A Study On Stabilization Of Cohesive Soils By Using Sisal Fiber
Authors:- Nadikota Srinivas, P.Hanuma
Abstract- Soil Properties which makes a significant effect on development exercises because of quick development of urbanization and industrialization. Particularly in broad soils are making overall hazardous soil these having enormous volumetric change conduct when it goes through an adjustment of the dampness content. Among those, dark cotton soil are one kind of extensive soils and they shows high enlarging and shrinkage conduct inferable from fluctuating water content. In India, dark cotton soil covers as high as 20% of the absolute land region and significantly in focal and south India. Assuming that it ought to be utilized as establishment material, Improvement of soil should be finished by embracing different strategies like soil adjustment, support and so forth Use of locally accessible admixtures is viable as far as simple versatility and economy.The principle objective of this study is to survey the chance of involving sisal fiber as settling specialist and to comprehend the adequacy of sisal strands in controlling a few properties of dark cotton soil under controlled lab conditions. To accomplish this objective a few exploratory investigations like ideal dampness content, compressive qualities tests (UCS), CBR, and so forth, were done with expansion of various rates of sisal in dark cotton soil test as experimentation process.In present review, the dirt examples arranged with expansion of sisal strands by 0.25%, 0.5%, 0.75%, and 1% the normal length of sisal fiber will use in this study is roughly 10-15mm. From the beginning, Optimum Moisture Content not entirely set in stone through delegate test. At those OMC, a few tests like CBR, UCS were led. CBR test was conveyed in both Unsoaked and splashed condition and most extreme qualities was acquired where 0.75% sisal fiber was added.
Comparison of Seismic Behaviour of an RC Frame with Odinary Brick and Fly-Ash Brick for Shear Wall and Base Isolation
Authors:- PG Scholar Mrs. S. Archana , Dr. S. Kapilan (HOD)
Abstract- Earthquake is the most important factor in design and construction of a structure as it produces collapse of structure, loss of life, property. From every past Earthquake it is clearly evident that they bring greater damage to all structures from residential buildings to tall structures, industrial buildings, power plats, etc and even has an effect to collapse them. So it is very important to clearly understand the seismic behaviour of structure to effectively design it. Even though the amount seismic load that can be occurring in a structure cannot be judged correctly in life time they have to be designed accordingly to withstand the load which has the most probability of occurring in its lifetime. Mainly all structures are of Reinforced Concrete construction and they are also heavily affected seismic loading.This thesis is an design and analysis of an RC framed structure with ordinary brick and fly ash brick for shear wall and base isolation for the purpose of an comparison of them. When compared to an steel building an RC structure will be more vulnerable to seismic forces. So an RC frame has been taken with ordinary brick and fly ash brick for shear wall and base isolation. RC frame with all said conditions are analysed and designed, which will give an comparison of each in seismic loading. Due to which the behaviour can be found.
A Survey on Relevant Text Data Searching Techniques and Feature in Cloud
Authors:- Astha Jain, Prof. Rajesh Nigam
Abstract- Internet access increases the volume of data for storage, analysis, fetching, etc. Out of different type of data text is most bulky and unorganized in nature. Many of researchers have proposed different models for data management and retrieval. This paper is a deep survey of cloud text data fetching and storage. Many of cloud application use encryption model for the stored data security. So a detailed survey of various authors work was summarized in the paper with type of data and techniques adopt. Features used in the text mining were also brief in the paper for the analysis of impact of type of text data application. Paper has brief some of evaluation parameters that needs for comparing of relevant data fetching models.
An Efficient Iris Segmentation Algorithm Using Deep Learning Techniques
Authors:- Sateesh Yaduwanshi, HOD Aditi Khemariya
Abstract- The iris segmentation algorithm is very essential in an absolute iris recognition system and has a direct influence on the verification and recognition results of the iris. However, traditional iris segmentation algorithms have poor adaptability and are not robust enough when used in noisy iris databases captured under infinite conditions. In addition, there is currently no large iris database. Therefore, the iris distribution algorithm cannot increase the benefits from the convolution neural network (CNN). Iris segmentation is a basic process of iris recognition. Iris segmentation plays an important role in maintaining the accuracy of the iris by limiting the current defects in the reorganization system. Under these no-ideal conditions, existing segmentation based on local operations cannot see the true iris boundary, and iris segmentation will result in failure. Iris recognition is a significant issue in system control in computer based communication. Human iris recognition is an important branch of biometric verification and has been widely used in many applications, such as attendance maintaining, video monitor system, human – computer interaction, and door control system and network security. This process develops iris recognition to address this problem and introduces a new algorithm using the feature extractions then the classification using vgg16 to significantly improve iris recognition. The execution has performed on the MATLAB software and the performance results carried out in terms of accuracy ,precision and recall,F1.
Static and Dynamic Analysis of A Single Plate Clutch
Authors:- Associate Prof. Mula Mahender, Asst. Prof. Gosula Suresh, R. Venkata Ramana, R. Srikanth, R. Kumaran, A.Sai Riteesh
Abstract- The energy necessary for the motion of a vehicle is transmitted by the engine to the wheels through the flywheel, the clutch system and the driveline. A Clutch is a machine member used to connect the driving shaft to a driven shaft, so that the driven shaft may be started or stopped at will, without stopping the driving shaft. A clutch thus provides an interruptible connection between two rotating shafts. The present used material for friction disc is Cast Iron and aluminum alloys. In this project analysis is performed using composite materials. The composite materials are considered due to their high strength to weight ratio. In this paper composite material E Glass Epoxy and Aluminum Metal Matrix Composite are taken. A single plate clutch is designed and modeled using solid works software. Static analysis and Dynamic analysis are done on the clutch to determine stresses and deformations using materials Grey Cast Iron, Aluminum alloy 7075, E Glass Epoxy and Aluminum Metal Matrix Composite. Analysis is done in Ansys.
Biofuels-Recent Advances and Case Studies
Authors:- Aadarsh Dwivedi
Abstract- Biofuels are essentially the fuels that are generated by the living or dead organisms and which are mostly in the form of the co-metabolized substrates or they are the products of the microbial metabolism. Biofuels include the bio-diesel as well as the bio-CNG which stands for the compressed natural gas that has been produced from the biological sources. Biofuels are needed in the modern world as we need the alternate renewable sources of energy which are less polluting than the fossil fuels and which can also be degraded by the microbes present in the environment. In this report the current state of the biofuels industry is described with the purpose of reviewing the recent advances that have been made in the biofuel industry as well as discuss the future prospects of the biofuels’ usage in various industries. Some case studies have been discussed to highlight the issues faced and the advantages that the biofuel usage has over the usage of the conventional fossil fuels, and also to analyze the practical utility and economic sustainability of the biofuels’ usage at the large scale as well as the individual consumer scale.
Planning For Ecotourism in Sahyadri Hills Region: A Case of Chinchli & Mahardar, Dang District, Gujarat
Authors:- Ar. Mayur Siddhapura
Abstract-One of the major revenue earners in tourism is tourism in hilly regions and areas. As it offers major lodestones like climate, clean air, unique landscapes and wildlife, scenic beauty, local culture, history and heritage and also the opportunity to experience snow and participate in snow-based or nature-related activities and sports. The Chinchli and Mahardar region of Dang offers some of the rarest ‘tourism’ products of nature with a wide ecological range and diversity. Apart from the many-splendored natural attractions and scenic beauty, the religious and socio-cultural dimensions of the tourist resource assume significance in the context of the hill districts lying in the lap of the Dang region. The paper is aimed at identifying the potential of Chinchli and Mahardar region in context of hill tourism as well as to determining future strategic options for effective management of its destinations for sustainable development. Data for this study was drawn from a review of secondary sources, consisting primarily of official government documents, several research articles, tourism websites and media reports in this context. Situation analysis of collected data was undertaken through SWOT.
A Review of Intrusion Detection System
Authors:- M.Tech. Scholar Megha Tomar, Asst. Prof. Avinash Pal , Trapti Ozha(HOD), Director Durgesh Mishra
Abstract- Computer networks are susceptible to being attacked in ways that are relevant to cyberspace because of the proliferation of internet usage. As a direct result of this, a number of different researchers have created several intrusion detection systems, sometimes known as IDSs. One of the most significant challenges in the field of network security research is the identification of network intrusions. As a preventive measure to ensure the network’s safety, it helps in the identification of unauthorised uses of the network as well as attacks on the network. Methods such as machine learning-based (ML) approaches, Bayesian-based algorithms, nature-inspired meta-heuristic techniques, swarm smart algorithms, and Markov neural networks are some of the examples of approaches that have been proposed to determine the most useful features and, as a result, increase the effectiveness of intrusion detection systems. The many ongoing research, which number in the hundreds, were compared to an extensive range of data sets over the period of several years. This paper presents a comprehensive analysis of various research articles that employed single, hybrid, and ensemble classification techniques. The analysis covers a wide range of topics. We compared and contrasted the outcomes measures, limits, and datasets used by the studied articles in the production of IDS. This was done so that we could draw conclusions about the quality of the research. In addition, a potential course of action for further prospective research is presented below.
RC4 Encryption and Machine Learning based Attack Detection
Authors:- Dhananjay Pareta, HOD Aditi Khemariya
Abstract- This method proposes a new image encryption plan based on chaotic tent cards. The image encryption system based on this card shows better performance. First, you need to modify the RC4 to generate a more appropriate key stream for image encryption. Steganography is such an innovation that supports security where secret data is embedded in the cover. After the information is hidden in the multimedia data, the information spreads rapidly and the digital technology has been developed, which improves the convenience of accessing digital information and thus realizes reliable, faster and efficient digital data storage, transmission and processing and leads to illegal Consequences of production and redistribution. Easy and undetectable digital media. In recent years, image encryption has become an attractive field of research. Based on chaotic cryptographic algorithms, some new effective methods are proposed to develop secure image encryption technology. The RC4 algorithm proposes some new and efficient methods for developing secure image encryption technology. This simulation has performed on MATLAB simulation platform.
Smart Village for Rural Development
Authors:-Anuradha M S, Ameeth Parshetty, Gadgi Vishal, K Vinay Kumar
Abstract- This paper presents different methods to implement GSM based smart village. Smart villages are rural communities which use innovative solutions to enhance their sustainability, built on local strengths and opportunities. The idea of smart village would help villages become self-reliable that can encourage foreign and domestic investors. Various techniques are also discussed, such as smart irrigation, safety, and soil testing, automatic street lights which are used for implementation of smart village.
An Electronic Load Controller for Micro Hydro System
Authors:- M.Tech. Scholar Sharad Kumar Pathak, Dr. Shweta Chourasia, Abhishek Dubey
Abstract- This paper presents different methods to implement GSM based smart village. Smart villages are rural communities which use innovative solutions to enhance their sustainability, built on local strengths and opportunities. The idea of smart village would help villages become self-reliable that can encourage foreign and domestic investors. Various techniques are also discussed, such as smart irrigation, safety, and soil testing, automatic street lights which are used for implementation of smart village.
Moder AgricultureWith Auto Pet Fedder System
Authors:- Prof. Harshalata Mahajan, Ms Sajiya Attar, Ms. Mansi Korde, Ms.Poonam Gawale, Ms. Hritika Swami
Abstract- The idea for this generated from following
Choice of technology:-
The project is based on Arduino uno and IoT technology. We have used automated cowshed and assistant for famers. We have been choosen this technology to make the work automated and easy for famers.
Eco friendly:-
Customer and authorized person get the acknowledge through the sms on mobile thus use of paper is avoided so deforestation is avoided and also avoid the use to pen for entering the data so the use of plastic is also reduced which is hazardous to nature.
Best use available resources:-
Due to use of Arduino uno and IoT it is fully automated. Automatic pet feeding system features machine which can feed pets automatically.
Social impact of project:-It is invented to give the farmer an assistant. As we know farmers and agriculture is India’s biggest power. So it agriculture system will be improved. Our India in agriculture field also get improved. For this the automated and modern agriculture is very useful for our farmers. So they can get more time to make agriculture system well and good get more time to make agriculture system well an good. This will help to improve the Indian agriculture system, utilize the resources very greatly and it is step towards Digital India”
functionality:- It is fully automated system works on Arduino and IoT .when process is started, food from motor is automatically down in front of animals. Water pumps are used to supply the water to the cowshed to clean the cowshed and other one is supplied to the farm. With the help of moisture sensor, water in the soil can be identified .temperature and humidity sensor senses the soil and all information regarding is notified to the farmer through IoT on the farmer’s mobile. This makes all the work automated and easy.
User friendliness:-In the project messaging / notification system is used to get all information about farm in absence of farmer and also feed the animals or pet in absence of farmer and farm work easy.
Aesthetic & completeness of project:-This system is implemented to reduce the human work and modify the cowshed according to technology. Project is executed as per our aim and we have completed its presentation using project demo
Power requirement: ¬Arduino-5v ,Nodemcu-3.3v, 4channel relay-5v ,power supply-23.
Performance Analysis of Missing Data Imputation Methods
Authors:- Harmanpreet Singh, Amrit Kaur, Harpreet Kaur
Abstract- Missing value can cause bias and makes the dataset not represent the actual situation. The selection of methods for handling missing values is important because it will affect the estimated value generated. This study aims to introduce basic concepts of missing data to a non-statistical audience, list and compare some of the most popular approaches for handling missing data in practice and provide guidelines and recommendations for dealing with missing data in scientific research. In this paper, we are going to compare mainly four imputation methods to handle missing values- K-Nearest Neighbor Imputation (KNNI), MICE (Multiple Imputation by Chained Equations) using PMM (Predictive Mean Matching) method, Multiple Imputations using Chained Random Forests and Likelihood via Expectation-Maximization algorithm. The difference in the way these methods work causes the estimation results to be different. Performance of the data imputation methods wasanalyzed using Normalized Root Mean Square Error (NRMSE) method. The results suggest that RF and KNN are.
A Study Keen on Computer Network Security Concerns
Authors:- Mr .Vinayak Pai, Mr. Senthil Jayapal, Anand M, Mr. Jeelani Basha Kattubadi, Dr. Ramesh Palanisamy
Abstract- Network security is a branch of computer security that focuses on computers and networks. Computer security aims to protect information and property against theft, corruption, and natural disasters while keeping it productive and accessible to its intended users. Computer system security refers to the methods and techniques that secure sensitive and essential information and services against dissemination, manipulation, or breakdown due to unauthorized activity, untrustworthy employees, and unanticipated incidents.
Experimental Investigation of Geopolymer Concrete by Replacing The Natural Coarse Aggregate Using Building Waste Material
Authors:- Assistant Prof. M. Brindha, PG Student M. Dhivya Jothi
Abstract- Cement is the integral part of building material, is a binding agent that sets, hardens and adhere with building ingredients. Whether building a new plant or upgrading existing operations which grow emission and environmental impact like degradation of landscape, pollution of water resources and atmosphere is high on coarse aggregate usage. At the same time waste aggregate increased by construction and demolishing which dumped in landfills. The purpose of this paper is to conduct an experimental investigation on Geopolymer concrete which replacing a coarse aggregate. The geopolymer concrete reduced the emission and eco-friendly for the environmental condition. In the geopolymer concrete fly ash are used instead of cement which improves binding and strength added for the alkaline solution to make the gels and added to fine aggregate, recycled coarse aggregate. Finally investigated and compare the compressive strength and flexural strength have been to tested on normal & geopolymer concrete.
Utilisation with Forecasting Of Demolish And Construction Waste In Environment Management
Authors:- Vianjal Badjatiya, Pallavi Gupta
Abstract- Construction waste leads to disasters, and the solution for that consists of 5 steps. For one, bring an end of being a part of causing waste by prevention. On the other hand, waste can be managed by recycling, reusing, recovering, and last option is to clearance or disposal. Also, other factors such as economical and marketing are considered to be effective answers.
Review of Fack News Analysis for Food Review on Twitter Data Set
Authors:- M. Tech. Scholar Anil Verma, Assistant Professor Megha Jat
Abstract- Today’s the modern era of the internet where people share the opinions, ideas of the people through such social media: microblogging sites, personal blog, reviews. various users review for a specific product, company, brand, individual, forums, company, brand and movies etc. sentiment analysis is a part of text mining where Analyzed, opinion of people and classified into tweets as good, bad, neutral. In this paper work data will be collected from twitter API and the sentiment of tweets and reviews published paper identified by searching particular keywords and then evaluate the polarity of tweets based on classified tweets as positive. Negative.After fed data into a supervised model for testing of new data sets. Machine learning techniques and tools are used. Machine learning classifiers such as Naive Bayes (NB), Maximum Entropy, Random Forest (RF), Support vector machine SVM classifiers are used for testing and training of the data sets and also evaluating the Polarity of sentiment of each tweet based on this analysis. Show that in result we get a performance of classifiers by evaluate parameters has highest accuracy. Using machine learning classifier RF, DTs, SVM and evaluate the accuracy of features and increasing the number of tweets. In the future work use of same methodology some more features can be added which are used for improving accuracy of prediction.
An Exploration of Methods for Empathetic Cluster Formation Using Mobile Computing Systems
Authors:- Naheeda Zaib, Saiba Jan
Abstract- A distributed system is a collection of independent units working to solve a problem that none could solve on their own. Specific tasks in a distributed system known as smartphones are carried out on base stations, whose location within the network changes over time. Distributed mobile systems introduce new issues such as mobility, a lack of a reliable, consistent store on mobile nodes, poor wireless frequency band, interruptions, and limited battery life. This paper discusses the problem of fault-tolerant computing in mobile distributed databases. The given processes are built on the concepts of checkpointing and flip restoration. We have also solved the challenge of recovering from simultaneous failures in a distributed computing framework. We have developed a novel strategy in which we have successfully dealt with lost and orphaned messages.
Review of Wormhole Attack on Mobile Ad-hoc Network
Authors:- M.Tech. Scholar Deepak Badgujar , Assistant Professor Lokendra Jat
Abstract- WSNs are unstable because to the wireless nature of communication since any attacker with the desire to steal the data may do so by inserting rogue nodes into the network. Attackers may carry out this by launching attacks such as wormhole, floods, grey hole, and others. The goal of routing protocols is typically to determine the shortest route between a source and a destination node. The hop count is used as a statistic to calculate the journey length. The wormhole attack, one of the several above-described attacks, is risky since it builds a tunnel by bypassing a few nodes in between them. The hop length is automatically decreased by the tunnel, resulting in a short route between the source and destination nodes. This article provides a concise overview of the methods or strategies for the identification and defence against wormhole attacks.
Energy Saving in Mobile Wireless Sensor Networks
Authors:- M. Tech. Scholar Pooja Vishwakarma, Asst. Prof. Megha Jat
Abstract- Many growing and upcoming Networks developments meet the requirements of ubiquitous communication systems. Remote Sensor Networks are a kind of best-in-class technology that focuses on energy efficiency and data collection. Bunch-based directing in WSNs boosts hubs’ energy production and innovative information collecting. (LEACH) Low Energy Adaptive Clustering Hierarchy has suggested several studies on network lifespan and data collection by allowing the group head to be rotated among sensor hubs and attempting to distribute energy use across all hubs. Cluster Head option affects WSN longevity since a CH uses more power than a Cluster (non-CH) hub. In this study, a power-efficient group head choice in Mobile WSN is developed, evaluated, and approved based on remaining energy and randomised hub selection. The suggested solution shows notable differences from LEACH and an Application Specific Network Protocol for Wireless sensor Networks norms for sensor hub energy use, system lifespan, and efficient information gathering due to low energy consumption during information transmission.
Secure VANET Using Trust Management System
Authors:- M. Tech. Scholar Ganesh Babu Lodha, Asst. Prof. Er. Lokendra Jat
Abstract- A vehicular ad hoc network (VANETs) accelerates the availability of secure, interoperable remote communications for vehicles, transporters, activity signals, phones, and other devices. VANETs require support against security risks due to growing reliance on advanced communication, training, and control. VANETs in-enlightened decency data trust, mystery, no renouncement, control, continual operational needs/demands, availability, What’s more security confirmation. VANETs might have better durability. Tom’s analysis focused on two main areas: information trust, or if and to what degree low-level activity data is credible, and focus trust, or how reliable those centre points are. VANETs appear in make. This study suggests an attack-safe trust association for VANETs that can remember. Adapt to malicious attacks Review the information and versant centres’ relentlessness. Vanets. A large portion of information trust may be evaluated based on data received from various vehicles; focus trust is analysed. On two estimates, helpful trust and suggestive trust, which indicate how risky a middle cam wood fulfil its comfort and door trustworthy those propositions starting with an inside to isolate centre points will be, freely. The symbolism plot’s sufficiency and competence may be tested extensively. Those specified trust association subjects may be significant with a broad arrangement regarding VANET requires to upgrade development prosperity, adaptability, and trademark security.
Omni Channel Inventory Planning in Retail
Authors:- Anand Sharma, Rahul Vavaldas
Abstract- Omni-channel retailing entails ensuring that businesses provide a consistent customer experience across all channels by employing more intuitive commitment channels that allow a customer to design his own living space. A 360-degree view of incoming and on-shelf activity is made possible by an Omni-channel strategy, which can also aid in enhancing advertising effectiveness. The Omni-channel perspective enhances system complexity by expanding client options, stock-keeping units, and product selection. However, it also assists with catering to the needs and expectations of particular customers. This research demonstrates that customer loyalty to their preferred Omni-channel e-retailers may be mostly based on their trust in the brand. In addition, consumers value personalisation, and an increasing number of visitors to web-based entertainment sites offer different reasons, while the majority of customers use mobile coupons to make purchases at home, on the move, or online (Mercier et al., 2014). Omnichannel e-retailers can provide customised experiences for customers if they collect data about them; yet, such data is difficult to collect due to consumers’ reluctance to disclose personal information. Direct delivery to the purchaser is likely the most crucial factor in achieving a pleasant beginning-to-end client experience. The traditional mindset of purchasing an item in a store and bringing it home is still prevalent, but it is losing way to more modern fulfilment strategies. This study examines the application of Omni-channel to four internet business processes with the purpose of enhancing the customer experience via personalization, instalment, designated development, and enhanced customer service.
Face Recognizationand IOT-Based Automobile Security and Driver Surveillance System
Authors:- M.Tech.Scholar Yogini B Jawale, Prof. S.V. Patil, Prof. O.K. Firke, Prof. Dr.A.M.Patil
Abstract-The Automobile industry is one of the largest and fastest-growing industries and the actual reason behind it is, the up-growing men to the vehicle ratio. Many new vehicles are launched in the market. And people spend much of money for it. The increased number of vehicles with advanced security features are available but vehicles thefts and security breaches is still a prevailing problem in our society. Hence this paper proposes a simple low-cost solution, based on a strong biometric mechanism that involves face authentication. This system that uses a night vision camera to capture the face of a person seating on the driver’s seat and some sensors to provide his surveillance in accidental situations. This system also gives us instant alerts with latest captured image of the vehicle’s interior on email. Index terms: Raspberry pi3b, Open CV,.
An Optimized Machine LearningAlgorithms for Solving Class Imbalance Problem in Credit Card Fraud Detection
Authors:- Md Shufyan, Dr. Prashant Prashun
Abstract- Class imbalance problem is more common with machine learning algorithm, it occurs when the ratio of data into different classes is not equal, in its data is divided into two classes, one is of majority classes and other is of minority classes. The sample present in the majority classes is too high as compared to the minority class, for very few numbers of samples is present in the minority classes.The algorithm is unable to read the data from minority classes. Thiscauses poor performance and often may cause overfitting when model get trained from skewed dataset. In this research work, to balance the dataset we applied SMOTE or Synthetic Minority Oversampling Technique in order to balance the dataset. Before balancing the dataset, it has to undergo through preprocessing phase in which we applied missing value removal and outlierdetection to reduce the dataset. When the dataset gets reduced, we applied the different algorithm like logistic regression, decision tree and extreme learning machine to detect the fraud, but it causes overfitting due to imbalance of dataset. SMOTE has been applied to balance the dataset and then ML algorithm has been applied and it has been noticed ELM is more feasible and effective as compared to remaining algorithm.
Maximum Power Utilization On Solar With Sofc Power Generation with Its Effects Analysis in Microgrid
Authors:- PG Scholar Sheetal Soni, Asst.Prof. Rahul Rathore
Abstract- Nowadays Renewable Energy plays a great role in power system around the world. It is a demanding task to integrate the renewable energy resources into the power grid .The integration of the renewable resources use the communication systems as the key technology, which play exceedingly important role in monitoring, operating, and protecting both renewable energy generators and power systems. This paper presents Review about the integration of renewable energy mainly focused on wind and solar to the grid.
Survey Paper of Wireless Sensor Network
Authors:- M. Tech. Scholar Ms. Pooja Vishwakarma, Asst. Prof. Ms. Megha Jat
Abstract- In recent years, the area of wireless sensor networks has seen rapid advancements in technology and innovation. In this paper, a brief introduction of wireless sensors and their associated applications, including those in the fields of condition, structure checking, keen home watching, industrial application, prosperity, the military, vehicle recognizable proof, blockage control, and RFID tagging, is provided. As work continues on WSN, more compact and straightforward sensor nodes will become accessible. These nodes will have the capacity for wireless communication, as well as the ability to recognise a variety of biological states and organize data. There are several types of coordinating traditions to choose from, based on the application and the creating of the framework. Traditions that guide provide a path inside the framework as well as the capability to multi-hop correlate. WSNs may be found in a variety of applications, including those used by regular natives and the military in general. These applications include getting a hold on enemy interference area, challenge following, calm watching, living space checking, firing acknowledgement, and cutting edge.
Trust-Based Protocol for Management of Trust in the VANET Network
Authors:- M.Tech. Scholar Ganesh Babu Lodha, Asst. Prof. Er. Lokendra Jat
Abstract- One of the main concerns in vehicular communications is the security of the Vehicular Ad-hoc Network (VANET), since each car must rely on messages sent by friends, some of which may include harmful content. Every vehicle must most likely evaluate, choose, and reply locally to the data obtained from various cars in order to protect VANETs from harmful actions. In this research, we study (separately and together) probabilistic and deterministic approaches to evaluate trust for VANET security. Based on available data, the probabilistic technique determines the buddy vehicles’ trust dimension. The message’s legitimacy is determined by the trust level, which determines whether it will be accepted for continued transmission across the VANET or deleted. The deterministic technique calculates separations using got flag quality (RSS) and the vehicle’s relocation to estimate the trust dimension of the received message (position facilitate). Better results are obtained when probabilistic and deterministic approaches are combined than when they are used separately. The suggested computations are shown with numerical results obtained through reenactments.
Impact of Tourism Sector on Poverty Reduction in Indonesia: Study Case West Java Province
Authors:- Paruta , Dedi Budiman Hakim, Yeti Lis Purnamadewi
Abstract- The goal of economic development should be to reduce poverty as well as promote growth. West Java’s present rapid economic expansion but not being followed by a decline in poverty. The tourism sector ideally has a strategic role in development in West Java. Activities related to tourism that not only concentrate on offering services but also work as a bridge between the primary and secondary industries can boost the economy and lessen poverty. There aren’t many empirical studies that examine how the tourist industry affects reducing poverty at the national and regional levels. Through by descriptive analysis and panel data methods were used in 27 regencies/cities in West Java from 2013 to 2019 to investigate this question. It was discovered that there was a link between the government spending for the tourist sector and the high school GER on poverty levels. The existence of the tourist industry as a base sector in a region and the high school GER as a proxy for tourism human resources are also recognized to have a major impact on lowering poverty in West Java, according to the Random Effect Model.
Solar Power Prediction by Artificial Immune Algorithm for Environmental Features Selection
Authors:- Sumit Kumar, Asst. Prof. Durgesh Vishwakarma
Abstract- The growing penetration of renewable energy resources poses a high degree of uncertainty in the electric grid’s behavior due to the intermittent nature of such resources. Handling the uncertainty becomes even more challenging when it is extended to the loads as well. Hence many of researchers work for this to predict the power of the solar panel plates. This paper has developed a model that identifies the features of the environment that affect the [solar power generation in terms of ratio. Artificial Immune System based Solar Power Prediction model finds the features and ratio that directly contribute the solar power in particular geographical location. Expeirment was done on real dataset of India geographical data. Result shows that proposed model has increases the evaluation parameter values as compared to previous models.
Research on Privacy-Preserving Technology for Cloud Computing
Authors:- Assistant Professor Jyoti Kaushal
Abstract- Consumers will be able to access applications and data anywhere in the world on demand by cloud computing which promises reliable services delivered through next-generation data centers. Cloud computing has a very broad application prospects such as virtualization, large-scale, dynamic configuration and many other characters. At the same time, there are many security risks such as privacy information leakage in the network by the rapid growing of network security threats. Security issues is the key issues constraining the development of cloud computing. The Privacy Protection Support Vector Machine (PPSVM) is widely concerned in secure multi-party computation (SMC). We propose a new optimized Privacy Protection Support Vector Machine classifier without Secure Multi-Party Computation for vertically partitioned data set which is not disclosing the private data. The novel approach is proved as being greater than traditional classification SVM on privacy-preserving by some experiments.
Research on Privacy-Preserving Technology for Cloud Computing
Authors:- Assistant Professor Jyoti Kaushal
Abstract- Consumers will be able to access applications and data anywhere in the world on demand by cloud computing which promises reliable services delivered through next-generation data centers. Cloud computing has a very broad application prospects such as virtualization, large-scale, dynamic configuration and many other characters. At the same time, there are many security risks such as privacy information leakage in the network by the rapid growing of network security threats. Security issues is the key issues constraining the development of cloud computing. The Privacy Protection Support Vector Machine (PPSVM) is widely concerned in secure multi-party computation (SMC). We propose a new optimized Privacy Protection Support Vector Machine classifier without Secure Multi-Party Computation for vertically partitioned data set which is not disclosing the private data. The novel approach is proved as being greater than traditional classification SVM on privacy-preserving by some experiments.
Analysis of Fiscal Decentralization Impact on the Human Development Index (HDI) and Poverty in Indonesia: Study Case South Sumatra Province
Authors:- Daniel Bonartua Malau, Wiwiek Rindayati, Yeti Lis Purnamadewi
Abstract- Fiscal decentralization in South Sumatra seems to have been going on for more than two decades but in terms of fiscal independence it has not yet been implemented properly. The realization of regional income and capital expenditure of South Sumatra is third ranked of all provinces on the Sumatra. However, the Human Development Index (IPM) and Poverty in South Sumatra are still in the poor category. This study purpose to the factors of fiscal decentralization that affect the human development index (HDI) and poverty in South Sumatra. The data used in this study is secondary data from 15 districts/cities in South Sumatra Province with the period 2013 – 2020. This study uses panel data regression analysis with the Fix Effect Model (FEM) method. Based on the results of data analysis shows that the degrees of fiscal decentralization, GRDP, educational facilities, and health facilities have significant effect the human development index (HDI) in South Sumatra Province. The variables of capital expenditure, GRDP, open unemployment rate, and Gini ratio have a significant effect on poverty in South Sumatra Province.
Ambulence at Traffic Light Yosing IOT
Authors:- Omer Mahmoud Abdallah Omer, Mrs. Sulthana A.S.R, Mca.M.Phil
Abstract- The idea of this project is to use the each second protectively to save a person Now a days many lives its not saved before the person reaches the hospital in emergency vehicle or the emergency vehicle is delayed to reach the accident zone at time should to such incidents we are in a situation to develop a system which makes us secure and provides us an efficient way in saving human lives the project we have structured a protocol is which that could reduce the delay caused by the emergency vehicle and to save his life of the patients as soon as possible by not disturbing other vehicle the same time alert is also given to other vehicles to make sure that an emergency vehicle is approaching Here we use microcontroller to control the traffic The most role of this project is control the traffic signals from the ambulance and make clearance the way of path automatically without any disturbance of public This project is use to save the time of delay in most efficient to save the life.
Polytechnic Curriculum & National Occupational Skills Standard Mapping Process
Authors:- Mohd. Nasir Bin Kamaruddin, Salhana Binti Sahidin Salehudin
Abstract- Malaysian Skills Certificate (SKM) is a certificate issued by the Skills Development Department (JPK), Ministry of Human Resources for skills programmes offered by Training Providers whether public or private. The benefits of this Skills Certification are recognised by the industry in Malaysia in providing opportunities for career paths and self-development that are comparable to career paths based on academic qualifications. Sultan Salahuddin Abdul Aziz Shah Polytechnic took the initiative to establish an Accredited Center to enable Mechanical Engineering Diploma (MED) students and the general public to obtain additional accreditation from the Skills Development Department, Ministry of Human Resources. The most important process in the establishment of an Accredited Center is related to the curriculum. To allow students who are following programmess at polytechnics or public institutions to obtain additional certificates, namely the Malaysian Skills Certificate or the Modular Certificate, JPK requires that the existing curriculum must meet the requirements of the National Occupational Skills Standard (NOSS). The mapping process is an important factor in the success and qualification of the awarding process as a Certified Center. The Mapping Guidebook was produced to be a special reference source for polytechnics in implementing accreditation programmess under the Skills Development Department. This book will have an impact on Accredited Centers in helping to make the SKM and Modular programmes a success to produce individuals with skill qualifications recognised by the current industry.
Poverty Determinants in the Provinces of Central Java and West Java with their Alleviation Strategies
Authors:- Muhammad Alif R, Muhammad Firdaus, Muhammad Findi Alexandi
Abstract- Pro-poor and pro-job economic growth is one of the goals in the 2020-2025 Indonesia mid-term development plan. Unfortunately, Central Java Province with economic growth above the national average still facing the highest poverty rate at Java Island in 2019, this in contrast to West Java Province which success to push poverty rate become lower. The purpose of this study is to map poverty and economic growth, to analyze the factors that influence poverty at the Provinces of Central Java and West Java in 2015-2019 using the Dynamic Panel Data SYS-GMM, and find priorities strategies of poverty alleviation for Central Java Province using the AHP model. The results showed that the poverty cluster in most of the cities/regencies at Central Java Province was high and West Java Province was categorized as medium, while the results of the klassen typology of Central Java and West Java Provinces were categorized as fast growing regions (Cluster III). Significant determinants affect the poverty rate in the two provinces was, poverty in the previous year, economic growth, inflation, Human Development Index (IPM), and the Open Unemployment Rate (TPT). The amount of savings only affects in Central Java Province, and inequality (gini ratio) only affects in West Java Province. Meanwhile, based on the judgement from the experts analyzed using AHP, the most priority target for alleviating poverty in Central Java Province is reducing the unemployment rate, with the most priority strategy being to create jobs.
Budgeting and Cost Control in a Construction Project Management
Authors:- M.Tech. Scholar Sujata Janardan Pawar, Assi. Prof. Trupti Kulkarni, Dr.Tushar Janardan Pawar
Abstract- In the construction field, most civil engineers are unaware of detailed project management,specifically budgeting and cost control of construction projects. It is difficult to asset information on budgeting and cost control even in the literature survey. This detailed study of project management would benefit civil engineering students to understand the explicit concept of budgeting and cost control. Basically, cost control is interdependent on the budget, so the knowledgeof budgeting and cost control is essential for the project’s success and profit. In this review, we present meticulous information of budget planning and cost control with a two-step mechanism..
Factors Affecting Financial Performance of Old Age Protection in BPJS Ketenagakerjaan (Indonesian Social Security) before and during Pandemic
Authors:-Lumban Benget Hutajulu, Wita Juwita Ermawati, Alim Setiawan Slamet
Abstract- This research aims to determine factors affecting financial performance of old age protection in BPJS Ketenagakerjaan. In this study, there are five independent variables such as: Solvability ratio, effectiveness of membership, effectiveness of fee, efficiency ratio, and varian ratio, while dependent variable is growth assets measured by Return on Net Asset Ratio. The method of multiple linear regression analysis was utilized twice before and during pandemic with using 2019 and 2020 data to determine the factors that influence financial performance. The result of this study shows that solvability and efficiency significantly affects the financial performance of old age security program before and during pandemic while effectiveness of membership, effectiveness of fee, and varian do not have significant impact. At the end, this study is expected to help management in BPJS Ketenagakerjaan improve financial performance of Old age security program.
Technological Improvements of Surveillance Drone
Authors:- Sanghapal Mangale, Prathamesh Chaudhari, Asst. Prof. Nitin Gotan Patil
Abstract- The focus of the project is to create new approaches and to the study of the new technology through the use of innovative aero- space technologies and to create a drone which can fulfil different requirements of the industry the presented article analyses, adopts and develops new solutions with regard to the aerial electric supply solutions for operational surveillance drones. This article proposes a number of innovative new usages and original solutions for continuously operating surveillance drones on a predefined path or around a predefined perimeter and, afterwards, steps are discussed which have to be followed to provide ingredients and end-to-end systems in order to transform this project into reality i.e., the aerial electric supply of surveillance drones. The presented article analyses, adopts and develops new solutions with regard to the aerial electric supply solutions for operational surveillance drones. This article proposes a number of innovative new usages and original solutions for continuously operating surveillance drones on a predefined path or around a predefined perimeter and, afterwards, steps are discussed which have to be followed to provide ingredients and end-to-end systems in order to transform this project into reality i.e., the aerial electric supply of surveillance drones. The presented article analyses, adopts and develops new solutions with regard to the serial electric supply solutions for operational surveillance drones. This article proposes a number of innovative new usages and original solutions for continuously operating surveillance drones on a predefined path or around a predefined perimeter and, afterwards, steps are discussed which have to be followed to provide ingredients and end-to-end systems in order to transform this project into reality. I.e., the aerial electric supply of surveillance drones. The presented report analyses, adopts and develops new solutions with regard to the aerial electric supply solutions for operational surveillance drones. This article proposes a number of innovative new usages and original solutions for continuously operating surveillance drones on a predefined path or around a predefined perimeter and, afterwards, steps are discussed which have to be followed to provide ingredients and end-to-end systems in order to transform this project into reality i.e., the aerial surveillance drones. Now days because of increase in modern technology there is equal growth in automobile this will creating huge amount of traffic jam, sound pollution and air pollution. In this situation lots of time gets wasted to reach one place to another place. Drone/quadcopter is a flying robot which is unmanned aerial vehicle (uav), controlling from ground with wireless remote. It has flexibility of tack-off and landing with wide range. To fly or operate drone rc controller is used and camera is used to send capture or record its audio-video visuals. We can use unmanned aerial vehicle in various sectors like disaster rescue, industry for delivery of material in lesser time, agriculture to check the condition of crops and the military use has gowned up as per the capability of drone to operate in critical region while keeping their operators at safe distance.
Seismic Analysis of RCC Building with or Without Shear Wall on Plain and Slopping Ground
Authors:- M. Tech. Scholar Aman Patel, Prof. Sandeep Gupta
Abstract- The hilly region’s fast urbanisation and economic growth have hastened real estate development and increased population density there significantly. As a result, there is a significant public pressure in that area for the construction of tall structures. In a hilly area, the shortage of flat land forces development to take place on hills. When subjected to lateral stresses brought on by an earthquake, hill structures perform differently from those in plains. This study involved the seismic analysis of a RCC building on spaced, sloping ground with or without a shear wall.
A Review Of Sentiment Analysis For Movies Reviews Using Deep Neural Network
Authors:- Research Scholar Sagar Mehta , Assistant Professor Nisha
Abstract- Sentiment analysis is a popular and growing topic of research in natural language processing (NLP) and text mining. It is quickly becoming one of the most important and exciting fields of research because the success of a product is largely determined by how well it is rated online. Sentiment analysis helps us to determine how natural text connects to how people feel or think. It allows us to observe how a person thinks about something very important to the person who created it. Nobody goes to the movies anymore unless they’ve heard positive things about it on social media or from film critics. The same is true when purchasing something. As a result, reviews are becoming an important aspect of marketing. it is important to make it easier and less error-prone to infer the sentiment of a review.
A Review of COVID-19 Patients in Chest X-Ray Images using InceptionV3
Authors:- Research Scholar Drishti Sharma, Assistant Professor Nisha
Abstract- COVID-19 is a viral infection caused by a novel coronavirus. It causes the lungs’ air sacs to enlarge. It can be diagnosed with a chest X-ray (CXR) imaging, which is usually less expensive and safer than a CT scan and is always available in small or remote facilities. X-ray machines, on the other hand, do not always diagnose COVID-19. Because the COVID-19 dataset is small and cannot be diagnosed from a CXR, coronavirus diagnosis can be done using pre-trained neural networks. The major purpose of this research is to use pre-trained deep transfer learning (DTL) architectures and traditional machine learning (ML) models to autonomously diagnose COVID-19 from CXRs. Because there aren’t many photos, DTL is employed to extract image features that aid in classification.
Comparative Study of Mechanical Properties of Recycled Coarse Aggregates Subjected to Different Treatments
Authors:- M.Tech. Scholar Ombir, Asst. Prof.Sonu Mor
Abstract- The majority of the solid waste produced worldwide is composed of construction and demolition (C&D) waste, which is disposed of in landfills. The concept of properly extracting, treating and reusing this treated material as a replacement of virgin coarse aggregates in fresh concrete, especially for lower level applications and it is very evident from past research by carried out by various researchers around the globe. The utilization of recycled aggregates (RA) made from C&D waste in concrete building is discussed in this paper. The study provides a description of the impact of recycled aggregates on the characteristics of both fresh and hardened concrete in addition to a brief explanation of the engineering features of recycled aggregates. However, this study demonstrates that high-quality concrete may be produced using recycled aggregates that are collected from site-tested concrete specimens. This paper examines how different treatment to the recycled aggregates affects the properties of concrete in fresh and hardens state. For this study replacement ration of 50% is considered i.e half of the requirement of coarse aggregates is used from recycled aggregates. The specimens are tested for slump values in fresh state and split tensile strength as well as compressive strength tests are carried out at the 28 days age. From tests it is evident that untreated recycled aggregates should not be used as such as a replacement of coarse aggregates because it imparts a lower compressive strength as well as tensile strength in harden state. Also due to porous nature of untreated recycled aggregates it absorbs a lot of water and hence workability of concrete i.e slump values are found to be lower then what is required at a given w/c ratio.
Survey of Recent Energy-Efficient Techniques in Wireless Sensor Networks
Authors:- F.Shakila Banu, Dr.S.Sankara Gomathi
Abstract- Wireless sensor networks are gaining a lot of traction in today’s IoT-enabled industrial and home applications that use either homogeneous or heterogeneous sensors to collect intent data. Because the application of WSNs is geographically dependent, they are designed to run on self-powered sensor nodes. These nodes must be energy efficient for the network to last as long as possible. Cluster head selection is an important step in a WSN architecture that focuses on reducing network energy usage. It groups sensor nodes in such a way that a complex network cluster is produced, which has a longer life span and uses less power. Because Wireless Sensor Networks (WSNs) are prone to resource constraints, maintaining the network’s correct operation is a prerequisite. In this study, we conducted a thorough examination of recent challenges in Wireless Sensor Networks and covered a variety of topics in relation to various scenarios and methodologies. In addition, the study focuses on contemporary techniques to reducing wireless sensor network energy consumption, as well as research into increasing the network lifetime by diverse authors.
Study on the Treatment of Landgfill Leachate Using Nanoparticles of Titanium Oxide
Authors:- M. Tech. Scholar Subhacini C, Asst. Prof. Prabavathi S
Abstract- Landfill leachate is the liquid that leaches or drains from a landfill. Leachate results from precipitation entering the landfill from moisture that exists in the waste when it is composed. It can be a toxic liquid, a chemical or any liquid material otherwise unsuitable for use. Nanotechnology has the efficiency in removing contaminants present in water including heavy metals e.g.: (cadmium, arsenic, copper, lead, Mercury, nickel, zinc etc.). Nanoparticles attract water and are repellent towards impurities and also repel organic matter and bacteria. Titanium dioxide or TiO2 has characteristics that make it suitable to many different applications. Ultrafine titanium dioxide nanoparticle has strong absorption against both UV- A and UV- B radiation. The photo catalytic activity of TiO2 can be used to decompose impurities in wastewater. The study of titanium dioxide on leachate as treatment is observed and the results are obtained by conducting an experiment.
Empowering Business with Analytics
Authors:- Atul Vashishtha
Abstract- Business intelligence and the use of information have been influenced by the Big Data phenomena, which refers to the amount, variety, and velocity of data. As part of business intelligence, new concepts have evolved, including data science and quick analytics. Timely data analysis is challenging due to the massive amounts of unstructured event data generated by business process executions across big and complicated supply chains. Users may assess and enhance the performance of business processes with the use of an architecture for integrating big data analytics into business performance management. There is currently a lack of a complete methodology for operationalizing analytics for diagnostic and interactive PMS. This article fills this gap by using an action research methodology and creating a framework that is then applied to a construction firm. The findings demonstrate that BPA can help uncover crucial performance indicators, possible sources of risk, and associated interdependencies in addition to fostering conversation. The implementation of data-based initiatives faces a variety of serious challenges, including data quality, organizational capabilities, and cultural transformations.
Design and Development of Security Algorithm Using Modified PGP Algorithm
Authors:- Prof. Sushila Ratre, Suprit Pandurangi, Ashwin Nair, Vinay Kondabathula, AyushGajbhiye
Abstract- With the rise of data protection regulations and the increasing fines, companies worldwide focus more on cybersecurity, especially on the safety and privacy of sensitive customer data. Source code can be related to a company’s ‘secret sauce’. At a fundamental level, one’s intellectual property is represented by the code. This has a vast range starting from code to the protocols for implementation to deployment to marketing and sales. Hence security of the source code plays a very vital role. In the proposed system a modified PGP encryption algorithm that is better than the STEK algorithm currently being used by Meta is planned to be implemented. This algorithm uses both symmetric and asymmetric encryption and decryption of data which makes it better than STEK. Using this algorithm, a more secure private key for securing the data can be implemented. A larger key shall be generated by twiddling the source code if needed so as to generate the key of size 8192 bits. A dynamic PGP virtual disc can be used to create the predefined size, so as to handle the requirement of a big sized encryption key. This will be beneficial in handling both the size of the data and the key values so as to achieve efficient and feasible secured data. But sometimes, the PGP algorithm can be slower when sharing data over public platforms, So AES can be used, which is quick and good for large databases. There are many algorithms available in the market for encrypting the data.
Systematic Analysis of Rapid Prototyping Machines to Enhance the Productivity and to Minimize the Cost of Raw Material and Production
Authors:- P.N.Gawande, Prof. S.G.Kamble
Abstract-In recent years, rapid prototyping technology (RPT) has been implemented in many spheres of industry, particularly in the area of product development. Existing processes provide the capability to rapidly produce a tangible solid part, directly from three dimensional CAD data from a range of materials such as photo curable resin, powders and paper. This paper gives an overview of the growth and trend of the technology, areas of applications. Although digital modeling and analysis methods are widely employed at various product development stages, still, building a physical prototype makes the present typical process expensive and time consuming. Therefore, it is necessary to implement new technologies, such as virtual prototyping, which can enable industry to have a rapid and more controlled decision making process.
Smart Agriculture Using IOT and Machine Learning
Authors:-Asst. Prof. Vaidehi Verma, Manoj.P, Shejan Shriram.R, Shreyas. U, Shyamsundar.B, Surya.S
Abstract- Agriculture plays a very important role in both fields, such as food necessity for human beings and providing necessary stocks for many food industries, and it is one of the most effective and the backbone of India. The future of innovation in creating farming methods is moderately reinforcing the crop yield to make it more commercial and reduce irrigation debris. In this research paper, we are pleased to introduce our prototype for Smart Agriculture using IoT and Machine Learning. Firstly, we will construct a greenhouse and then test different kinds of crops grown inside. By using IoT devices, we will collect various datasets consisting of moisture, temperature, and humidity, which are the three most vital parameters that are required in any agriculture field. This system comprises temperature, humidity, and moisture sensors, installed in the greenhouse, and sends data through an Arduino Board, developing an IoT device with the cloud. Machine learning algorithms are applied to the dataset which is collected from the greenhouse field to predict results proficiently.
Multistory Building Design and Performance Parameters Optimization Using Anova Method
Authors:- M.E Student Brijesh Pandey ,Prof. Rajeev Chandak
Abstract- Designing and analysis of structural buildings by manual calculation is a complicated and time-consuming work, it is not always the better option when compared with computer aided software. A computer aided program named Staad.Pro is available which allows to design and analyze a structural building in an easier way and consumes less time prior to the construction. Staad.Pro can be used in order to apply static and dynamic loads and their combinations in a quite simple method. The Staad.Pro software can design and analyze a structure for different types of a materials such as concrete, steel, timber and user defined material with the use of suitable properties.
Sketch To Face Generation
Authors:- Pulkit Dhingra, Ritika Pandey
Abstract- Automation has been impacting various industries on a large scale by efficiently tackling challenging tasks. In a criminological investigation, eyewitnesses play a vital part in putting the accused behind bars. Sketches of criminals drawn from the information provided by the eyewitnesses make it easier to identify the accused. Often this process of sketch generation is time-consuming. Modern-day machine learning models are capable enough to tackle various extreme situations. With the help of technology, we can produce models that eliminate the demand for a sketch artist. The paper deals with the use of a generative adversarial network to build a machine learning model that can be used by eyewitnesses to draw a free-hand sketch and get a colored image as the output from the model.
Modelling and Analyzing Land-Use Pattern Using GIS
Authors:-Asst. Prof. Prof. Kalyani.N.Kulkarni, Sanket Anil Bhame, Akshay Gaware, Kunal Ghule, Adhiraj Kotwa
Abstract- This paper delivers the modelling of the region under Pune Metropolitan Region Development Area (PMRDA) to evaluate the area on Geographic Information System. Later on analysing the various projects that are making an enormous change in a significant area causing ecological, social, and physical changes in the same area under study. This takes place because of urbanization which is a physical and socio-economic spatiotemporal approach that transforms the rural terrain into urban form. It is attaining pace worldwide and is the most elemental cause of global land change. The rate of growth poses a great challenge for urban planners, as the development of cities often outpaces the planning cycle. This leads to additional challenges for metropolitan planners, namely: i) the database for the planning is usually obsolete and ii) methods and patterns of arbitrary urban growth are not accounted for properly. This study presents an approach to address these challenges by using commonly open and affordable remote sensing data to study: i) land use and land cover transformation and ii) by examining the extent of urban areas to explore the patterns and methods of urban development. There is a need for land use cover change to be studied on spatial and temporal scales to understand its possible impacts on the environment. We assessed land-use/land-cover data from 1991 to 2021 using multi-temporal Landsat datasets. The dynamics of urban growth were quantified using various metrics of metropolitan development. Urban land has increased significantly at the cost of grasslands, barren and agricultural lands, and our study confines in predicting and mapping this and giving us a fair percentage change in the tabular form by conversion and processing accordingly.
Performance Analysis of Intrusion on Detection Using Machine Learning Techniques
Authors:- Ishita Bansal
Abstract-As cyber attacks become more common, cyber security is quickly becoming one of the most important things for every company to worry about. Artificial Intelligence (AI) and Machine Learning (ML), especially Deep Learning (DL), can be used as key enablers for cyber-defence because they can help find threats and even tell cyber analysts what to do next. This makes it possible to use these technologies as key tools for cyber-defence. For AI and ML to be used more quickly in cyber security and for effective cyber defence systems to be made, the private sector, academic institutions, and the government need to work together on a global scale. In this research, we look into the different deep learning techniques that are used to find network intrusions, and we present a DL framework that can be used in a variety of cyber security applications. Machine learning is being used more and more in many different fields because it has been shown to work better than traditional algorithms that are based on rules. Different cyber-detection systems are currently adding these techniques in order to help the first level of security analysts or even replace them in the long run. Even though the goal of fully automating detection and analysis is appealing, machine learning needs to be looked into very carefully to see if it can help with cyber security. We go over some of the ways that machine learning has been used to find intrusion, malware, and spam. This analysis is for people who work in security. The goal is twofold: first, to figure out how mature these solutions are right now, and second, to find out what the main problems are with these solutions that keep them from being used right away in machine learning cyber detection strategies. Our conclusions are based on a thorough review of the relevant research that has already been published, as well as the results of experiments done on real enterprise systems and real network traffic.
Stock Price Prognostication using Machine Learning Model (LSTM
Authors:- Mansimar Singh, Prabhnoor Singh, Ms. Shipra Raheja
Abstract- – A stock/equity is a financial instrument that reflects ownership of a portion of a company. This empowers the stock owner to share a share of the corporation’s assets and profits according to the amount of stock they own. Shares are the units of stock. A stock is a wide term that refers to any company’s holding certificates. Market forces influence stock prices on a daily basis. This means that stock prices differ due to supply and demand. When there are more people who want to purchase a stock than there are those who want to trade it, the price rises. If more people wanted to sell a stock than acquire it, the supply would surpass the demand, and the price would fall. It’s simple to understand supply and demand. What’s harder to recognize is what makes individuals like one stock and dislike another. It all comes down to decide what news is good for a corporation and what news is bad. There are numerous solutions to this problem, and almost every investor you speak with will have their own thoughts and techniques. However, the main premise is that a stock’s price fluctuation reflects what investors believe a firm is worth. Don’t mistake a company’s worth for its stock price. A company’s market value is calculated by multiplying the stock price by the number of remaining shares.
A Review on Renewable Energy Sources and Bidirectional DC-DC Converter
Authors:- Vikram Sirohi, Assistant Professor Somya Agarwal, Dr. Raghavendra Patidar
Abstract- A critical overview of renewable energy is provided, including descriptions of renewable energy sources, technologies, assessments, comparisons and planning as well as energy technologies that facilitate renewable energy sources .Depletion of natural resources like gas, oil, coal along with environment pollution increased the popularity of the Renewable Energy Sources (RES). Power Electronic converters are utilized for conversion of power from RES to coordinate the stand-alone load and utility grid. MPPT control is also established by these converters to supply the standalone or grid-connected load despite of the RES's unpredictable nature. In order to reduce the number of switches used for integrating RES to drive loads, Multi- port converters are developed. These converters have the capability to supply more than one load simultaneously. Furthermore, more number of RESs are connected using these converters in order to drive common loads.
A Low-Cost Monitoring Design for Photovoltaic System Using IOT
Authors:- Peniel David, R. Krishna Prasath, S. Pranesh Supervisor, Mrs. B. Suganya
Abstract- Internet of Things (IoT) technology in photovoltaic (PV) systems is an important aspect for monitoring, supervising and performance evaluation. The main aim of this system is to design a low-cost monitoring system for the maximum power point tracking in photovoltaic (PV) systems. In addition, the monitored real time data will be sent to the user’s mobile app through IoT. The LDR is used to find the light intensity of sun and makes the photovoltaic cell to turn to the respected side. Based on the monitored data the users can identify the working of the system.
Neural Network-Based Advanced Cancer Prediction and Classification for Enhanced Diagnosis and Prognosis Accuracy
Authors:- Valarmathi P, Rubadharshini A K, Subashini P, Arullakshmi A
Abstract- One of the main areas of contemporary machine learning and data mining research is medical diagnostics. Since single nucleotide polymorphisms (SNPs) contribute significantly to the variability of the human genome, they have been linked to a number of illnesses, including cancer. The most prevalent malignant growth in women, breast cancer, has become much more prevalent during the last 20 years. Several methods have been used on Genetic data to make distinctions between these tumorous and benign data. The large amount of features in SNP data, which makes classification difficult, is one of the main issues.The dimensionality problem for the diagnosis of cancer in women is addressed in this research by an innovative blended intelligence technique based on Association Rules for Harvesting (ARM) and neural network technology (NN) who employs the Evolutionary Computation (EA). While NN is employed to achieve successful classification, ARM optimized by Grammatical Evolution (GE) is used to obtain relationships between SNPs, diminish dimension, which and find the most useful features. The NCBI GEO (Gene Expression Omnibus) website’s carcinoma SNP dataset was used to test the suggested NN-GEARM technique. Up to 90% consistency has been achieved by the developed model.
DOI: 10.61137/ijsret.vol.8.issue4.467
A Comparative Analysis Of Einstein AI Vs. Microsoft CoPilot In CRM Contexts
Authors: Sarosh Ameen
Abstract: The rapid advancement of artificial intelligence (AI) has revolutionized customer relationship management (CRM), empowering businesses with tools to automate workflows, personalize customer interactions, and drive data-driven decision-making. Two of the most prominent AI solutions in the CRM landscape are Salesforce Einstein AI and Microsoft CoPilot. This article presents a thorough comparative analysis of these platforms, focusing on their architectures, core functionalities, integration capabilities, security and privacy frameworks, customization options, use cases, and overall business impact.Salesforce Einstein AI is an advanced suite of AI-powered tools natively integrated into the Salesforce CRM ecosystem. It leverages machine learning, predictive analytics, and natural language processing to deliver intelligent insights, automate routine tasks, and enhance customer engagement. Einstein AI is renowned for its robust data security, extensible platform, and seamless integration across Salesforce’s Sales, Service, Marketing, and Commerce Clouds. The platform’s Einstein Trust Layer ensures data privacy and responsible AI usage, making it a trusted choice for enterprises seeking to harness AI without compromising sensitive information.Microsoft CoPilot, on the other hand, is an AI assistant embedded across Microsoft’s productivity and business applications, including Dynamics 365. CoPilot leverages large language models (LLMs) to provide real-time assistance, automate data entry, generate insights, and streamline workflows. Its integration with Microsoft 365 and Dynamics 365 enables users to access AI-powered features within their existing work environments, fostering productivity and collaboration. Microsoft CoPilot prioritizes data privacy and security through its multi-layered approach, aligning with Microsoft’s comprehensive compliance and regulatory framework.This article explores the unique strengths and limitations of both platforms, their real-world applications, and their potential to transform CRM operations. By examining their architectures, security models, customization capabilities, and business outcomes, this analysis aims to provide a comprehensive understanding of how Einstein AI and Microsoft CoPilot are shaping the future of CRM.
DOI:
Implementation Strategy For Salesforce Einstein Copilot In Enterprise CRMs
Authors: Ayesha Farzana
Abstract: Salesforce Einstein Copilot represents a transformative leap in intelligent customer relationship management (CRM), leveraging the power of generative artificial intelligence (AI) to enhance user productivity, automate workflows, and deliver contextually aware recommendations. As enterprises strive to remain competitive in an increasingly data-driven and customer-centric business landscape, the integration of Einstein Copilot into existing CRM infrastructures provides a strategic edge. This article explores a comprehensive implementation strategy for deploying Salesforce Einstein Copilot across enterprise-level CRM systems. It begins by outlining the business rationale and technological foundations that underpin Einstein Copilot, including its reliance on AI, machine learning (ML), and natural language processing (NLP). It then delves into detailed planning methodologies, governance frameworks, and organizational change management approaches necessary for successful integration. Key focus areas include architecture alignment, data security and privacy, customization techniques, performance optimization, and cross-platform scalability. Emphasis is placed on aligning business goals with AI capabilities, ensuring data quality, managing user adoption, and integrating with external systems through APIs and MuleSoft. The article also covers the technical prerequisites for Einstein Copilot setup, sandbox testing strategies, KPI tracking, and iterative feedback loops. Real-world case studies illustrate practical lessons and benefits achieved, while challenges such as AI model bias, integration complexities, and user resistance are addressed with actionable solutions. The article concludes with a forward-looking perspective on the role of generative AI in CRM evolution and outlines best practices for ensuring long-term success with Einstein Copilot. The goal is to provide CXOs, CRM managers, architects, and developers with a clear, strategic, and technically grounded roadmap for deploying Einstein Copilot to drive innovation, operational efficiency, and enhanced customer engagement in the enterprise CRM landscape.
Adaptive Load Balancing in Ldoms Using Edge AI Models
Authors: Komal Jain, Ajeet Kumar, Shravanthi R, Ritu Chauhan
Abstract: Oracle Solaris Logical Domains (LDOMs) offer flexible, high-performance virtualization at the hardware layer, enabling fine-grained resource allocation across critical workloads. However, as enterprise infrastructures grow in complexity and scale particularly in edge and hybrid environments the need for dynamic and intelligent load balancing becomes paramount. Traditional static and reactive policies fall short in addressing modern demands marked by workload volatility, bursty usage patterns, and constrained physical resources. In this context, Edge AI models present a transformative approach to adaptive load management. This review explores how AI particularly Edge-deployed supervised, unsupervised, time-series, and reinforcement learning models can be leveraged to predict resource saturation, detect faults, and proactively manage LDOM reallocation and live migrations. Emphasis is placed on integrating AI pipelines with Solaris-native telemetry tools (kstat, vmstat, prstat) and automating control actions using the ldm command suite. Real-world case studies across telecom, financial, and healthcare sectors are analyzed to demonstrate improvements in SLA compliance, resource efficiency, and fault avoidance through AI-assisted decisions. We further address system-level integration with Oracle Ops Center, highlight governance concerns such as model explainability and override control, and explore lightweight inference frameworks suitable for constrained control domains. Challenges in data quality, model trust, and automation safety are also discussed. The review concludes by outlining future directions including federated learning, policy-aware AI agents, cross-domain telemetry fusion, and convergence with AI-Ops ecosystems. By embedding intelligence directly into the LDOM infrastructure, organizations can evolve from static resource provisioning to a self-optimizing virtualization platform—capable of continuous learning, rapid adaptation, and resilience at the edge. This shift is vital to meet the performance and operational demands of modern digital infrastructure.
DOI: https://doi.org/10.5281/zenodo.15846618
Challenges in SAP HCM Payroll Schema Customization for USA: Practical Lessons
Authors: Balakrishna Teja Pillutla
Abstract: Customizing SAP HCM payroll schemas for the USA is a nuanced process requiring navigation of complex federal and state regulations, alignment with client-specific requirements, and technical consistency across custom wage types and SAP Time Management. This article examines practical challenges in schema customization for U.S. payroll, including retroactive calculations, custom wage types, and multi-state taxation. It details schema customization techniques, personnel calculation rules (PCRs), and validation logic, supported by a real-world use case from a multi-state employer. Lessons learned and best practices offer actionable guidance for consultants. The goal is to equip SAP HCM functional consultants with knowledge to build accurate, maintainable, and compliant U.S. payroll systems.
DOI: https://doi.org/10.5281/zenodo.16446115
Leveraging Artificial Intelligence To Streamline Operations, Reduce Costs, And Improve
Authors: Srinivas Madduru
Abstract: This article explores how businesses can strategically leverage Artificial Intelligence (AI) to streamline operations, reduce costs, and improve customer loyalty. In an increasingly data-driven and competitive environment, AI enables organizations to automate repetitive tasks, optimize resource use, and deliver highly personalized customer experiences. The article outlines how AI enhances operational efficiency through intelligent automation, reduces expenses via smarter workflows and predictive planning, and strengthens customer relationships through real-time engagement and personalization. Real-world applications, implementation best practices, and future outlooks are discussed, offering business leaders a comprehensive roadmap to integrating AI in a way that’s scalable, ethical, and impactful.
DOI: http://doi.org/10.5281/zenodo.16742168
Longevity-as-a-Service: Founders Leveraging AI To Disrupt Health And Wellness
Authors: Vijayalakshmi Sadasivam
Abstract: The convergence of biohacking and artificial intelligence (AI) is creating a powerful new category of entrepreneurial opportunity at the intersection of health, wellness, and technology. This article explores how modern startups are leveraging AI to develop personalized, data-driven solutions that optimize physical and cognitive performance, extend longevity, and promote preventative health. From real-time biomarker tracking to genetic analysis and adaptive supplement regimens, AI enables scalable, hyper-personalized health offerings that are attracting both consumers and investors. Entrepreneurs are building platforms, wearables, and SaaS models that deliver continuous insights, automate recommendations, and integrate seamlessly into users’ daily routines. While the commercial potential is immense, it also brings ethical challenges related to privacy, accessibility, and scientific rigor. This article analyzes key business models, leading case studies, and the future trajectory of the AI-biohacking movement, while highlighting the responsibility founders bear in ensuring safety, transparency, and long-term trust. The future of health is not just digital—it’s intelligent, personalized, and increasingly entrepreneur-led.
DOI: http://doi.org/10.5281/zenodo.16742192
Merging AI And CRM To Deliver Seamless, Adaptive, And Context-Aware Customer Journeys
Authors: Ashwin Thupakula
Abstract: This article explores how the integration of Artificial Intelligence (AI) with Customer Relationship Management (CRM) systems is transforming the way businesses engage with customers. As expectations for personalized, real-time experiences rise, traditional CRM platforms struggle to keep up. AI addresses this gap by bringing automation, predictive insights, and context-aware interactions into the CRM ecosystem. It enables organizations to deliver seamless, adaptive, and emotionally intelligent customer journeys across all touchpoints. The article examines the evolution of CRM, the role of AI in predictive analytics, conversational automation, and dynamic segmentation, and how these technologies together enable real-time journey orchestration. It also outlines the benefits—such as improved customer loyalty, operational efficiency, and higher marketing ROI—while addressing the technical, ethical, and organizational challenges of implementation. Finally, it looks ahead at how AI will continue to shape CRM into an autonomous, emotionally intelligent engagement platform that helps businesses build deeper, lasting relationships with customers.
DOI: http://doi.org/10.5281/zenodo.16742206
Emerging Trends In AI For Healthcare Diagnostics
Authors: Samaira Lodh
Abstract: Artificial Intelligence (AI) is revolutionizing healthcare diagnostics by providing unprecedented capabilities in data analysis, pattern recognition, and predictive modeling. AI-powered tools have demonstrated potential in increasing diagnostic accuracy, reducing diagnostic errors, optimizing treatment pathways, and ultimately improving patient outcomes. The integration of AI with healthcare diagnostics stands at the forefront of digital transformation, leveraging advancements in machine learning, deep learning, and natural language processing. These technologies enable precise identification of diseases from various forms of medical data, including imaging, genomics, and patient records. Despite remarkable progress, the field faces challenges such as data privacy concerns, ethical dilemmas, integration with existing healthcare workflows, and the need for transparency and explainability in AI-driven decisions. Emerging trends like explainable AI, federated learning, and the use of AI for point-of-care diagnostics are shaping the future of healthcare diagnostics. This article explores these trends, evaluates their potential impact, and discusses the implications for practitioners, patients, and policymakers. The ultimate aim is to provide an in-depth understanding of how AI is redefining healthcare diagnostics, the directions in which the field is evolving, and the unresolved questions that must be addressed to leverage the full potential of AI while safeguarding ethical and clinical standards.
DOI: https://doi.org/10.5281/zenodo.16979367
A Review Of Cloud-Native Security Solutions
Authors: Arhaan Madavi
Abstract: Cloud-native security has become an essential paradigm in modern computing, aligning security strategies with the dynamic and scalable architecture of cloud-native applications. As enterprises transition from traditional on-premises environments to distributed, containerized, and microservices-based infrastructure, the security landscape shifts dramatically. This review synthesizes current research and best practices in cloud-native security, outlining critical challenges, innovative solutions, and industry trends. Cloud-native environments are characterized by their reliance on containers, Kubernetes, service meshes, and serverless functions, which bring new opportunities alongside new threats. The paper discusses how traditional perimeter-based security approaches are being replaced by identity-driven, zero-trust models, embedding security into every layer of application design and deployment. Topics such as secure software supply chains, runtime protection, compliance automation, and infrastructure-as-code security are explored. This review aims to provide a single resource for researchers, DevSecOps practitioners, and enterprise architects seeking a comprehensive understanding of cloud-native security, emphasizing the importance of collaboration between development, operations, and security teams. Through an in-depth analysis of technologies, frameworks, and strategies, the article clarifies how organizations can address the unique risks present in modern cloud-native ecosystems while enabling agility and continuous delivery. By surveying academic literature and industry reports prior to 2014, we situate key advancements in their historical context, revealing the trajectory toward the current state of cloud-native security. The findings underscore the necessity for proactive, automated, and scalable security practices that evolve with cloud-native application lifecycles.
DOI: https://doi.org/10.5281/zenodo.16979595
The Role Of Bioinformatics In Neuroscience Research
Authors: Ishira Venkatesh
Abstract: Bioinformatics has become a pivotal force in transforming neuroscience research, enabling deep insights into the structure and function of the brain. By integrating computational approaches with experimental data, neuroscientists can now analyze complex neural networks, decipher molecular mechanisms, and unravel the genetic underpinnings of neurological disorders. The surge in large-scale data—from genomics and transcriptomics to neuroimaging and electrophysiology—has created both opportunities and challenges, necessitating advanced analytical tools capable of processing and interpreting vast datasets. Bioinformatics methods have empowered the identification of novel biomarkers, the understanding of brain development, and the discovery of therapeutic targets, bringing precision and efficiency to neuroscience studies. Moreover, bioinformatics facilitates interdisciplinary collaborations, connecting computer scientists, biologists, and clinicians to resolve intricate questions related to cognition, behavior, and disease. The application of machine learning, network analysis, and data mining techniques has enhanced the predictive accuracy for diagnosis and treatment strategies. As neural data repositories expand, bioinformatics supports the harmonization and sharing of information, promoting reproducibility and fostering the growth of open science. Despite these advances, challenges remain, including data standardization, the need for high computational power, and the integration of multi-modal data. Continuous development of bioinformatics tools is required to address these challenges while ensuring ethical considerations are met in data management. Ultimately, bioinformatics is reshaping neuroscience, fueling discoveries that have the potential to transform our understanding of the brain, mental health, and neurological diseases.
DOI: https://doi.org/10.5281/zenodo.16979834
Digital Transformation Through Salesforce CRM And Cloud Systems
Authors: Riyan Dastoor
Abstract: Digital transformation embodies the fundamental integration of digital technology into all facets of business, revolutionizing how organizations operate and deliver value to customers. At the core of this transformation lies Customer Relationship Management (CRM) systems, with Salesforce CRM being a leading platform that harnesses cloud technology to empower businesses. Salesforce’s cloud-based CRM eliminates traditional IT burdens by offering scalable, flexible, and seamlessly integrated solutions that centralize customer data and automate essential processes. This unification fosters enhanced collaboration, data-driven decision-making, and personalized customer experiences. As companies face rising customer expectations and increasing competitive pressure, digital transformation fueled by Salesforce CRM provides a strategic advantage by enabling agility, efficiency, and innovation. Through advanced AI capabilities, automation, and a robust cloud infrastructure, Salesforce CRM transcends simple contact management and becomes the backbone of customer-centric business models. This article explores the multifaceted role Salesforce CRM and cloud systems play in driving digital transformation, discussing its impact on operational processes, customer engagement, scalability, and organizational success.
DOI: https://doi.org/10.5281/zenodo.16980036
The impact of predictive analytics on enhancing cybersecurity readiness
Authors: Rohan Verma
Abstract: Predictive analytics has emerged as a transformative force in the field of cybersecurity, enabling organizations to proactively identify, assess, and mitigate cyber threats before they materialize into severe security breaches. This article explores the evolving role of predictive analytics in enhancing cybersecurity readiness by leveraging historical data, machine learning algorithms, and real-time information to anticipate potential vulnerabilities and attack vectors. The integration of advanced analytics tools in cybersecurity frameworks has revolutionized threat detection and response strategies, shifting the paradigm from reactive to proactive defense. Predictive models analyze diverse data sources—including network traffic, user behavior, and threat intelligence feeds—to identify anomalous patterns and predict future attacks with increasing accuracy. This capability supports not only the detection of known threats but also the anticipation of novel, sophisticated cyberattacks. Additionally, predictive analytics facilitates better resource allocation, enabling organizations to prioritize cybersecurity efforts based on risk assessments and probabilistic forecasts. The article also addresses challenges such as data privacy, model accuracy, and the evolving landscape of cyber threats, emphasizing the need for continuous innovation and adaptation. By comprehensively examining the technological foundations, applications, benefits, and limitations of predictive analytics, this exploration highlights how predictive techniques contribute significantly to strengthening cybersecurity posture in a digital-first world. The discussion extends to case studies illustrating successful implementations, underscoring a transition towards dynamic, intelligence-driven security operations. Overall, predictive analytics stands as a critical enabler of cybersecurity readiness, providing a competitive edge in defending against ever-evolving threats.
DOI: https://doi.org/10.5281/zenodo.17708624
The influence of AI in improving fault tolerance in distributed computing systems
Authors: Nandini Iyer
Abstract: Artificial Intelligence (AI) has emerged as a transformative force in the field of distributed computing, particularly in enhancing fault tolerance mechanisms. Fault tolerance, the ability of a system to continue operating properly in the event of the failure of some of its components, is critical in distributed systems that involve numerous interconnected nodes and components. AI brings new capabilities to fault tolerance by enabling systems to predict, detect, and respond to faults more efficiently and accurately than traditional methods. By leveraging machine learning algorithms, anomaly detection techniques, and predictive analytics, AI enhances the robustness and resilience of distributed computing environments. This article explores the integration of AI into fault tolerance strategies within distributed computing systems. It discusses the key challenges faced in maintaining fault-tolerant distributed systems, the role of AI-driven predictive maintenance, and anomaly detection, and the application of reinforcement learning to dynamic resource allocation and recovery processes. It also covers AI-assisted decision-making in fault diagnosis and recovery, and how AI helps optimize system performance while minimizing downtime and operational costs. Additionally, the article evaluates case studies from cloud computing, edge computing, and critical infrastructures where AI-based fault tolerance has been successfully implemented. By synthesizing current research and technological advancements, this article aims to provide a comprehensive understanding of the potential and limitations of AI in improving the reliability and fault tolerance of distributed computing systems. The outlook on future trends and challenges highlights ongoing research directions and emerging technologies that promise to further transform this area. Keywords include fault tolerance, distributed computing, artificial intelligence, predictive maintenance, and anomaly detection.
DOI: https://doi.org/10.5281/zenodo.17708723
Published by: vikaspatanker
