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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, G. Rajganesh, N. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur Chennai India

ISBN: (纸本)9798331509675

The non-intrusive detection of Autism Spectrum Disorder (ASD) marks a significant advancement in early diagnosis and intervention. This approach allows users to upload videos to a web interface, where visual and auditory data are processed separately. Visual data is analyzed using a customized VGG19 Convolutional Neural Network (CNN) to identify facial expressions and movements indicative of autism, while auditory data is evaluated by a Recurrent Neural Network (RNN) to detect auditory cues like tone and rhythm. A voting mechanism across 10 frames determines the visual classification, labelling the subject autistic if most frames indicate autistic traits. Audio analysis provides an independent prediction. The subject is classified as autistic if either analysis indicates autistic features. By leveraging deep neural networks to process multimodal data, this method offers an accessible, early ASD detection tool for caregivers and professionals, promoting timely interventions and improved outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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Developed a Machine Learning and Deep Learning Model for 5G MIMO Data Based Beam Selection and Intelligent Network Analytics

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SN computer science 2025年第3期6卷 1-21页

作者： Ashok, P. Suganya, R. Lekha, P. Pandian, Poornima Subashini, M. Nithya, M. Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Tamilnadu India Department of Computer and Communication Engineering Sri Sai Ram Institute of Technology Tamilnadu India Department of Electronics and Communication Engineering Sri Sairam Engineering College Tamilnadu India Department of Computer Science and Engineering Sri Sairam Engineering College Tamilnadu India

Introduces an innovative application of 5G MIMO data in singular value decomposition (SVD) focusing on deep learning beam-selection and intelligent network analytics. This research explores the potential of leveraging 5G MIMO data to enhance network performance and efficiency through advanced ML techniques. Deep learning algorithms are employed for beam-selection, optimizing the transmission of data in 5G networks to improve throughput and reliability. Intelligent network analytics are applied to extract valuable insights from the vast amount of data generated by 5G MIMO systems, enabling proactive network management and optimization. By harnessing the capabilities of deep learning and intelligent analytics, this research aims to address key challenges in 5G network optimization and management. The integration of SVD techniques with 5G MIMO data offers a novel approach to enhancing network performance, enabling more efficient utilization of resources and improved quality of service for users. The insights gained from intelligent network analytics facilitate informed decision-making and proactive maintenance leading to greater reliability and resilience in 5G networks. This research contributes to the advancement of 5G technology by leveraging ML to unlock the full potential of MIMO data for optimizing network performance and enabling intelligent network management. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G Beam-selection Deep learning Intelligent management MIMO Network analytics Network performance Optimization Proactive maintenance Singular value decomposition

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Enhancing Online Learning: A Comparative Analysis of Machine Learning Models and Data Visualization Techniques for Predicting Learner Flexibility

Enhancing Online Learning: A Comparative Analysis of Machine...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Priya, P. Kanaga Suguna, R.K. Sujitha, S. Devi, G. Nandhini, D. Kalyan, G. Ramesh KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India SNS College of Technology Department of Computer Science and Engineering Coimbatore India SNS College of Engineering Department of Computer Science and Technology Coimbatore India

ISBN: (纸本)9798350319200

The fast growth of online learning has increased the demand for efficient tools and approaches to measure and improve student's capacity to adapt to online learning. This paper gives a thorough comparative comparison of Machine Learning (ML) models and data visualization techniques for predicting the flexibility of learners in the realm of online education. In this study, various data visualization techniques are investigated to learn more about how learners behave, engage with, and perform in online courses. Understanding patterns and trends through data visualization is essential for educators and institutions to make well-informed decisions to enhance learner's learning journeys. To find out how effective different visualization techniques are in revealing useful insights, IBM Cognos and Python are used. The suitability of several ML algorithms for predicting learners' flexibility levels is also examined in this study. The predictive accuracy or mean accuracy score of several ML models, including XGBoost, Random Forests, CatBoost, Logistic Regression, and K Nearest Neighbour, is compared. The dataset used for this investigation contains a variety of learner-related variables, such as demographic data, past academic achievement, and interactions with online courses. For educators and educational institutions looking to use data-driven techniques to improve online learning experiences, the findings of this comparative study offer invaluable advice to them. This study contributes to the ongoing attempts to raise the caliber and efficacy of online education by identifying the most efficient data visualization strategies and ML models for forecasting learners' flexibility. The study's conclusions enable educators to proactively identify learners who are at risk of having difficulty with online learning, enabling prompt interventions and individualized support for academic success. © 2023 IEEE.

关键词： accuracy and flexibility IBM Cognos machine learning online learning visualization

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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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SEEK Mobility Adaptive Protocol Destination Seeker Media Access Control Protocol for Mobile WSNs

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第1期4卷 130-145页

作者： Shaker, Atheel Sabih Youssif, Omar F. Aljanabi, Mohammad Abbood, Zainab Ali Mahdi, Mahdi S. Computer Engineering Techniques Baghdad College of Economic SciencesUniversity Baghdad Iraq Computer Engineering Techniques Ibn Khaldun University College Baghdad Iraq Department of computer science Alsalam university college Baghdad Iraq Department of computer Aliraqia university college of education Baghdad Iraq Computer Technology Engineering dep Al-Esraa University College Baghdad Iraq Department of computer technology engineering Al-Esraa University college Baghdad Baghdad Iraq

The mobile wireless sensor network (WSN) is an emerging field as it widens the scope of applications where sensor networks can be applied. Generally, mobility in wireless communications degrades how well each pair of nodes communicate with one another. In WSN, the effect is higher because nodes have limited communication and computational capabilities. Those limitations create a challenging environment for the operation of sensor node communications. This paper proposes an energy-efficient media access control (MAC) protocol with mobility adaptive throughput based on a carrier-sense multiple access with collision avoidance MAC mechanism called the SEEK-mobility adaptive protocol (SEEK-MADP). SEEK-MADP uses a unique control packet operation to transfer data packets through as many nodes as possible in one duty cycle. The control packets, SYNC and RTS, are merged into one packet (SEEK). SEEK is then transferred to the downstream nodes to establish a connection between a stream of nodes in a duty cycle. This process minimizes the energy consumed by the handshaking process between the connected nodes. To increase throughput, the data period is adaptive to minimize/maximize the data packets according to the movement speed of the sender/receiver nodes. The proposed algorithm is assessed via extensive simulations in mobile scenarios using Network Simulator version 2. The final results show that the SEEK-MADP outperforms MAC protocols, sensor MAC (S-MAC), and SMAC with adaptive listening, which have shown good performance in mobile scenarios. The performance of the proposed algorithm is better than that of IEEE 802.15.4 standard MAC mechanism at mobile scenarios. © 2023 Authors. All rights reserved.

关键词： Medium access control

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Generative Adversarial Networks for Motor Imagery Classification using Wavelet Packet Decomposition and Complex Morlet Transform

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Multimedia Tools and Applications 2025年 1-24页

作者： Shirodkar, Vaishali Edla, Damodar Reddy Kumari, Annu Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim403703 India Information Technology Department Goa College of Engineering Farmagudi Goa Ponda403401 India

The area of brain-computer interface research is widely spreading as it has a diverse array of potential applications. Motor imagery classification is a boon to several people with motor impairment. Low accuracy and datasets with few trial recordings present challenges for this classification. This research offers a novel approach to these problems in electroencephalography signal classification for efficient control of neuroprosthetic devices. By using wavelet packet decomposition the proposed method divides the EEG signal into eight frequency bands. Each of these bands were then evaluated by a one-dimensional convolution neural network to find the most discriminative band. Only EEG signals filtered with these bands were used to generate time-frequency images called scalograms using complex Morlet transform. These scalograms were then classified using a two-dimensional convolution neural network optimized for spatial-temporal feature extraction and classification. To mitigate data scarcity, generative adversarial networks were used for effective data augmentation. Experimental evaluation on a benchmark dataset demonstrated substantial improvement: the two-dimensional convolution neural network achieved 93.63% accuracy, surpassing the 85% achieved by the one-dimensional convolution neural network baseline. Precision, recall, and F1-score metrics validated robust performance across motor imagery tasks. Comparative analysis with the latest methods underscored the proposed model’s superiority in accuracy and reliability. Using wavelet transformations, convolution neural network architectures, and generative adversarial network-driven data augmentation presented a promising framework for advancing brain-computer interface applications, particularly in enhancing neuroprosthetic device control. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

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Majority voting ensembled feature selection and customized deep neural network for the enhanced clinical decision support system

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International Journal of computers and Applications 2022年第10期44卷 991-1001页

作者： Fathima, M. Dhilsath Samuel, S. Justin Natchadalingam, R. Kaveri, V. Vijeya Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering PSN Engineering College Tamil Nadu India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Heart disease and diabetes are global health issues that affect people worldwide. Diabetes is becoming a significant concern, and Diabetes patients have a substantially higher risk of heart disease morbidity and mortality than people without diabetes. These conditions are associated with hospitalizations and emergency room visits, which raises healthcare expenses. An important strategy to improve health care outcomes and reduce unnecessary costs is to identify and anticipate them in patients. Clinical Decision Support Systems (CDSS) assess patient data from clinical datasets to help disease prediction and enhance treatment options for heart disease and diabetes, and other disorders. According to the literature, most CDSS have used machine learning algorithms for predicting heart disease and diabetes. These algorithms performed worthily, but the accuracy of these machine learning (ML) algorithms is lacking, especially in medical data, which contains numerous complex attributes such as resting blood pressure, serum cholesterol, fasting blood sugar, and thalassemia value. This proposed work developed a majority voting ensembled feature selection (MVEFS) technique and customized deep neural network (CDNN) to develop a CDSS for heart disease and diabetes prediction. This deep neural network-based CDSS best performing than ML-based CDSS. There are several input attributes in the clinical dataset. Some attributes are not associated with disease and have negative consequences when used in clinical data analysis for disease prediction. As a result, feature selection is essential for removing unimportant features. The feature selection significantly minimizes system learning time, which improves CDSS performance efficacy. The MVEFS selects the associated heart disease and diabetes-related features from the clinical dataset. The classifier execution time, accuracy, sensitivity, precision, specificity, and F1-score are the performance metrics used to evaluate the proposed CDSS.

关键词： Forecasting

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Deep Convolutional Multi-Relational Graph Attention Network for Autonomous Drone Navigation in Urban Wind Zones

Deep Convolutional Multi-Relational Graph Attention Network ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Pabitha, C. Joshua, K. Paul Pradeep, S. Sophia, S. Sharma, R. Rajesh Prabhu, D. Karthik Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India MAlla Reddy Engineering College for Women Department of Computer Science and Engineering Telangana Secunderabad India Karunya Institute of Technology and Sciences Department of computer Science and Engineering Tamil Nadu Coimbatore India Alliance University Department of Computer Technology Alliance College of Engineering and Design Bangalore India Ramco Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Rajapalaiyam India

ISBN: (纸本)9798331509828

Drones are essential for civil engineering operations like logistics and data collecting. Current autonomous drone studies mainly concerns itself with safe path planning in static scenarios;however one of the major challenges of over urban environment is often dynamic factors such as wind, building layouts, and signal coverage that can significantly impact direction stability. For autonomous navigation across dynamic wind zones, this study proposed a new Shape-aware Mesh Dual Lightweight Deep Convolutional Multi-Relational Graph Attention Network with Growth Optimizer (SAMDLDCMRGANet-GO) that leverages local data from RGB cameras and GPS. The method incorporates a Dual Aggregation Transformer for feature extraction, Shape-aware Mesh Normal Filtering for preprocessing, and the LDCMRGAN et for efficient path planning. The proposed approach demonstrates its efficacy in navigating complicated surroundings while retaining high precision in obstacle avoidance and target acquisition, achieving an impressive 99.14% recall, 99.15% accuracy, an outstanding EVA of 0.99, and a low RMSE of 0.41. © 2025 IEEE.

关键词： Mesh generation

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Knowledge-based genetic algorithm approach to optimise gated recurrent unit for semantic web service classification

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International Journal of Intelligent Systems Technologies and Applications 2023年第4期21卷 366-385页

作者： Sridevi, S. Karpagam, G.R. Kumar, B. Vinoth School of Computer Science and Engineering VIT University Tamilnadu Vellore632014 India Department of Computer Science and Engineering PSG College of Technology Peelamedu Tamilnadu Coimbatore641004 India Department of Information Technology PSG College of Technology Peelamedu Tamilnadu Coimbatore641004 India

The expeditious proliferation of web services over the current web-cyberspace becomes a significant challenge for the web service classification. Recent reports suggest a deep learning algorithm namely, gated recurrent unit, performs well. But the performance of the GRU classifier hinges on structure of the GRU classifier namely, hidden neural network (HNN) and training data. Hence, it is significant to predict optimal HNN and training dataset. The recent studies revealed that genetic algorithm (GA) is most suited for this optimisation problem. However, GA is inefficient in terms of convergence rate and generation of prudent solution. For that, domain knowledge is incorporated in GA operator in order to accelerate convergence rate called as knowledge-based genetic algorithm (KBGA). The research work adopts KBGA to optimise the structure of GRU, so that the classifier outperforms the classification process. Extensive analysis and statistical hypothesis test have been done to show the efficacy of KBGA. © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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On the Complexity Aspects of Various Sub-classes of Linear Languages 9

On the Complexity Aspects of Various Sub-classes of Linear L...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Praveen, G.L. Sunil, K.S. Mar Baselios College of Engineering And Technology Department of Computer Science & Engineering Kerala Thiruvananthapuram India Government Engineering College Idukki Department of Computer Science & Engineering Kerala Painavu India

ISBN: (纸本)9798350394474

The non-terminals in the underlying grammar play an important role in determining different characteristics of the language generated. The languages generated by the grammars are determined by a unique non-terminal known as the start symbol. Even without altering any of the production rules, the grammar produces a distinct language if we swap the start symbol for any other non-terminal. Occasionally, we purposefully include extra non-terminals to attain specific characteristics in both the grammar and parse trees. One such scenario is when we convert a language to Chomsky's Normal Form (CNF), in which case we add additional non-terminals to convert the parse tree into a binary tree. In this paper, we consider different sub classes of CFLs by restricting the number of nonterminals. We study different characteristics like the generative power of the grammar, the stack height, and the number of turns of the PDA which accept the languages produced by the grammars. We show that these characteristics are influenced by the number of nonterminals in the grammar. © 2024 IEEE.

关键词： Binary trees

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