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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kansal, Vipashi Husain, Saif O. Kumar, Rakesh Chinthamu, Narender Kumar, Manish Aswini, S. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Mit Cto DallasTX United States Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research explores the integration of edge computing and unified learning procedures for peculiarity locations in Internet of Things (IoT) situations. Four inconsistency discovery calculations - Isolation Forest, Local Outlier Factor, One-Class SVM, and Recurrent Neural Organize - are assessed over assorted IoT datasets to survey their adequacy. Results show that the Repetitive Neural Network outperforms other calculations with exactness, review, F1-score, and AUC-ROC values of 0.90, 0.92, 0.91, and 0.95 individually. The study illustrates the possibility of leveraging edge computing and unified learning for productive and privacy-preserving inconsistency locations at the organised edge. By passing on computational assignments to edge contraptions and collaboratively planning models utilizing combined learning, our approach finishes flexible and adaptable irregularity revelation in IoT systems. The investigate contributes encounters into tending to challenges such as inertness, security, and flexibility characteristics in centralized idiosyncrasy disclosure approaches, clearing the way for more capable and privacy-preserving courses of action in IoT applications. © 2024 IEEE.

关键词： Anomaly detection

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Detection of Retinal Diseases in Retinal Optical Coherence Tomography Images 2

Detection of Retinal Diseases in Retinal Optical Coherence T...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, M. Sheeba, T.M. Princella, A.S.Anshy SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Valliammai Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350397635

In biomedicine, disease image classification is a laborious, manual process. However, recent developments in computer vision have made applying artificial intelligence for the detection and categorization of medical images a more practical option. It is essential to analyse retinal illnesses utilising Optical Coherence Tomography (OCT) scans. For patients to survive, these disorders must be accurately detected and classified. The analysis of retinal illnesses is currently done by doctors on a regular basis by looking at the OCT pictures. The manual diagnosing process is laborious, though. In order to aid clinicians in their diagnosis, an automatic detection and classification technique for retinal illnesses has been developed in this work. Utilizing OCT scans, a deep multilayered Artificial Neural Network (ANN) has been employed to identify and categorise the retinal anomalies. On a 150 image open source retinal OCT dataset, the suggested technique has been used. © 2023 IEEE.

关键词： Optical tomography

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Cricket shot classification using Deep Learning 2

Cricket shot classification using Deep Learning

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kallur, Shanta Thotad, Puneeth N. Yaligar, Suman Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798350388916

Cricket is a popular sport that involves various batting techniques and shot types. Accurate and real-time detection of cricket shots can provide priceless visual images for coaches, players, and analysts. This study proposes an artificial intelligence-based approach for cricket shot detection aimed at automatically recognizing and classifying different shots played by batsmen. Images are extracted and pre-processed to enhance their quality. The pre-processed images extract key features like bat trajectory, player movement, and batsman position. A training dataset is used to enable shot classification, consisting of labeled samples of different cricket shots. Neural network models, such as a convolutional neural network, VGG16, VGG19, are trained using an image set to learn the patterns and characteristics of each shot type. The model is then employed to classify shots in real time for predicting the corresponding shot type based on the selected features. The proposed approach is evaluated on a diverse dataset of cricket match images, encompassing a broad extent of shots played by professional batsmen. Execution metrics such as accuracy, precision, recall, and F1-score are used to assess the effect of the shot detection system. A VGG16 model in cricket shot prediction of the dataset yielded an impressive accuracy of 95.81%. The solution establishes the efficiency of the planned approach in accurately detecting and classifying cricket shots in real-time. The developed system has the potential to enhance cricket coaching, player performance analysis, and broadcast coverage by providing valuable insights into the shot selection and execution of batters. © 2024 IEEE.

关键词： Convolutional neural networks

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An Extensive Examination of Different Phases Available in Enhancing the Power of WSN 4

An Extensive Examination of Different Phases Available in En...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Gill, Rupali Kumar, Rakesh Pant, Shivani Deepika, A. Al-Fatlawy, Ramy Riad Angadi, Sanjeevkumar Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India Gla University Department of Computer Engineering & Applications Mathura India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science Engineering Dehradun248007 India India The Islamic University Najaf Iraq Computer Science and Engineering Nutan College of Engineering and Research Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

In this whole test, we delve into the evolution and transformative ability of Wireless Sensor Networks (WSNs), emphasizing the pivotal position of graphical statistics in elucidating the technological improvements and stressful conditions interior this region. By harnessing the energy of the IEEE 802.15.4 general, this research provides an in-intensity analysis of WSNs' operational efficiencies, energy consumption styles, and network resilience through meticulously designed graphs and information visualizations. These graphical representations now not only offer a easy and concise overview of WSN universal performance metrics but additionally spotlight the giant enhancements completed thru the implementation of a singular WSN framework. The examiner's findings underscore the vital importance of empirical statistics visualization in advancing our knowledge of WSN technologies, offering valuable insights into the optimization strategies that may result in extra power-inexperienced, reliable, and scalable sensor networks. This research contributes to the continued communicate in the subject, paving the manner for future enhancements and programs of WSNs across several sectors. © 2024 IEEE.

关键词： Energy utilization

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Enhancing Remote Sensing Image Classification using Arithmetic Optimization Algorithm with Deep Learning Approach 2

Enhancing Remote Sensing Image Classification using Arithmet...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Shanthi, R. Balasubramaniam, D. Venkatesh, R. Sankari, C. Manimaran, V. Basha, Shaik Hedayath Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Chengalpattu Tamilnadu Kattankulathur603203 India Department of Biomedical Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India Ramco Institute of Technology Rajapalayam India Chennai Institute of Technology India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu Erode638060 India Department of ECE R.M.K. College of Engineering and Technology India

ISBN: (纸本)9798350383867

The classification of Remote sensing image (RSI)is a vital task in field of geospatial analysis where aerial or satellite images are classified into predefined classes or land cover sorts. It plays a main part in diverse fields namely agriculture, disaster management, environmental surveillance, urban planning, and natural resource management. With the machine learning (ML) and deep learning (DL) models, these images examined and categorized depending on their spatial, spectral and temporal features. The exact detection of remote sensing images simplifies decision-making procedures, aiding governments, environmental agencies, and companies to make knowledgeable decisions and policies. It authorizes experts to observe variations in lands, discover land use trends, and evaluate environmental effects and making it a vital device in addressing present difficult challenges connected to weather change, urban development, and natural resource protection. This study presents an enhanced remote sensing image classification utilizing arithmetic optimization algorithm with DL (ERSC-AODL) Approach. The ERSC-AODL technique concentrates on the accurate recognition of the RSI into dissimilar classes. It begins with the use of an EfficientNet feature extractor, which derives complex spectral and spatial data, and acts as a base of proper representation of the images. Moreover, the recognition process takes place by a bidirectional long short-term memory (BiLSTM). Finally, the hyperparameter optimization procedure is performed by the ERSC-AODL technique. To portray the proficient classification outputs of the ERSC-AODL technique, a range of experiments were utilized. The investigational outputs stated that the ERSC-AODL technique attains improved accomplishment over other models. © 2024 IEEE.

关键词： Decision making

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Hybrid Approach for the Classification and Detection of Brain Tumor 2

Hybrid Approach for the Classification and Detection of Brai...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Rekha, S. Geetha, K. Gireesh Gokulavasan, B. Patil, Harshal Maranan, Ramya Psg College of Arts and Science Department of Computer Science Coimbatore India Sri Krishna Adithya College of Arts and Science Department of Computer Applications Coimbatore India Mohan Babu University Department of Electronics and Communication Engineering Tirupati India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350373110

Brain tumors (BT) are a leading cause of global mortality, affecting all ages, with early detection being crucial for successful treatment. Traditional biopsy-based categorization is invasive and often occurs post-surgery, highlighting the need for non-invasive diagnostic methods. This study introduces a hybrid machine learning (ML) model combining Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB) to classify glioma, meningioma, pituitary tumors, and normal brain tissue from MRI scans. Trained on MRI images from Kaggle, the hybrid model's performance was compared to standalone models (SVM, GNB, Linear Discriminant Analysis, and Random Forest), achieving superior accuracy (96.19%), TPR (95.83%), TNR (96.53%), precision (96.06%), and F1-score (98.50%). BTs are notoriously aggressive, and misdiagnosis can significantly reduce patient lifespans. The hybrid model addresses the limitations of traditional ML models, enhancing diagnostic accuracy, reducing unnecessary surgeries, and expediting treatment initiation, thereby improving patient outcomes. © 2024 IEEE.

关键词： Support vector machines

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Predictions of Basketball Match Results using Autoencoder with Gated Recurrent Unit Algorithm 1

Predictions of Basketball Match Results using Autoencoder wi...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Kavitha, K.R. Almusawi, Muntather Vybhavi, Gummagatta Yajaman Edwin Prabhakar, P.B. Saranya, N. Naga Sona College of Technology Department of Electronics and Communication Engineering Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350352931

This research explores basketball match predictions within the field of sports analytics utilized Deep Learning (DL) technique to analyse the outcomes of National Basketball Association (NBA) games based on player performance and team statistics. The challenge in predicting NBA games outcomes and performance analysis various factor, as the high dimensionality of the data and uncertainty surrounding player due to difficult to predictions of basketball game. This paper, proposed Autoencoder with Gated Recurrent Unit (AE-GRU) algorithm to efficiently analyse and predict NBA game outcomes. The data was initially collected from NBA games between 2009 - 2021, focusing on five teams and pre-processing involved min-max normalization to uniformly scale the feature vectors. The Principal Component Analysis (PCA) utilized for feature selection, effectively reducing the high dimensionality of the NBA game data across various teams. The AE-GRU technique then captured the complex feature of basketball game data and handled uncertainty data and enhances performance. The performance of DL model evaluated the various metrics such as accuracy, precision, recall and f1-score metrics on NBA game. © 2024 IEEE.

关键词： Deep learning

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ENERGY-EFFICIENT ANT ROUTING ALGORITHM FOR OPTIMIZED PATH SELECTION NETWORK LONGEVITY IN WIRELESS SENSOR NETWORKS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第2期84卷 21-42页

作者： Zelani, Shaik Khader Babu, D. Nagabhushana Surendra, Doddaga Rahman, Mohammad Zia Ur Department of Electronics and Communication Engineering Sri Vasavi Institute of Engineering and Technology-Autonomous Pedana India Department of Data Science Malla Reddy University Telangana Hyderabad India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India

The exponential growth of wireless sensor networks (WSNs) in various applications underscores the critical need for energy-efficient routing protocols to extend network lifespan. This paper introduces the Energy-Efficient Ant Routing Algorithm, a novel bioinspired routing strategy derived from ant colony optimization (ACO), which integrates traditional pheromone mechanisms with dynamic energy-aware adaptations to prioritize routes through nodes with higher residual energy levels, thereby balancing load and prolonging network functionality. The model’s adaptive pheromone updating mechanism, which considers both the cumulative path energy cost and the minimum residual energy of nodes along the path, encourages the use of energy-efficient routes and mitigates rapid energy depletion in critical network areas. This approach not only extends operational network lifetime but also supports higher data reliability and throughput in challenging WSN scenarios. The results of the proposed model demonstrated significant improvements over traditional ACO and other common energy-aware routing protocols. The network lifespan is extended by ~ 25% compared to standard ACO and by 15% compared to the best-performing conventional energy-aware routing protocol under the same conditions. Furthermore, EEARA achieved a 20% reduction in average energy consumption per node and enhanced the data delivery ratio by 10% due to more stable routing paths. The results underline the model’s potential as a scalable, efficient routing solution for energy-limited networks, promising substantial improvements in sustainable network operations. © 2025 by Begell House, Inc.

关键词： Ant colony optimization

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Predicting Pest Infestation Patterns with Graph Convolutional Networks (GCN) in Precision Farming 3

Predicting Pest Infestation Patterns with Graph Convolutiona...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Sireesha, Marri Hussein, Laith Manju, M. Johri, Prashant Kuppuraj, T. Aanandha Saravanan, K. Department of Computer Science and Engineering CMR Technical Campus Hyderabad India College of technical engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Kongu Arts and Science College Department of Computer Science TamilNadu Erode India School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331542375

Due to the increases pest impacts on agricultural productivity, effective pest prediction models are required for efficient pest control in precision farming. The framework outlined in this research uses a Graph Convolutional Network (GCN) to estimate pest infestation by exploiting spatial and temporal relationships present in agri-ecosystems. The methodology combines weather, soil, pest, and crop health data into a graph-based representation of spatial units, and the directed connections between them that reflect their proximity and climatic similarity. The GCN converts these graphs to learn such complex relationship as the infestation prediction at various severity levels. The proposed model has produced a better accuracy of 94.8%, better precision of 95.2%, better recall of 94.5% and better F1-score of 94.8% as compared from Random forest, SVM, LSTM, CNN as per the comparison of table 3. Comparing them focuses on graph construction approaches and temporal aspects, mixed P/S and temporal emb has improved the model performance. This framework is practical for further pest management and employing the result without harming the environment. The results show the effectiveness of GCNs for use in pest prediction in precision farming and the opportunity to develop sustainable and scalable data-based agriculture. © 2025 IEEE.

关键词： Weed control

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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