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检索条件"机构=Computer Science and Engineering I.T.S Engineering College"

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An implementation of the DCF-MAC protocol based VANEt 4

An Implementation of the DCF-MAC protocol based VANET

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4th international Conference on smart systems and inventive technology, iCssit 2022

作者： sahayaraj, J. Martin Ramesh, t. suresh, J. Pratha, s. Jaya Department of Electronics and Communication Engineering Sri Indu College of Engineering and Technology T.S Hyderabad501510 India Department of Computer Science and Engineering R.M.K Engineering College Kavaraipettai Tamilnadu Thiruvallur601206 India Department of CSE CARE College of Engineering Tamil Nadu Trichy620009 India Department of Computer Science and Engineering Annamalai University T.N Chidambaram608 002 India

ISBN: (纸本)9781665401180

VANEt (Vehicular Ad Hoc Network), a rapidly expanding wireless network using multiple routing systems to provide vehicle network connectivity interconnection between vehicles is rapidly changing because of the frequent movement of VANEts. this frequently results in network topologies, resulting in path errors on these networks. Furthermore, the movement-induced track failure problem is a key priority in VANEt to provide efficient network access to vehicles with an optimal Qos without disrupting service. this paper has randomly roamed the performance of various ad hoc mobile node routing protocols that have not been reported in previous studies of iEEE 802.11g VANEt based on OFDM and that have a high data rate of 54 Mbps. this work showed the implementation of the DCF-MAC protocol to load VoiP traffic with OPNEt Modeler 14.5. © 2022 iEEE

关键词： Vehicular ad hoc networks

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Real time social distance detection using Deep Learning 1

Real time social distance detection using Deep Learning

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1st international Conference on Computational intelligence and sustainable engineering solution, CisEs 2022

作者： salagrama, shailaja Kumar, H. Hemant Nikitha, R. Prasanna, G. sharma, Kanhaiya Awasthi, shashank University of the Cumberland's Information Technology WilliamsburgKY United States Anurag University Computer Science and Engineering Hyderabad India Anurag University Computer Science and Text Engineering Hyderabad India G.L. Bajaj I.T.M. Computer Science and Engineering Greater Noida India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

in the Covid-19 pandemic, if residents do not take action to prevent the virus from spreading, the process of softening the curve of the coronavirus will be complicated in the face of the worldwide Covid-19 scenario. Without vaccination, the only method to combat the disease is social isolation. the proposed system employs the You Only Look Once, Version 3 (YOLOv3) object detection model to identify persons in the background and bind boxes around them, and assign iDs for in-depth tracking of recognized people. this study focuses on public space surveillance and determining whether or not people maintain social distance as per Covid-19 guidelines. YOLOv3 is an efficient tracking method that produces positive results with a moderate mean Average Precision(MAP) and Frame Per second (FPs) score for monitoring community deviations in real-time. in this study, YOLOv3 is used for object capture, and the OpenCV library is used for image processing. Proposed work is helpful in areas where big crowds are expected, such as retail malls, movie theatres, railway stations, airports, and public places. © 2022 iEEE.

关键词： Object detection

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Performance Evaluation and Comparison of Clustering Algorithms for social Network Dataset 6

Performance Evaluation and Comparison of Clustering Algorith...

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6th international Conference on Contemporary Computing and informatics, iC3i 2023

作者： Ramakrishna Kumar, M.V.N.M. sharma, Vibhoo Gupta, Keshav Jain, Abhishek Priya, Bhanu Prasad, M.s.R. College of Engineering Department of Electrical and Communication National University of Science and Technology Muscat Oman Hindustan College of Science & Technology Department of I. T. Uttar Pradesh Mathura India GRD Institute to Technology and Management Uttara Khand Dehradun India Uttaranchal University Department of C.S.E. Uttara Khand Dehradun India Teerthanker Mahaveer University Department of Computer Science & Engineering U.P. Moradabad India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350304480

Evaluation of the effectiveness of various clustering methods using an enhanced K-Mean algorithm is shown in this paper. Essential data mining techniques, such as clustering techniques, divide things into proper subgroups. Data can be described and analyzed using clustering. Compared with K-Means, Hierarchical, and E.M. Algorithms in this study is the improved K-Means Clustering Algorithm. the algorithms are used to compare the performance of the clusters based on how long it takes to assemble them. the enhanced K-Mean Algorithm performed much better results than traditional algorithms. Additionally, the Confusion Matrix is revealed. © 2023 iEEE.

关键词： Clustering Data Mining EM algorithm Hierarchical K-Mean

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Feature-based intrusion Detection system with support Vector Machine 1

Feature-based Intrusion Detection System with Support Vector...

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1st iEEE international Conference on Blockchain and Distributed systems security, iCBDs 2022

作者： Khodaskar, Manish Medhane, Darshan ingle, Rajesh Buchade, Amar Khodaskar, Anuja Pune Institute of Computer Technology Dept. of Information Technology Pune India MVPS's K.B.T. College of Engineering Dept. of Computer Engineering Nashik India Dept. of Computer Science & Engineering Naya Raipur India Mit School of Engineering Mit Art Design and Technology University Department of Computer Science & Engineering Pune India Sipna College of Engineering and Technology Dept. of Computer Engineering Amaravati India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

today billions of people are accessing the internet around the world. there is a need for new technology to provide security against malicious activities that can take preventive/ defensive actions against constantly evolving attacks. A new generation of technology that keeps an eye on such activities and responds intelligently to them is the intrusion detection system employing machine learning. it is difficult for traditional techniques to analyze network generated data due to nature, amount, and speed with which the data is generated. the evolution of advanced cyber threats makes it difficult for existing iDs to perform up to the mark. in addition, managing large volumes of data is beyond the capabilities of computer hardware and software. this data is not only vast in scope, but it is also moving quickly. the system architecture suggested in this study uses sVM to train the model and feature selection based on the information gain ratio measure ranking approach to boost the overall system's efficiency and increase the attack detection rate. this work also addresses the issue of false alarms and trying to reduce them. in the proposed framework, the UNsW-NB15 dataset is used. For analysis, the UNsW-NB15 and NsL-KDD datasets are used. Along with sVM, we have also trained various models using Naive Bayes, ANN, RF, etc. We have compared the result of various models. Also, we can extend these trained models to create an ensemble approach to improve the performance of iDs. © 2022 iEEE.

关键词： support vector machines

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A Distributed Data Mining and Cloud Analysis for Predictive Gas Level, Dynamic Booking, and smart Energy Optimization

A Distributed Data Mining and Cloud Analysis for Predictive ...

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2024 international Conference on Advances in Data engineering and intelligent Computing systems, ADiCs 2024

作者： sugumar, D. Ramadevi, R. Prasad, J. Mohankumar, N. Joy Winnie Wise, D.C. Velmurugan, s. Care College Of Arts And Science Department Of Commerce Tamil Nadu Trichy India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Saveetha Nagar Department Of Biomedical Engineering Tamil Nadu Chennai India Kpr Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Tamil Nadu Coimbatore India B.V. Raju Institute Of Technology Department Of Electronics And Communication Engineering Telangana India Rajalakshmi Institute Of Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India T.J.S. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

the state-of-the-art internet of things (iot) innovation known as the Autonomous Gas Reservoir Management system (AGaRMs) totally changes the most common way of overseeing gas supplies. AGaRMs uses sensors that monitor gas levels and a distributed architecture that forecasts patterns in gas use to ensure that gas refills occur at the right time. Complex Ai calculations examine verifiable information, current weather patterns, and usage patterns to estimate when gas will run out. this, then, prompts robotized booking sales to be transported off the supplier. Likewise, AGaRMs appeals to the savvy progression of energy, which empowers strong gas usage. the framework's flexibility and versatility make it reasonable for use in private and business settings, further developing production network productivity, reducing working expenses, and improving the client experience. the AGaRMs system offers a smart method for managing gas leaders that work with gathering effective power energy practices and streamlining gas provisioning processes. AGaRMs optimizes the gas supply chain and improves the user experience by catering to a wide range of residential and industrial applications with its flexible and scalable design. © 2024 iEEE.

关键词： internet of things

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Diagnosing Progressive Face Recognition from Face Morphing Using Vit technique through DL Approach

Diagnosing Progressive Face Recognition from Face Morphing U...

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Networking and Communications (iCNWC), international Conference on

作者： M.K. Mohamed Faizal s. Geetha A. Barveen Department of Computer Science and Engineering M.I.E.T. Engineering College Trichy Tamilnadu Department of Computer Applications University College of Engineering Trichy Tamilnadu

the face-morphing attack, which occurs in private, public, and governmental institutions, is one of the most well-known in today’s world. Face recognition systems tend to be vulnerable if the face images are manipulated with duplicate images. Manipulated images are combined with the original image so that the images look like legitimate ones. several face recognition studies are being conducted to determine whether the face images are manipulated. Using the DL algorithm, the face image is trained to attain the original and morphed face images by recognizing the face images. DL algorithms determine the images by classifying whether they are morphs or not recognizable to humans. in this paper, the foremost emphasis is on diagnosing the face recognition from those face-morphed images using the different DL techniques. Different DL techniques are effectively compared, where the Vit transformer attains improved accuracy when compared to Resnet, RNN, and CNN, respectively. this paper provides an overview of the various deep learning algorithms for detecting those face recognition images that focus on challenges and issues in the facial datasets from Face Recognition Kaggle dataset with training and testing image dataset. it determines the higher contrast in image efficiency and the evaluation of the face recognized images with an improved image.

关键词： training Deep learning image recognition Face recognition transformers Classification algorithms testing

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smartFit: Ai-Powered training Recommendations with Biometric integration 5

SmartFit: AI-Powered Training Recommendations with Biometric...

引用

5th iEEE Global Conference for Advancement in technology, GCAt 2024

作者： Parwej, Firoj suganya, A. Naresh, Burri Goud, Veer sudheer Kaliappan, s. Manikandan, G. Mandsaur University Department of Computer Science and Engineering Daulatpura Madhya Pradesh Mandsaur India Karpagam College of Engineering Department of Information Technology Tamilnadu Coimbatore India Cvr College of Engineering Department of Cse -CS Telangana Hyderabad India St. Martin's Engineering College Deptartment of Ai&ds Secunderabad India Lovely Professional University Division of Research and Development Delhi G.T.Road Punjab Phagwara India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350376685

this research introduces smartFit, a state-of-the-art technology that seamlessly integrates biometric data and Ai-powered training recommendations. smartFit monitors the user's pulse rate, oxygen saturation, and muscle activity in real time. subsequently, it implements machine learning algorithms to develop customized training regimens. this ongoing adjustment to the user's evolving fitness levels and objectives guarantees that the workout regimens are both highly effective and customized to their unique requirements. the proposed system is designed to optimize exercise efficacy, promote extended engagement in fitness activities, and improve user satisfaction. the novel method significantly enhanced the fitness ratings and overall satisfaction of participants in comparison to more conventional forms of exercise, as evidenced by a three-month pilot study. the results demonstrate the potential for smartFit's data-driven, individualized training recommendations to transform the fitness industry. © 2024 iEEE.

关键词： Adversarial machine learning

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Cloud-integrated Clean Fuel Generation for solar-Hydrogen Production with Wireless sensor Networks 7

Cloud-Integrated Clean Fuel Generation for Solar-Hydrogen Pr...

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7th international Conference on inventive Computation technologies, iCiCt 2024

作者： Naveenkumar, N. sridevi, V. Bharathi, R. Mishra, Nilamadhab Murugan, s. Velmurugan, s. Department of Computer Science And Engineering Namakkal Tamil Nadu Rasipuram India Academy of Maritime Education And Training Deemed To Be University Electrical And Electronics Engineering Tamil Nadu Chennai India Srm Institute of Science And Technology Faculty of Management Chennai Tamil Nadu Kattankulathur India Vit Bhopal University School of Computing Science And Engineering Madhya Pradesh India Saveetha Institute of Medical And Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India T.J.S. Engineering College Department of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

this research study proposes a novel system that integrates Wireless sensor Networks (WsNs) with cloud-based platforms to efficiently generate renewable energy and produce clean fuel. By enhancing the safety, efficiency, and reliability of solar power and hydrogen generation, the system integrates optimization strategies, data analysis, and real-time monitoring. Critical parameters such as temperature, sun irradiance, electrolysis efficiency, and hydrogen purity are monitored using WsNs, with data transmitted and processed via cloud techniques to identify patterns, outliers, and areas for improvement. Proactive decision-making and rapid adjustments are facilitated through remote monitoring and control capabilities, leading to maximized energy production and clean fuel output. security measures including encryption, access controls, and regular audits safeguard data privacy and integrity, while multiple sensor deployments, robust communication protocols, and a resilient cloud infrastructure ensure system reliability. this integrated approach offers an effective solution for advancing renewable energy generation and clean fuel production, contributing to environmental sustainability and a greener future. © 2024 iEEE.

关键词： Energy efficiency

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Effective & smart Urban Mobility system at Low Cost

Effective & Smart Urban Mobility System at Low Cost

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international Conference on Computational and Characterization techniques in engineering & sciences (CCtEs)

作者： Hari Jyothula Y Laxmana Rao K. Vara Prasad Chiriki Usha i Lakshmi Manikyamba Department of Computer Science and Engineering Aditya University Surampalem India Department of IT Vignan's Institute of Engineering for Women Visakhapatnam India Department of Computer Science and Engineering Avanthi Institute of Engineering & Technology I Department of Computer Science and Engineering Dadi Institute Of Engineering And Technology Department of Computer Science and Engineering University College of Engineering Science & Technology Hyderabad Jawaharlal Nehru Technological University Hyderabad India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

there is no established metric for measuring urban smartness at this time. Various writers use various metrics to assess smart city transportation systems. the assessment indicators for the transport system that were offered in the scholarly works were reviewed, and the most relevant and impactful indicators were chosen. A hierarchical assessment model for smart city transport systems is developed in this paper. there are five categories, or "factors," that the indications fall into. several indicators are assigned to each of the categories. this study used the hybrid MCDM (multi-criteria decision-making) method to evaluate urban transport systems and identify which of the given characteristics were most important. top Priority sorting based on ideal solution similarity (tOPsis), COPRAs, and simple Additive Weighting (sAW) were the multi-criteria assessment methods in use. the importance of the components and indicators was determined using an analytical hierarchy process (AHP), rankings (prioritization), and direct evaluation (experts evaluate each criterion as a percentage; total assessments of all criteria equal 100%). there were two phases to the evaluation and comparison of mobility systems: first, smart mobility systems were evaluated comprehensively according to the integrated importance of the indicators; and second, multi-criteria evaluations were conducted according to the indicators of every factor separately. Based on the amount of intelligence, a top city is determined and rated. Building a hierarchical model for evaluating smart transportation systems is the focus of this paper.

关键词： Costs smart cities Buildings Analytic hierarchy process time measurement smart transportation Weighted sum model sorting

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Design of Arrow shaped Microstrip Patch Antenna for X and Ku- Band Applications 1

Design of Arrow Shaped Microstrip Patch Antenna for X and Ku...

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1st international Conference on technologies for smart Green Connected society 2021, iCtsGs 2021

作者： Yuvaraj, D. Nageswara Rao, M.V. shanmuga priya, P. Naveneetha krishanan, s. Department of Computer Science Cihan University Kurdistan region Duhok Iraq Dept. of ECE GMR Institute of Technology RajamSrikakulam Andhra Pradesh India Department of Computer Science and Engineering M.I.E.T Engineering College Trichy India Department of Electronics and Communication Engineering SACS MAVMM Engineering College Madurai India

ISBN: (纸本)9781607685395

A small size printed patch antenna to cover applications of X and Ku band is suggested in this paper. the suggested antenna consists of an arrow shaped patch on front view of FR 4 substrate and a partial ground on the other side. initial design started with a rectangular patch of dimensions 10.7 mm x 7 mm. then it is modified into arrow shaped patch in order to improve the reflection coefficient at lower and higher *** measurement of the antenna is 12 x 15 x 1.6 mm3. the antenna parameters are optimized using FEM based HFss software. the operating range of frequencies include from 6.75 GHz to 18.96 GHz. the proposed antenna displays a good omnidirectional radiation pattern and maximum gain of 7.41 dBi at 17 GHz. simulated s11 parameters, VsWR, Gain and current distributions are offered in this paper and s11 simulated parameters are in good agreement with measured parameters. © the Electrochemical society

关键词： Arrow shaped Ku band Printedmicrostrip antenna X band

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