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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Jamunadevi, C. Pandikumar, S. Naveen, N. Prasath, S. Dhivyashree, S. Pg Kongu Engineering College Department of Computer Technology Perundurai Erode India Acharya Institute of technology Department of Mca Bangalore India Excel Engineering College Department of It Komarapalayam India college Department of Computer Science Thindal India Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798350377972

Cybersecurity experts continue to be concerned about phishing attacks because it causes a real risk to people, businesses, and economies all around the world. Phishing attacks cause a critical threat to global cybersecurity, necessitating effective classification methods for phishing URL operations. So, this work aims to categorize phishing URL operations into distinct types: deceptive, spear, whaling, and pharming. Leveraging machine learning techniques including logistic regression, support vector machine, Naive Bayes, decision tree, K-NN, and Perceptron, evaluate their efficacy in accurately identifying and classifying phishing attacks. The findings underscore the importance of these models in providing cybersecurity professionals with actionable insights into the diverse tactics employed by malicious actors. Among the evaluated models, logistic regression emerges as the preferred choice, demonstrating superior performance in accurately categorizing phishing URLs. This work contributes to enhancing cybersecurity defenses by offering a comprehensive framework for understanding and mitigating the impact of phishing attacks on digital ecosystems. © 2024 IEEE.

关键词： Phishing

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Design and Implementation of an Astute Infant Monitoring System Based on YOLO v8 Algorithm 15

Design and Implementation of an Astute Infant Monitoring Sys...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunkumar, T. Maheswaran, S. Dineshkumar, P. Geetha, K. Sureshkumar, A. Praveenkumar, S. Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India Kongu Engineering College Department of Electronics and Communication Engineering Tamilnadu Perundurai India K.S.Rangasamy college of Technology Department of Information Technology Tamilnadu Tiruchengode India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Namakkal India

ISBN: (纸本)9798350370249

Parents today have a greater workload because they are working hard at their careers. As a result, there is less opportunity of giving babies better care. Children's health may suffer greatly as a result of this. Even if the infants are appropriately and consistently monitored, there is a risk of losing life. For this, we use cameras to monitor the external conditions. The system is designed to monitor and alert the baby emotions to the parents. The monitoring of baby's motion, emotions and surrounding is done by the camera that is fixed in an angle that it can cover the baby to the maximum extend. The camera that are fixed turn according to the baby's movement, so that it continuously monitors the baby. The system will alert the user if there in some negative emotion given by the baby. The system is trained in a way that it monitors the surroundings around the baby carefully. (eg: fire). This is done using YOLO v8. YOLO v8 is an anchor-free model. © 2024 IEEE.

关键词： Neonatal monitoring

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IoT based Automated Rehabilitation using 3D Printed Hand and Health Monitoring for Paralyzed Patients 5

IoT based Automated Rehabilitation using 3D Printed Hand and...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Govindaraj, S. Maheswari, S. Loganathan, P. Sharanya Nivasini, J.S. Ponkarthika, M. Velalar College of Engineering and Technology Department of Biomedical Engineering Erode India Excel Engineering College Department of Electronics and Communication Engineering Namakkal India Erode Sengunthar Engineering College Department of Computer Science and Design Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350389609

Paralysis is a condition characterized by the loss of muscle function in part of the body, leading to impaired movement and control. It can significantly impact an individual's daily life, mobility, and overall well-being. Paralysis can result from various factors, including spinal cord injuries, strokes, traumatic injuries, neurological disorders, and certain medical conditions. According to the World Health Organization (WHO), millions of people worldwide live with some form of paralysis, with varying degrees of severity. Tracking the improvement of patients using these techniques might be difficult. Rehabilitation results inadequate monitoring of critical health factors including temperature and blood oxygen levels. The suggested approach is creating a 3D - printed hand incorporating Internet of Things sensors. Paralysis patients can benefit from this hand real-time sensory feedback and customized therapy routines. IoT sensors are incorporated in the hand to track the patient's hand movements, blood oxygen saturation (SpO2), heart rate, and muscle activity (EMG). Arduino UNO serves as the central controller that facilitates automation, while the Wi-Fi module and voice control enhance user interaction. Additionally, Peltier modules keep the affected extremities at their ideal temperature, improving patient comfort and promoting blood flow. Suggested approach provides a multi-dimensional approach to enhance their entire quality of life and health condition for people with this difficult medical condition by combining all those features. © 2024 IEEE.

关键词： Sensory feedback

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NeuroSpectrum Deep Learning Architecture for Automated Spectrum Analysis Resource Optimization 3

NeuroSpectrum Deep Learning Architecture for Automated Spect...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Tamilselvan, K. Senthil, T. Buvaneswari, B. Santhiya, S. Soni, Mukesh Avs Engineering College Department of Information Technology Salem India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering chittoor India Panimalar Engineering College Department of Information Technology Chennai India Excel Engineering College Department of Computer Science and Engineering Namakkal India Lovely Professional University Department of Division of Research and Development Phagwara India

ISBN: (纸本)9798331508456

Reducing the availability of spectrum, as well as the nature of allocating channels in the present wireless telecommunication networks, is one of the critical issues facing modern wireless technology. Such spectrum sensing methods may not be highly flexible or efficient in environments where the radio spectrum undergoes frequent changes. This work proposes an improved deep learning spectrum sensing model based on CNN with reinforcement learning. The proposed system design calls for a pioneering dual mechanism, incorporating both spectrum analysis and a resource allocation learning model. The framework achieved remarkable performance metrics: 96 The spectrum sensing method achieved a 3% occurrence rate, a 94.8% precision rate for resource allocation, and a 92.7% efficiency in dynamic spectrum use. In the present investigation, we have employed it for different topologies and got 45% improvement in spectrum efficiency and 87% less interference than the conventional techniques. The work challenges deep learning-based spectrum sensing as well as management and indicates that they are prepared for the following generation of the wireless communication network. © 2025 IEEE.

关键词： Cognitive radio

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Systolic optimized adaptive filter architecture designs for ECG noise cancellation by Vertex-5

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Aerospace Systems 2023年第1期6卷 163-173页

作者： Jayapoorani, S. Pandey, Digvijay Sasirekha, N.S. Anand, R. Pandey, Binay Kumar Electronics and Communication Engineering Excel Engineering College TamilNadu Komarapalayam India Department of Technical Education Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh Lucknow226021 India Electronics and Communication Engineering Sona College of Technology TamilNadu Salem636005 India Electronics and Communication Engineering Sri Eshwar College of Engineering TamilNadu Coimbatore636005 India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Uttarakhand Pantnagar India

The adaptive sign least mean square (SLMS) filter may change dynamically depending on the filter output. Noise cancellation is one of the most common adaptive filter applications. In real-time applications such as medical computing, the speed of the process producing hardware is critical, hence, this study proposes the hardware implementation of the SLMS adaptive filter utilizing the Xilinx System Generator. The suggested design seeks to improve performance while decreasing convergence rate and route latency. In this paper, (i) we proposed a modified architecture for an 8-tap SLMS adaptive filter is built, and (ii) multiplier-less structure for a Modified DLMS Filter is introduced and compare the same. The ECG signal was evaluated using the intended architecture. The algorithm’s functionality is tested in MATLAB using varied ECG data from the MIT-BIH database as input. Using Xilinx system generator, the LMS and SLMS are developed, simulated, synthesized, and implemented on a Virtex-5 FPGA. When comparing Systolic Sign LMS Filter to LMS Filter, the result reveals a 5% drop in total real-time router completion and a drop in the number of adders and subtractors, as well as a 48.84% reduction in maximum combinational path latency. © 2022, Shanghai Jiao Tong University.

关键词： Adaptive filters

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Application of Natural Gradient Boosting and Grid Search in Fault Detection and Diagnosis of Electrical Power Grids 2

Application of Natural Gradient Boosting and Grid Search in ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Alzubaidi, Laith H. Prashanth, V. Ilakkia, T. Kumar, S. Senthil Devipriya, A.G. Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electrical and Electronics Engineering Excel Engineering College Namakkal India

ISBN: (纸本)9798331504960

In recent years, the demand of electrical power grids had increased tremendously since;girds are primary sources for meeting the electricity demand. However, due to occurrence of faults continuously the power grids are failing in providing constant supply. Several methods are employed for detecting the faults but they failed in effectively classifying types of faults. Therefore, this research proposes Natural Gradient Boosting and Grid Search (NGBoost-Grid Search) for detection and diagnosis of electric faults in electrical power grid. Initially, the data is taken from publicly available electric fault line detection dataset. Then, these current signals are pre-processed by using Interquartile Range (IQR), Hilbert Huang Transformation (HHT) and min-max normalization. After that, Elastic Net technique is employed for selecting the optimal features from pre-processed signals for eliminating irrelevant signals. Finally, selected optimal features are classified by using NGBoost for distinguishing the fault current signals and healthy current signals, Grid search is integrated to hyper tune for improving the performance of NGBoost and gave effective classification of fault and normal current signals. The proposed NGBoost-Grid Search achieved attained better results such as accuracy (96.47%), precision (97.63%), Recall (89.94%) and F1-score (93.29%) respectively when compared with existing Support Vector Machine (SVM). © 2024 IEEE.

关键词： Hilbert-Huang transform

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Generative Adversarial Network Based Real-Time Malware Detection in Cloud Environments 3

Generative Adversarial Network Based Real-Time Malware Detec...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Bittla, Srinivasa Rao Mahmood, Abothar Kokila, D. Siddiqua, Ayesha Ajitha, P.R. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Excel Engineering College Department of Artificial Intelligence & Data Science Namakkal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering Technology Department of Artificial Intelligence and Data Science Mamallapuram India

ISBN: (纸本)9798331508456

Recently, the rapid growth of cloud computing has created a vast attack surface for malware threats. The existing Support Vector Machine (SVM) accurately detected malwares although the dynamic nature of cloud environments led to high-dimensional data and class imbalance issues. Hence, this research proposes a Generative Adversarial Network (GAN) to address the class imbalance by creating synthetic malware samples. Initially, the input data is collected and fed into the analysis phase with Cuckoo Sandbox to generate malware behavior report in JavaScript Objective Notation (JSON) format. After that, Trend Micro Locality Sensitive Hashing (TLSH) is employed to measure the similarity among files and then the clustering phase groups the similar files as a cluster according to hash values. Further, the features are extracted from JSON format which contains information on Application Programming Interface (API) calls and their return codes. Then, the optimal features are selected using Chi-square to calculate the statistical independence on each feature and finally, GAN model is used to detect malware in cloud environments. From the results, the proposed GAN achieved better results in terms of accuracy (99.61%) when compared to existing Artificial Neural Network (ANN). © 2025 IEEE.

关键词： Computer viruses

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Hybrid Binary Whale Optimization Algorithm for Feature Selection Optimization Problem 1

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10th International Conference on Innovations in Computer Science and engineering, ICICSE 2022

作者： Ramya, V. Kumar, E. Vinay Gopika, G.S. Manoj, G. Excel Engineering College Tamilnadu Komarapalayam India Andhra Pradesh Visakhapatnam India Sathyabama Institute of Science & Technology Tamilnadu Chennai India

ISBN: (数字)9789811974557

ISBN: (纸本)9789811974540

In order to address feature selection (FS) optimization problems, a binary variant of hybrid particle swarm optimization (PSO) and whale optimization algorithm (WOA) is suggested in this article. The original PSOWOA is a hybrid algorithm that takes advantage of both PSO and WOA abilities. Although the higher performance, the initial hybrid solution is ideal for ongoing search space challenges. However, the selection of the feature is a binary task. Thus, a binary variant of the hybrid PSOWOA named HBPSOWOA is suggested to obtain a more significant reduction in dimension under the presumption of the feature selection accuracy. The K-nearest neighbors (KNN) is used to find the best possible solution. A benchmark database from the UCI machine learning repository is used to evaluate the HBPSOWOA algorithm. The findings show that when using a variety of performance metrics, best features, run time, and accuracy, HBPSOWOA significantly exceeded grey wolf optimizer (GWO), PSO, sine cosine algorithm (SCA), and WOA. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Attention-Based RNN for Alzheimer's Diagnosis: A Multi-Modal Approach with Enhanced Feature Retention Algorithm 1

Attention-Based RNN for Alzheimer's Diagnosis: A Multi-Modal...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Ramalingam, S. Gowsikraja, P. Loganathan, E. Logeswaran, A. Ramkumar, N. Geetha, K. Sri Eshwar College of Engineering Department of ECE Coimbatore India Kongu Engineering College Department of Computer Science and Design India Erode Sengunthar Engineering College Department of CSE Erode India Velalar College of Engineering and Technology Department of IT India SR University Department of ECE Warangal India Excel Engineering College Department of CSE India

ISBN: (纸本)9798331518943

Alzheimer's disease (AD) affects millions, and early detection is key. This paper introduces a new deep learning algorithm, Attention-Based Recurrent Neural Network (ABRNN), to detect AD using multi-modal data, including MRI scans and clinical features. ACO is used for preprocessing to enhance image quality and retain important patterns in MRI scans for diagnosis. SegNet isolates the brain regions that indicate the progression of AD through image segmentation. ResNet is used for feature extraction to capture deep hierarchical features from the segmented MRI scans to identify subtle but important features of brain degeneration. A memory-augmented block is added to store key feature patterns so the network can recall important information for AD diagnosis. A hybrid attention mechanism combines imaging and non-imaging features to focus on the relevant brain regions and clinical indicators. ABRNN is compared to 4 existing algorithms: CNN, LSTM, VGG-16, and Random Forest. In experiments, ABRNN achieved 97.85% accuracy, outperforming CNN (94.2%), LSTM (93.5%), VGG-16 (95.1%), and Random Forest (92.3%). ABRNN's performance is better because it retains important features through memory augmentation, extract features well with ResNet, and segments well with SegNet, so it's more accurate than traditional methods © 2024 IEEE.

关键词： Image segmentation

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A Review on Segmented Flood Attack Detection in Cloud Networks Using Machine Learning Methods 5

A Review on Segmented Flood Attack Detection in Cloud Networ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Dineshkumar, P. Geetha, K. Viswanathan, R.V. Sujatha, S. Rajan, C. Sona College of Technology Department of Information Technology Tamilnadu Salem India Excel Engineering College Department of CSE Tamilnadu Komarapalyam India Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Trichy India University College of Engineering Anna University Department of Computer Applications Tamilnadu BIT Campus Tiruchirappalli India K. S. Rangasamy College of Technology Department of AIML Tamilnadu Namakkal India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

Distributed Denial of Service (DDoS) attacks are one of the several issues disrupting the Internet at present. Cloud environments must have robust security mechanisms in place to ensure continuous service availability and lessen the impact of such attacks. The reason DDoS attacks are so difficult for Intrusion Detection Systems (IDS) is that they overwhelm system resources and complicate detection by flooding networks with malicious traffic. IDSs in the cloud use a variety of data mining and cyber analytics methods to detect and eliminate these threats. Numerous detection systems rely on machine learning algorithms, which have demonstrated potential in spotting malicious behavior and analyzing network patterns. With the help of the most recent datasets, this study seeks to improve detection skills by offering a thorough overview of the current DDoS threat landscape. Machine learning methods such as Random Forest and Gradient Boosting Machine (GBM) are used to categorize and forecast various DDoS attack types. To get into the study, a thorough method for anticipating and evaluating DDoS attacks was demonstrated. A comprehensive approach for predicting and analyzing DDoS attacks was examined to enter the research. © 2024 IEEE.

关键词： Network intrusion

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