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

作者： Rajathi, N. Divya, S. Anitha Elavarasi, S. Saritha, G. Jeya ramya, V. Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

The increasing integration of Cyber-Physical Systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection Systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection System (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments. © 2024 IEEE.

关键词： Network intrusion

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Chaotic Hippopotamus based Energy Efficient Routing for Wireless Sensor Networks 4

Chaotic Hippopotamus based Energy Efficient Routing for Wire...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Vedanarayanan, V. Jayaraman, Venkatesh Lakshmi, R.T.Thivya Surendran, R. Sathyabama Institute of science and technology Department of Electronics and Communication Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

The energy efficient routing in wireless sensor network (WSN) is crucial for enhancing the network longevity. For this, energy efficient routing protocol is introduced with cluster based routing. Initially, the sensor nodes are deployed randomly in the network area and are clustered together using the Chaotic Hippopotamus Optimization (ChHipO) Algorithm. The algorithm considers remaining energy, delay and distance as the multi-objective fitness factor for choosing the cluster head (CH) using the ChHipO Algorithm. The proposed ChHipO Algorithm integrates iterative chaotic mapping in the hippopotamus optimization algorithm for improving the rate of convergence. The analysis of ChHipO Algorithm based on residual energy, network lifetime, throughput and end-to-end delay acquired the values of 0.87, 99.728, 99.589 and 3.01 respectively. © 2024 IEEE.

关键词： Sensor nodes

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PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight Factor of Whale Optimization Algorithm 2

PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight F...

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2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Habelalmateen, Mohammed I. Archana Reddy, R. Reddy, Chetana Nagarjuna, N. Pradeep Kumar, S. Universiti Tun Hussein Onn Malaysia Parit Raja Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Telangana India Oxford College of Engineering Department of Electronics and Communication Bangalore India Cvr College of Engineering Department of Csit Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

In the landscape of optimization for mitigating the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, prevalent techniques like the tone reservation scheme and l-norm Cyclic Algorithm (LNCA) algorithm grapple with inherent challenges linked to computational complexity and iterative algorithm derivation. While strides have been made with models such as Twin Symbol Hybrid Optimization by the premise of Partial Transmit Sequence (TSHO-PTS) and Advanced Switching Differential Evolution (ASDE), persistent concerns related to symbol overlaps and algorithmic limitations underscore the need for further refinement in computational efficiency within OFDM-based communication systems. In response to these challenges, the proposed Nonlinear Inertia Weight Factor within the Whale Optimization Algorithm (NIWF-WOA) emerges as a solution. Comparative analysis against existing methods like LCNA, Modified Dragonfly Optimization algorithm (MDOA), TSHO-PTS, and ASDE reveals the superior performance of NIWF-WOA. Numerical validation affirms its efficacy, with a recorded in Bit Error Rate (BER) of 10-2.5, SNR 10-3.4 and PAPR of.10-4.2, underscoring the substantial improvement achieved by NIWF-WOA in optimizing PAPR reduction for MIMO-OFDM systems. © 2024 IEEE.

关键词： Landscape of optimization MIMO-OFDM system Non-linear weight factor Peak to Average Power Ratio Whale optimization

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Fingerprint Matching for Noisy and Distorted Patterns Using a Siamese Network With ResNet50 and Multihead Attention

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IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Kumar Lehana, Parveen Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii Ali Alzubi, Ahmad University of Jammu DSP Laboratory Department of Electronics Jammu180006 India University of Jammu Department of Computer Science and IT Jammu180006 India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab Rajpura140401 India Manipal University Jaipur Department of Computer Science & Communication Engineering Jaipur303007 India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa42-200 Poland Faculty of Informatics and Software Engineering National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv03056 Ukraine King Saud University Community College Department of Computer Science Riyadh11451 Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5033-5040页

作者： William, P. Patil, Jaikumar M. Panda, Sunita Venugopal, Anita Vidyullatha, Pellakuri Kumar, Nellore Manoj Jandwani, Aman Department of Information Technology Sanjivani College of Engineering SPPU Department of Computer Science and Engineering Shri Sant Gajanan Maharaj College of Engineering SGBAU Department of Electrical Electronics and Communication Engineering GITAM School of Technology IT Unit Dhofar University Department: Computer Science and Engineering Koneru Lakshmaiah Education Foundation Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Research Task

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (%) and power consumption (%). The results of the study reveal that the proposed approach is impactful in the smart collection and management of waste. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： And hygienic environment Deep learning Internet of things (IoT) Smart cities Smart waste

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A Hybrid CNN-LSTM-PSO Framework for Enhanced Cybersecurity Threat Detection and Classification

A Hybrid CNN-LSTM-PSO Framework for Enhanced Cybersecurity T...

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

作者： Akilandeswari, S. Soumya, T.R. Sumathi, S. Sushith, Mishmala Brinda, V. Rajan, S. E.G.S. Pillay Engineering College Department of Artificial Intelligence & Data Science Nagapattinam611002 India Prathyusha Engineering College Department of Computer Science Engineering Tiruvallur602025 India Velammal Engineering College Department of Electronics and Communication Engineering Chennai600066 India Adithya institute of Technology Department of Information Technology Kurumbapalayam India K.Ramakrishnan College Of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode638012 India

ISBN: (纸本)9798331509828

With the advancement of digital era comes an increased value for cybersecurity, and these need to be dealt with using advanced techniques. Deep learning - metaheuristic optimization hybrid model for advanced threat detection and mitigation is presented in this paper. With a Convolutional Neural Network (CNN) for feature extraction and Long Short-Term Memory (LSTM) for sequence analysis, the proposed framework integrates all the modules in order to provide robust cyber-attack detection. In order to optimize the parameters of the model, a Particle Swarm Optimization (PSO) algorithm is used in order to achieve better classification accuracy and faster computation. Benchmark datasets are used for extensive experiments, where existing methods outperformed in terms of precision, recall, and Fl-score. The results demonstrate that with detected flagged FPs and the hybrid model, the increased detection rates are achieved without the reduction in true negatives, rendering it a practical solution for real world applications. This research adds to the field by providing a solution to cybersecurity problems that is scalable and adaptable to the present and beyond. © 2025 IEEE.

关键词： Convolutional neural networks

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An Empirical Study of Covid-19 Effect on Health Care Workers' Career Development

An Empirical Study of Covid-19 Effect on Health Care Workers...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Acar, Kutay Orman, Gunce Keziban Turhan, Sultan Nezihe *** Electronic Publishing and Communication Services Inc Istanbul Turkey Galatasaray University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350324457

The COVID-19 pandemic has affected the lives and health of many people. Among them, health care workers indeed played the biggest role. In this work, we investigate the health care workers job applications, and the job postings change with the time series analysis perspective. The dataset is provided by ***, which is the foremost job-searching company in Turkey. Since the data set includes job applications over every region of country, the results reveal important facts about health care workers in Turkey. We aim to study the possible effect of vaccination on health care workers job search decisions. © 2023 IEEE.

关键词： COVID-19 Healthcare Data Healthcare Workers Healthcare Workers' Career Development Healthcare Workers' Wellbeing

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Extraction of Electroencephalography (EEG) Features to categorize the Mental Disorder 15

Extraction of Electroencephalography (EEG) Features to categ...

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

作者： Sarvalingam, Parameswaran Vajravelu, Ashok Palpandi, S. Abinaya, B. Prasad, B. Sathesh, S. Kavin Kumar, K. Manivel, Murugesan Ksr College of Engineering Department of Biomedical Engineering Tiruchengode637215 India Faculty of Electrical and Electronic Engineering Uthm Malaysia Adhiparasakthi College of Engineering Department of Information Technology India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Sona College of Technology Department of Electronics and Communication Engineering India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350370249

Time series analysis techniques are major in quantitatively studying the electroencephalogram (EEG). Whether conducting fundamental neurophysical or clinical research, a quantitative approach to EEG is essential. It can also be significant for strictly clinical research for psychological effects in indoor. Millions of neurons make up the human brain, which is crucial for regular behavior and the body in response to both internal and external inputs and motor and sensory impulses. These neurons regulate how the body and brain communicate, and decipher brain signals or pictures utilized to comprehend how the brain processes information. Here, it is examine EEG data that show variations in frequency bands among several mental conditions, including bipolar illness and autism, to identify patterns among diseases. Different signals are seen in patients with autism syndrome disorder, general epilepsy, and normal controlled participants (ASD). For every reading genre, subjects are sorted and chosen at random: Four participants were chosen for ASD, and three each for controlled subjects and general epilepsy. The recordings can achieve the best classification accuracy for the subjects that match their waveforms thanks to the inferred characteristics that have been retrieved. The values of the attributes are crucial to analyze and determine the sort of condition the person is experiencing. As previously indicated, although the readings may differ, Kuppuswamy's socioeconomic status scale classifies the degree of harshness of the recordings, which is quite helpful in predicting whether the person is prone to the kind of mental condition. The findings are transmitted to healthcare professionals via the Internet of Things (IoT) for additional consultation. © 2024 IEEE.

关键词： Autism Spectrum Disorder (ASD) Electroencephalography (EEG) Epilepsy Kuppuswamy's socioeconomic status scale

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Empowering Women's Safety: Artificial Intelligence and IoT-Enabled Smart Wearable Device for Real-Time Monitoring and Emergency Assistance 8

Empowering Women's Safety: Artificial Intelligence and IoT-E...

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8th International Conference on electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Clement, Jayapratha Ramya, R. Gomathi, T. Surendran, R. Kalyani, G. Karpaga Vinayaga College of Engineering and Technology Department of Computer science and engineering Tamil Nadu Chengalpattu India Chennai Institute of Technology Department of Biomedical Engineering Chennai India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India RuralCare Innovators LLP Tamil Nadu Madurai India

ISBN: (纸本)9798331520762

Women's safety remains a pressing concern worldwide, necessitating multifaceted approaches to address physical, emotional, and social threats. Despite strides in gender equality, women still face significant risks of violence and harassment in diverse settings, from homes and workplaces to public spaces. It is estimated that globally, 137 women are killed each day by an intimate partner or a family member. Furthermore, in the United States, 19.3% of women have experienced rape at some point in their lives, and 43.9% have been subjected to sexual violence other than rape. Technological innovations, like safety apps and improved public transport security, offer additional protective layers. Many safety apps and devices are inaccessible due to cost, lack of awareness, or technological literacy, particularly in underprivileged or rural areas. Our aim is to create a system that will help make every location and every minute safer for women. The goal of this invention is to design an Android app that women may use in any type of scenario in day-to-day life. The proposed Android-based application will subsequently update the user's position from dawn to evening. When an individual crosses the geographical region, an SMS alert is delivered to the selected contact already updated in the mobile app and also send an alert when the user vital signs exceeds threshold values. © 2025 IEEE.

关键词： Emergency services

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A Semi-Supervised Deep Learning Approach for Intrusion Detection and Classification for the Internet of Things

Biomedical Materials and Devices

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Biomedical Materials and Devices 2025年 1-17页

作者： Uthradevi, G. Thiruvasagam, P. Mythili, S. Manoj, S. Oswalt Department of Electronics and Communication Engineering Chennai Institute of Technology Tamil Nadu Kundrathur Chennai 600069 India Department of Electrical and Electronics Engineering Chennai Institute of Technology Tamil Nadu Kundrathur Chennai 600069 India Department of Biomedical Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul 624622 India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India

Intrusion Detection Systems (IDS) are essential for protecting networks from cyber threats and ensuring the security of critical infrastructures. This paper introduces an innovative hybrid model that combines Deep Belief Networks (DBNs), Principal Component Analysis (PCA), and Support Vector Machines (SVM) with adversarial learning to enhance the detection of both known and emerging cyber threats. The proposed model achieves a strong balance of accuracy, efficiency, and scalability, making it highly effective for real-world applications. By incorporating adversarial learning, the system can detect zero-day exploits—previously unseen attacks—and adapt to evolving threats. This is achieved by training the model on adversarial samples, which improves its resilience to variations in attack patterns. SVM further enhances the model’s ability to classify known and unknown threats with high precision. The model was rigorously tested on two benchmark datasets, NSL-KDD and CICIDS2017, achieving outstanding results. It demonstrated a high accuracy rate of 99.73% on NSL-KDD and a low false positive rate of 0.55%, while on CICIDS2017, it achieved a false positive rate of 0.73%. The model also outperformed existing approaches in precision, recall, and F1-score, particularly in detecting complex attack types such as R2L (Remote-to-Local), U2R (User-to-Root), and Botnet attacks. Additionally, PCA reduced the feature space by 40%, significantly lowering the model’s inference time to just 9.0 ms per sample. This makes the model highly suitable for real-time intrusion detection in high-traffic network environments, where speed and efficiency are critical. Overall, this hybrid model strengthens cybersecurity defenses and contributes to building resilient infrastructures. By effectively detecting and mitigating cyber threats, it supports the creation of safer, fairer, and more inclusive digital communities. The integration of adversarial learning and dimensionality reduction ensures rob

关键词： Adversarial learning Cyber security Deep belief networks Dimensionality reduction Feature extraction IDS Networks PCA SVM

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