made by a user known as the client or network provider. Today’s most popular applications employ the Global Positioning System (GPS) to give position data.
A Review of Human Facial Expressions Recognition Using Artificial Intelligence K-Nearest Neighbor (KNN) Algorithm
Authors:-P.G. Scholor Shivam Sharma, Asst. Prof. Hemant Amhia
Abstract- Facial expression is one of the most important form of non-verbal communication. Facial expressions emit the feelings of a person, and it allows judging that person by others. Some can understand facial expressions of underlying emotions to some extent, whereas many of us cannot. Facial Expression recognition (FER) system is a system to recognize expressions from a person’s face. It plays an important part in today’s world in fields of mental disease diagnosis, and human social/physiological interaction detection. Various methods of FER exist. This paper provides a summary of various processes involved in FER.
Analysis of Multiserver Retrial Queue of Homogeneous with Simple Death Process
Authors:- M. Renisagayaraj, P Suganthi, R. Sujatha
Abstract-We analyzed an M /M /2 retrial queueing model with vacation taken by the server. Then server providing service one by one, also provides the second server with a fixed size 𝑘 ≥ 1. The customers are queued up for the first service, which is essential for all customers and the second server gives an optional service when there is a demand for some of the customer where as the others leave the system after the first server provide the service.
Computer Aided Diagnosis System for Brain Tumor Detection
Authors:- Konda Shiva Teja Ravinder, Devaraju Sai Vandith, Dr. A Venkataramana, AkkepallyAvanthika, A Jaya Krishna Murthy
Abstract- A brain tumor is a mass of abnormal cells in the brain. A brain tumor occurs when abnormal cells form within the brain.This paper presents a brief review of brain tumor detection methods. The computer aided diagnosis system for brain tumor detection consists of step by step procedures namely input of brain images, filtering, thresholding, morphological operations, bounding box, getting tumor outline encoding, inserting the outline in filtered image and displaying images. Simulation results are carried out by considering standard brain images and found that this algorithm works well in detecting the brain tumor.
Drive Assist
Authors:- Pratik Patil, Harshal Bhangale, Janavi Kadam, Vaishnavi Konduru, Co-Ordinator Mrs. V.T. Thakare
Abstract- Contemporary solutions are needed for modern issues. There’s no need to postpone your trip due to vehicle troubles or getting troubles at remote locations because DRIVE ASSIST will guide you how to fix it. User will be able to smoothly use the website with no problems as it is a user- friendly website. If there is sudden destruction of the vehicle, DRIVE ASSIST provides users with the closest garage, serviceman, hotels for resting and even the opportunity to rent automobiles to continue their journey in the event of an unexpected vehicle breakdown. The website also lists the closest gas stations and charging facilities for electric vehicles. DRIVE ASSIST was developed to provide users with support. To use the services they require, users only need to register on the website. Contact information will be available on the website so that customers can speak with service providers directly to ask questions and to discuss pricing. The supervisor will keep track of all customers and service provider information.
Sign Language Translator
Authors:- Asst. Prof. Ms. Pragati V. Thawani, Ms.Kaanchi Mukati, Ms.Shreya Jaiswal
Abstract- Individuals who have trouble hearing can communicate by using sign language. Since it might be difficult for regular people to communicate with deaf people, this technique is beneficial for helping them. The system suggested in this research seeks to partially address this issue. In order to create a real-time sign language dataset using a computer or laptop web camera and then use the Tensor Flow model and LSTM Deep Learning Model, along with many other technologies, to create a real-time sign language recognition system and aid in closing the gap between signers and non-signers, the authors proposed a method. Four modules—image capture, pre-processing, classification, and prediction—make up the proposed system.
The Impact of Fiscal Policy on Unemployment in Indonesia
Authors:- Siti Mewah Siregar, Muhammad Findi, Wiwiek Rindayantil
Abstract- This research investigates fiscal policy’s role in influencing Indonesia’s unemployment rate. This study uses secondary data with the Vector Error Correction Model (VECM) analysis method from 1970-2021. The study results a show that foreign debt, government expenditure, and government revenue do not significantly affect unemployment in Indonesia in the short term. However, in the long term, foreign debt and government expenditurehave a significant positive effect on unemployment, while government revenue has a significant negative effect on unemployment. The IRF test results show that the unemployment variable responds negatively if there is a shock to government expenditureand positively if there is a shock to foreign debt and government revenue. The FEVD test shows that government expenditureis the most effective fiscal policy in reducing the unemployment rate in the short term.
OCTA X – Analysis and Extermination of Space Debris
Authors:- Yash Nigam, Ayush Gupta, Mohd. Aaqib
Abstract- As we are heading towards the age of communication and advanced space exploration for the betterment of life and in the search for life on other planets in the universe. As we head towards the age of this modernisation spacecraft and satellites are being launched into free space as well as into the orbit of the earth and other planets too, for various purposes, as all machines have a life cycle so do the spacecraft and satellites have, as we know the fundamental law that material can’t be created nor be destroyed so, all the junk satellites and spacecraft just revolve around the orbit or in the free space creating the junk debris of the dead spacecraft which causes functional difficulties for the other space operations for the other spacecraft. Day by Day the problem is increasing and creating a major concern of the junk that envelops the planet hence creating hindrances for further space missions. And by OCTA-X is a robotic octopus-shaped debris cleaner and eliminator, the robotic AI Arms have big claws with a big cavity that can collect junk material and suck inside the main body of the robot, pack it throws it in the direction of the star (sun) which will culminate the junk material, hence cleaning the space debris with help of an AI Octopus Shaped Robot.
Analysis of New Computational Models in Analysis of Time Series Data for Rainfall Forecasting of Indian city
Authors:- Prashant Shivhare, Shivank Sonia
Abstract- Autoregressive integrated moving average (ARIMA) is a data mining technique that is generally used for time series analysis and future forecasting. Climate change forecasting is essential for preventing the world from unexpected natural hazards like floods, frost, forest fires and droughts. It is a challenging task to forecast weather data accurately. In this paper, the ARIMA based weather forecasting tool has been developed by implementing the ARIMAalgorithm in R. Sixty-five years of daily meteorological data (1951-2015) was procured from the Indian Meteorological Department. The data were then divided into three datasets- (i)1951 to 1975 was used as the training set for analysis and forecasting, (ii)1975 to 1995 was used as monitoring set and (iii)1995 to 2015 data was used as validating set. As the ARIMA model works only on stationary data, therefore the data should be trend and seasonality free. Hence as the first step of R analysis, the acquired data sets were checked for trend and seasonality. For removing the identified trend and seasonality, the data sets were transformed and the removal of irregularities was done using the Simple Moving Average (SMA) filter and Exponential Moving Average (EMA) filter. ARIMA is based on method ARIMA (p,d,q) where p is a value of partial autocorrelation, d is lagged difference between current and previous values and q is a value from autocorrelation. In the present study, we worked on ARIMA (2,0,2) for rainfall data and ARIMA (2,1,3) fortemperature data. As a result, it estimated the future values for the next fifteen years. The root means square error values were 0.0948 and 0.085 for rainfall data and temperature data respectively which show that the algorithm worked accurately. The resulted data can be further utilized for the management of solar cell station, agriculture, natural resources and tourism.
A Review On Analysis Of Connecting Rod Using Finite Element Method
Authors:- Pradeep Kumar Dwivedi, Prakash Kumar Pandey
Abstract- The connecting rod belongs to the group of critical components of piston engines. The connecting rod transfers loads from the piston onto crankshaft. In modern diesel engines the large value of torque achieved at low speed of rotation causes high stresses in pistons, crankshafts, connecting rods and another engine components. Amplitude of operational stresses has significant in- fluence on the fatigue life of the connecting rod. Additional factors which limit its fatigue strength are: incorrect shape (design), material defects or technological errors (defects created during the production process).The failure analysis of the connecting rods of piston engines was described in many publications. Several typical and uncommon failure modes in connecting rods of combustion engines were reported in work. The author’s attention is focused on description of failure mode and the stress analysis of investigated connecting rod.
Assessment on Manpower Training Practice (The Case of Nib International Bank, Ethiopia)
Authors:- Abreham Tesfaye Abebe (PhD)
Abstract- The document assesses the manpower or workforce training practices of Nib International Bank. The Bank that operates in Ethiopia, a country in the horn of Africa. The researcher deployed a research methodology that fits for the purpose of the research and come up with a recommendation. Training and development has impact on the performance of individual employees, which in turn affects the organization’s performance at large.
A Survey On Heat Pipe Heat Recovery Systems
Authors:- M.Tech.Scholar Babli Lodhi , Prof. Shrihar Pandey
Abstract- Abstrct- The development of heat pipe arrangements has been accelerated by advances in computer research, which have shown multiphase flow regimes and highlighted the vast potential of the respective technology for passive and active applications. This analysis aims to assess the utility of contemporary heat pipe systems for heat recovery and renewable applications. Regarding the operational temperature profiles of the evaluated industrial systems, fundamental characteristics and constraints are explained together with theoretical comparisons. Working fluids are compared using the figure of merit for the temperature range. The analysis determined that typical tubular heat pipe systems offer the broadest operational temperature range compared to other systems and, as a result, offer optimization and integration opportunities for renewable energy systems..
Music Recommendation System Using Facial Features
Authors:- Chandani Mourya, Rugved Patil,Siddhi dorage, Shweta Saindane,Priyanka Sherkhane
Abstract- One of the most challenging and complex processes ever attempted in the paradigm of image processing is the analysis of facial expressions. Since humans express most of their emotions through facial expressions, other uses for facial expressions include determining a person’s mood. The ability to recognize a person’s mood is one of the most beneficial implementations since it may be put to use in a variety of ways to enhance a person’s quality of life.For many people, listening to music is a crucial part of their existence. Numerous studies and advancements have been made in the field of music organizing and search, which directly relates to the issue of locating or streamlining the process of choosing a certain song to listen to. One option is the song’s recommendation, which is becoming more and more popular in modern times as it aids in choosing music for a range of events. Because music is a fantastic form of entertainment for people and may be used to unwind, concentrate, manage stress, and maintain a balance between mental and physical tasks. This paper will discuss the recommendation system which will enable users to receive song recommendations merely by looking at their facial expressions when we combine artificial intelligence technology with a generalized music approach.
A Novel Approach in Power gated Adiabatic Logic for Ultra Low Power Applications
Authors:- Rishabh Singh, Uday Panwar
Abstract-With the continuous scaling down of device technology in the field of VLSI circuit design, low power dissipation has become one of the primary concern of the research field. With the increasing demand of low power portable devices, adiabatic logic gates prove to be an effective solution. This paper presents different types of adiabatic logic families such as 2N-2N2P, PFAL (Positive Feedback Adiabatic Logic), DCPAL (Differential Cascode and Pre-resolved Adiabatic Logic) and a proposed circuit based on the PFAL logic circuit. In this paper, various adiabatic logic approaches have studied and compared with a proposed adiabatic logic based on PFAL logic circuit. Adiabatic logic styles such as 2N-2P, 2N2N-2P, DCPAL and PFAL are considered and their average power dissipation and delay at different frequencies are compared with the proposed circuit. Simulations are done by using HSPICE 32nm technology. Finally results of Power Delay Product obtained from simulations are plotted on bar graphs at various frequencies.
An Investigate on Cyber Security in the Digital Banking Industry
Authors:- Asst. Prof. Mr. S. Kirubakaran
Abstract-Online technology is modernized with excellent performance and is widely used by all users in the twenty-first century. The top five industries that often utilize online technology include the digital banking sector. Despite the increased use of online banking, cybercrime in the banking industry has been rising. According to reports, 50 percent of cybercrime involves ATMs, debit cards, and online banking. Compared to other industries, the banking industry is more frequently the target of cyberattacks. This article examines cyber assaults on the banking industry and methods for defending against them.
MNIST Digital Classification and Handwritten Digit Recognition
Authors:- P Masthan, M Vinay Kumar, P Akhil, P Dileep Kumar, Asst. Prof. Mrs K Anuranjani
Abstract- One of the most well-known issues in computer vision and machine literacy operations is the handwritten digit recognition challenge. There are several machine literacy techniques that have been used to solve the handwritten number recognition issue. In this paper, neural network methods are the main topic. Deep neural networks, deep belief networks, and convolutional neural networks are the three most widely used Neural Network techniques. In this paper, the three neural network approaches are compared and estimated in terms of numerous factors similar to delicacy and performance. Recognition, delicacy rate and performance, still, isn’t the only criterion in the evaluation process, but there are intriguing criteria similar to prosecution time. Random and standard dataset of handwritten numbers have been used for conducting the trials. The results show that among the three neural network approaches, convolutional neural network is the most accurate algorithm; it has a 98.08 delicacy rate. Still, the prosecution time of convolutional neural networks is similar to the other two algorithms.
I Draw Hand Writing Robot
Authors:- Bhairavi P.Chamate, Komal Meshram, Nimisha Ghatole, Raviksha Dhomne, Prof. Swati Dhabarde
Abstract- In industrial use, most of the chunks are obtained from accomplish to make it understandable, In this system, we have understood and formed I draw handwriting robot The main idea is to develop an I- draw handwriting robot that can be taken to any place with comfort. So the controller. This robot can draw both parallel and upstanding. Its single design structures a writing head that spreads beyond the machine, making it possible to draw on objects greater than the machine itself. The major benefit of the machine is that it can be located over the hardcover because the core XY extends the design of the machine.
Design of Chilled Water Distribution Systems
Authors:- Suhasini Pyarasani
Abstract- A chilled water plant can be conceptually well designed but implemented in a manner that unnecessarily increase first costs. This paper evaluates different chilled water distribution systems configurations, for chilled water plant. These chilled water distribution systems configurations include Primary-only-variable flow, and Primary-Secondary, Primary-distributed secondary, and Primary -coil secondary.Different analyses are performed in a model, and results are tabulated and plotted to compare energy costs. This paper offers recommendation to assist designers and engineers to select the chilled water distribution systems, without significant effort on designing.
Wearable Health Monitoring System using IOT
Authors:- Jagruti Kotkar, Pooja Sonawane, Saurabh Kothawale, Prof- Megha Beedkar (Guide)
Abstract- IoT is one of the emerging technologies which is leading to smart health monitoring. IoT helps in connecting the people by empowering their health and wealth in a smart way through wearable gadgets. IoT is the network of physical objects that are embedded with sensors, software and other technologies for exchanging of data over the network. Now a day’s people are suffering from a lot of acute and chronic diseases, and they do not acknowledge it earlier and due to lack of immediate treatment the death rates among these patients are increasing. This type of problems can be encountered through wearable gadgets that continuously monitor the activity and condition of the patient in a predictable method. The main aim of this work is to provide an extensive research in capturing the sensor data’s, analyzing the data and providing a feedback to patients based on different health parameters.
Online Examinaton System
Authors:- Shital Ghodake,Sakshi Nikam, Madhuri Paithankar, Sakshi Mokal, S.A Bhad
Abstract- The Online Exam System is a web-based platform that allows you to manage and conduct exams on the Internet. It provides a convenient, cost-effective and efficient means of assessing candidates’ knowledge and skills regardless of their geographic location. Systems typically include a user interface that allows students to log in, access test materials, and complete their exams online. Tests can be taken in a variety of formats, including multiple-choice, essay, and open-ended questions. The system also includes features for evaluating, reporting and analyzing test results. Online testing systems have several advantages over traditional paper-based testing. It saves time and money because there is no need to print and distribute test papers and no physical space is required to conduct the exam. The system is more secure because it can prevent cheating and protect the integrity of the exam. In addition, the system allows for more efficient examination administration, such as scheduling examinations, appointing examinees and communicating with students. Overall, online testing systems provide a flexible and efficient way to conduct testing in a digital environment.
Review of Gender identification using Machine Learning Techniques
Authors:- Lecturer Md. Arifuzzaman , Lecturer Jannatul Afroj Akhi, Lecturer Tamim Hossain , Lecturer Md. Rezaur Rahman Shipon , Lecturer Shamima Yasmin Sejuti, Prof. Dr. Muhammad Abdul Goffar Khan
Abstract- This paper is about the “Thermal sensor based Temperature Measuring Robot” using Arduino Uno circuits. In this technology, Temperature Measuring Robot measured the temperature of the human body and the temperature of any object. The MLX90614 infrared thermometer is a contactless temperature sensor module for Arduino compatible device. An infrared thermometer works to measure the object temperature by the infrared radiation in the form of an electromagnetic wave through the light emitted on the object. MLX90614 is a powerful infrared sensing device with a very low noise amplifier with a 17 bit ADC. It utilizes non-contact temperature sensing to collect the temperature info without touching any surface of the object. The construction is equipped with many sensors. Hardware and software architecture and integration with Robot operating system is described in details. In the last part of the paper we presented the results of implemented measurement technologies and draw conclusions.
Design and Implementation of Thermal Sensor Based Temperature Measuring Robot Using Arduino Uno
Authors:- M.Tech. Scholar Chahat Vaishnav, Assistant Professor Aditi Khemariya
Abstract- Gender classification has recently received a lot of interest because genders include a lot of information about male and female social activities. It’s difficult to extract discriminating visual representations for gender classification, especially with faces. Gender classification is the process of determining a person’s gender based on their appearance. Automatic gender classification is gaining popularity due to the fact that genders contain a wealth of information about male and female social activities. In recent years, such classification has become increasingly significant in a variety of fields. In a conservative society, a gender classification system can be utilised for a variety of objectives, such as in secure settings. Identifying the gender type is critical, especially in sensitive areas, to keep extremists out of safe areas. Furthermore, such a system is used in situations where women are segregated, such as female railway cabins, gender-specific marketing, and temples.
An Analysis of the Use of Machine Learning Models in the Detection of Skin Cancer
Authors:- M. Tech. Scholar Payal Yadav, Assistant Professor Aditi Khemariya
Abstract- Skin cancer, sometimes referred to as cancer of the skin or SC for short, is one of the most common forms of cancer in the world. Even while a clinical examination of skin lesions is essential for determining the features of the illness, it is constrained by the amount of time it takes to complete and the many different interpretations it might lead to. approaches such as machine learning (ML) and deep learning (DL) have been created to aid dermatologists in establishing an early and correct diagnosis of SC. This is essential for boosting the patient’s chance of survival, hence the approaches have been developed. In this article, we conduct a comprehensive analysis of the published research on the categorization of skin lesions using machine learning. Our intention is to provide those who are new to the topic a firm foundation upon which they may build the studies and contributions they make in the future. Searches were conducted across a number of different internet databases using inclusion/exclusion criteria. Documents were chosen for this evaluation based on their capacity to offer an accurate description of the processes that were carried out as well as an exhaustive explanation of the results that were obtained. A total of 68 papers were chosen, the great majority of which depend on DL methods for detecting and classifying skin cancer, in particular convolutional neural networks (CNN), with a smaller number of research relying on ML techniques or hybrid ML/DL approaches. The articles were selected because of the value they provide to the process of diagnosing and classifying skin cancer. In order to classify skin lesions, many ML and DL algorithms provide findings that are considered to be state-of-the-art. The encouraging results that have been obtained up to this point give hope that these methods will eventually be used in clinical practice.
Literature survey on Different Technique used for Detection of Depression using EEG Signal
Authors:- Research Scholar Pritam Prabhat, Associate Professor & HOD Dr. Bharti Chourasia
Abstract- Electroencephalogram (EEG) plays an important role in E-healthcare systems, especially in the mental healthcare area, where constant and unobtrusive monitoring is desirable. EEG signals can reflect activities of the human brain and represent different emotional states. Stress is a feeling of emotional or physical tension. It can come from any event or thought that makes you feel frustrated, angry, or nervous. Mental stress has become a social issue and could become a cause of functional disability during routine work. A machine learning (ML) framework is effective for electroencephalogram (EEG) signal analysis. This paper reviews of depression emotion recognition from EEG for e-healthcare applications.
Smart Helmet Using IOT Technique
Authors:-Rai Abhishek, Kamisetty Bindusri Laxmi, Machkuri Venkatesh, Dr. A Venkataramana, M Rajamouli
Abstract- Smart Helmet is system used to design a helmet that provides safety to bike riders. It detects whether the rider met with an accident. A smart system can help to decrease death rates on road accidents. This smart system turns on the ignition only when the helmet is worn and no alcohol is consumed by rider. A smart system also helps to detect the obstacle and responds through automatic brake system. The system provides an alert and inform to the family or friends about the accident faced by the rider. Smart helmet system is developed using Arduino Uno for controlling the entire process, RF module to provide communication between helmet and bike unit, ultrasonic sensor for automatic break system, GSM and GPS module for SMS and current location identification, vibrating sensor for accident detection and node MCU to store the data of alcohol consumed by rider. The developed system is tested and works well.
Sales force E-commerce Microservice
Authors:- A Pravin
Abstract- To develop an exclusive e-commerce platform for artisans to sell their products. The demand forecast of the items required, automatic quality checks on the items as well as Sentiment analysis with next recommendation actions for the artist shall be added. To promote the Indian handicraft industry globally. Providing a common platform to make, market, and sell highquality handicrafts and goods. The micro service approach encourages enterprises to become more agile, with crossfunctional teams responsible for each service. Micro service architecture structures the application as a set of loosely coupled,collaborating services. Micro services are inherently distributed systems. Implementing such a company structure, as inSpotify or Netflix, can allow you to adapt and test new ideas quickly, and build strong ownership feelings across the teams.
Home Service Provider Application
Authors:- Shital Mohite, Alisha Pathan, Bhumi Lohar, Atharva Pisal, Riyaan Chatterjee
Abstract- We are building an web application for all the web users. The project “Home service provider” is used to automate all processes of the booking service Which deals with booking of services, providing it confirmation and user details. This will help you to get service from anywhere at any time. This webpage will take input from the user and provide suitable options for you. You can also provide your service here on our platform.Which would save the customers time and efforts significantly.
Brain Tumour Detection using Deep Learning
Authors:-Rohit Chahal
Abstract-The fragmentation of human-assisted manual categories can lead to inaccurate predictions and diagnoses, so classification of the brain tumor is one of the most important and difficult challenges in the field of medical imaging. Moreover, if there is big data that should be national, it is a frustrating task. Because tumors in the brain have a wide range of appearance and because normal tissue and tissue are very similar, it is difficult to distinguish tumor regions from images. We suggested how to remove brain tumor from 2D magnetic resonance brain imaging (MRI) using the Fuzzy C-Means clustering algorithm, followed by classification classification and convolutional emotional networks in this study. Tests were performed on real-time databases with tumors of various sizes, locations, forms, and firmness. In the traditional classification category, we used six classical dividers used in scikit-learn: Vector Support Machine (SVM), K-Nearest Neighborhood (KNN), Multilayer Perceptron (MLP), Logistic Retreat, Naive Bayes, and -Random Forest. After that, we moved on to Convolutional Neural Networks (CNNs), which were built using Keras and Tensorflow and produced much better results than the ancient neural networks. CNN achieved a 97.87 percent accuracy rate in our study, which is surprising. The main purpose of this study was to use textual and mathematical knowledge to discriminate between normal and aberrant pixels.
Secured Routing Energy Efficient Protocol (Sreep)
Authors:-Research Scholar R.S.Karthik, Dr.M. Nagarajan
Abstract-The routing mechanism in wireless sensor networks (WSNs) is crucial for a variety of monitoring applications, such as those focusing on the environment and traffic. In this section, the comprehensive contributions made to routing in WSN are examined. This study concentrates mainly on the challenges that WSN confronts as well as the various protocols that are employed. The SREEP algorithm is a brand-new proposal for assuring the secure transmission of data packets. The proposed technique reduces energy consumption while preserving a high level of security. The efficacy of the algorithm is assessed based on energy consumption, transmission duration, latency, and throughput.
Review: Cloud Computing and Internet of Things Plays Vital Role in Smart Stadium
Authors:-Asst. Prof. Shubham Gangrade, Associate Prof. Dr.Kapil Chaturvedi, Asst. Prof. Ankita Awasthi, Asst. Prof. Brij Mohan Sharma, Asst. Prof. Ashutosh Pandey, Associate Prof. Dr.Vijay Bhandari
Abstract-As we see in daily life how technology plays important role in routine life. There are few technologies are based on cloud computing like azure Microsoft server and the IoT and both are very important part in our lives. In our working life and the follow-up of all operations that we must follow before any match is held on any stadium in the world. An aspect of precautionary measures is discussed here before every match. In this research, based on discussion as per the understanding we will conduct on how to integrate cloud computing and the IoT and use them to work in developing stadiums in the word and made it smart. Several existing and new models of smart stadium are although explained.in this paper we are seeing cloud computing techniques which helps to iot applications.
Data Privacy using Block Chain and AI
Authors:-Asst. Prof. G. Kiran Kumar, A.Hari Prasad, B. Balaraju, N. Mehamood Hussain, B. Guna Sai Reddy, M. Jaya Kishore
Abstract-While data is the fuel that drives AI algorithms, it is difficult to approve or authenticate its use in the complex internet where it resides because of its dispersed nature and the fact that its diverse stakeholders do not trust one another’s stewardship. Due to this, it is challenging to facilitate data exchange in cyberspace for true big data and true powerful AI. In this paper, we propose the SecNet, an architecture that integrates three key components to enable secure data storage, computing, and sharing in the large-scale Internet environment, with the goal of creating a safer online environment rich in authentic big data and, by extension, a more robust artificial intelligence thanks to a larger pool of relevant information from which to draw. 1) Blockchain-based data sharing with ownership guarantee, allowing trustworthy data sharing in the large-scale environment to produce genuine big data. 2) An AI- based safe computing platform that may generate smarter security rules and so contribute to the development of a more reliable digital environment. As a result, greater AI performance may be attained by promoting data sharing and using a trusted value-exchange system for buying security services, which gives participants a chance to earn monetary benefits for supplying their data or service. In addition, we cover the usual deployment of SecNet and its applications.
Spammer Detection and Fake User Identification on Social Networks
Authors:-B.Sekhar, P.Shiva Prasad, J.Bharath Kumar, J.Mahesh, A.Pradeep, B.S.Muzammil
Abstract-Thousands of people across the globe utilize online services. Certain social media platforms, like as Face book and Twitter, get a profound impacts on people’s lives of their consumers, although they can also have unwanted consequences. Hackers are using the most popular social media websites as a distribution service for their unwanted as harmful content. When it comes to spammers, Face book, for examples, became one of the greatest widely utilized websites of any and all time. To responsible for the greater or companies, fraudulent start sending out unwanted messages to authenticated traffic, which not only affects the legitimate customers and also disrupts the usage of resources. In addition, the ability of spreading damaging content to consumers via the use of fictitious accounts has grown[10]. It is becoming increasingly customary in the field of online social networks to study fraudsters and false accounts on Tweets (OSNs). In this study, we start with a review of methods to determine scammers using Tweets as a test bed for their activity. Also included in this paper is a taxonomy of Twitter spam detection algorithms which categorizes the strategies that focus on their capacity to detect: 1 false contents, 2 is spamming depending upon Urls, (3) spamming within hot topics, and (iv) fraudulent accounts. All of these aspects are taken into consideration as well as schedule and user actions. We believe that this research will serve as useful resources for scholars looking for the most cur The y-axis in the above graph reflects the number of tweets containing either a false account or spam terms, while the x- axis represents the total number of tweets. Rent achievements in Twitter malware detection.
Automated Product Identification System For Visually Impaired
Authors:-Subhadip Ghosh, Soumen Maity, Sourav Chowdhury, Sudip Kumar Ghosh, Debmitra Ghosh
Abstract-This model intends to create a speech recognition system. A novel dataset is created, which consists of spoken words. The dataset is to train our system as well as to test the performance of the system. This dataset is not the same as the other conventional datasets generally used for this recognition system. The exciting and challenging aspects of this project are discussed. The content of the dataset and its collection and verification process are also discussed. Along with the system details, a methodology is used to reproduce and compare metrics to check the accuracy of this task. In the end, the performance result of the model is shown.
Land Registry Management System Using Blockchain
Authors:-Sanjana Gate, Rohan Temgire, Atharva Bankar, Rohit Chavan, Prof. Priyanka Sherkhane
Abstract-The present Land Registration System is a time consuming process and it involves a lot of vulnerabilities and fraudsters use it to cheat the common people and the government. The incomplete/improper registration leads to dispute of ownership and litigations of the land. In this project we make use of blockchain technology to overcome some vulnerability in the existing system. We use Metamask to proceed with the transactions and for verifying the users on our system. This application provides a simple and intuitive user interface, where users buy and sell their lands. The Land Inspector is the one who verifies and approves all the transactions and user accounts. With this system, users can ensure enhanced security.
Smart Wheelchair to Disability person Using Arudino UNO
Authors:-Lecturer S.Senthil, Lecturer E.Nambirani
Abstract-In the design of a smart, motorized, voice and app-controlled wheelchair using embedded system. Proposed design supports voice activation system for physically differently abled persons incorporating manual operation. The “Voice-controlled Wheel chair” for the physically differently abled person where the voice command controls the movements of the wheelchair. The voice command is given through a cellular device having Bluetooth and the command is transferred and converted to string by the BT Voice Control for Arduino and is transferred to the Bluetooth Module HC -05 connected to the Arduino board for the control of the Wheelchair. For example, when the user says „Go‟ then chair will move in forward direction and when he says „Back‟ then the chair will move in backward direction and similarly „Left‟, „Right‟ for rotating it in left and right directions respectively and „Stop‟ for making it stop. This system was designed and developed to save cost, time and energy of the patient. Ultrasonic sensor is also made a part of the design and it helps to detect obstacles lying ahead in the way of the wheelchair that can hinder the passage of the wheelchair. On addition to this an IOT device was integrated using NodeMCU where relay was connected to the microcontroller, using the application we can control the device using web application anywhere around the world.
Agriculture That Makes Use of the IOT
Authors:-Assistant Prof. Mr. N.V.S. Prasad, S.Zakeer Hussain, G.Anil Kumar, K.Bala Kumar,
K.Charan Kumar Reddy, S.Mahammed
Abstract-The widespread adoption of IoT technology has resulted in revolutionary changes across all walks of the average person’s life. The Internet of Things, or IoT, is a system where devices create their own network topology. Intelligent Smart Farming Internet of Things (IoT) based devices are changing the game in agriculture by improving yields, cutting costs, and maximaising efficiency. The purpose of this report is to propose an Internet of Things (IoT) based Smart Farming System that will help farmers get Live Data (Temperature, Soil Moisture) for efficient environment monitoring, thereby boosting both yield and product quality. This report proposes an IOT- based Smart Farming System that utilities Arduino technology combined with various Sensors and a Wi-fi module to generate a live data feed that can be accessed online via Thingsspeak.com. The proposed product has been tried and tested in real agricultural fields, yielding data feed accuracy rates of 98% or higher.
Experimental Investigation of Machine Learning Techniques for Predicting Software Quality
Authors:-Asst. Prof. V.Lakshmi Chaitanya, Nikhitha Sutraye, A.Sai Praveeena,U.Naga Niharika,
P.Ulfath, D.P.Rani
Abstract-There are several points in the software development process when estimating software quality is useful. Quality assurance planning and benchmarking are two potential applications. Multiple criterion linear programming and quadratic programming are two approaches that have been utilised in prior research to estimate software quality. In addition, we tested out C5.0, SVM, and a Neutral network to determine how best to estimate quality. The precision of these research is rather poor. The purpose of this research was to enhance estimate precision by using important properties of a large dataset. In order to improve accuracy, we used a feature selection technique and a correlation matrix. We have also tried our hands at several newer techniques that have proven effective in other prediction challenges. Xgboost, Random Forest, and Decision Tree are only few of the machine learning algorithms used to analyse the data and draw conclusions about the software’s quality and its relationship to its development qualities. Results from experiments demonstrate that machine learning algorithms can accurately predict the quality of software.
Predictive Analysis of Aged and Faulty Electronic Appliances in Smart Home
Authors:-Asst. Prof. Sathess Lingam. P, UG Scholar Krithik Gokul. S, UG Scholar Sagar.T.N, UG Scholar Prasanna Venkatachalapathi.B
Abstract-The use of smart homes and Internet of Things (IOT) devices has become increasingly common in recent years. As a result, there is growing interest in using predictive analytics to detect failures in electronic devices and managing medications in smart homes, especially smart homes used by sensors. The purpose of this study is to explore the use of predictive analytics in smart homes to detect errors in electronic devices and improve medication management. To do this, it uses data from a variety of sensors and devices to identify patterns and anomalies that indicate possible errors or problems in devices and medicines. The research focuses on using machine learning algorithms to analyze data from sensors such as temperature, humidity, motion and light to identify patterns of device use and medication administration. Algorithms then use this data to predict the likelihood of failure or problems with devices and medicines. The study also explores how natural language processing (NLP) techniques can be used to analyze text-based data such as drug labels and instructions for use. This allows us to better understand how medicines are used and administered correctly. Overall, this research contributes to the development of predictive analytics techniques that can improve the management of smart homes, especially electronics and medicines used by the elderly.
Need of Technology in Trade Andbusiness
Authors:-Research Scholar Seelesh Sharma
Abstract-The old order changeth yielding place to new and God fulfills himself in many ways lest one good custom should corrupt the world.”Alfred Lord TennysonMeans old policies, methods changed and new policies, methods take their place because along with the old methods some defects arise in those systems, which start causing damage.Exactly the same thing we can do in the context of technology, the techniques which were used for trade and business in ancient times, if compared to the present, all those methods have become very old, and very time consuming.At present, where cut-throat competition is going on, doing work smartly and doing it quickly and accurately has become an essential requirement of business and business, so to fulfill this need, technology has taken birth has become an integral part of business.Technology has transformed all business and business processes from complexity to organization whether it is a matter of information technology or other means of technology, every means of business and business seems to be closely related.The importance of information technology in business has grown impressively over the past two decades. The modern economy places a premium on the acquisition, processing and fair use of information in all its forms and formats. IT helps companies innovate, grow and reach new customers.The most important means of technology in trade include electronic communication such as email, text, fax and virtual conferences. Tracking methods for shipping and purchasing is another huge technological innovation, as it allows businesses to verify the delivery of goods and the amount of inventory purchased. Electronic spreadsheets and databases are other inventions that allow international companies to more easily manage and store their information. Technology has revolutionized the lives of consumers and businesses alike. The increased array of products on the shelves, lower costs of goods and services, and ease of access to information are just a few of the ways technology has enhanced society. The field of international trade is particularly sensitive to technological innovations. Technology used to protect confidential business information using quality. The birth of the Internet and online social networking sites has brought down the cost of conducting business. This gives companies an easy to use Six Sigma management approach. The level of technology is an important determinant of economic growth. Rapid rate of growth can be achieved by high level of technology. Schumpeter is of the view that only innovation or technological advancement is the determinant of economic progress, but if the level of technology stagnates then the process of development stops.At present, there has been an amazing increase in the production and distribution of goods and services through technology. This technology has made business world-wide. We can do shopping from any place in the whole world by simply pressing a few buttons while sitting at our home and can buy the things we want.Through technology, the level of efficiencies of products and services, and when the level of capabilities, comes down costsAnd when there is a reduction in the cost, there is definitely an increase in the profits and the growth of the systems is moreRemember those days when we were born, would technology have developed so much at that time, anyway, it is said that necessity is the mother of invention, so as we need great new inventions were born and technology is also such a thing. It is the invention that replaces our need. Talking about business, any person sitting at home can inquire about any particular service in the whole world, can get any information about it and can also order online after being satisfied.Technology has made business so easy that if any person wants to do business to become self-reliant, then being free from complications, one can easily think about business. Technology is no less than a miracle word for business and business.Just as finance is said to be the lifeblood of business, in the same way if we call technology the heartbeat of business in the present context, then it will not be an exaggeration. Internet has become such a system that has erased all the distances of the world, whether it is family or business or business, no matter how far away we can operate the system and business activities.
Predictive Analysis of Aged and Faulty Electronic Appliances in Smart Home
Authors:-Asst. Prof. Sathess Lingam. P, UG Scholar Krithik Gokul. S, UG Scholar Sagar.T.N, UG Scholar Prasanna Venkatachalapathi.B
Abstract-The use of smart homes and Internet of Things (IOT) devices has become increasingly common in recent years. As a result, there is growing interest in using predictive analytics to detect failures in electronic devices and managing medications in smart homes, especially smart homes used by sensors. The purpose of this study is to explore the use of predictive analytics in smart homes to detect errors in electronic devices and improve medication management. To do this, it uses data from a variety of sensors and devices to identify patterns and anomalies that indicate possible errors or problems in devices and medicines. The research focuses on using machine learning algorithms to analyze data from sensors such as temperature, humidity, motion and light to identify patterns of device use and medication administration. Algorithms then use this data to predict the likelihood of failure or problems with devices and medicines. The study also explores how natural language processing (NLP) techniques can be used to analyze text-based data such as drug labels and instructions for use. This allows us to better understand how medicines are used and administered correctly. Overall, this research contributes to the development of predictive analytics techniques that can improve the management of smart homes, especially electronics and medicines used by the elderly.
A Cloud Computing
Authors:- Ms. Rashmi. R.Kamat
Abstract-Cloud computing is the practice of using a network of remote servers hosted on internet to store, manage and process data on demand and pay as per use. It provides access to a pool of shared resources instead of local servers or personal computers. As it do not acquire the things physically, it saves managing cost and time for organizations. Cloud computing is a completely internet dependent technology where client data is stored and maintain in the data center of a cloud provider like Google, Amazon, Microsoft etc. Cloud computing is an emerging domain and is acclaimed throughout the world. There are some security issues creeping in while using services over the cloud. This research paper presents a review on the cloud computing concepts as well as security issues inherent within the context of cloud computing and cloud infrastructure. This paper also analyzes the key research and challenges that presents in cloud computing and offers best practices to service providers as well as enterprises hoping to leverage cloud service to improve their bottom line in this severe economic climate and boost up its usage. The main emphasis of our study based on existing literature and to understand the concept of multi- tenancy securityissue.
A High Grade Type Light Weights Concrete Design Using Epoxy Material
Authors:- Ajay Bhardwaj, Asst. Prof.Kishor Patil
Abstract-The aim of this study is to determine the performance of concrete by adding the fly ash and silica fume in concrete by the partial replacement of cement and fine aggregate by some percentage and this will be done by different percentage at the gap of some percent and what will be effect on basic properties of concrete as from the other research paper it is noted silica fume and fly ash both are added separately in concrete by some partial replacement so result will be tremendous so here in this study by considering or reading all the previous data from the research paper the new work should also be positive fly ash is the waste product of coal combustion product also known as the fuel ash and silica fume is also known as the micro silica fume is nonmetallic and nonhazardous material.
A Modelling and Analysis Multistory Building Load Analysis Using Staad Pro Software
Authors:- Sumit Kanade, Associate Professor Rahul Yadav
Abstract-In order to compete in the ever growing competent market it is very important for a structural engineer to save time. As a sequel to this an attempt is made to analyze and design a multistoried building by using a software package staad pro. For analyzing a multi storied building one has to consider all the possible loadings and see that the structure is safe against all possible loading conditions. There are several methods for analysis of different frames like FEM method, cantilever method, portal method, and Matrix method. The present project deals with the design & analysis of a multi storied residential building of consisting each floor. The dead load &live loads are applied and the design for beams, columns, footing is obtained STAAD Pro with its new features surpassed its predecessors and compotators with its data sharing capabilities with other major software. We conclude that staad pro is a very powerful tool which can save much time and is very accurate in Designs. Thus it is concluded that staad pro package is suitable for the design of a multistoried building.
A Review On Solar, Wind & Grid Connected Fact Device Design And Noise Estimation
Authors:- Shubhanshu khare, Prof. A. K. Sharma
Abstract-The world is witnessing a change-over from its present centralized generation to a future with greater share of distributed generation. Hybrid energy systems are inter-connected with wind power, photovoltaic power, fuel cell and micro-turbine generator to generate power to local load and connecting to grid/micro-grids that decrease the dependence on fossil fuels. The hybrid system is a better option for construction of modern electrical grids that includes economic, environmental and social benefits. An overview of different distributed generation technologies has been presented. This paper puts forward a comprehensive review of optimal sizing, energy management, operating and control strategies and integration of different renewable energy sources to constitute a hybrid system. The feasibility of the different controllers such as microcontroller, proportional integral controller, hysteresis controller and fuzzy controller are presented. The controller is a closed loop feedback mechanism used for power regulation which achieves zero steady state error and the output signal generated from the controller produces desired output response.
Tribological Behaviour Of Aluminium Metal Matrix Composite Reinforced With Boron Nitride And Carbon Fibre
Authors:- Assistant Professor Sugan V, Manimuthu R, Professor & Head Subramaniam D
Abstract-The increasing need for low weight alloys and composites for engineering and structural applications motivates researchers to investigate the prospect of developing novel processes to generate high-performance materials. The current study addresses the manufacturing of metal matrix composites (MMCs) employing Stir casting procedures with aluminium as the base metal and carbon fibre and boron nitride as reinforcements. The primary goal of incorporating reinforcement into a metal matrix is to improve thermal, structural, and tribological qualities by increasing yield strength, tensile strength, and hardness at ambient temperatures. A hardness test will be performed to investigate the tribological and mechanical properties of the AMMCs, and a SEM image will be captured to investigate the microstructure.
Turtlebot Maze Solving With ROS
Authors:- N Harshavardhan Reddy, G Vamshi Krishna, Shaik Shahid Ali
Abstract-In this project, we are building a maze solver using Turtlebot. It is predicted that usage of automated robotic systems will increase tremendously in both industrial and domestic applications such as path planning bot, robot-based room cleaning system, robot-based waiter, etc. Currently, during the pandemic, it has become important to minimize human-to-human contact to stop transmission of disease hence there is a necessity to use them for pathfinders such as in restaurants. As a result, there is a need for robots to be equipped with this technology. Due to this, we have decided to find an approach for a bot to find to navigate and move the bot from one place to another automatically. For this approach, we have used ROS, modified Turtlebot package, and Gazebo. After compiling and integrating various nodes using ROS we were successfully able to simulate bot which could solve a maze and avoid obstacles in it’s path.
Criminal Face Detection Using Machine Learning
Authors:- Prof. Anup Ganji, Rashmi Kamat, Swapnali Chougule Pooja Biradar, Srushti Gadivaddar
Abstract-In practice, identification of criminal in Malaysia is done through thumbprint identification. However, this type of identification is constrained as most of criminal nowadays getting cleverer not to leave their thumbprint on the scene with the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. However, because of limited software developed to automatically detect the similarity between photo in the footage and recorded photo of criminals, the law enforces thumbprint identification. In this paper, an automated facial recognition system for criminal database was proposed using known Principal Component Analysis approach. This system will be able to detect face and recognize face automatically. This will help the law enforcements to detect or recognize suspect of the case if no thumbprint present on the scene. The results show that about 80% of input photo can be matched with the template data.
An Investigation on the Diagnostic Potential of Machine Learning for Glaucoma
Authors:- Tasneem Shaikh, Sudha Sharma, Durgesh Mishra
Abstract-This review article will study the application of a range of image processing algorithms with the goal of providing an automated diagnosis of glaucoma. The objective of the paper is to fulfill this goal. Glaucoma is a degenerative illness that affects the visual nerve and is brought on by trauma to the neurological system. If the problem is not treated and is allowed to continue without being monitored, it is possible for a person to gradually lose all or part of their vision if the issue is not handled. It is a fact that a large percentage of people residing in the world’s rural and semi-urban areas suffer from eye difficulties; nevertheless, the exact same can be stated for every other location as well. At this point in time, the diagnosis of retinal illnesses is nearly totally completed via the processing of images that are obtained by the study of photographs of the fundus of the retina. Some of the essential image processing methods for detecting eye illnesses include image registration, picture fusion, image segmentation, feature extraction, image enhancement, morphology, pattern matching, image classification, analysis, and statistical measures. Image enhancement, morphology, and pattern matching are some examples of other methods.
Factors Influencing the Process of Internationalization of Small and Medium Enterprises (SME’S)
Authors:- Research Scholar Manish Ranjan, Professor Dr. Ashok Kumar
Abstract-The internationalization process of small and medium-sized enterprises (SMEs) is influenced by a variety of factors. A growing economy creates more opportunities for SMEs to expand their businesses internationally, as it indicates a higher demand for goods and services in international markets. SMEs need to consider the political and economic stability of their target markets before entering them. They need to have a thorough understanding of the political situation, economic growth prospects, and exchange rates of the target country to ensure their success in the international market. The present study aims to study the factors influencing the process of internationalization of SMEs. In the present research study, the researcher has used descriptive research design.To develop stable item attributes, adequate sample sizes are required. The sampling population include 473 SMEs.
Train Car Auto-Pilot to Traffic Sign Detection and Recognition Using Deep Learning
Authors:-Jadhav Sakshi Chhabu, Wakhare Nisha Maruti, Kamble Snehal Govind, Kaitake Jijabai Baban
Abstract-Traffic Sign Detection and Recognition is a crucial task for enhancing the safety of autonomous driving systems. Deep Learning has been proven to be a powerful tool for solving this problem. In recent years, Convolutional Neural Networks (CNNs) have been widely used for Traffic Sign Detection and Recognition due to their ability to automatically learn and extract features from images. This approach has achieved high accuracy rates in detecting and recognizing traffic signs under different weather and lighting conditions. This paper proposes a comprehensive review of recent advances in Traffic Sign Detection and Recognition using Deep Learning. We discuss the challenges of Traffic Sign Detection and Recognition, the state-of-the-art methods, and the datasets commonly used for evaluation. Furthermore, we analyse the limitations of current approaches and highlight the future research directions in this field.
Developments in Forensic Science Technology, Such As New Methods for Analyzing Evidence or New Tools for Gathering Evidence
Authors:-Andrew Roy
Abstract-Forensic technology has undergone significant advances in recent years, resulting in new methods for analyzing evidence and new tools for gathering it. These advancements have revolutionized the way forensic science is conducted and have led to increased accuracy and efficiency in criminal investigations. This research paper aims to explore the latest developments in forensic science technology, including the various methods for analyzing evidence and tools for gathering it. The paper will also discuss the benefits and drawbacks of these new technologies and their potential impact on the field of forensic science.
Analyzing the Impact of Mobile Commerce on Consumer Behavior and E-Commerce Sales
Authors:-Pawan Dadoria
Abstract-The advent of mobile commerce (m-commerce) has transformed the way consumers shop online, creating new opportunities and challenges for e-commerce retailers. The studies is analyze effect in m-commerce in consumers behavior and e-commerce sales, using a mixed-methods approach that combines a literature review, survey data, and secondary data analysis. The literature review covers the definition and evolution of m-commerce, as well as theoretical frameworks for analyzing the relationship between m-commerce and consumer behavior. The survey data is collected from a sample of online shoppers in the United States, and includes measures mobile commerce consumer impact in m-commerce, and shopping behavior across different channels (desktop, mobile, and in-store). The secondary data analysis draws on publicly available data from leading e-commerce retailers and industry reports, and includes measures of e-commerce sales growth, channel mix, and mobile traffic and conversion rates. The results show that m-commerce has a significant positive impact on e-commerce sales, especially for retailers that invest in mobile-friendly websites and apps, personalized promotions, and convenient payment and delivery options. Moreover, the findings suggest that m-commerce adoption is associated with changes in consumer shopping behavior, such as increased frequency and convenience of purchases, reduced search and decision-making costs, and greater reliance on social proof and mobile reviews. The implications of these findings for e-commerce retailers and mobile commerce developers are discussed, along with limitations and future research directions.
A Cinema – Online Movie Ticket Booking System
Authors:-Aarya Nanndaann Singh M N, Akash Hegde P, Abhilash R, Akash Kumar, Prof. Priyadarshini R
Abstract-This paper presents the design and implementation of an online movie ticket booking system. The system is designed to provide a convenient and user-friendly platform for customers to purchase movie tickets online, eliminating the need to wait in long lines or visit physical ticket counters. The system includes features such as movie selection, seat selection, payment processing, ticket confirmation, ticket rescheduling, ticket transferring. The system also employs various recommendation algorithms to suggest movies to users based on their previous selections and browsing history. Additionally, the system includes security features such as user authentication, data encryption, and secure payment processing to ensure the protection of customer information. The implementation of the system involved the use of various programming languages, frameworks, and databases. Overall, the system offers an efficient and streamlined approach to movie ticket booking, enhancing the overall movie going experience for customers.
Emotion Based Music Player
Authors:-Prof. R. K. Sahare, Isha Bhoyar, Diksha Borkar, Amruta Shedame, Achal Deotale, Sheetal Mistry
Abstract-Everyone wants to listen music of their individual taste, mostly based on their mood. Average person spends more time to listen music. Music has high impact on person brain activity. User always faces the task to manually browse the music and to create a playlist based on the current mood. This project is efficient which generate a music playlist based on the current mood of user. How ever the proposed existing algorithms in use are comparably slow, less accurate and sometimes even require use of additional hardware like EEG or sensors. Facial expression is a easy way and most ancient way of expressing emotion, feelings and ongoing mood of the person. This model based on real time extraction of facial expression and identifies the mood. In this project we are using HAAR cascade classifier to extract the facial features based on the extracted features from HAAR cascade, we are using COHN KANADE dataset to identify the emotion of user. If the user’s detected emotion is neutral then the background will be detected and the music will play according to the background. For example. If it detects gym equipment, the algorithm will automatically create a workout song playlist from the captured image of the background.
Survey on Healthcare Image Steganography Techniques and Features
Authors:-Ph.d. Scholar Arun Kumar Sonaniya, Prof. Laxmi Singh
Abstract-One of important part of human life is health and some of information storage provide such valuable data. In order to increase the trust on such type of stored data steganography technique was applied by various researchers. This paper has summarized the features of image processing and its application in different areas. This paper has detailed various models proposed by scholars of image steganography processing. It was obtained that image may undergo some set of attacks that may disturb geometrical and spatial information, so list of such attacks was also summarized in the paper. Some of algorithm measuring parameters were also mentioned in the paper.
Integrating Green/Sustainability concept in Nigeria’s Property Market
Authors:-Habibu Sani, Ibrahim Bashir Bello
Abstract-The study was conducted to explore the need for integrating green sustainability concept into property development and valuation with a view to improving compliance to green sustainability concept and practice into real property market indices. The study was conceived on a survey design to appraise the need of integrating green issues/sustainability into property valuation process. The study used literature analysis approach to review real estate surveyors practices/approach to value indices perception using questionnaires to scope the importance of a range of sustainability features on market value for a hypothetical property, based on social, economic and environmental features constituting the triple bottom line of sustainability. Finding srevealed that energy waste and water management, preservation of biodiversity and environmental indoor/health quality are breakpoints for the integration of green issues into property valuation practice in developing country like Nigeria. There are already growing awareness of the need to integrate sustainability into real estate valuation practice. The study therefore concludes by establishing the significance of integrating green concept/sustainability into real estate valuation and its effect on the general perception of the Nigerian property market players.
Sustainable Investment Appraisal of Latent Values in Undeveloped Sites in Barnawa Kaduna, Nigeria
Authors:-Habibu Sani, Ibrahim Bashir Bello
Abstract-Real estate investment is considered the most complex and sophisticated form of investment compared with stocks, bonds and other finance sector investment, due to the influence of such factors as location and social trends as well as obsolescence (functional/physical) on its performance. Real estate investment decisions must be guided on intuition to avoid colossal loss thus the need for employing appraisal techniques as highest and best use technique to justify resource allocation to productive and best use among competing opportunities. Comparative analysis technique of yields from investment potentials of some selected sites was adopted.The study revealed there exist some vacant plots along AliyuMakama Road Barnawa Kaduna, having untapped latent value for investment but were left vacant thus termed vacant for speculation even though speculation is statutorily discouraged in the land use act 1978 with no visible machinery of enforcement to deter offenders. This has not only thwarted the aesthetics of the area but breaded security threat as being a rearing ground for criminals, rodents and reptiles thus jeopardizing sustainable city growth enshrined in the city master plan.
Sustainable Low Income Housing Delivery in Nigeria: Rent to Own Model.
Authors:-Habibu Sani, Ibrahim Bashir Bello
Abstract-Nigeria being the most populated country in West Africa region is faced with numerous challenges including housing delivery. Existing housing stock are by far short of expected number while the population of the country is geometrically increasing without a corresponding increase in housing delivery even though there are deliberate policies instituted to ameliorate housing problems. This article is predicated on the overview on Nigeria’s housing delivery journey using a policy and document review technique. The research concluded that a rent-to-own model is a workable strategy to adopt by Nigerian government whose interest to improve low income housing is objective and resolute to alleviate sufferings of the low income earners whose savings could hardly grow within a reasonable time frame to purchase a property from the open market on a cash and carry basis as is the tradition in the country.
strong>Scalable Data Ingestion and Analytics: Leveraging Azure Data Explorer for IoT Performance Optimization
Authors:-Seetaiah B, Technology Manager
Abstract-The proliferation of IoT devices has led to a significant increase in telemetry data, creating challenges in data ingestion and processing using traditional SQL databases. As device counts grow, SQL database performance degrades, resulting in slower data handling and inefficient query responses. This paper explores the implementation of Azure Data Explorer (ADX), a fully managed data service, to overcome these challenges. By leveraging ADX, the system achieved faster data streaming, improved performance, and greater scalability. This case study presents a detailed analysis of the migration process, performance improvements, and future scalability considerations.
DOI: 10.61137/ijsret.vol.9.issue2.196
strong>Design and Fabrication of Fixed-Wing Unmanned Aerial Vehicle (UAV) With Dropping Mechanism
Authors:-Assistant Professor Mr.N.Kawin, P.Tharun, VP.Vaishnav, M.Yogesh
Abstract-Unmanned Aerial Vehicles (UAVs) which are also known as Drones are aircrafts without a human pilot on board, -usually controlled by a ground-based controller and a communication system or by the ability of being programmed with various degrees of autonomy. The reason of the rapid advancement on drone technology is the need for more precision, accessibility, safety and cost effectivity in many fields. They currently lack the flexibility and adaptability of manned aircrafts. By some measures, 80% of the global drone industry revenues are related to agriculture, in some way. (R1) Unmanned aerial vehicles (UAVs) are one of the most promising innovative technologies invented in recent years to promote precision agriculture and smart farming. UAVs can not only reduce labour requirements but also increase production output, reduce the use of pesticides, and protect the environment. The main objective of our project is to design an aircraft that is capable of lifting as much weight as possible while taking into account the available power and aircraft’s length, width, and height requirements. A special attention has been devoted to Dropping Mechanism of the payload which can help us in gaining more ways to deliver the goods we need. The following report is a synopsis of the design process, Fabrications, component selection and Fly with maximum payload.
DOI: 10.61137/ijsret.vol.9.issue2.197
strong>Evaluation of Workability Properties in Light Weight Geopolymer Concrete Using Bamboo Aggregates with Different Percentage of Superplasticizers
Authors:-S. Kavipriya, K. Vani, V.Siva, S. Jeeva Bharath
Abstract-The light-weight concrete is a concrete which has a density of 300 to 1850 kg/m3.There are many advantages of having low density. It helps in reduction of dead load, increases the progress of building. The weight of a building on the foundation is an important factor, in case of weak soil. This research focus on reducing the density of concrete by replacing coarse aggregates with bamboo aggregates at different proportion with 10%,20%,30% and 40% respectively. Nowadays,research focus on reducing the self weight of structures, this paper contributes in that area to evaluate the density and workability properties of geopolymer concrete by reducing its density by replacing coarse aggregate with bamboo aggregates. Geopolymer concrete is one of the sustainable concrete in future for developing greener environment by reducing the emission of carbondioxide. This study emphasis to reduce the weight of geopolymer concrete which will help to develop more precast products. Sodium hydroxide of 12M is used in this research. Superplasticizer conplast SP430-Fosroc is used in this study to improve its workability. Low calcium based flyash is used as source material and M-Sand is used as fine aggregate. Density and fresh properties of geopolymer concrete by adding 0.25%,0.5%,0.75% and 1% of superplasticizer by volume of concrete replacing bamboo aggregates are tested.
DOI: 10.61137/ijsret.vol.9.issue2.198
strong>A Soil Moisture Sensor and Accelerometer are Part of an Internet of Things-based Landslide Detection and Monitoring System
Authors:-Jayapal N, Dharanidharan S A, Ajay Aravinth M, Ajithkumar R, Gokul R
Abstract-One major natural hazard that can harm property, infrastructure, and human life is landslides. In this work, we suggest an Internet of Things (IoT)-based landslide monitoring and detection system that gathers and analyzes data on precipitation, slope stability, and other environmental conditions using a variety of sensors and communication technologies. In addition to software components for data processing and visualization, the suggested system also includes hardware components including sensors, microcontrollers, and communication devices. To assess the system’s effectiveness in identifying and tracking landslides, we carried out field tests and contrasted the outcomes with those of other approaches. Our research demonstrates that the Internet of Things-based approach can enhance early warning and risk management initiatives by offering precise and up-to-date information on landslide risk. The consequences of our findings’ ramifications for further study and real-world applications are examined.
DOI: 10.61137/ijsret.vol.9.issue2.199
strong>The Intelligent Grocery Distribution System in India
Authors:-Bharathi V, Aarthy M, Boomika V G, Kowsika P, Kaviya S
Abstract-The Public Distribution System (PDS) in India is a government initiative that provides goods to those in need at set prices. On the other hand, manual material weighing results in imprecise measurements and unlawful usage of consumer goods. We currently have a system in place where the products are personally sent to the customer after their fingerprint is verified and their ration card is scanned. However, this was insufficient to halt the corruption. As a result, a two-step verification process has been suggested as part of the system. However, we deploy automation in place of manual labor when it comes time to handle the commodity. Customers are given an RFID card with a unique identifying number that serves as a ration card. An RFID reader scans the card.
DOI: 10.61137/ijsret.vol.9.issue2.200
strong>IoT-based Wireless Charging for E-vehicles
Authors:-Saranya S, Keerthana B, Kiruba M, Gobika S and Kavipriya R
Abstract-The demand for electric vehicles is rising as the automotive industry transitions quickly from IC engine vehicles to electric vehicles. As a result, there are now more charging stations. This concept uses an inductive coupling to wirelessly charge the car using a wireless charging system. All we have to do is park the vehicle in the charging area. Wireless power transmission is the process of moving electrical energy from a source to a load remotely without the need of wires or cables. Nikola Tesla’s greatest invention was the wireless power transfer concept. There is no need for human contact with this technology. One technology that may represent a step ahead in the future is wireless power transmission. This idea has the potential to create new wireless charging opportunities for everyday use. Magnetic resonance technology, or wireless power transfer (WPT), has the potential to free people from obnoxious cords. In actuality, the WPT uses the same fundamental theory—known as inductive power transfer—that has been established for at least 30 years. In recent years, WPT technology has advanced quickly. With a load efficiency greater than 90%, the power transmission distance rises from a few millimetres to several hundred millimetres at milliwatts to kilowatts of power level. Electric vehicle (EV) charging applications find the WPT highly appealing because to its advancements in both static and dynamic charging settings. The capabilities of wireless charging systems are further enhanced by IoT integration. The charging process gets smarter and more linked with IoT. It is possible to gather and analyse data in real time, which makes managing charging stations more effective. Features like dynamic load balancing, predictive maintenance, and remote monitoring are made possible by IoT. Issues with charging time, range, and cost are resolved by the smooth integration of WPT and IoT in EV charging. Because of this convergence, conventional battery technology is no longer as important for the widespread use of EVs. The project’s goal is for academics to use these cutting-edge successes to propel WPT further and encourage the wider use of electric vehicles.
DOI: 10.61137/ijsret.vol.9.issue2.201
strong>Modified LUO Based Boost Converter with Single Input and Multi Output Topology
Authors:-C.Gowrishankar, K. Divyabharathi, K.Lalithapriya, S.Pooja, A.Srilekha
Abstract-A power electronics device that transforms a low voltage input to a higher voltage output is the Modified Luo Based Boost Converter with Single Input and Multi-Output Topology. This paper proposes a modified Luo converter utilizing a coupled inductor and a diode-capacitor network to achieve multiple output voltages. The proposed circuit operates in continuous conduction mode (CCM), ensuring stable output voltage despite input variations. Simulation and experimental results validate the converter’s performance, demonstrating superior efficiency and reduced output voltage ripple compared to conventional converters. The high voltage gain and effective energy conversion make this topology ideal for various industrial and consumer applications, including telecommunications, renewable energy systems, and embedded power supplies.
DOI: 10.61137/ijsret.vol.9.issue2.202
strong>An Emprical Study on Substituting Sugarcane Bagasse Ash Aor a Portion of Cement in Mortar
Authors:-S.Gowtham, R.C.Haniska, C.Haniskaa, R.Pradheepa
Abstract-We are aware that a lot of damage is done to environment in the manufacture of cement. It involves lot of carbon emission associated with other chemicals. The researches has shown that every one ton of cement manufacture releases half ton of carbon dioxide, so there is an immediate need to control the usage of cement. The hand materials wastes such as Sugar Cane Bagasse Ash is difficult to dispose which in return is environmental Hazard. The Bagasse ash imparts high early strength to mortar and also reduce the permeability of mortar. The Silica present in the Bagasse ash reacts with components of cement during hydration and imparts additional properties such as chloride resistance, corrosion resistance etc. Therefore the use of Bagasse ash in concrete not only reduces the environmental pollution but also enhances the properties of mortar and also reduces the cost. It makes the mortar more durable. This project mainly deals with the replacement of cement with Bagasse ash in fixed proportions and analysing the effect of SCBA blended mortar. The concrete mix designed by varying the proportions of Bagasse ash for 0%, 5%, 10%, 15%, 20%, 25%, 30% the cubes are been casted and cured in normal water for ages of 7, 14 and 28 days. The test result indicate that the strength of mortar increase up to 10% Sugar cane bagasse ash replacement with cement.
DOI: 10.61137/ijsret.vol.9.issue2.203
strong>Interlinking of Local Water Bodies in the Villages of Thethakkudi, Mayiladuthurai District, Tamilnadu, India
Authors:-Sridhar N, Jeevitha M, Kamalini V, Kokila K
Abstract-Interlinking of water bodies involves the process of diverting surplus water through a network of canals. Through which the water bodies can holds water for a much longer period than in the past. This would cover additional areas for irrigation, remove the imbalance in availability of water and create the way for effective utilization of available water resources. Therefore, this project will offer interlinking of local water bodies through the link water channels at micro-level in the village of Thethakkudi. The Thethakkudi is a village in Mayiladuthurai district in Indian state of Tamil Nadu. The present population of the village is 358. Out of these 129 are men, 141 are women and 88 are children. The village is administered by the Kathiruppu Panchayat, which covers an area of 0.98km2. Thethakkudi has an average elevation of 4m and is located 11km from the coast of Bay of Bengal. Even this income they get from outstations because of the lack of quantity and quality of water resources. Therefore, the agriculture in this village is also being destroyed. Currently there are more than 20 excavated ponds and puddles are available. Very few ponds are seasonal at best, and their water does not last beyond monsoons. Most of the ponds are get water from rainfall, also dry up as early as March. So, the process of diverting surplus pond water through a network of canals to relatively drier areas are more useful for agriculture development of the village. Therefore, the aim of this project is to improve the agricultural practices through interlinking of ponds depending upon the local topographical survey using Remote Sensing and GIS. The Remote Sensing and GIS with DEM Techniques are used to study topography of the ground and to analyze the morphologic characteristics easily, quickly and at low cost. The benefits accruing from this project are crop diversification, better farm practices, improving food productivity, rejuvenation of groundwater and improving revenue of farmers.
DOI: 10.61137/ijsret.vol.9.issue2.204
strong>Miscellaneous Trends Detection of Neovascularization in Fundus Images Using Convolutional Neural Network
Authors:-Associate Professor Dr.F.R.Shiny, Malar, Abishega A G, Anusree A, Christeena Joy A, God Shaly J
Abstract-Visual impairment is one of the major health prob- lems in the world. The main reasons for visual impairment are lifestyle factors and limited eye care resources. Therefore, early screening and timely treatment are the keys to prevent vision damage. This project proposes a detection of neovascularization in fundus images using convolutional neural network. Wiener filter is used for preprocessing. In preprocessing noise is removed from the input dataset. Image segmentation is a critical step in image processing. One of the most common image segmentation methods is fuzzy c-means clustering. Fuzzy c-means clustering methods have a lot of potential when it comes to extracting detailed features from image pixels. Fuzzy c-Means (FCM) clustering is a popular unsupervised learning algorithm. The selected characteristics are fed into the Convolutional Neural Network (CNN) classifier for data classification using machine learning. This CNN classifier model attempts to reduce the number of features in a dataset. Finally, the CNN classification method is used to improve accuracy.
DOI: 10.61137/ijsret.vol.9.issue2.205
EARLY DETECTION OF DEEP VEIN THROMBOSIS USING DEEP LEARNING
Authors:-Assistant Professor Mr.C.Bastin Rogers, Asphini A, Jaisha R, Saranya M, Surjith Ribitha S
Abstract-The detection of Deep Vein Thrombosis (DVT) during the early stage is critical for preventing any adverse effect. DVT is one of the major causes of diseases that are related to blood circulatory system in human. This article proposes a methodology for the early detection of DVT through the photographic images captured using smartphones as edge de- vices. Unlike the traditional methods, the proposed methodology utilizes the edge computing as a green computing initiative. The manifestation of telangiectasia is used as the early bio marker. The proposed image analysis model uses Convolutional Neural Network (CNN) for training the detection model. The experiments were done with the globally available DVT and Varicose vein images as well as the photographic images captured through smartphones. The proposed robust approach produces excellent results without requiring any restricted environment for capturing the images.
DOI: 10.61137/ijsret.vol.9.issue2.206
Predicting Different Types of Paddy Leaf Diseases Using Convolutional Neural Network (CNN)
Authors:-Assistant Professor Mrs. G.Santhiya, Anusubha k , Jenisha Joy MJ, Reshma V, Snekha D .
Abstract-Most of the countries are depends on agriculture, where Tamil nadu is the land of agriculture. Here paddy cultivation is major source of earning. People in Tamil nadu, consumes rice as main meal for three times in a day. Various factors such as diseases on paddy leaf, pest attack etc., the production of paddy will be affected approximately 40stage to protect the paddy because it will destroy the entire farm land. If the diseases are identified in initial stage there is no need to spray a high dose fertilizer on the paddy crops. To overcome this, the proposed system uses pre-processing, transfer learning Inception V3 method, neural network are trained by deep learning based Convolutional Neural Network(CNN) classification algorithm to identify the paddy leaf diseases like bacterial leaf blight, brown spot and rice blast. This method produces good accuracy. Scope of this project is to detect disease on paddy crops and too notify the types of diseases to farmer so that the farmers can take early action to protect the paddy crops.
DOI: 10.61137/ijsret.vol.9.issue2.207
E-DEPARTMENT
Authors:-Assistant Professor DR.A.S.Selva Reegan, Aslin Stephy.D.M , Aswathy.N.S, Babisha.N, Sneha.R.
Abstract-Most of the countries are depends on agriculture, where Tamil nadu is the land of agriculture. Here paddy cultivation is major source of earning. People in Tamil nadu, consumes rice as main meal for three times in a day. Various factors such as diseases on paddy leaf, pest attack etc., the production of paddy will be affected approximately 40stage to protect the paddy because it will destroy the entire farm land. If the diseases are identified in initial stage there is no need to spray a high dose fertilizer on the paddy crops. To overcome this, the proposed system uses pre-processing, transfer learning Inception V3 method, neural network are trained by deep learning based Convolutional Neural Network(CNN) classification algorithm to identify the paddy leaf diseases like bacterial leaf blight, brown spot and rice blast. This method produces good accuracy. Scope of this project is to detect disease on paddy crops and too notify the types of diseases to farmer so that the farmers can take early action to protect the paddy crops.
DOI: 10.61137/ijsret.vol.9.issue2.208
CNN Based Analysis and Visualization of Crime Against Women
Authors:-Associate Professor Dr. M.Supriya , Ajisha J, Amishya Renjai R. J, Aspiya S, Babis Dania T
Abstract-Women’s safety and protection remains a critical global concern, with rising incidence of crimes including rape, sexual harassment, domestic violence, dowry deaths, and acid attacks. The substantial volume of crime data generated through reporting systems presents a valuable opportunity for analysis, visualization, and prevention strategies. This paper proposes a comprehensive investigation of crimes against women in India using Convolutional Neural Networks (CNN). Raw data un- derwent preprocessing to eliminate anomalies, rectify invalid locations, and determine geographical coordinates. Descriptive analysis categorized crimes by type and district, while heat maps were generated to visualize crime distribution patterns. The CNN-based approach enables the identification of spatial crime hotspots through deep learning techniques, analyzing data collected from crime records, social media, news articles, and public databases. The methodology incorporates various attributes including crime type, location, temporal patterns, and demographic factors. Results provide decision-makers with valuable insights for crime prediction and prevention strategies, ultimately contributing to enhanced women’s safety measures.
DOI: 10.61137/ijsret.vol.9.issue2.209
Experimental Investigation of Sisal Fiber and Slag-Based Bio-Fiber Composites: Mechanical, Chemical, Acoustical, and Morphological Analysis
Authors:-E. Prakash, Lerindeoni S, Reny R
Abstract-The growing demand for sustainable materials has spurred significant interest in bio-fiber-reinforced composites. This study investigates the development and characterization of a novel composite material composed of sisal fiber and industrial slag as key constituents. The composite was fabricated using varying fiber weight fractions and thoroughly analyzed for its mechanical, chemical, acoustical, and morphological properties. Mechanical tests revealed that optimal fiber loading improved tensile and flexural strengths, highlighting the load-bearing capability of sisal fibers. Chemical analysis through FTIR and XRD confirmed successful interfacial bonding and the presence of pozzolanic reactions between slag and the binder. Acoustic tests demonstrated promising sound absorption properties, especially in mid-frequency ranges, making the material suitable for noise reduction applications. SEM micrographs illustrated uniform fiber dispersion and good matrix-fiber adhesion, while EDS validated the elemental composition. Overall, the sisal fiber and slag-based composite exhibited a balanced combination of mechanical strength, chemical stability, acoustic damping, and morphological integrity, suggesting its potential use in eco-friendly construction and automotive applications.
DOI: 10.61137/ijsret.vol.9.issue2.210
AI And Big Data Analytics In Pharmaceutical Supply Chain Management
Authors: Nandini Bhatt
Abstract: Artificial Intelligence (AI) and Big Data Analytics are reshaping pharmaceutical supply chain management by enabling greater efficiency, transparency, and resilience. This paper examines the transformative impact of AI-driven big data technologies on pharmaceutical supply chains, highlighting their roles in demand forecasting, inventory management, quality control, and risk mitigation. It discusses the challenges of integrating AI and big data in complex, regulated environments and explores ethical and operational considerations. The study emphasizes how leveraging AI and big data analytics enhances supply chain agility, reduces costs, and improves patient access to medicines while addressing issues such as data security and regulatory compliance.
DOI: http://doi.org/
Market Analysis Of AI-Based Health Technologies: Trends And Forecasts
Authors: Faria Khan
Abstract: The integration of Artificial Intelligence (AI) into healthcare systems has revolutionized the landscape of medical diagnostics, treatment planning, patient care, and healthcare operations. With exponential growth in data generation and computational capabilities, AI-based health technologies are being rapidly adopted across clinical, administrative, and research domains. This paper provides a comprehensive market analysis of AI-based health technologies, exploring the current trends, key market drivers, challenges, regional developments, and future forecasts. As AI continues to evolve, its impact on healthcare systems is expected to increase significantly, transforming traditional healthcare models into more predictive, personalized, and efficient systems. Through data-driven insights and strategic foresight, this study aims to highlight the critical factors influencing the market trajectory and predict the future scope of AI in the global healthcare sector.
DOI: http://doi.org/
Regulatory Considerations For AI Applications In The Biomedical Industry
Authors: Mamata Gowda
Abstract: Maharani’s CollegeAs Artificial Intelligence (AI) transforms the biomedical industry, regulatory bodies face the critical task of ensuring that these innovations are safe, ethical, and effective for public use. From diagnostic algorithms and AI-enhanced drug development to robotic surgeries and personalized medicine, AI technologies are redefining clinical practices and research methodologies. However, their rapid integration raises significant regulatory challenges, particularly in areas concerning data privacy, algorithmic transparency, clinical validation, and liability. This paper provides an in-depth exploration of the current regulatory landscape governing AI in biomedical applications. It analyzes the roles of major regulatory agencies such as the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), and others in shaping guidelines for AI deployment. Furthermore, it highlights the complexities involved in classifying AI tools, updating compliance frameworks for adaptive algorithms, and harmonizing international standards. By dissecting case studies and emerging trends, this paper offers insights into how regulatory frameworks can evolve to balance innovation with patient safety and public trust in the age of AI-powered healthcare.
DOI: http://doi.org/
Investment Strategies In AI-Driven Nanomedicine Ventures
Authors: Keerthi Kumar
Abstract: The convergence of Artificial Intelligence (AI) and nanomedicine has sparked a transformative wave in the biomedical and pharmaceutical industries, opening new pathways for disease diagnosis, treatment, and drug delivery at the nanoscale. As AI technologies enhance the design, functionality, and application of nanomaterials, nanomedicine ventures have become highly attractive to investors seeking long-term value and breakthrough innovations. This paper presents a comprehensive analysis of investment strategies in AI-driven nanomedicine ventures, focusing on the unique technological, financial, and regulatory dynamics of this rapidly growing domain. From venture capital and private equity to public funding and strategic partnerships, the investment landscape surrounding AI-nanomedicine is evolving, driven by innovation potential, patient demand, and the promise of market disruption. By examining investment trends, risk management techniques, key success factors, and emerging market opportunities, this paper offers a strategic framework for stakeholders aiming to capitalize on this cutting-edge intersection of technology and healthcare.
DOI: http://doi.org/
Ethical Implications Of AI In Healthcare Business Practices
Authors: Manasa Jain
Abstract:
DOI: http://doi.org/
Ethical Implications Of AI In Healthcare Business Practices
Authors: Manasa Jain
Abstract:
DOI: http://doi.org/
Ethical Implications Of AI In Healthcare Business Practices
Authors: Manasa Jain
Abstract:
DOI: http://doi.org/
Applications Of Nanotechnology In Regenerative Medicine
Authors: Yasik Sharma
Abstract: – Regenerative medicine aims to restore or replace damaged tissues and organs, offering new therapeutic strategies for conditions previously considered incurable. Nanotechnology, through the manipulation of materials at the nanoscale, has emerged as a transformative tool in this field by enabling precise control over cellular behavior and tissue microenvironments. Nanomaterials such as nanoparticles, nanofibers, and nanotubes exhibit unique physicochemical properties that facilitate enhanced scaffold design, targeted drug delivery, and real-time monitoring of tissue regeneration. This paper reviews current advancements in applying nanotechnology to regenerative medicine, focusing on its role in tissue engineering, stem cell modulation, and biomolecular delivery. Key challenges including biocompatibility, toxicity, and scalability are discussed, alongside future prospects that suggest integration of nanotechnology with biofabrication and personalized medicine could revolutionize regenerative therapies.
DOI: http://doi.org/
Nanoparticle-Based Drug Delivery Systems: Overcoming Biological Barriers
Authors: Nanditha Das
Abstract: Nanoparticle-based drug delivery systems represent a transformative innovation in modern pharmacology, providing novel solutions to long-standing challenges in medicine. These systems leverage the unique properties of nanoparticles to improve drug solubility, enhance bioavailability, and ensure targeted delivery, particularly across complex biological barriers. This paper explores the diverse types of biological barriers that impede therapeutic efficacy, such as the blood-brain barrier, gastrointestinal tract, and tumor microenvironment. It further delves into the various nanoparticle platforms developed to navigate these barriers, including liposomes, dendrimers, polymeric nanoparticles, and lipid-based systems. The role of surface modification techniques, targeting ligands, and stimuli-responsive mechanisms in improving delivery efficiency is analyzed in depth. Additionally, the paper evaluates the pharmacokinetics, biodistribution, and safety concerns associated with these systems, while discussing the latest clinical advances and translational hurdles. Ultimately, this comprehensive overview underscores how overcoming biological barriers using nanoparticles has opened new frontiers in precision medicine and is revolutionizing drug therapy paradigms.
DOI: http://doi.org/
The Role Of Nanotechnology In Developing Next-Generation Vaccines
Authors: Ravi Kumar
Abstract: – Nanotechnology has emerged as a transformative platform in the development of next-generation vaccines, enabling precise delivery of antigens and immunomodulatory agents to the immune system. By mimicking natural pathogens at the nanoscale, nanoparticle-based vaccines enhance antigen stability, promote targeted delivery to antigen-presenting cells, and improve immunogenicity. This paper explores various nanomaterial platforms used in vaccine development, including lipid nanoparticles, virus-like particles, polymeric and inorganic nanoparticles, and their role in overcoming limitations of traditional vaccines. Mechanisms of immune activation, strategies for improving vaccine stability and targeted delivery, and challenges in clinical translation and regulatory approval are discussed. The convergence of nanotechnology with immunology and bioinformatics is poised to revolutionize vaccine development, enabling rapid, safe, and highly effective vaccines against infectious diseases, cancers, and emerging pathogens.
DOI: http://doi.org/
Nanorobotics In Targeted Cancer Therapy
Authors: Faiza Shaik
Abstract: The use of nanorobotics in cancer therapy represents a groundbreaking evolution in the field of biomedical sciences, offering precision, efficiency, and adaptability in targeting malignant cells. These nanometer-scale devices are engineered to perform complex tasks at the cellular and molecular levels, enabling the direct delivery of anticancer agents to tumors while minimizing damage to healthy tissue. This paper explores the foundational principles of nanorobotics, their diverse types, and the technological advancements enabling their application in oncology. It delves into the mechanisms through which nanorobots navigate biological environments, recognize cancerous cells, and administer therapeutic agents with unmatched specificity. Additionally, the paper addresses the integration of sensors, actuators, and logic gates within nanorobots to enhance decision-making and responsiveness in real-time conditions. Challenges such as biocompatibility, immune response, power sources, and regulatory hurdles are discussed in detail. Furthermore, current experimental studies, clinical trials, and future perspectives in the development of nanorobotics for cancer therapy are critically analyzed. The convergence of nanotechnology, robotics, and medicine through nanorobotics holds the promise of redefining cancer treatment paradigms with higher survival rates and lower side effects.
DOI: http://doi.org/
Advancements In Nanotechnology For Targeted Cancer Therapies
Authors: Raj Kumar
IJSRET_V9_issue2_196.pdf
Abstract: Nanotechnology has revolutionized the landscape of targeted cancer therapies by providing innovative tools to deliver therapeutic agents directly to tumor cells while minimizing damage to healthy tissues. This paper explores the latest advancements in nanotechnology applications for targeted cancer treatment, emphasizing the design and functionalization of various nanoparticle platforms, advanced drug delivery mechanisms, and tumor targeting strategies. It further discusses the integration of multifunctional nanoparticles enabling combination therapies, challenges related to biocompatibility, immune evasion, and clinical translation barriers. The paper concludes by highlighting future directions, including personalized nanomedicine and stimuli-responsive delivery systems, that promise to enhance therapeutic efficacy and patient outcomes in oncology.
DOI: http://doi.org/
Nanorobotics In Targeted Cancer Therapy
Authors: Faiza Shaik
Abstract: The use of nanorobotics in cancer therapy represents a groundbreaking evolution in the field of biomedical sciences, offering precision, efficiency, and adaptability in targeting malignant cells. These nanometer-scale devices are engineered to perform complex tasks at the cellular and molecular levels, enabling the direct delivery of anticancer agents to tumors while minimizing damage to healthy tissue. This paper explores the foundational principles of nanorobotics, their diverse types, and the technological advancements enabling their application in oncology. It delves into the mechanisms through which nanorobots navigate biological environments, recognize cancerous cells, and administer therapeutic agents with unmatched specificity. Additionally, the paper addresses the integration of sensors, actuators, and logic gates within nanorobots to enhance decision-making and responsiveness in real-time conditions. Challenges such as biocompatibility, immune response, power sources, and regulatory hurdles are discussed in detail. Furthermore, current experimental studies, clinical trials, and future perspectives in the development of nanorobotics for cancer therapy are critically analyzed. The convergence of nanotechnology, robotics, and medicine through nanorobotics holds the promise of redefining cancer treatment paradigms with higher survival rates and lower side effects.
DOI: http://doi.org/
Real-Time Security Compliance Enforcement Using Tripwire in Solaris
Authors: Daria Kuznetsova, Sergey Belov, Anna Fedorova, Viktor Pavlov
Abstract: As Solaris continues to serve mission-critical workloads across healthcare, government, and financial sectors, maintaining system integrity and regulatory compliance has become increasingly complex. Traditional security controls often lack the real-time responsiveness and policy-driven rigor required for hardened UNIX environments. This review explores the application of Tripwire a widely trusted file integrity monitoring solution for enforcing real-time security compliance on Solaris platforms. The article delves into how Tripwire enables continuous monitoring of system files, binaries, libraries, and configuration artifacts using cryptographic checksums and customized policies. Through automated scans, deviation detection, and audit-ready reporting, Tripwire ensures alignment with frameworks such as HIPAA, FISMA, and PCI-DSS. The review further examines operational deployments of Tripwire within Solaris Zones, legacy AIX integrations, and hybrid infrastructures. Challenges related to system overhead, false positives, and policy maintenance are also analyzed, with optimization techniques offered to minimize performance impact. Emphasis is placed on Tripwire’s integration with SIEM platforms, service management facilities (SMF), and compliance dashboards, enabling seamless escalation, incident tracking, and forensics. The framework's ability to enforce baseline configurations, detect unauthorized modifications, and generate tamper-proof audit evidence makes it invaluable in regulated UNIX environments. Looking ahead, Tripwire's role is evolving through alignment with AIOps, Compliance-as-Code, and GitOps pipelines, paving the way for dynamic and automated security enforcement. This article concludes by asserting that Tripwire, when strategically configured and integrated, provides a scalable and proactive compliance solution tailored for Solaris-based infrastructures strengthening operational resilience while satisfying stringent audit requirements.
DOI: https://doi.org/10.5281/zenodo.15847881
AI-Driven Anomaly Detection in Nagios and Zabbix Logs
Authors: Anirudh Narayan, Bindu Lakshmi, Haritha Gopal, Vivek Vardhan
Abstract: In the evolving landscape of IT infrastructure monitoring, the volume and velocity of log data generated by tools such as Nagios and Zabbix present significant challenges for timely and accurate anomaly detection. Traditional rule-based approaches, which rely on static thresholds and manual configurations, often fail to capture subtle or emerging issues, leading to alert fatigue or missed incidents. To address these limitations, the integration of artificial intelligence, particularly machine learning, into log-based monitoring has emerged as a transformative solution. By analyzing patterns in historical logs and adapting dynamically to changes in system behavior, AI models ranging from supervised classifiers to unsupervised clustering algorithms and deep learning architectures can enhance the detection of anomalies within Nagios and Zabbix environments. This review examines the application of AI to anomaly detection in logs generated by Nagios and Zabbix, focusing on key log types such as performance metrics, event logs, alert logs, and syslogs. It explores how AI improves detection precision, reduces false positives, and enables earlier incident prediction. The paper also compares data handling mechanisms in both tools and outlines common AI integration pipelines including log preprocessing, model training, and real-time inference. Furthermore, implementation case studies and evaluation metrics are discussed to highlight real-world benefits and performance trade-offs. Ultimately, this article positions AI-driven anomaly detection as a critical enabler for modern observability and proactive IT operations, especially in large-scale or mission-critical infrastructures.
Cloud-Based Business Intelligence: Leveraging Cognitive CRM Models In Practice
Authors: Nargiz Eldar qizi Aliyeva, Kamran Vidadi oglu Mustafayev, Lala Elshan qizi Mammadova, Emil Rovshan oglu Gurbanov
Abstract: – In the era of hyper-personalized customer engagement, businesses are increasingly turning to cloud-based Business Intelligence (BI) systems integrated with Cognitive Customer Relationship Management (CRM) models to gain competitive advantage. Cognitive CRM extends traditional CRM by embedding AI capabilities such as natural language processing, machine learning, and sentiment analysis to generate deeper insights from structured and unstructured data. This article explores the practical application of Cognitive CRM within cloud-based BI ecosystems, focusing on architecture, integration strategies, real-time analytics, and decision automation. It highlights case studies where companies have successfully leveraged these models to optimize customer retention, improve service personalization, and boost operational efficiency, while also addressing challenges like data privacy, system complexity, and model governance.
DOI: https://doi.org/10.5281/zenodo.16311390
Securing Salesforce In Multi-Tenant Cloud Environments: A Compliance Perspective
Authors: Niloofar Farrukhzoda Rajabova, Daler Bahromovich Toshmatov, Sherzod Mahmudzoda Nasimov, Aziza Akbarzoda Komilova
Abstract: As enterprises increasingly migrate to cloud-native platforms like Salesforce, the security of multi-tenant environments becomes paramount, particularly in regulated industries. Salesforce’s multi-tenancy architecture provides scalability and cost-efficiency, but also raises concerns around data isolation, regulatory compliance, and shared infrastructure risks. This article offers a compliance-oriented examination of Salesforce security in multi-tenant clouds, exploring the architecture, built-in controls, shared responsibility models, and strategies for adhering to regulations such as GDPR, HIPAA, and SOC 2. By aligning platform capabilities with compliance mandates, organizations can ensure secure operations without sacrificing agility and innovation.
DOI: https://doi.org/10.5281/zenodo.16312240
AI-Powered Virtualization Models For Enterprise Bioinformatics
Authors: Elen Rafayelovna Sargsyan, Hayk Vahagnovich Ghazaryan,, Anzhela Viktorovna Grigoryan, Karen Samvelovich Melikyan, Tatevik Aramovna Harutyunyan
Abstract: The explosive growth of genomic and proteomic datasets has propelled bioinformatics into the enterprise computing domain, demanding scalable, secure, and high-performance infrastructure. Traditional physical server models have proven inadequate for managing the dynamic and compute-intensive nature of bioinformatics workflows. In response, AI-powered virtualization models are emerging as transformative solutions, combining intelligent workload orchestration with flexible virtual environments. This paper investigates how artificial intelligence enhances virtualization strategies in enterprise bioinformatics settings by enabling predictive resource allocation, automated fault detection, and real-time optimization. Through architectural analysis and case study evaluation, the research presents a practical framework for deploying AI-integrated virtual infrastructure that meets the evolving needs of large-scale biological computation.
DOI: https://doi.org/10.5281/zenodo.16312734
Enhancing Security Incident Detection and Automated Response Using AI-Powered Security Information and Event Management (SIEM) Systems
Authors: Kiran Desai
Abstract: – As cyber threats evolve in complexity and frequency, traditional security monitoring systems struggle to keep pace with modern enterprise needs. Security Information and Event Management (SIEM) systems have long served as a cornerstone for centralized logging and alerting, but the sheer volume of alerts and incidents now threatens to overwhelm human operators. This has led to a critical shift toward integrating artificial intelligence (AI) and machine learning (ML) into SIEM platforms. AI-driven SIEM systems automate detection, triage, and even response to incidents, enabling security teams to operate more efficiently and effectively. These systems can analyze vast datasets in real time, identify anomalous behaviors, and recommend or initiate appropriate countermeasures with minimal human intervention. This article explores the architecture, algorithms, integration strategies, and real-world applications of AI-enhanced SIEM systems. It also examines key challenges such as data quality, model drift, and regulatory compliance, while offering insights into future trends like explainable AI and predictive threat modeling. The goal is to provide a comprehensive understanding of how AI transforms SIEM into an intelligent, adaptive shield against modern cyber threats
DOI: https://doi.org/10.5281/zenodo.16751895
Applying Digital Forensics Techniques To Secure And Investigate Threats In Healthcare Information Systems And Electronic Medical Records
Authors: Shashi Tharoor
Abstract: In the digital era, healthcare organizations are increasingly reliant on information systems to manage sensitive patient data and streamline clinical workflows. However, the growing digitization has also rendered these systems prime targets for cyberattacks, internal misuse, and accidental breaches. Digital forensics offers a critical framework for detecting, investigating, and mitigating security incidents in healthcare information systems. This paper explores the multifaceted application of digital forensics within healthcare, encompassing threat identification, evidence preservation, legal compliance, and technological challenges. As medical data is governed by stringent regulations such as HIPAA and GDPR, the role of digital forensics becomes indispensable in ensuring confidentiality, integrity, and availability of patient records. The unique nature of healthcare environments, including legacy systems, third-party integrations, and life-critical devices, necessitates a tailored forensic approach. Moreover, the integration of artificial intelligence and blockchain in forensics is transforming incident response and audit mechanisms. This review delves into forensic readiness, methodologies, tools, case studies, and future directions, emphasizing the critical need for a proactive stance in safeguarding healthcare information. By aligning forensic practices with risk management and compliance, healthcare organizations can build resilient infrastructures capable of withstanding the evolving threat landscape.
DOI: https://doi.org/10.5281/zenodo.16751920
Integrating Kerberos Authentication To Strengthen Security And Access Control In Samba-Based File Sharing Environments
Authors: Rohit Gore
Abstract: In an increasingly hybrid IT ecosystem, secure and scalable authentication mechanisms are essential for managing file-sharing services across diverse network environments. Samba, an open-source reimplementation of the SMB/CIFS protocol, enables seamless file and print services for SMB/CIFS clients, most notably Microsoft Windows. While Samba supports several authentication methods, integrating it with the Kerberos authentication protocol significantly strengthens its security posture, especially in enterprise environments. Kerberos, a time-tested network authentication protocol, facilitates secure and mutual authentication without transmitting passwords over the network. This article explores the integration of Samba with Kerberos, focusing on configuration strategies, performance implications, and real-world deployment considerations. It discusses the internal mechanisms of both technologies and illustrates how their integration can simplify centralized identity management using services such as Microsoft Active Directory and MIT Kerberos. Additionally, it reviews security enhancements, troubleshooting practices, and future considerations in the context of Linux-based servers and heterogeneous network environments. By aligning Samba with Kerberos authentication, organizations can achieve a unified and secure authentication architecture that minimizes administrative overhead, strengthens compliance, and provides a resilient foundation for secure file-sharing operations
DOI: https://doi.org/10.5281/zenodo.16751947
Implementing Scalable And Efficient Network File Sharing Solutions Using The Samba Protocol For Seamless Cross-Platform Access And Management
Authors: Ashwin Sanghi
Abstract: Financial institutions operate in a dynamic and high-stakes environment where data integrity, system availability, and uninterrupted service are paramount. In recent years, the increasing complexity of IT infrastructures, along with the growing threat of cyberattacks and natural disasters, has prompted a strategic shift toward virtualized disaster recovery (VDR) models. VDR enables the replication and recovery of data and critical systems through virtual environments, offering increased flexibility, faster recovery times, and reduced reliance on physical infrastructure. This article presents a comprehensive review of the adoption and implementation of virtualized disaster recovery strategies in financial institutions. It evaluates technological architectures, regulatory requirements, integration challenges, and case studies to illustrate real-world applications. Furthermore, it delves into cost-benefit analyses, risk mitigation tactics, and the role of automation and orchestration in streamlining recovery processes. Through this analysis, we aim to demonstrate how VDR can enhance business continuity, improve compliance postures, and provide a robust response mechanism to both anticipated and unforeseen disruptions.
DOI: https://doi.org/10.5281/zenodo.16751967
Financially Sustainable Big-Data In The Cloud: Governance, Lifecycle, And Tactical Strategies For Cost Optimization
Authors: Sudhir Vishnubhatla
Abstract: As financial and digital enterprises adopt cloud-native big-data systems, the focus has shifted from feasibility to cost-effectiveness. Elastic compute, multi-tiered storage, and managed services have removed barriers to scalability but introduced new challenges of cost predictability, governance, and optimization. This article synthesizes two decades of research and practice to articulate cost-optimization strategies for big-data systems in the cloud. It frames cost not as a narrow technical knob but as a discipline spanning architecture, governance, lifecycle management, and multi-cloud alignment. Three diagrams, the cost optimization model, the iterative cost lifecycle, and the levers of cost control—are used to illustrate how modern organizations can manage the financial sustainability of their big-data ecosystems without sacrificing agility, resilience, or compliance
DOI: http://doi.org/10.5281/zenodo.17452344
Leveraging AI To Optimize Clinical Data Management And Analytics Through SAP Digital Health Platforms For Enhanced Healthcare Outcomes
Authors: Parthiv Yodhan
Abstract: The rapid expansion of clinical data and the growing demand for personalized, efficient healthcare necessitate innovative approaches to data management and analytics. Traditional clinical data management (CDM) processes often struggle with data fragmentation, manual processing, and delayed insights, which can negatively impact patient outcomes and operational efficiency. This article explores the integration of Artificial Intelligence (AI) with SAP Digital Health platforms as a transformative solution for optimizing clinical data management and analytics. AI technologies, including machine learning and natural language processing, enhance data cleaning, validation, predictive modeling, and decision support, while SAP platforms provide a secure, interoperable, and scalable infrastructure for data integration and real-time analytics. By leveraging this synergy, healthcare organizations can improve diagnostic accuracy, enable personalized care, optimize operational workflows, and accelerate clinical research. The article also examines implementation challenges such as data privacy, interoperability, adoption barriers, and ethical considerations, and highlights emerging trends including real-time patient monitoring, genomics integration, and telemedicine analytics. Ultimately, AI-powered SAP Digital Health platforms offer a pathway toward a data-driven, patient-centric healthcare ecosystem, where predictive insights and proactive interventions significantly enhance clinical outcomes, operational efficiency, and population health management.
DOI: http://doi.org/10.5281/zenodo.18169570
A Unified Artificial Intelligence Framework For Secure Cloud And IoT Integration In Healthcare And Financial Systems
Authors: Atharv Joshi
Abstract: The convergence of Artificial Intelligence (AI), Cloud Computing, and the Internet of Things (IoT) has enabled intelligent, data-driven transformation across healthcare and financial systems. However, the integration of these technologies presents significant challenges related to security, scalability, interoperability, and real-time decision-making. Healthcare and financial domains demand highly reliable and secure architectures due to the sensitive nature of their data and strict regulatory requirements. Existing solutions often address these technologies in isolation, resulting in fragmented architectures and increased exposure to operational and security risks. This paper proposes a unified artificial intelligence framework that securely integrates cloud and IoT infrastructures to support intelligent healthcare and financial applications. The framework adopts a layered architecture encompassing IoT data acquisition, cloud-based storage and processing, AI-driven analytics, and embedded security mechanisms. Machine learning and deep learning models are employed to enable predictive analytics, anomaly detection, and decision support while ensuring data confidentiality, integrity, and availability. The framework supports both real-time and batch data processing, enabling scalable and low-latency operations. The proposed framework is validated through healthcare and financial use case scenarios, including remote patient monitoring and real-time financial transaction analysis. Performance evaluation demonstrates improved system efficiency, enhanced decision-making accuracy, and robust security compared to traditional siloed systems. The results confirm that the unified framework effectively addresses integration challenges while maintaining compliance and adaptability. This research contributes a comprehensive and scalable solution for next-generation intelligent healthcare and financial ecosystems, offering a foundation for future advancements in AI-enabled cloud and IoT integration.
DOI: http://doi.org/10.5281/zenodo.18169572
Machine Learning–Based Credit Scoring Models Integrated With SAP Financial And Banking Applications
Authors: Ishvik Reddy
Abstract: Traditional credit scoring methods often fail to capture the multi-dimensional complexities of modern financial risks, particularly in volatile markets and for borrowers with limited credit histories. This review article investigates the integration of Machine Learning (ML)-based credit scoring models within the SAP financial and banking ecosystem. We evaluate the transition from legacy logistic regression scorecards to advanced ensemble methods like XGBoost and Random Forests, implemented through the SAP HANA Predictive Analytics Library (PAL) and SAP Business Technology Platform (BTP). The study highlights how the "embedded" and "side-by-side" architectural patterns in SAP S/4HANA enable real-time, data-driven credit decisioning by processing transactional data at the source. Furthermore, the article addresses the critical requirement for Explainable AI (XAI) using SHAP and LIME to meet regulatory standards like Basel IV and GDPR. We explore diverse use cases, including retail loan automation, dynamic corporate credit limit management, and SME financing via alternative data. The study concludes by discussing the future impact of Generative AI and Quantum Machine Learning on credit risk reporting and simulation. By synthesizing technical implementation strategies with financial risk theory, this paper provides a strategic roadmap for banks aiming to deploy transparent, accurate, and high-performance scoring systems within their enterprise landscape.
DOI: http://doi.org/10.5281/zenodo.18229000
Cloud-Based Decision Support Systems For Managing Healthcare Operations And Financial Risks
Authors: Reyvik Taluk
Abstract: The modern healthcare landscape is defined by the critical need to optimize operational efficiency while mitigating complex financial risks. Traditional on-premise systems are increasingly inadequate for handling the high-velocity data required for real-time institutional decision-making. This review article investigates the role of Cloud-Based Decision Support Systems (CDSS) as a transformative solution for managing healthcare operations and financial stability. We examine how cloud architectures, utilizing standards like HL7 and FHIR, enable the integration of disparate data sources—from electronic health records to supply chain logs. The study explores analytical models for patient flow optimization, staffing resource management, and revenue cycle enhancement, demonstrating their impact on institutional throughput and cash flow. Furthermore, we address the significant hurdles of data privacy (HIPAA/GDPR), cybersecurity, and the ethical requirement for Explainable AI. By synthesizing current research with emerging trends like digital twins and generative AI for executive briefings, this article provides a strategic roadmap for healthcare leaders. Ultimately, we demonstrate that the synergy between cloud scalability and proactive data analytics is the essential foundation for building resilient, sustainable, and patient-centric healthcare organizations in a digitally connected age.
DOI: http://doi.org/10.5281/zenodo.18229034
Quantization Aware Training Techniques for Efficient Transformer-Driven Large Language Models
Authors: Sai Sukesh Reddy Tummuri
Abstract: Large language models powered by transformers have grown quickly, resulting in previously unheard-of performance improvements, but at the expense of high computational complexity, memory usage, and energy consumption. Their deployment in real-time and resource-constrained environments is hampered by these limitations. In order to improve inference efficiency while maintaining predictive accuracy, this paper proposed a novel Dynamic Sensitivity-Aware Quantization-Aware Training (DSA-QAT) framework. The suggested method adaptively adjusted quantization precision based on layer-wise sensitivity and training dynamics, in contrast to traditional quantization approaches that apply uniform precision reduction. This allowed for more informed precision allocation across transformer components. Using representative performance and efficiency metrics, controlled simulation experiments were used to assess the suggested framework. According to experimental results, the quantized model maintained balanced precision, recall, and F1-score values while achieving prediction accuracy above 97%. The model also demonstrated strong robustness against quantization noise, decreased inference latency, a smaller memory footprint, improved energy efficiency, and stable training loss convergence. Additionally, a notable decrease in model size was noted, allowing for effective deployment without sacrificing performance. Overall, the findings demonstrated that the suggested DSA-QAT framework successfully reduced the trade-off between accuracy and model efficiency. The study demonstrated the potential of adaptive quantization-aware strategies for the high-performance, scalable, and sustainable deployment of large language models in practical applications.
Automation And Control Mechanisms For Cloud-Based Enterprise Systems
Authors: Manasa Gowda
Abstract: Cloud-based enterprise systems have fundamentally transformed organizational computing by replacing static, hardware-bound infrastructures with scalable, distributed, and service-oriented architectures. Enterprises increasingly rely on cloud environments to deliver highly available digital services, support global user bases, and enable rapid innovation cycles. However, the dynamic, heterogeneous, and continuously evolving nature of cloud platforms introduces significant operational complexity. Maintaining performance stability, cost efficiency, reliability, and security in such environments requires advanced automation and adaptive control mechanisms rather than traditional manual administration. This review examines the foundational automation principles and control strategies that underpin modern cloud operations. Key mechanisms discussed include Infrastructure as Code (IaC) for reproducible provisioning, orchestration frameworks for lifecycle management of distributed services, and continuous integration and deployment pipelines for reliable software delivery. The paper further analyzes runtime control approaches such as auto-scaling algorithms, observability-driven feedback loops, and policy-based governance frameworks that regulate system behavior in real time. Integration of control theory concepts—feedback regulation, elasticity management, and self-healing—is explored to demonstrate how cloud systems achieve adaptive stability under fluctuating workloads. In addition, the review evaluates the growing role of Artificial Intelligence for IT Operations (AIOps) in predictive failure detection, anomaly identification, and automated remediation. Key operational challenges including configuration drift, multi-cloud interoperability, security compliance, and unpredictable demand patterns are critically discussed. Finally, emerging paradigms such as autonomous cloud infrastructures and intent-based management are presented as future directions toward self-governing enterprise platforms. Overall, this paper provides a comprehensive conceptual and technical overview of automation and control frameworks that enable resilient, scalable, and efficient cloud-based enterprise operations.
DOI: https://doi.org/10.5281/zenodo.18711882
Designing Enterprise-Scale Systems For Cloud And Network Integration
Authors: Pooja Kulkarni
Abstract: The rapid pace of digital transformation has compelled organizations to redesign their information technology infrastructure to support large-scale, distributed operations. Modern enterprise applications are no longer confined to centralized data centers but instead operate across public clouds, private infrastructure, hybrid platforms, and edge environments. This distribution enables global accessibility and scalability but also introduces complexity in coordinating computing resources, networking paths, and data consistency. As a result, enterprises must adopt integrated architectural approaches that unify cloud computing and network management into a cohesive operational model. Integrating application services, storage systems, and communication networks across heterogeneous environments presents several architectural and operational challenges. These include maintaining low latency across geographically dispersed components, ensuring system scalability during fluctuating workloads, enforcing consistent security policies, and preserving service reliability during failures or outages. Organizations must also address interoperability between different vendors and technologies while minimizing operational overhead and cost. Consequently, system design has shifted from infrastructure-centric deployment to architecture-centric planning, where resilience and adaptability are primary goals. This review analyzes the fundamental architectural models and enabling technologies that support cloud-network integration in enterprise environments. It explores the role of microservices-based architectures in improving modularity and fault isolation, software-defined networking in enabling programmable traffic control, and API-driven communication in supporting interoperability. Additionally, containerization and orchestration platforms are discussed as mechanisms for achieving portability and automated scaling, while observability frameworks provide real-time insight into system performance and operational health. The study further examines critical challenges faced by modern enterprise systems, including interoperability across platforms, implementation of zero-trust security strategies, network segmentation for risk containment, and performance optimization in distributed infrastructures. Addressing these challenges requires coordinated architectural planning, automation, and continuous monitoring rather than isolated configuration efforts. Security and reliability are therefore treated as integrated design principles rather than supplementary operational tasks. Finally, the review highlights best practices and emerging technological trends shaping the future of enterprise systems. These include edge computing for latency reduction, service mesh frameworks for internal service communication control, and artificial intelligence-driven network management for predictive optimization and fault detection. Collectively, these advancements support the development of resilient, scalable, and adaptable enterprise ecosystems capable of meeting evolving performance, security, and operational requirements.
DOI: https://doi.org/10.5281/zenodo.18711899
Adaptive Query Intelligence: AI-Enabled Optimization Strategies For High-Volume SQL And NoSQL Processing In Regulated Industries
Authors: Dr. Matteo Rinaldi, Hiroshi Nakamura, Elena Petrova, Daniel Sørensen, Ananya Kulkarni
Abstract: This paper explores how machine learning–driven query optimization can elevate the performance, scalability, and operational resilience of SQL and NoSQL database systems deployed in high-volume financial and healthcare environments. Conventional rule-based and cost-based optimizers frequently encounter limitations when confronted with volatile workloads, uneven data distributions, and rapidly shifting access behaviors that define contemporary transaction processing and clinical data infrastructures. The central inquiry of this study examines whether adaptive, data-aware optimization models—trained on historical execution traces, telemetry signals, and workload metadata—can deliver superior efficiency and stability in such dynamic contexts. The research employs a blended methodological approach that integrates architectural framework design, algorithmic prototyping, and comparative benchmarking across representative relational and non-relational database platforms operating under large-scale transactional and analytical loads. Empirical evaluation indicates that learning-enabled optimizers meaningfully lower query response times, improve compute and memory utilization, and enhance predictability during peak data surges when compared to traditional strategies. Core contributions include the development of predictive cost estimation models, context-aware index adaptation mechanisms, and real-time execution plan adjustments powered by supervised and reinforcement learning paradigms. Collectively, the study advances the theoretical foundations of intelligent data management by embedding adaptive learning into optimization workflows, while offering practical guidance for engineering robust, high-throughput database infrastructures capable of sustaining accuracy, compliance, and responsiveness in mission-critical financial and healthcare systems.
DOI: https://doi.org/10.5281/zenodo.19104981
A Resilient Multi-Cloud Intelligence Layer For Modern Enterprises: Coordinating AI, Microservices, And ERP-Based Workforce Platforms At Scale
Authors: Kai Lorenz, Elena Kovarik, Mateo Serrano, Tariq Al-Nadim, Ananya Kulkarni
Abstract: Distributed enterprise infrastructures increasingly connect operational applications, workforce management platforms, and analytical services across multiple cloud environments. Coordinating these interconnected systems while maintaining reliability, scalability, and intelligent decision support presents significant engineering challenges for large organizations. Conventional enterprise architectures frequently depend on tightly coupled systems and centralized analytical platforms that struggle to manage rapidly evolving services deployed across hybrid and multi cloud infrastructures. This study introduces a resilient multi cloud intelligence layer designed to coordinate artificial intelligence services, microservice based applications, and ERP driven workforce platforms within large scale enterprise ecosystems. The proposed architecture establishes an intermediary intelligence layer that aggregates operational data streams, orchestrates service communication across distributed cloud environments, and enables predictive analytics capabilities to operate directly alongside operational systems. Microservices provide modular and scalable service components that support flexible integration between enterprise applications, while containerized deployment models ensure portability across cloud infrastructures. Artificial intelligence models integrated within the intelligence layer analyze operational signals to support workforce optimization, operational forecasting, and anomaly detection across enterprise processes. The framework also incorporates resilience mechanisms such as distributed service orchestration, automated scaling, and cross cloud workload coordination to maintain operational continuity under dynamic workloads. By integrating microservices architecture, machine learning driven analytics, and ERP based workforce management platforms within a unified multi cloud intelligence framework, the proposed approach enables organizations to transform fragmented enterprise infrastructures into coordinated intelligent ecosystems capable of supporting scalable operations and continuous analytical insight.
Federated Learning For Privacy-Preserving Security Systems
Authors: Vikram Iyer
Abstract: The rapid escalation of cyber threats in decentralized environments has necessitated the development of collaborative defense mechanisms that do not compromise data sovereignty. Traditional centralized machine learning requires the aggregation of sensitive telemetry data, creating significant privacy risks and regulatory hurdles. This review explores the paradigm of Federated Learning (FL) as a transformative solution for privacy-preserving security systems. By enabling the training of global threat detection models across distributed nodes—such as edge devices, corporate branches, or mobile endpoints—without transferring raw data to a central server, FL addresses the fundamental tension between collective intelligence and individual privacy. This article categorizes current FL architectures, including horizontal, vertical, and transfer-based federated systems, and examines their application in intrusion detection, malware analysis, and anomaly-based behavioral monitoring. We analyze the integration of Differential Privacy and Secure Multi-Party Computation within the FL pipeline to mitigate data leakage from model updates. Furthermore, the review addresses the challenges of communication overhead, non-independent and identically distributed (non-IID) data, and vulnerability to poisoning attacks. By synthesizing recent research and industrial implementations, this paper provides a strategic roadmap for the deployment of self-evolving, privacy-aware security frameworks. The findings suggest that Federated Learning not only complies with stringent data protection mandates like GDPR but also enhances model robustness by training on diverse, real-world datasets that were previously inaccessible due to privacy constraints.
DOI: https://doi.org/10.5281/zenodo.19427310
Graph-Based Machine Learning Models For Network Attack Detection
Authors: Sneha Pillai
Abstract: The increasing complexity and interconnectedness of modern digital infrastructures have rendered traditional, point-based network security measures largely ineffective. Conventional machine learning models often treat network traffic as independent, identically distributed (IID) data points, failing to capture the structural dependencies and relational context inherent in sophisticated cyber-attacks. This review explores the paradigm shift toward Graph-Based Machine Learning (GML) for network attack detection. By representing network entities—such as IP addresses, MAC addresses, and service ports—as nodes, and their interactions as edges, graph-based models can effectively map the "topology of intent" behind malicious activity. This article categorizes current GML methodologies, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Temporal Graphs, which account for the dynamic nature of traffic flows. We examine how these models excel at detecting "lateral movement," "botnet command-and-control," and "distributed denial-of-service" (DDoS) attacks by identifying anomalous structural patterns that are invisible to tabular analysis. Furthermore, the review addresses the challenges of scalability in massive-scale networks and the necessity for real-time graph processing. By synthesizing recent academic breakthroughs and industrial applications, this paper provides a strategic roadmap for deploying graph-based "Relational Intelligence" within Security Operations Centers. The findings suggest that GML significantly reduces false positives by providing contextual awareness, making it a cornerstone for the next generation of resilient, self-aware network defense systems.
DOI: https://doi.org/10.5281/zenodo.19427318
Promoting Peace Education Through Spiritual Pedagogy Insights from Ramakrishna Mission
Authors: Amitesh Sarkar
Abstract: Peace education has become an important element in promoting peace, morality, and social unity in the modern societies. This paper examines how spiritual pedagogy, especially those applied by the Ramakrishna Mission can be used to enhance peace education. The study is descriptive and analytical and incorporates both philosophical and empirical information. This research points out the importance and impact of value based education based on spirituality on increasing emotional intelligence, ethical reasoning, and conflict management in learners. The main dimensions of peace education such as ethical awareness, emotional stability, social harmony, and conflict resolution are assessed with the help of a structured dataset. The results indicate that spiritual pedagogy plays a very important role in holistic growth and harmonious coexistence. It is concluded that the concept of incorporating spiritual values into the contemporary education systems can reinforce the peace-building processes at the international level.
A Study On Intelligent Automation In IT Systems
Authors: Neha Gupta
Abstract: Intelligent automation in IT systems represents the integration of advanced technologies such as artificial intelligence, machine learning, robotic process automation (RPA), and cognitive computing to enhance operational efficiency and decision-making. It enables organizations to automate repetitive tasks, optimize workflows, and improve service delivery with minimal human intervention. This study explores the role of intelligent automation in modern IT environments, focusing on its ability to streamline IT operations, reduce costs, and improve system reliability. It examines key components such as automated incident management, predictive maintenance, intelligent monitoring, and self-healing systems. The study also highlights the integration of AI-driven analytics to enhance automation capabilities and enable real-time decision-making. Furthermore, it discusses major challenges such as implementation complexity, integration with legacy systems, security concerns, and workforce adaptation. Emerging trends such as autonomous IT operations (AIOps), hyperautomation, and AI-driven orchestration are also analyzed. The findings indicate that intelligent automation significantly enhances efficiency, scalability, and resilience in modern IT systems.
DOI: https://doi.org/10.5281/zenodo.19657761
Cloud-Based Solutions For Big Data Processing
Authors: Vikram Singh
Abstract: Cloud-based solutions for big data processing have become essential in managing the massive volume, velocity, and variety of data generated in modern digital environments. Traditional data processing systems are often insufficient to handle large-scale datasets efficiently due to limitations in storage, computing power, and scalability. Cloud computing addresses these challenges by providing on-demand resources, distributed computing frameworks, and scalable storage systems for efficient big data processing. This study explores the role of cloud platforms in enabling real-time analytics, batch processing, and distributed data management. It examines key technologies such as Hadoop, Spark, and cloud-native data processing services that support parallel processing and fault tolerance. The study also highlights the integration of big data analytics with artificial intelligence and machine learning to derive meaningful insights from complex datasets. Furthermore, it discusses major challenges including data security, latency, data governance, and cost management. Emerging trends such as serverless computing, edge-cloud integration, and hybrid cloud architectures are also analyzed. The findings indicate that cloud-based big data solutions significantly enhance scalability, efficiency, and flexibility in data-driven applications.
DOI:
Mathematical Reasoning In Environmental Decision-Making And Policy Formation
Authors: Jag Pratap Singh Yadav
Abstract: Mathematical reasoning is now essential in the making of environmental decisions and policies in that it offers a means by which environmental dynamics can be modeled in order to identify uncertainties and evaluate policy alternatives. Mathematics not only serves to assist institutions in making sound environmental decisions; it defines for such institutions what constitutes an environmental problem and what can be done about it legally. The current essay explores the use of mathematical reasoning in the development of environmental policy. Specifically, it will examine the mathematical methodological basis of dynamical system theory, probability theory, optimization theory, and game theory in order to explore their implementation into regulatory regimes through integrated assessment models, cost-benefit analysis, and threshold regulation. With references to the development of cap-and-trade programs, management of fish stocks by targeting maximum sustainable yield, and carbon valuation through the social cost of carbon, the article shows how mathematical modeling can result in extremely successful policy frameworks when used in combination with institutional coherence and ecological sensibility, but also how false precision, biased assumption and value-laden ethical considerations can be concealed behind formal mathematical modeling. At the same time, the limitations of the conventional approach to the use of mathematical models in environmental policy making are discussed in relation to uncertainties and political tensions, as well as the dangers associated with excessive formalization and optimization, which can lead to indecision or to the depoliticization of value disputes. The key thesis developed in the paper is the need to recognize that mathematical models have power and must be subjected to reflection, criticism and democratic debate because they form the mediating language for making sense of the world and cannot remain apolitical and value-free.
DOI: https://doi.org/10.5281/zenodo.19835245
Stochastic Processes As Tools For Managing Uncertainty In Real-World Systems
Authors: Jag Pratap Singh Yadav
Abstract: Systems in reality are characterized by uncertainties. There are uncertainties associated with nature, economics, communications, healthcare delivery, production systems, and social systems, among others, which cannot be modeled using deterministic equations. Stochastic processes facilitate the formulation of mathematical models of systems whose behavior is affected by some random elements. They help in assessing risks, optimizing resource allocations, forecasting future behaviors, and enhancing system robustness. The paper centers on stochastic processes as techniques for dealing with uncertainty in systems. First, the concept of stochastic processes will be defined. Major types of stochastic processes, including Markov chains, Poisson processes, Brownian motion, random walks, and queueing models, will be discussed. Then, applications of stochastic models in various areas, such as financial modeling, engineering, health care, climate studies, operations management, telecommunications, and machine learning, will be explored. Additionally, this paper will examine advantages and disadvantages associated with stochastic modeling. In particular, problems associated with assumptions used in building stochastic models and computational complexities of such models will be analyzed. It will be concluded that stochastic processes represent powerful tools for studying and managing uncertain systems because they enable turning randomness from an issue into a quantifiable phenomenon.
DOI: https://doi.org/10.5281/zenodo.20021901
Published by: vikaspatanker
