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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Kumar, M. Ashok Pradeepa, P. Muthu, A. Essaki Krishnan, R. Santhana Saravanan, K. Narayanan, K. Lakshmi SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India PSNA College of Engineering and Technology Department of Information Technology Dindigul India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India

ISBN: (纸本)9798350397208

Women's safety has become one of the chief concerns in India, since quite a large number of violence takes place against women's like sexual assault, abuse and murder. Women's are worshiped as God in this country yet her safety is at stake. Even when Women's started to equally flourish in every sectors starting from a Taxi Driver to a CEO of the top MNC, there are major safety issues which still prevent them to excel in their sector. Women's of all ages are affected by some sort of problems against their rights. It tends to increase as we let things grow without noticing them. The reports from National Crime Records Bureau (NCRB) which was released in 2021 which clearly states that more than 4, 00,000 cases were registered under crime against women. Hence, there should be a system which helps them to seek help when they are feeling unsecured. Therefore we propose a system which will assist the women to seek help when they are in danger. This system facilitates them to contact their relative when they experience unsecured situations. © 2023 IEEE.

关键词： Crime

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Pattern Analysis and Regressive Linear Measure for Botnet Detection

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computer Systems science & engineering 2022年第10期43卷 119-139页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Department of Computer Science and Engineering Easwari Engineering CollegeChennaiIndia Department of Computer Science and Engineering United Institute of TechnologyCoimbatoreIndia

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets *** simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

关键词： BotNet threat intrusion features linearity and non-linearity redundancy regressive linear measure classification redundancy eliminationbased learning model

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Parkinson's Disease Detection from Spiral and Wave Drawings Using CNN - RNN With GSM Alert

Parkinson's Disease Detection from Spiral and Wave Drawings ...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Manjula Devi, C. Gobinath, A. Padma Priya, S. Neha, J.J. Jothi, M. Harivarshini, M. Velammal College of Engineering and Technology Department of Information Technology Madurai India PSNA College of Engineering and Technology Department of Information Technology Dindigul India School of Electrical and Electronics Engineering SRM Institute of science and Technology Trichy India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528126

This paper detects Parkinson's Disease (PD) using a hybrid model of CNN and RNN when analyzing spiral and wave drawings. The model incorporates both spatial and temporal pattern recognition, which contributes to a significantly improved diagnostic accuracy for this condition. The CNN represents the spatial features, such as edge detection and stroke thickness, whereas the RNN captures temporal dependencies in the sequences of drawings that reflect the motor impairments associated with PD. This non-invasive objective approach also supports early detection which can then be intervened upon promptly. The model's performance was promising in accuracy, precision, recall, and F1-score values. Diagnostic information is transmitted quickly to ensure that an embedded GSM alert system communicates PD status to patients. © 2024 IEEE.

关键词： Neurodegenerative diseases

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computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

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computer Systems science & engineering 2023年第3期44卷 2759-2774页

作者： R.Punithavathi A.Delphin Carolina Rani K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan Department of Information Technology M.Kumarasamy College of EngineeringKarur6391 I3India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112India Department of Electronics and Instrumentation Easwani Engineering CollegeTamil Nadu600089India Pondichery University Puducherry605014India Department of computer science and engineering The Oxford college of EngineeringBangalore560068India Department of Computer Science Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Computer and Information Science Faculty of ScienceAnnamalai University Chidambaram608002India

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in influencing crop *** wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are *** the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision *** proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as *** proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed *** parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

关键词： Precision agriculture smart farming weed detection computer vision deep learning

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An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第3期4卷 87-100页

作者： Swapna Rani, S. Suganya, V. Selvi, S. Ashokkumar, N. Prema, S. Engineering College Telangana Hyderabad501510 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Electrical and Electronics Engineering Panimalar Engineering College Chennai600 123 India Tirupati517102 India Department of Chemistry PSNA College of Engineering and Technology Tamil Nadu 624622 Dindigul624622 India

Lung cancer is one of the most serious and prevalent cancers in the globe. Early detection of lung cancer can increase a patient's chances of life. Computed Tomography (CT) scan images are difficult for clinicians to utilize in order to determine the stages of cancer. computer-aided systems can assist researchers in more precisely predicting the stages of lung cancer in recent times. This study demonstrates the use of technology that is made possible by machine learning and image processing to accurately classify and predict the lung cancer from CT images. The existing tumor detection frameworks have the major difficulties in terms of high complexity, overfitting and error prediction. Therefore, the proposed work aims to formulate a simple and accurate automated system for the prediction and classification of lung cancer from CT images. Before classifying the input lung scan image, an adaptive median filtering approach is used to improve its contrast and quality. From the segmented lung parts, the histogram and texture features are derived. The most relevant characteristics are chosen using the Lion-Butterfly Optimization (LBO) method for training and testing operations. Eventually, the input CT picture is correctly predicted as either healthy or disease-affected using the Stacking Ensemble Learning Classification (SELC) algorithm. In this study, a thorough performance evaluation is conducted utilizing several measures in order to analyze the outcomes. © 2023 college of Education, Al-Iraqia University.

关键词： Learning systems

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Smartcart - Automatic Billing Cart for Shopping 10

Smartcart - Automatic Billing Cart for Shopping

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Joyce, Alice Antony, Angaline Joseph, John Johnson, Sneha Uma, E.S. Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350384369

This paper presents a revolutionary project to transform shopping experiences in malls through a cost-effective automated billing system. By eliminating specialized baskets and intricate hardware, we significantly reduce implementation costs. A user-friendly mobile app offers real-time purchase tracking, secure UPI payments, and personalized experiences, simplifying mall operations for a mutually beneficial retail evolution. Our innovative approach addresses traditional retail challenges, ushering in a new era of efficiency and satisfaction. The sys- tem promises increased customer satisfaction and operational effectiveness, marking a significant step towards the evolution of shopping centers. © 2024 IEEE.

关键词： Customer satisfaction

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A Comprehensive Analysis of Human Action Recognition for Noisy Videos Using CNN and LSTM

A Comprehensive Analysis of Human Action Recognition for Noi...

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Suma, S. Patel, Punyaban Tiwari, Rajesh Gundu, Lavanya Devi, Guntoju Kalpana Raju, K. Srujan Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798331505134

Human action identification has advanced significantly as a result of the development of deep learning algorithms. Convolutional Neural Networks (CNNs) are known for their adeptness at extracting crucial information within video frames, especially shapes and postures. However, understanding movements necessitates not just individual frames as well as the temporal flow of the movement;this is wherein LSTM (long short-term memory) variants of Recurrent Neural Networks (RNNs) come into play. By analyzing frame sequences, LSTMs are particularly effective in gathering temporal connections, recalling prior motions, and overcoming the gap amongst static features as well as dynamic actions. Even with CNNs and LSTMs' remarkable accuracy in human action recognition (HAR), challenges remain. © 2024 IEEE.

关键词： Convolutional neural networks

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Air and Water Quality Management for Precision Agriculture with IoT and Mobile App Integration 2

Air and Water Quality Management for Precision Agriculture w...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Sheela, K Santha Deepalakshmi, R. Vijayalakshmi, R. Surya, M. Kumar, M. S. Abishek Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350328202

Food security and population support are fundamentally dependent on agriculture. The agricultural industry, though, must address several issues, such as the need for higher production, effective resource management, and environmental sustainability. In this setting, precision agriculture has evolved as a game-changing strategy that uses technology to improve farming methods while lowering environmental impact. Monitoring the quality of the air and water inside agricultural environments is a crucial component of precision agriculture. The quality of the air and water all directly impacts crop health, yield, and the surrounding ecosystem. Traditional techniques for monitoring these variables require costly and time-consuming procedures, making it difficult for small and medium-sized farmers to get access to vital information. We suggest a mobile-based monitoring system that takes advantage of the capacity of widely used mobile phones to gather and examine real-time data on air and water quality to address this problem. © 2024 IEEE.

关键词： Air quality

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Tech Meets Agriculture - Precise Plant Disease Detection Using Deep Learning Algorithm

Tech Meets Agriculture - Precise Plant Disease Detection Usi...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Arunasakthi, K. Akilandeswari, V. Shalini, K.S. Archana, K. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Plant disease prediction is crucial aspect of agricultural field. Plant diseases are one of the major concerns for agriculture in recent times;most of the crops are prone to damage caused by these diseases, which later hampers global food security. The traditional methods used for disease analysis rely strictly on visual inspection. These methods are time-consuming and prone to errors. Deep learning has therefore emerged as a significant and formidable instrument for the automation of plant disease detection. This project uses the deep learning models, YOLOv5 and YOLOv9. It ranks high on accuracy and ensures efficiency in the system to detect diseases related to plant leaves. It uses annotated datasets for several plant leaf diseases with a convolutional neural network to extractfeatures and enable classification. The system provides real-time automated solutions for the identification of plant diseases through the integration of deep learning models into agricultural practices, with a view to allowing growers to take timely action to avoid further damage to crops. The proposed technique not only heightens the rate of accuracy for disease detection but also contributes toward the growing dimension of precision agriculture, within which technology acts as an assistant in the optimization of crop management and enhancement of yield. © 2024 IEEE.

关键词： Convolutional neural networks

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