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7th International Conference on electronics, communication and Aerospace Technology, ICECA 2023

作者： Sudhakar, K. Mahaveerakannan, R. Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350340600

The convergence of the IoT and the Cloud makes our lives better by allowing for frictionless communication between humans and inanimate objects. Projecting analytics in the medical arena may help transform a sensitive healthcare one, and cutting-edge artificial intelligence technologies are increasingly making their way into the healthcare industry. In order to provide early intervention and preventative therapy to people at risk, it is crucial to accurately predict the beginning of diseases. As the use of electronic clinical data spreads, it becomes more important than ever to refine existing prediction models. With larger medical datasets, clinicians have to put in more effort to understand complicated feature connections and diagnose illnesses in advance. Therefore, this research study simplifies the process of classifying heart disease by identifying the most important risk variables from a high-dimensional dataset. We have employed two datasets with a wide range of clinical characteristics for our investigation. To begin, we examined the interplay and dependency between a variety of clinical characteristics and coronary heart disease. Last but not least, we looked into a hybrid machine learning classification model (Support Vector Machine with K-Nearest Neighbour) by testing it with both full and reduced feature subsets. Accuracy, ROC curve, and F1-Score were used to assess the trained classifiers, while Galactic Swarm Optimisation (GSO) was used to choose the appropriate kernel weight of the SVM. When associated to models trained on a complete feature set, the presentation of classification models trained with a slighter feature set enhanced melodramatically with a reduced amount of training period. © 2023 IEEE.

关键词： Internet of things

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Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

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Recent Patents on engineering 2025年第2期19卷 57-70页

作者： Babu B, Ravindra Saxena, Swati Widjaja, Gunawan Vinodha D, Vedha Omarov, Batyrkhan Ramaiah, Gurumurthy B. Vats, Prashant Adama Ethiopia Department of Computer Science KCG College of Technology Chennai India Universitas Krisnadwipayana Jawa Barat 13077 Indonesia Department of Electronics and Communication Engineering JCT College Of Engineering and Technology Pichanur Coimbatore India International University of Tourism and Hospitality Turkistan Kazakhstan Technical and Vocational Training Institute Addis Ababa Ethiopia Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Methods: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users. © 2025 Bentham science Publishers.

关键词： Cloud security

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Review On the Techniques Used for Detection of Fabric Defects Using AI 1

Review On the Techniques Used for Detection of Fabric Defect...

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1st IEEE International Conference on Advances in electronics, communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Batra, Isheeta Prasad, S A Hari Arvind, K.S. Faculty of Engineering & Technology Department of Computer Science and Engineering Karnataka India Faculty of Engineering & Technology Department of Electronics and Communication Engineering Karnataka India

ISBN: (纸本)9798350348057

The garment industry is the second-most polluting industry after oil. These mass-produced clothes if rejected are dumped and have an enormous impact on the environment. Therefore, to save the cost post production it is important to identify any sorts of defects pre-production when clothes are in a textile form, so that in case the fabric lacks somewhere in quality it can always be replaced, saving all the time and cost. This detection is possible using techniques like segmentation, or on the basis of texture or by using deep learning algorithms each having its own advantages and disadvantages to efficiently identify the defects in textile fabric. This paper is a comprehensive study of these three techniques and comparison made on basis of brief analysis which say that the deep learning technique is the best of the three for detection of defects keeping in contrast the type of research and developing technologies. © 2023 IEEE.

关键词： Deep learning

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A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

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computer Systems science & engineering 2023年第4期45卷 427-443页

作者： A.Gautami J.Shanthini S.Karthik Department of Electronics and Communication Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering Dr.N.G.P Institute of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

In Wireless Sensor Networks(WSN),attacks mostly aim in limiting or eliminating the capability of the network to do its normal *** this misbehaviour is a demanding *** so far the prevailing research methods show poor ***3 centred efficient Intrusion Detection Systems(IDS)is proposed in WSN to ameliorate the *** proposed system encompasses Data Gathering(DG)in WSN as well as Intrusion Detection(ID)*** DG,the Sensor Nodes(SN)is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means(DFFF)algorithm chooses the Cluster Head(CH).Then,the data is amassed by the discovered ***,it is tested with the trained *** IDS encompasses‘3’steps:pre-processing,matrix reduction,and *** pre-processing,the data is organized in a clear ***,attributes are presented on the matrix format and the ELDA(entropybased linear discriminant analysis)lessens the matrix ***,the output as of the matrix reduction is inputted to the QN3 classifier,which classifies the denial-of-services(DoS),Remotes to Local(R2L),Users to Root(U2R),and probes into attacked or Normal *** an experimental estimation,the proposed algorithm’s performance is contrasted with the prevailing *** proposed work attains an enhanced outcome than the prevailing methods.

关键词： Distance fruit fly fuzzy c-means(DFFF) entropy-based linear discriminant analysis(ELDA) Quasi-Newton neural network(QN3) remote to local(R2L) denial of service(DoS) user to root(U2R)

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Diagnosis of COVID-19 from the X-Ray images using BAT Algorithm with Deep Convolutional Neural Network 3

Diagnosis of COVID-19 from the X-Ray images using BAT Algori...

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3rd International Conference on Advances in Electrical, Computing, communication and Sustainable Technologies, ICAECT 2023

作者： Mohanappriya, K. Vennila, C. Sathiaparkavi, J. Shapnarani, E. Saranathan College of Engineering Department of Computer Science and Engineering Trichy India Saranathan College of Engineering Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9781665494007

The COVID-19 widespread has posed a chief contest to the scientific community around the world. For patients with COVID-19 illness, the international community is working to uncover, implement, or invent new approaches for diagnosis and action. A opposite transcription-polymerase chain reaction is currently a reliable tactic for diagnosing infected people. This is a time- and money-consuming procedure. Consequently, the development of new methods is critical. Using X-ray images of the lungs, this research article developed three stages for detecting and diagnosing COVID-19 patients. The median filtering is used to remove the unwanted noised during pre-processing stage. Then, Otsu thresholding technique is used for segmenting the affected regions, where Spider Monkey Optimization (SMO) is used to select the optimal threshold. Finally, the optimized Deep Convolutional Neural Network (DCNN) is used for final classification. The benchmark COVID dataset and balanced COVIDcxr dataset are used to test projected model's performance in this study. Classification of the results shows that the optimized DCNN architecture outperforms the other pre-trained techniques with an accuracy of 95.69% and a specificity of 96.24% and sensitivity of 94.76%. To identify infected lung tissue in images, here SMO-Otsu thresholding technique is used during the segmentation stage and achieved 95.60% of sensitivity and 95.8% of specificity. © 2023 IEEE.

关键词： COVID-19

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Improving Tomato Disease Diagnosis Through ResNet50: An Innovative Approach 4

Improving Tomato Disease Diagnosis Through ResNet50: An Inno...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Dhamotharan, R. Raj, J. Relin Francis Krishnan, R. Santhana Kavitha, S. Soundiraraj, N. Kumar, V. Vinoth Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331540364

Tomato crops are vulnerable to numerous diseases that can severely affect both yield and quality. Early and precise identification of these diseases is indispensable for implementing active management and intervention measures. This research introduces an advanced approach to tomato disease detection by employing the ResNet50 architecture, pretrained on the ImageNet dataset. Through transfer learning, the ResNet50 model leverages its pre-learned features to classify tomato plant images affected by diseases such as Tomato Mosaic Virus (ToMV), Bacterial Spot, Blossom End Rot, Early Blight, and Late Blight. The ResNet50 model is fine-tuned with custom classification layers tailored to the specific diseases, allowing it to achieve high accuracy in disease detection. Preliminary results indicate that the ResNet50 model significantly outperforms traditional machine learning approaches, demonstrating its efficacy in accurately diagnosing tomato diseases. This study provides an effective solution for improving disease detection in tomatoes and highlights the transformative role of deep learning in advancing agricultural practices, fostering sustainability, and enhancing productivity. © 2024 IEEE.

关键词： Fruits

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A Comparative Study of Random Forest, SVM, and Naive Bayes for Sentiment Analysis Optimization 2

A Comparative Study of Random Forest, SVM, and Naive Bayes f...

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2nd IEEE International Conference on Innovations in High-Speed communication and Signal Processing, IHCSP 2024

作者： Pandey, Ranu Patidar, Praveen Kumar Verma, Pushpalata Khan, Gousia Hazra Anjum Harne, Sachin Tiwari, Rovin Shri Rawatpura Sarkar University Department of Computer Science and Engineering Raipur India Parul University Parul Institute of Technology Department of Computer Science and Engineering Vadodara India Bhilai Institute of Technology Department of Computer Science & Engineering Raipur India Government Engineering College Department of Computer Science and Engineering Raipur India Gh Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India Rgpv Department of Electronics and Communication Engineering Bhopal India

ISBN: (纸本)9798350368949

This study presents an in-depth evaluation of sentiment analysis models applied to a US Twitter dataset. The performance of machine learning models considered in this study are Random Forest, Support Vector Machine (SVM) in both optimized and non-optimized forms, Multinomial Naive Bayes, AdaBoost, and Gaussian Naive Bayes. In this work TFIDF is utilized for vectoize text. For machine learning algorithms the Random Forest and SVM achieve training accuracies of around 99%, but showing the lower test accuracies which denotes the overfitting condition. Other models such as multinomial Naive Bayes and AdaBoost showed consistent performance across both training and testing data. Gaussian Naive Bayes' underperforms in the test data due to the models sensitivity to the data properties. Highest accuracy is achieved using the optimized support vector machine that represents the importance of hyperparameter tuning on model generalization. © 2024 IEEE.

关键词： Emotion Recognition

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Directional Beam Based LED Deployment for a Short Range Indoor LED Light communication System

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Journal of Optical communications 2022年第3期43卷 411-420页

作者： Sharma, Ram Charan Kumari, A. Aggarwal, Mona Ahuja, Swaran Department of Electrical Electronics and Communication Engineering NorthCap University Haryana Gurgaon India Department of Electrical Electronics and Communication Engineering School of Engineering NorthCap University Gurgaon India Department of Computer Science and Engineering School of Engineering NorthCap University Gurgaon India

There is now an increasing awareness about the possibility of health hazards due to excessive electromagnetic radiations around us within the indoor environment. Hence, the alternative communication technologies such as indoor mobile communication system based on LED lighting are gaining more attention in the present scenario. The dual use of LEDs i. e. for illumination as well as for setting up of an indoor communication network infrastructure simultaneously makes it an attractive preposition. The LED lighting based communication system is qualified to be part of green technology networks. This paper investigates the optimal LED placement strategy for such a system to optimize the overall communication system parameters including the transmit power optimization. It is observed that the overall optimization of LEDs improve the signal to noise ratio and also a significant reduction in root mean square delay at lower semi-angle values besides improving the mobile coverage area. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Light emitting diodes

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Dermo classify: A dermatologist skin disease detection and classification using DCNN

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Sudar, K. Muthamil Nagaraj, P. Muneeswaran, V. Panda, Baidyanath Bhoi, Akash Kumar Department of Computer Science and Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India LTIMindtree 1 American Row 3 Floor HartfordCT06103 United States Directorate of Research Sikkim Manipal University Gangtok Sikkim737102 India

Purpose: In current circumstances, investigators who are doing exploration in various regions remote from their old neighborhood are experiencing different skin issues like dermatitis, dry skin, and psoriasis because of environment or weather condition changes, food sensitivities, and water changes. This results in itching and dry patching on their skin. An affected individual should investigate and analyze the skin sickness, take appropriate precautions to keep the illness from spreading and seek therapy from a skin expert as quickly as time permits. Since the investigators come from different parts of the country, they are unfamiliar with hospitals and places, so they avoid such skin diseases without realizing the severity of the sickness. To address this, we propose a computer-aided system that detects and classifies skin diseases early, helping individuals to identify their condition and seek appropriate treatment quickly. Methods: To detect skin disease at an early stage, this work epitomizes a strategy using a computer-aided model according to sophisticated deep learning neural network techniques such as convolutional neural network (CNN), Residual neural networks, and Xception neural network to detect skin disease. We use the HAM-10000 data set, which contains data on seven different types of skin diseases. Results: The CNN model produced the most significant performance accuracy of 98.3718% in the experiments. Conclusion: The paper presents a promising approach for skin disease detection and classification using DCNNs. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2024.

关键词： Convolutional neural networks

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Wavelet scattering and scalogram visualization based human brain decoding using empirical wavelet transform

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International Journal of Information Technology (Singapore) 2023年第3期15卷 1699-1708页

作者： Lakshmipriya, B. Jayalakshmy, S. School of Computer Science Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Electronics and Communication Engineering IFET College of Engineering Villupuram India

A prediction model to decode the human brain activities from brain signals is proposed in this work. The existing works available in the Neuroscience field are higher in the classification of brain related diseases like brain tumor, Alzheimer’s etc. whereas only very few works are done to understand the cognitive state of the human brain. In this work, decoding of brain signals is performed by analysing the time frequency representations of the signal using wavelet scattering and scalogram coefficients. The proposed approach decomposes the brain signals captured using functional near-infrared spectroscopy (fNIRS) into several intrinsic mode functions (IMFs) obtained through Empirical Wavelet Transform (EWT). Subsequently, the time frequency representations obtained from the wavelet scattering and scalogram coefficients of every IMFs are given as input to a popular transfer learning architecture, GoogLeNet for training and testing. The classification results obtained for every IMFs are compared and contrasted with the existing works. Wavelet scalogram representation of filter bank 2 of IMF 3 coefficients records better classification accuracy of 91.67%. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Brain decoding Empirical wavelet transform GoogLeNet Mental arithmetic Scalogram Wavelet scattering transform

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