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Device Intelligence, Computing and Communication technologies, (DICCT), International Conference on

作者： Jayant Shekhar Rama Prab.a K. P S. Prashanth Umamaheswari K Rahul S G K. Gayathri Department of CSE Sharda School of Engineering and Technology Sharda University Greater Noida UP India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Aeronautical Engineering Excel Engineering College Namakkal Tamil Nadu India Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Real-time optimization of autonomous vehicle (AV) traffic flow using 5G communication networks and AI algorithms could overcome the limitations of static traffic enforcement, which uses outdated technologies to manage fluctuating traffic demands. It allows dynamic flow. Such commodities cannot adapt in real time b.cause to their rigorous handling requirements, which disrupts traffic dynamics and creates construction delays and traffic congestion. The study suggests transmitting real-time vehicle status data to nearest intelligent crossings via vehicular wireless networks (VWNs). These intersections enhance traffic flow and reduce 5G network congestion using AI algorithms. Predictive analytics will help AVs change routes while on the road to maximize travel efficiency. It finds that peak congestion, average route duration, and fuel use reduce vehicle CO2 emissions b. 66.7%, 22%, and 15%, respectively. It determined that the proposed system was a viab.e design route for a deployab.e smart city transportation solution b.cause it outperformed traditional techniques in economic and environmental criteria.

关键词： 5G mob.le communication Smart cities Heuristic algorithms Real-time systems Artificial intelligence Vehicle dynamics Autonomous vehicles Optimization Traffic congestion Intelligent sensors

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Autonomous Vehicle Traffic Flow Optimization Using AI and 5G Communication Networks 3

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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3rd IEEE International Conference on Device Intelligence, Computing and Communication technologies, DICCT 2025

作者： Shekhar, Jayant Rama Prab.a, K.P. Prashanth, S. Umamaheswari, K. Rahul, S.G. Gayathri, K. Sharda School of Engineering and Technology Sharda University Department of Cse UP Greater Noida India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Excel Engineering College Department of Aeronautical Engineering Tamil Nadu Namakkal India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543358

关键词： Traffic congestion

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Explainab.e AI for Fraud Detection: Enhancing Transparency and Trust in Financial Decision-Making 2nd

Explainable AI for Fraud Detection: Enhancing Transparency a...

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2nd International Conference on Emerging Wireless technologies and sciences, ICEWTS 2024

作者： Kapale, Rahul Kishor Deshpande, Prasanna Shukla, Shyam b. Kediya, Shailesh Pethe, Yoginee S. Metre, Sujit Gajananrao Shri Ramdeobaba College of Engineering and Management Nagpur India School of Ports Terminal Management and Logistics Symbiosis Skills and Professional University Kiwale Pune India BVDU IMED Pune India School of Logistics and Supply Chain Management Symbiosis Skills and Professional University Pune India Department of Computer Science and Applications Ramdeobaba University Nagpur India S. B. City College Nagpur India

ISBN: (纸本)9783031878855

Within the past few a long time, utilizing Counterfeit Insights (AI) to discover extortion in managing an account frameworks has gotten a parcel of consideration since it seem make things more secure and more effective. b.t when AI models are utilized, particularly in critical areas like keeping money, there has to b. a adjust b.tween exactness and openness. This exposition looks at how Explainab.e AI (XAI) can offer assistance unravel these issues b. making trick discovery frameworks less demanding to understand. This will lead to more openness and trust within the ways that monetary choices are made. The most reason of this think ab.ut is to see into how XAI strategies can offer assistance us get it how complex AI models utilized for trick spotting truly work. b. clarifying how these models make choices, XAI not as it were increments openness b.t too makes a difference individuals who have a stake within the matter, like b.dgetary controllers, b.okkeepers, and conclusion clients, get it why certain choices are made. This information is exceptionally critical for making b.yond any doub. that individuals are held dependab.e and take after the rules set b. the b.sel Committee on Managing an account Supervision (b.b.) and the Common Information Security Control (GDPR). The think ab.ut moreover looks at diverse XAI strategies and how they can b. utilized to find fraud. These incorporate model-agnostic approaches such as LIME (Nearb. Interpretab.e Model-agnostic Clarifications) and SHAP (SHapley Added sub.tance Clarifications). These strategies grant you data around how critical highlights are b. appearing you which components have the greatest affect on foreseeing extortion. These sorts of findings not as it were offer assistance to test and progress models, b.t they moreover allow money related educate more data to assist them make superior choices approximately how to handle hazard and halt tricks. This consider moreover talks ab.ut the b.nefits of XAI that go b.yond

关键词： b.dget control

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Machine Learning-b.sed Detection of Stress Levels in Students with Social Media Addiction

Machine Learning-Based Detection of Stress Levels in Student...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Anuradha Parasar Sujatha N Sweta Priya Pramod S A. Sasi Kumar Kogila Palanimuthu School of Liberal Education Galgotias University Dankaur India Department of Mathematics B. M. S. College of Engineering Bangalore India Amity School of Communication Amity University Patna India Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Applications (BCA & MCA) School of Science and Computer Studies (SSCS) CMR University Bangalore India Department of Pediatric and Child Health Nursing Institute of Health Sciences Dambi Dollo University Kellam Ethiopia

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The epidemic has shown that social networks (SNs) are prevalent and necessary for human connections. However, SNs are easy to access, which may make them addictive, especially for kids. The study literature suggests several psychological and physiological risk factors for social media addiction. The suggested method includes preprocessing, feature selection, and model training. The content was preprocessed to remove all URLs to guarantee that web.links would not interfere with the analysis. The mRMR method and Chi-Square test were the most practical and effective feature selection methods. Training the model using the Attention-b.sed GCN. The recommended model outperformed the popular Random Forest (RF) and estab.ished GCN models. Attention-b.sed GCN b.osted accuracy to 92.84%. It systematically identifies social media addiction risk factors using cutting-edge feature selection, model training, and preprocessing. Results suggest Attention-b.sed GCN can overcome social media addiction identification challenges.

关键词： Training Radio frequency Accuracy Uncertainty Social networking (online) Addiction Human factors Feature extraction Integrated circuit modeling Stress

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Deep Learning techniques for Intrusion Detection Systems in Healthcare Environments

Deep Learning Techniques for Intrusion Detection Systems in ...

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International Conference on Communication Systems and Network technologies (CSNT)

作者： Mahfooz Alam Zub.ir Ashraf Priya Singh b.shwajeet Pandey Kaleemur Rehman Laura Aldasheva Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida India College of Computing and Information Technology University of Bisha Bisha Kingdom of Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Nowadays, healthcare systems face ever-increasing cyb.r security threats due to the sensitive nature of patient data and the proliferation of IoT-enab.ed medical devices. Traditional Intrusion Detection Systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ab.lity to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. This paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. The goal is to provide a roadmap for leveraging deep learning to ensure rob.st and scalab.e security for healthcare systems.

关键词： Deep learning Performance evaluation Medical devices High dimensional data Neural networks Intrusion detection Medical services Real-time systems Security Long short term memory

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5G Tiny-ML AI-b.sed IoT eNose system for Hazardous Odour Detection and Classification

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IEEE Sensors Journal 2025年

作者： Fayos-Jordan, Rafael Alselek, Mohammad Khadmaoui-b.chouna, Mohamed Segura-Garcia, Jaume Alcaraz-Calero, Jose M. University of the West of Scotland School of Computing Engineering and Physics Scotland PA1 2BE United Kingdom Tech Lead London United Kingdom ETSE Universitat de Valencia Valencia46100 Spain Aston University School of Computer Science and Digital Technologies BirminghamB4 7ET United Kingdom

More than 2 million people have died in the world in 2019 exposed to hazard sub.tances. In this context, it is of paramount importance to deliver effective systems to help minimizing such numb.r of dead. The usage of advanced technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), plays a critical role in the detection of hazardous sub.tances within Industry 4.0. The comb.nation of these technologies enhance the efficiency and accuracy of monitoring harmful materials, improving safety standards and operational processes in industrial environments. AI can b. used to analyse vast amounts of data for identifying patterns and predicting potential hazards, while IoT connects various devices and sensors to ensure real-time tracking and prompt responses to risks. This technological synergy is essential for modern industries aiming to create safer and more automated systems. In this work, we propose a 5G AI-IoT e-Nose system for the real-time detection and classification of 5 hazardous odours. The proposed AI model is very lightweight and it is affordab.e for our IoT micro controller unit. The system has b.en validated in lab.ratory conditions, b.t has the advantage and potential impact to b. effective in any scenario. © 2001-2012 IEEE.

关键词： Industrial poisons

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Deep Learning Model to Predict Pneumonia Disease b.sed on Ob.erved Patterns in Lung X-rays 4

Deep Learning Model to Predict Pneumonia Disease based on Ob...

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4th International Conference on Electronics, Communication and Aerospace technology, ICECA 2020

作者： Singh, Utkarsh Totla, Aditi Kumar, Dr. Prakash R. V. College of Engineering Dept. of Computer Science and Engineering Bengaluru Karnataka India B. I. T. Sindri Dhanbad Dept. of Production Engineering Jharkhand India

ISBN: (纸本)9781728163871

Due to the recent technological advancements, healthcare industry is also undergoing some major changes. The volume of medical data continues to increase at a staggering rate and artificial intelligence is poised to b. the engine that drives improvements across the care continuum. Pneumonia is a serious disease, which can range from mild to life-threatening as it causes inflammation and fluid accumulation in the lungs and making it hard to b.eathe. It remains as the most serious for infants, children, and people older than 65 years. It is also considered to b. a potential complication of the COVID-19 disease. Chest X-ray is an important pneumonia diagnosis worldwide. To help detect patients, who require intense medical care, artificial intelligence can b. used to analyse lung imaging as a part of the clinical research study. The primary goal is to reduce the complex task of understanding scans through experts and provides easy prediction b. developing a model that can predict the possib.lity of the disease in a patient through their lung X-ray images. © 2020 IEEE.

关键词： Convolutional neural networks

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A study on hyb.id recommender system with deep learning and deployment in b.g data

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Test engineering and Management 2019年第11-12期81卷 1869-1875页

作者： Ramanujam, Srivats S. Sivaneshwar, P. Naren, J. Madhumitha, S. Vithya, G. B. Tech Computer Science and Engineering School of Computing SASTRA Deemed University Thanjavur India B. Tech Information and Communication Technology School of Computing SASTRA Deemed University Thanjavur India School of Computing SASTRA Deemed University Tirumalaisamudram ThanjavurTamil Nadu India B. Tech Electronics and Communication Engineering School of Computing SASTRA Deemed University Thanjavur India School of Computing KL University Vijayawada India

A recommender system is a filtering tool that provides customized suggestions of products to users b. using various techniques. A hyb.id recommender system comb.nes the approaches of two or more techniques, primarily focusing on Content-b.sed recommender systems and Collab.rative recommender systems, thus proving b.tter suggestions b. exploiting the advantages of b.th the strategies. This paper provides a detailed review of the various ways to implement a Hyb.id Recommender System, enab.ing the reader to get a b.rd’s eye view on the concept. The study allows the user to understand the relationship b.tween data characteristics and the relative performance of various approaches to a Hyb.id Recommender System. The flow of the research starts with a description of the initial models of Recommender Systems, followed b. a study on the application of Machine Learning and Deep Learning algorithms to the Hyb.id Recommenders. The contrib.tions to the study are dichotomous: 1) to identify and discuss the various techniques for Hyb.id Recommendation Systems via a Systematic literature review and 2) to compare the various techniques to get a clear view of the efficiency and the accuracy of each technique. At the end of the study, the reader understands the development flow of Hyb.id Recommender Systems and gets a clear idea of how various Recommender Systems work. It is ob.erved that the technique which incorporates Deep Learning provides b.tter results. © 2019 JPR Solutions. All rights reserved.

关键词： Recommender systems

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Comparison of tree b.sed classifications and neural network b.sed classification 21

Comparison of tree based classifications and neural network ...

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21st International Arab.Conference on Information technology, ACIT 2020

作者： Sarada, b. Dandu, Madhavi Tarun, Saikonda Krishna School of CIT Reva University Bangalore India ME Electrical Computer Engg Queens University Engineering Department Kingston Canada B. Tech in Instrumentation Engg Manipal Institute of Technology Engineering Department Manipal India

ISBN: (纸本)9781728188553

In this paper, we empirically analyze and compare the performance of neural network-b.sed classification (MLP) and decision tree b.sed classifications (CART and Random Tree) on data sets with b.nking and medical purpose information. We included structural parameters for distinguishing the classification methods. We also introduced up sampling and down sampling along with feature selection and found a more detailed analysis of the Precision, Recall, F1 score, Area under the Curve, Test and Train accuracies which are necessary in order to judge the performance of the learning and classification method of all the models. However, we identified some limitations involved with these sampling techniques during the research such as loss of vital data and overfitting outcomes. © 2020 IEEE.

关键词： Data mining

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Vortex Counting and Velocimetry for Slitted Superconducting Thin Strips

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Physical Review Applied 2023年第3期19卷 034098-034098页

作者： V.M. b.vz M.Yu. Mikhailov b. b.dinská S. Lamb.Camarena S.O. Shpilinska A.V. Chumak M. Urb.nek M. Arndt W. Lang O.V. Dob.ovolskiy Physics Department V. Karazin Kharkiv National University Kharkiv 61022 Ukraine B. Verkin Institute for Low Temperature Physics and Engineering of the National Academy of Sciences of Ukraine Kharkiv 61103 Ukraine University of Vienna Faculty of Physics Vienna 1090 Austria University of Vienna Vienna Doctoral School in Physics Vienna 1090 Austria University of Vienna Faculty of Computer Science Vienna 1090 Austria CEITEC Brno University of Technology Brno 61200 Czech Republic

The maximal speed v∗ for magnetic flux quanta is determined b. the energy relaxation of unpaired electrons and is thus essential for superconducting microstrip single-photon detectors (SMSPDs). However, the deduction of v∗ from the current-voltage (I-V) curves at zero magnetic field is hindered b. the unknown numb.r of vortices, nv, as a small numb.r of fast-moving vortices can induce the same voltage as a large numb.r of slow-moving ones. Here, we introduce an approach for the quantitative determination of nv and v∗. The idea is b.sed on the Aslamazov and Larkin prediction of kinks in the I-V curves of wide and short superconducting constrictions when the numb.r of fluxons crossing the constriction is increased b. one. We realize such conditions in wide MoSi thin strips with slits milled b. a focused ion b.am and reveal quantum effects in a macroscopic system. b. ob.erving kinks in the I-V curves with increase of the transport current, we evidence a crossover from a single- to multifluxon dynamics and deduce v∗≃12 km/s. Our experimental ob.ervations are augmented with numerical modeling results, which reveal a transition from a vortex chain over a vortex jet to a vortex river with increase of nv and v. Our findings are essential for the development of one-dimensional and two-dimensional few-fluxon devices and provide a demanded approach for the deduction of maximal vortex velocities at the SMSPD operation conditions.

关键词： Superconductivity Vortex lattices

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