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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Gloria Jeyaraj, Josephine Pon Uthayakumar, P. Muthuraman, M.S. Surendran, S. Bhuvana, R. Sheeba, M.R. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Mathematics PSNA College of Engineering and Technology Tamilnadu Dindigul India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Dept of Computer Science AM JAIN College Tamilnadu Chennai India Department of Computer Science Government Arts and Science College for Women Thoothukudi District Sathankulum India

ISBN: (纸本)9798350305418

India's compulsory primary education emphasizes mathematics. Math helps boost students' logical and rational thinking. It supports our nation's scientific development and modern students' scientific and cultural growth. Students learn elementary school mathematics by facing a lot of data, thinking, and checking results. Teaching materials are not the only educational technology in the primary school mathematics teaching reform. Math teachers have started to focus on the need for more teaching resources and new tools and technology. Due to the lack of complete and systematic teaching materials, primary school mathematics technical resource development has new goals and requirements. Resource mining with fuzzy clustering is great. Fuzzy clustering algorithm can introduce fuzzy mathematics into cluster analysis, use membership function clustering to determine sample affinity, and analyze and aggregate a large number of text resource information into meaningful classes or clusters for primary school mathematics education technology resource mining and development. An optimization strategy for cluster number is given to meet classic fuzzy clustering algorithm initialization requirements. The optimized fuzzy clustering algorithm optimizes the initial clustering center, overcoming its sensitivity to the beginning value. Thus, the data set is fuzzy-divided unsupervised. This article analyzes created resources to inform primary school campus mathematics education development. © 2023 IEEE.

关键词： educational technology Fuzzy clustering algorithm primary school mathematics resource development

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Machine Learning Approaches for IC Fault Localization and Diagnosis 9

Machine Learning Approaches for IC Fault Localization and Di...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Saidala, Ravi Kumar Dwivedi, Yagya Dutta Aparna, P. Prasad, S. J. Suji Dineshkumar, S. Hemalatha, R. Alliance University Department of Computer Science Engineering Karnataka Bangalore560076 India Institute of Aeronautical Engineering Department of Aeronautical Engineering Telangana Hyderabad500043 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Puduvoyal601206 India Kongu Engineering College Department of Electronics And Instrumentation Engineering Tamil Nadu Erode638060 India M.Kumarasamy College of Engineering Department of Electrical And Electronics Engineering Tamil Nadu Karur639113 India St. Joseph's College of Engineering Omr Department of Computer Science And Engineering Tamil Nadu Chennai600119 India

ISBN: (纸本)9798350365092

The conducted researchprovide a machine learning-driven method for thermal testing of integrated circuits. This method involves comparing the current temperature distribution with a reference distribution that is suitable for the energy state of the system. Furthermore, the research explores a methodology for positioning temperature sensors. This placement approach is advised for utilization in the identification and troubleshooting of issues with integrated circuits (ICs). Our failure localization method utilizes the average temperatures in several sub- areas of the integrated circuit, together with the interconnections between those temperatures. This research provides the simulation results for the sensor placement technique, fault detection, and localization. The results on the productivity of the testing approach are presented utilizing statistical methodologies. © 2024 IEEE.

关键词： Timing circuits

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An Integrated Approach for Analysis of Electronic Health Records Using Blockchain and Deep Learning

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Recent Advances in computer science and Communications 2023年第9期16卷 1-10页

作者： Singhal, Pooja Gupta, Shelly Deepak Singh, Jagendra ABES Engineering College Ghaziabad Dr. A.P.J. Abdul Kalam Technical University Lucknow India School of Computer Science Engineering and Technology Bennett University Greater Noida India

Blockchain is used to assess health records digitally, preserving the security and immutability of the records. The goal of this study is to make it easier for patients to access their medical records and to send them alert messages about important dates for their check-ups, healthy diet, and appointments. To achieve the above-mentioned objective, an integrated approach using Blockchain and Deep learning is initiated. The first approach is Hyperledger Fabric in Blockchain, i.e., private Blockchain, for storing the data in the medically documented ledger, which can be shared among hospitals as well as Health organizations. The second approach is incorporated with a deep learning algorithm. With the help of algorithms, we can analyse the ledger, after which an alert i.e., consultation, health diet, medication, etc., will be sent to the patient’s registered mobile device. The proposed work uses nine features from the dataset;the features are identification number, age, person gender, disease, weight, consultation date, medication, diagnosis, and diet specification. The study is conducted with several features to give accurate results. The integrated model used in this suggested piece of work automates the patient's alert system for a variety of activities. In terms of precision, recall, and F1 score, testing data demonstrate that the LSTM performs better than the other models. By working together with the calendar software on android mobile devices, alert systems can be improved in the future. © 2023 Bentham science Publishers.

关键词： Blockchain

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Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

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computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT *** proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBRMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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Hybrid arithmetic optimization algorithm with deep transfer learning based microarray gene expression classification model

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International Journal of Information technology (Singapore) 2024年第6期16卷 3923-3928页

作者： Gowri, B. Shyamala Nair, S. Anu H. Kumar, K. P. Sanal Department of Computer Science and Engineering Annamalai University Annamalai Nagar Tamil Nadu Chidambaram 608002 India Department of Computer Science and Engineering Faculty of Engineering and Technology Deputed To WPT Annamalai University Chennai India Department of CS RV Government Arts College Chengalpattu India

Microarray gene expression (MGE) data classification is a vital challenge in biomedical and genomics research, designed to understand the difficult relations among genes and numerous biological states. In this procedure, high-dimensional gene expression data is primarily pre-processed and converted to create it responsive to machine learning (ML) techniques. Feature selection and dimensionality reduction models are frequently used to remove the most appropriate gene signatures. medicine, and drug improvement, making it a vital element of recent genomics studies. Therefore, this manuscript presents a hybrid arithmetic optimization algorithm with a deep transfer learning-based MGE data classification (HAOADTL-MGEDC) model. The HAOADTL-MGEDC technique initially undergoes the conversion of gene expression data into image format. Then, the preprocessing of the images takes place using the median filtering-based noise removal and contrast enhancement approach. Besides, the feature extraction procedure is implemented by the usage of the capsule network (CapsNet) model. Meanwhile, the CapsNet hyperparameters can be optimally selected by the use of HAOA. Finally, the parallel bidirectional gated recurrent unit (BiGRU) methodology has been used for the classification of GE data. The experimental values of the HAOADTL-MGEDC technique have been tested on a benchmark dataset. Extensive simulation analysis inferred the higher of the HAOADTL-MGEDC technique compared to other DL approaches. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep Transfer Learning Gene Expression Hyperparameter Tuning Median Filtering Microarray

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Attribute Based Encryption in Healthcare Application 1

Attribute Based Encryption in Healthcare Application

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Suresh Kumar, S. Chidambaram, G. Vijayalakshmi, S. Dhayanandh, A.T. Computer Science and Engineering PSG College of Technology Coimbatore India

ISBN: (纸本)9781665460842

Data volumes are increasing due to the growth of technologies such as IoT, Cloud Computing, and mobile internet. Data encryption is the solution, which protects data privacy by giving specific access to encrypted data can be better understood using Attribute-based (ABE) encryption. When patients use web-based commercial systems to store their personal health data, the privacy of personal health records (PHRs) can be a major problem. When it comes to establishing policies to control access and protect data confidentiality, common access control systems, such as Work-Based Access Control, have major limitations. In this research work a novel healthcare application has been developed based on the ABE scheme approach. Advanced Encryption Standard (AES) algorithm and Secure Hash Algorithm (SHA) are used for the encryption and decryption processes in the ABE scheme. This allows only designated authorized personnel, such as a patient or their respective doctors, to access personal data recorded in medical records, with a vision to improve the privacy and security of user details. © 2022 IEEE

关键词： Data privacy

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An Optimal Deep Learning for Cooperative Intelligent Transportation System

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computers, Materials & Continua 2022年第7期72卷 19-35页

作者： K.Lakshmi Srinivas Nagineni E.Laxmi Lydia A.Francis Saviour Devaraj Sachi Nandan Mohanty Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyTelangana500075India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Engineering College of Engineering PunePuneMaharashtra411005India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related *** the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS *** the same time,prediction of short-term traffic flow is highly essential to manage the traffic *** to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow *** paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so *** presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow *** IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count *** addition,deep belief network(DBN)model is applied for the prediction of short-term traffic *** further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of *** results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

关键词： Cooperative intelligent transportation systems traffic flow prediction deep belief network deep learning vehicle counting

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Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI 2

Real-Time Enhanced GAN-Powered Intrusion Prevention System: ...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Sutherlin Subitha, G. Seldev Christopher, C. Wilfred Blessing, N.R. Sheeja Kumari, V. Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Nagercoil India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Saveetha School of Engineering Simats Univeristy Department of Computational Intellegence Chennai600025 India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

In an era of escalating cyber threats and evolving networks, the demand for robust intrusion prevention is paramount. Introducing the Real-Time Enhanced GAN-Powered Intrusion Prevention System (RT-EGAN-IPS), a state-of-the-art solution fortified with advanced AI. RT-EGAN-IPS harnesses the power of Generative Adversarial Networks (GANs) to revolutionize real-time intrusion detection. GANs, renowned for their data synthesis capabilities, excel at uncovering subtle anomalies and emerging threats within network data. Its real-time adaptability enables it to swiftly identify novel attack patterns by continuously learning from incoming network data. Deep reinforcement learning and predictive analytics empower RT-EGAN-IPS to not only detect intrusions but autonomously respond, bolstering network protection. Versatile in handling multi-modal data streams, from text logs to multimedia content, RT-EGAN-IPS thrives on a collaborative approach. Security experts collaborate with the system for real-time validation and feedback. Dynamic thresholding ensures responsiveness to evolving threats, supported by low-latency responses and an ensemble approach. This abstract unveils RT-EGAN-IPS, a holistic AI-driven network security paradigm, promising a new level of protection in the ever-changing realm of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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Two-Stage, Global-Local Approach for Cell Nuclei Segmentation in Histopathology Images 8

Two-Stage, Global-Local Approach for Cell Nuclei Segmentatio...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Shakya, Amit Saharan, Revat Gupta, Chetan Kumar, Rupesh Sharma, Lalit Naidu, Srivatsava Murala, Subrahmanyam Arora, Chetan Yamaha Motor Solutions Emerging Technology and Innovation Lab India Indian Institute of Technology Ropar Department of Biomedical Engineering India School of Computer Science and Statistics Trinity College Dublin Ireland Indian Institute of Technology Delhi Department of Computer Science and Engineering India

ISBN: (纸本)9798331531539

Effective management of high-resolution, and spatially wide contextual cues is fundamental to the accurate semantic segmentation. Traditional approaches like multi-resolution feature maps, and skip-connection are effective but require changes in the backbone architecture, restricting utilization of newer models and architectures for the problem. In this work we propose an architecture-agnostic, two-stage, global-local frame-work, called GoLo, for the semantic segmentation, which can use arbitrary semantic segmentation models within its two stages. We focus on segmenting cell nuclei in histopathology image analysis, where accurate segmentation of cell nuclei boundaries is one of the key issues. The proposed framework consists of first stage with Global and second stage with Local learning approach. The first stage is proposed to process the image globally and provide the coarse nuclei segmentation map. In the second stage, to process the image locally, coarse segmentation map and input image is first converted into patches. These patches are then fed as input to the second stage to get the fine- grained segmentation map. Both stages are trained with a combination of dice and binary cross entropy loss. To show the effectiveness of our approach, we test 4 state-of-the-art segmentation architectures (ACC-UNet, UCTransnet, Swin-UNet, and Vanilla U-Net), on 4 different benchmark datasets (MoNuSeg, CPM-17, CoNSep, and TNBC). We evaluate performance of each technique before and after using our framework. We report an average improvement of 4.82 % in mIoU, and 4.52% mDSC score, across techniques, and datasets. © 2024 IEEE.

关键词： Semantic Segmentation

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Development of a Movie AI Question Answering System Based on Knowledge Graph 4

Development of a Movie AI Question Answering System Based on...

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4th International Signal Processing, Communications and engineering Management Conference, ISPCEM 2024

作者： Xu, Yihan Xie, Nan Hu, Xinjun Chen, Weimin College of Computer Science and Technology Zhejiang University of Water Resources and Electric Power Zhejiang Hangzhou China College of Mechanical and Electrical Engineering China Jiliang University Zhejiang Hangzhou China

ISBN: (纸本)9798331528676

With the increasing influence of the internet, information retrieval technology needs to be continuously upgraded. The development of the internet drives the transformation of the film industry, with an increase in the number of online movies, a flourishing of professional film websites and forums, and an increasing influence of online comments. There are still shortcomings in the relationship between the internet and film evaluation, and further exploration is needed. The development of this system adopts a front-end and back-end separation mode, with Vue technology architecture used in the front-end and Django framework used in the back-end. The knowledge graph can organize and express movie information, providing data support for intelligent question answering and comment systems. The system constructs a graph by collecting movie data, achieving storage, management, and querying, providing intelligent and personalized services, and designing movie review functions to provide comprehensive information and evaluation references;The question answering function is implemented through natural language processing technology (NLP) and graph database technology, which can recognize the user's query intention and retrieve relevant information, generate accurate and concise answers. Its core module is the movie AI question answering function based on Neo4j. After the user raises a question, the comment system will parse the question and construct a Cypher query statement. After execution, the result will be returned and displayed to the user, achieving intelligent answers and personalized services. In system design, the connection between the movie AI question answering module and the Neo4j database is crucial. This connection enables effective communication and information exchange between user questions and movie knowledge graphs, providing users with convenient and accurate movie related information. Through deep integration with the Neo4j database, the system can bette

关键词： Information management

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