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Assessment and Summary of Inverter and Induction Motor Efficiency in Dual Electric Vehicle Drive Systems Across Urban and Highway Driving Conditions

Assessment and Summary of Inverter and Induction Motor Effic...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Manjunatha B Naresh Amit Dutt Prateek Chaturvedi A Balaram Y Krishna Bargavi Department of Mechanical Engineering New Horizon College of Engineering Bangalore Karnataka India Electronics and Communication Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Lovely Professional University Phagwara Punjab India Amity School of Engineering & Technology Amity University Greater Noida Uttar Pradesh India Department of Computer Science & Engineering MLR Institute of Technology Hyderabad Telangana India Department of Computer Science & Engineering GRIET Bachupally Hyderabad India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Recent studies have shown that battery-powered motors and power electronic inverters are crucial parts of contemporary dual electric vehicle driving systems. Consequently, the traction engine and its accompanying drive must run at peak efficiency all the way through the driving schedule. The traction motor and drive system in a hybrid electric vehicle (HEV) typically utilize the full range of torque and speed operating conditions. With this in mind, the objective of this work is to estimate the efficiencies of the inverter and motor over common urban and highway driving patterns. The significant losses within a standard three-phase DC/AC traction inverter, including switching and conduction losses in the insulated-gate bipolar transistors and antiparallel diodes, are modelled and approximated for both urban and highway driving. Using an Automobile Simulator program, a medium-sized SUV equipped with an induction motor was simulated, accounting for copper losses in the stator, rotor, and core. The average efficiencies of the inverter drive and induction traction motor are then evaluated and summarized for both urban and highway driving conditions. Finally, the overall efficiency of the traction motor drive system is assessed based on the individual component performance analyses.

关键词： Road transportation Stator cores Urban areas Switches Propulsion Traction motors Inverters Semiconductor diodes Hybrid electric vehicles Standards

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The Multi Layer Security Network Authentication System Development Through Blockchain technology 2

The Multi Layer Security Network Authentication System Devel...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Reddy, M Vijay Bhasker Kumar, Rajiv Bag, Akash Hagar, Abdulnaser A. Vaithecswaran, G. Tripath, Vikas Geethanjali College of Engineering and Technology Department of It India Gl Bajaj Institute of Technology & Management Department of Computer Science Greater Noida India Amity Law School Amity University Chhattisgarh Raipur India Dr. Babasaheb Ambedkar Marathwada University Department of Computer Science & Information Technology Aurangabad India Stack Research and Development Tamil Nadu Tiruchirappalli India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

As block chain technology is a new approach in security world. So, hereby we will describe the multilayer security network authentication system development through block chain technology as this article describes a multi-layer secure Internet of Things network concept block chain - based. The approach decreases the difficulties of real block chain technology deployment by separating the Iot devices into a cross de-centric networking and adopting block chain platform at all stages of the network, while keeping the block chain technology's heavily guarded and credibility assurance. It offers an Internet of Things wide-area networking solution. Block chain is a series of information-containing blocks. The data recorded within a block is determined by the kind of block chain. A Bit coin Block, for example, comprises information on the Sender, Receiver, and the quantity of bit coin to be transmitted. The Genesis block is the initial block in the chain. The block chain's objective is to distribute information among all persons that access it through an application. Reading and writing accessibility to this ledger can be unfettered ('permission less') or controlled (,permission'). It is now possible to securely communicate, read, and save digital information using block chain. Furthermore, it employs cryptographic encryption to safeguard each transaction. Banks may raise their present security and transparency standards to new heights by doing so. © 2022 IEEE.

关键词： Authentication

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A Holistic,Proactive and Novel Approach for Pre,During and Post Migration Validation from Subversion to Git

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computers, Materials & Continua 2021年第3期66卷 2359-2371页

作者： Vinay Singh Mohammed Alshehri Alok Aggarwal Osama Alfarraj Purushottam Sharma K.R.Pardasani School of Computer Science University of Petroleum&Energy StudiesDehradunIndia Department of Information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadhSaudi Arabia Amity School of Engineering&Technology Amity UniversityUttar PradeshNoidaIndia Department of Mathematics Bioinformatics&Computer ApplicationsMaulana Azad National Institute of TechnologyBhopal462003India

Software development is getting a transition from centralized version control systems(CVCSs)like Subversion to decentralized version control systems(DVCDs)like Git due to lesser efficiency of former in terms of branching,fusion,time,space,merging,offline commits&builds and repository,*** is having a share of 77%of total VCS,followed by Subversion with a share of 13.5%.The majority of software industries are getting a migration from Subversion to *** a few migration tools are available in the software ***,these too lack in many features like lack of identifying the empty directories as premigration check,failover capabilities during migration due to network failure or disk space issue,and detailed report generation as post-migration *** this work,a holistic,proactive and novel approach has been presented for pre/during/post-migration validation from Subversion to *** scripts have been developed and executed run-time over various projects for overcoming the limitations of existing migration software tools for a Subversion to Git *** premigration,none of the available migration tools has the capability to fetch empty directories of Subversion,which results in an incomplete migration from Subversion to *** Scripts have been developed and executed for pre-migration validation and migration preparation,which overcomes the problem of incomplete *** was conducted in SRLC Software Research Lab,Chicago,*** the migration process,in case of loss of network connection or due to any other reason,if migration stops or breaks,available migration tools do not have capabilities to start over from the same point where it *** Scripts have been developed and executed to keep the migration revision history in the cache(elastic cache)to start from the same point where it was left due to connection *** post-migration,none of the available version control migration tools generate a detailed repo

关键词： Databases and information systems subversion Git version control system trunk tag translational settings author mapping

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Evaluating Cyber-Crime Using Machine Learning and AI Approach for Environmental Sustainability 2nd

Evaluating Cyber-Crime Using Machine Learning and AI Approac...

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2nd International Conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Chauhan, Ritu Mehtar, Khushi Kaur, Harleen Alankar, Bhavya Artificial Intelligence and IOT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031717284

The online world has become an essential part of everyday life in India due to its fast expansion and technical improvements. But as technology has advanced, cyber-crimes have also increased dramatically, posing serious risks to the economy, security, and privacy. This research examines the intricate terrain of cybercrimes targeting minors in India, scrutinizing more than 16 lakh cases that have been documented during the three years preceding this one. The study takes an extensive approach, applying machine learning modelling such as Gradient Boosting, AdaBoost, Random Forest, Gaussian Naive Bayes, Bernoulli Naive Bayes, Linear SVM, Logistic Regression, and Gradient Boosting in conjunction with exploratory assessment of data. The findings show different accuracy rates;Random Forest, Gradient Boosting, Linear SVM, and Gaussian Naive Bayes all show an impressive 88% accuracy. AdaBoost and Logistic Regression reach 75%, but Bernoulli Naive Bayes comes in lower at 62%. The comprehension of computational interconnections is improved with correlation matrices. This thorough analysis provides important insights for academics, policymakers, and stakeholders in the field of child cybersecurity by exposing common cyber threats targeting minors and highlighting the role that algorithms for learning may play in detecting and averting these risks for environmental sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Dynamic Price Management System in a Distributed Cloud Environment

Dynamic Price Management System in a Distributed Cloud Envir...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Gurjapna Anand Priyanka Vashisht Simar Preet Singh Department of Computer Science and Engineering Amity University Haryana India School of Computer Science and Technology Bennett University Greater Noida Uttar Pradesh India

With the increase in the number of e-commerce applications on the internet, the demand and supply of commodities have increased. Though a static pricing system can be used in today's evolving market, prices of a commodity ought to be dynamically updated to improve revenues. The main challenge is to design a system dealing with real-time price updating based on the analysis of the available and complex data. The fundamental challenge is to design a dynamic price management system that meets the demand of customers and the availability of a commodity to maximize revenues with minimum cost and time involvement. In this paper, a Dynamic price management system (DPMS) for a flexible and connected smart nation has been designed to control and monitor the pricing of a commodity from selling platforms like retail, wholesale, the company's website, and e-commerce. Enormous pricing management system models have been researched, but in this paper, our objective is to provide a flexible, versatile, and intellectual pricing management system that handles the pricing of a commodity through a single interface for all the selling platforms. Smart pricing is the dynamic adjustment of prices or repricing of a commodity based on the market demand for a product. This model simplifies the vendors' problem of controlling a homogenous commodity's price component on different selling platforms like retail, wholesale, and e-commerce for a fully connected smart nation.

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Grid-Assisted Federated Learning in Vehicular Crowdsensing: A Mobility-Aware and Probabilistic Vehicle Selection Approach

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IEEE Transactions on Vehicular technology 2025年

作者： Chang, Xing Obaidat, Mohammad S. Ma, Jingxiao Xue, Xiaoping Tongji University College of Electronic and Information Engineering Shanghai201804 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India

Recently, Federated Learning (FL) has been advanced for Vehicular Crowdsensing (VCS), termed F-VCS, to enable collaborative learning without sharing private data, thus promoting privacy. However, the mobility of Intelligent Connected Vehicles (ICVs) leads to dynamic changes among FL participants, becoming one of the main challenges limiting F-VCS performance, as these dynamics can affect model training stability and accuracy. Current research primarily addresses these dynamics through ICV selection and resource optimization mechanisms. However, due to the lack of an effective method for characterizing ICV mobility, these approaches often rely on making binary judgments based on certain constraints (e.g., learning delay) rather than comparing the ICVs' mobility characteristics. As a result, they may not yield the most efficient outcomes for adapting to vehicular network dynamics, affecting overall system performance. This paper introduces a Grid-Assisted FL framework for VCS, named VCS_GAFL, which measures ICV mobility using a grid-cell model. Unlike traditional binary selection methods, our approach utilizes participation probabilities to capture the mobility differences among individual ICVs, guiding more precise and effective ICV selection and enhancing the VCS system's overall performance and adaptability. Specifically, we introduce an MMP-V model to predict ICV participation probabilities in F-VCS tasks. We then derive expressions for the impact of ICV participation probabilities on expected FL loss and ineffective energy consumption and formulate an optimization problem to jointly optimize ICV participation probabilities, wireless resource allocation, and ICV selection to minimize these impacts. By solving this problem, we derive a closed-form solution for wireless resource allocation and propose a probability-guided ICV selection strategy. Experimental results demonstrate that VCS_GAFL significantly enhances global accuracy, highlighting its potential and valu

关键词： Resource allocation

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Predictive Modelling of Economic Indicators using Machine learning and Data Analytics

Predictive Modelling of Economic Indicators using Machine le...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Akshita Singh Ramendra Singh Davendra Kumar Chauhan Ashok Kumar Shrivastava Haider Alabdeli Amity International School Noida India Department of Computer Science (IoT) Raj Kumar Goel Institute of Technology Ghaziabad India Department of Electrical & Electronics Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science Engineering Amity University Gwalior India The Islamic University Najaf Iraq

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

This study defines the use of machine learning models to predict car production volume for better planning and correspond them with market desirability. This work assesses three different models: Artificial Neural Network (ANN), Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), using an extensive dataset composed of 10,332 historical production volume records over the past ten years accompanied by raw material prices, energy costs along with labor cost attributes as well as experiments will market demand, seasonal trends economic indicators. The dataset was preprocessed with some techniques like handling of missing data, normalization and also encoding for categorical variables to make it strong. The models were trained and tested using a 70-30 training - test split, evaluated on different performance metrics such as accuracy, precision and other criteria. The bestcase accuracy of 97.75 % was achieved by the ANN, hence showing its capability to capture complex non-linear relationships within data. The SVM reached 93.40% accuracy, much stronger with high precision and recall tradeoff post update to the split point. The LSTM model providing an accuracy rate of 91.22%, considering it was best suited for handling sequential data. The above results have shown the ANN model better accuracy in predictions and because of this, we can use an ANN for future demand prediction efficiently. This has valuable implications for car manufacturing and is a good direction to elaborate on predictive models in the future.

关键词： Support vector machines Accuracy Biological system modeling Production Machine learning Predictive models Data models Automobiles Forecasting Long short term memory

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Optimizing Fresh Food Supply Chains: Leveraging 3PL for Carbon Reduction and Profit Maximization - A Game-Theoretic Analysis

Optimizing Fresh Food Supply Chains: Leveraging 3PL for Carb...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Anil Dhanda Divya Aggarwal Lovely Jain Priyansh Tyagi Mandeep Mittal Abhay Bansal Department of Mathematics Amity Institute of Applied Sciences Amity University Noida Uttar Pradesh India Department of Mathematics School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Mounting pressure on governments to reduce carbon emissions has forced them to impose regulations on supply chain members. Carbon cap-and-trade regulation has been widely adopted as a means to reduce carbon emissions. A fresh food supply chain involving a Manufacturer, 3PL, and a retailer is introduced. The 3PL is used by the manufacturer for its logistics because of its expertise, resources, and sustainable practices, Also many third-party logistics companies(3PL) offer refrigerated logistics services to the retailer and the 3PL has an overabundance of carbon permits. Trading of permits occurs and the impact on the retail price, the price of refrigerated logistics services in different carbon trading options, without carbon trade, inner carbon trade, and inner and outer carbon trade is studied. The prices are optimized through a game-theoretic approach, we model the trading of carbon permits as a leader-follower game with the 3PL as leader and Retailer as followers. The items are assumed to be of imperfect quality when delivered to the retailer by the 3PL.

关键词： Costs Supply chains Government Carbon dioxide Emissions trading Games Regulation

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Data Visualization in Transforming Raw Data into Compelling Visual Narratives

Data Visualization in Transforming Raw Data into Compelling ...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Prithu Sarkar Yadavalli Devi Priya Pravina B. Patel Prasanta Chatterjee Biswas Sreenivasulu Arigela Sravanthi Sallaram Amity School of Communication Amity University Kolkata Computer Science and Engineering Vignan's Institute of Information Technology Duvvada Vishakapatanam Andhra Pradesh Department of Computer Science Ganpat University Mehsana Gujarat India CDOE Parul University Vadodara Mohan Babu University Tirupati Andhra Pradesh Department of Computer Science and Engineering St.Martin's Engineering College

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

One of the main ways to evaluate information is through visual learning, which traditionally combines textual reading with visual aids like graphical representations. However, separating the visual method of learning to distinguish between text and visuals has been suggested in light of advancements in learning styles. The ability to swiftly create complex visual information and inexpensively distribute it digitally is another way that technology has improved visual presentation. The purpose of this research paper is to conduct a detailed investigation of the Virtualization of data, storytelling, and Data Visualization’s Narrative, understand how Instruments and methods are used in visualization, and select the appropriate tool for presenting visual data stories and upcoming projects. To address this purpose, this research has reviewed a wide range of different journals and articles to gather information about data visualization, storytelling, and data narratives. Based on the analysis, this research paper finds an all-encompassing perspective on the visual data narrative process, incorporating the phases of discovering insights (data exploration), developing these insights into a tale (creating a story), and sharing this story with others (telling a story). Through the pursuit of these novel research directions, visualizations can facilitate more potent data storytelling.

关键词： Visualization Quantum computing Instruments Data visualization Market research Virtualization Business

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Machine Learning in Fraud Detection for Financial Sustainability in Credit Card 2nd

Machine Learning in Fraud Detection for Financial Sustainabi...

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2nd International Conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Kaur, Harleen Chauhan, Ritu Shamsi, Yezdanul Haque Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031717284

In today’s financial world, various technologies have been emerged and with the evolution in the technologies it has led to the increase in the fraud cases. The sustainable growth of businesses and the financial markets are severely harmed by financial statement fraud. Consequently, it is crucial and vital to build an efficient methodology to identify financial statement fraud in businesses. Usage of credit card are very frequent these days and due to loopholes in the system fraud cases in credit card is rising which causes huge financial losses. In this paper the focus is on credit card fraud detection in real world with the help of machine learning. In this case Logistic Regression, Decision tree method and SVM (support vector machine) are applied on credit card fraud detection for knowledge discovery among the data. The applied methods can detect the fraudulent transactions with highest accuracy. However, the results are predicted using evaluation metrics such as accuracy, error rate, specificity, sensitivity, and ROC curves are calculated to assess each model's performance. Hence, the predicted results suggested that logistic regression is the most accurate model for prediction of fraud datasets in financial sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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