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5th International Conference on Smart Sensors and Application, ICSSA 2024

作者： Saranya, S. Sharmila, B. Shashank Kanna, R. Nesasudha, M. Karthikeyan, T.A. Department of Electronics and communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Biomedical Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electrical Engineering and Computer Science Monash University Malaysia Department of ECE Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354294

Around 50 million people in the world are affected by chronic seizures and epilepsy. Seizures are usually diagnosed by CT scans, MRI scans, EEGs, and blood tests. However, there is a need for devices that help people to detect and receive alerts before the onset of a seizure episode. This paper addresses the need to design a seizure detection device that works on the principle of changing muscle permittivity. As chronic seizures can be life-threatening, there is a need for devices that detect seizures before their onset, to help a person take precautionary measures. Though there are a few devices that work for this application, there is always room for improvement and innovation. Therefore, we have proposed a novel microstrip patch antenna that acts as a miniaturised biosensor. One of the major symptoms of seizure is the change in muscle stiffness. Considering this factor, our antenna is designed to detect the change in the permittivity caused due to the change in muscle tension which is reflected through the S parameter graph that gives us the information to detect a seizure attack beforehand. The return loss of the antenna is improved - 27.596 dB by introducing the concept of substrate integrated waveguides. The antenna will be integrated into an embedded system design to enable it to function as a wearable device. Also, the SAR properties are analysed and optimized as per the FCC standards as it would be modelled as a wearable device. The antenna has been designed, simulated, and analysed using the ANSYS HFSS tool. Further, a comparative study on the various patch shapes, substrate materials, and SIWs was also made using ANSYS HFSS. © 2024 IEEE.

关键词： Wearable antennas

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A Robust Methodology to Detect Glaucoma Disease using Modified Deep Learning Principles with Image Processing Logics

A Robust Methodology to Detect Glaucoma Disease using Modifi...

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

作者： Charulatha, R.T. Nirmaladevi, G. Vasumathi, G. Hemajothi, S. Suresh, K. Karthikeyan, C. Rajalakshmi Institute of Technology Department of Ece Chennai India Sri Sairam Institute of Technology Dept of Computer Science and Engineering Chennai600044 India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Ece Chennai India Qis College of Engineering and Technology Department of Electrical and Electronics Engineering AndhraPradesh Ongole India R.M.K. Engineering College Department of Ece Kavaraipettai Chennai India

ISBN: (纸本)9798331543617

Glaucoma is a leading cause of irreversible blindness, requiring early and accurate diagnosis to prevent vision loss. This study proposes a robust methodology for detecting glaucoma using a hybrid approach that integrates deep learning (Autoencoder) with a machine learning classifier (Multi-Layer Perceptron). The methodology utilizes image processing techniques, including noise reduction and data augmentation, to enhance the quality of retinal fundus images. Feature extraction was performed using Convolutional Neural Networks (CNNs) and Gabor filters, followed by dimensionality reduction through Principal Component Analysis (PCA) and wavelet decomposition. The model was tested on multiple publicly available datasets, including RIGA, HRF, ACRIMA, and Kaggle. The hybrid model achieved an accuracy of 97.1% on the RIGA dataset, 95.6% on the HRF dataset, and 96.3% on the ACRIMA dataset. The model outperformed traditional classifiers such as Support Vector Machine (SVM) and Random Forest, which achieved accuracies of 92.3% and 94.5% on the RIGA dataset, respectively. The study demonstrates that combining deep learning with machine learning classifiers, along with effective preprocessing, significantly improves glaucoma detection accuracy. Furthermore, explainable AI techniques were employed to enhance model interpretability, ensuring that clinical practitioners can trust and understand the model's decisions. © 2024 IEEE.

关键词： Wavelet decomposition

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Chaotic Particle Swarm Optimization Based Shortest Path Routing in VANET 7

Chaotic Particle Swarm Optimization Based Shortest Path Rout...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Ramya, R. Yazhini, A. Geetha, T. Devi, R. Sahila Kavin, K.S. Dhanalakshmi, S. Saveetha School of Engineering Department of Nano Electronics Materials and Sensors ECE Chennai India Anna University College of Engineering Department of Computer Science & Engineering Chennai600025 India Agni College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Palkulam629401 India Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Erode Sathyamangalam638401 India

ISBN: (纸本)9798350372816

The present study examines the Vehicle Ad hoc Networks (VANETs) exhibit unexpected behavior due to their high mobility and random network structure. Including ad hoc networks, a variety of problems, including repeated connected breakdowns, flexibility, multi-hop transmission of information, and information loss, impact Transmission Control Protocols (TCP). To overcome this problem, this paper suggests employing zone-based routing in VANETs while taking mobility into account. The routing technique is implemented with a hybrid optimization strategy. The most significant swarm intelligence (SI) algorithm for solving optimization issues is the chaotic particle swarm optimization (CPSO) algorithm. Due to its simplicity, which involves just a few parameters, CPSO has grown to be considered one of the most frequently used optimization techniques. When resolving challenging optimization problems, the routing algorithms experience high overhead or incompatibilities. A CPSO based shortest path routing method is presented for VANET that offers a high-quality path for connection among nodes, improving the distribution of packets percentage and preventing end-to-end delay, in order to enhanced routing effectiveness and throughput in prevailing approaches. These achievements show that CPSO performs adequately and is experiencing significant progress. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Hybrid Xception-LSTM Model for Remote Sensing: Advanced Urban Heat Island and Land Use Analysis

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Remote Sensing in Earth Systems Sciences 2024年第1期8卷 132-144页

作者： Singh, Ashutosh Kumar Deepika, Ch L. N. Shahnaz, K.V. Bhagyalakshmi, L. Sharada, K. Sarupriya, S. Suman, Sanjay Kumar Department of Electronics and Communication Engineering I.E.T. Dr. Rammanohar Lohia Avadh University Ayodhya India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of ECE Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology TN Chennai India Department of ECE Rajalakshmi Engineering College TN Chennai India Department of Computer Science and Engineering Gitam (Deemed to Be University) AP Visakhapatnam India Department of ECE Velammal Engineering College TN Chennai India Department of ECE St. Martin’s Engineering College Telangana Secundrabad India

Urban heat islands (UHI) and land use changes are critical environmental issues affecting urban areas, contributing to increased temperatures, reduced air quality, and adverse health effects. This study proposes a Hybrid Xception-LSTM Model to address these challenges by leveraging both spatial and temporal data for accurate UHI and land use analysis using remote sensing imagery. The Xception model, pre-trained on ImageNet, is fine-tuned for spatial feature extraction, focusing on land surface reflectance, vegetation indices, and surface temperature data. The LSTM layer captures long-term dependencies in temporal sequences to enhance prediction accuracy. This hybrid model was evaluated against nine existing models, including CNN, ResNet50, and EfficientNet. The proposed model achieved an accuracy of 94.38%, outperforming DenseNet121 (90.22%) and EfficientNet (90.12%). In addition, the model demonstrated superior precision (0.93), recall (0.91), and F1-score (0.92) compared to other models while maintaining competitive computational efficiency with a training time of 110.6 s and inference time of 3.0 s. These results highlight the effectiveness of combining spatial and temporal analysis for UHI prediction and land use classification. The proposed approach can aid urban planners in mitigating UHI effects and making informed land use decisions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Hybrid model Land use analysis LSTM Remote sensing Spatial features Temporal dependencies Urban heat island Xception

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GUI Based Modeling and Simulation of Maximal Calcium Ion Bifurcation on Neuron Response and its Applications 4

GUI Based Modeling and Simulation of Maximal Calcium Ion Bif...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Ahmed, Taslima Ishrat, Zaiba Vats, Satvik Bharadwaj, Sandhya Singh, Hitendra Iimt College of Engineering Department of Electronics and Communication Engineering Greater Noida India Meerut Institute of Technology Department of Electronics and Communication Engineering Meerut India Graphic Era Hill University Department of Computer and Science Engineering Dehradun India Iimt College of Engineering Department of Ece Greater Noida India Delhi Technological University Delhi India

ISBN: (纸本)9798350307757

In spite of sodium, potassium and chloride ions, calcium, magnesium and cobalt ions also plays a vital role for complex signaling in brain. Calcium ions (Ca2+) regulate neuronal activity in homo- and hetero cellular control brain physiology. Without calcium ion signaling, all transcriptional networks, and ion exchangers in brain may cause sudden stop of all physiological functions. Wrong interactions between Ca2+ and reactive other species signaling coordinate in neurodegenerative disorders, perturbed energy metabolism, and alterations of disease-related synaptic dysfunction. Therefore, in this paper, review of Ca2+ has been modeled with original H-H equations and discusses deregulation of Ca2+ for different neurological related diseases as Alzheimer, Parkinson, coma etc. These channels activate due to depolarization and inactivate very little and slowly during the whole action potential generation phase. Calcium ion bifurcation was first characterized by Fatt and Katz (1953). From researcher's simulation and formulation it was concluded, there are various subtypes channel in neuronal *** sub-channels serve a vital role in cellular signaling in neuron cell. Based on the neural functionality, calcium ion bifurcation is depended on calcium channel types Lca, Tca and Nca type. Lca -type currents are the main calcium ion response observed in muscular cells, where they initiate function of the respective cells. Tca type calcium ion response is activated by depolarization of ion. Nca type calcium ion channel provided only neuronal *** paper represents the bifurcation level of calcium ion response on the neuron functionality and an investigation on the properties of Ca2+ ion response has been observed. This paper also explains the extension of original H-H model for neuron at excitable state and is analyzed with GUI panel with proper simulated version. © 2024 IEEE.

关键词： Neurons

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Traffic Congestion Reduction Using Fuzzy Optimization Scheme in Heterogeneous Vehicular Communication Network

Traffic Congestion Reduction Using Fuzzy Optimization Scheme...

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

作者： Nagababu, K. Vanathi, A. Ratheesh, R. Nalini, Chekuri Karthikeyan, G. Vinnarasi, F.Sangeetha Francelin Aditya University Department of Computer Applications Andhra Pradesh Surampalem India Rajalakshmi Institute of Technology Department of Ece Chennai India Agni College of Technology Department of Electronics and Communication Engineering Chennai600130 India School of Engineering Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Department of Ece Sree Sainath Nagar Andhra Pradesh Tirupati India Department of Vlsi Microelectronics Chennai India St.Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331543617

VANETs emphasize their similarities and challenges compared to MANETs, particularly concerning dynamic node movement and routing protocols. It highlights existing research efforts, including routing protocol proposals and optimization algorithms, addressing congestion, delay reduction, and resource management. Proposed TCRFOS approach for congestion reduction in VANETs through fuzzy optimization techniques. VANETs, a type of MANETs, pose unique challenges due to the dynamic nature of vehicle movements and network conditions. Existing routing protocols adapted from MANETs struggle to address the rapid changes in node locations and wireless channels. To tackle this, the TCRFOS approach integrates fuzzy logic and resource management to accurately predict resource survival time, optimizing traffic flow and minimizing congestion. Through fuzzification, fuzzy inference, and defuzzification, TCRFOS effectively manages resources, enhancing network efficiency. The document describes the system model, explaining how the method considers aspects such as the likelihood of road collapse and the road utilization ratio to enhance the road network while reducing congestion. Additionally, the fuzzy optimization process is explained, highlighting the components involved and their roles. Results from experimentation using the proposed ERCRMs protocol demonstrate significant improvements in various metrics such as network throughput, delay, routing overhead, transmission accuracy, and energy efficiency compared to existing methods. This underscores the effectiveness of the TCRFOS approach in enhancing communication efficiency, reliability, and energy conservation in VANETs, positioning it as a promising solution for congestion mitigation in dynamic vehicular environments. © 2024 IEEE.

关键词： Traffic congestion

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LDCP: A Novel Approach to Predict Fake Reviews in Online Social Network by using Learning based Data Classification Principle

LDCP: A Novel Approach to Predict Fake Reviews in Online Soc...

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

作者： Mercy, S.Sudha Venkatesan, S. Bharaneedharan, M. Nayagan, S. Venkateswarlu, L. Padmaja, Nimmagadda R.M.K. Engineering College Department of Computer Science and Design Chennai India Qis College of Engineering and Technology Department of Electrical and Electronics Engineering AndhraPradesh Ongole India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India Qis College of Engineering and -Technology Department of Electrical and Electronics Engineering Andhra Pradesh Ongole India Department of Ece Tirupati India

ISBN: (纸本)9798331543617

The proliferation of online reviews has significantly impacted consumer decision-making processes, making the detection of fake reviews crucial for maintaining trust in e-commerce platforms. This study presents a novel approach for detecting fake reviews using machine learning (ML) and deep learning (DL) models, with a focus on achieving high accuracy through advanced feature extraction techniques and data augmentation. Various models, including classical ML algorithms such as Support Vector Machine (SVM) and Random Forest, alongside DL models like Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Cascaded Multi-Layer Perceptron (MLP), were evaluated. Feature extraction methods like TF-IDF, GloVe, and combined approaches were used to enhance performance. The DL model RoBERTa achieved the highest accuracy of 99.5%, while Cascaded MLP reached 99.2%. Classical models like Random Forest and SVM also performed well, achieving accuracies of 97.1 % and 96.8%, respectively. Data augmentation techniques, such as back-translation, further improved the models' ability to handle imbalanced datasets. The results demonstrate that combining DL architectures with advanced feature extraction leads to superior performance in fake review detection. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

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Intelligent Automation & Soft Computing 2023年第4期36卷 929-955页

作者： Lamiaa A.Abou Elazm Walid El-Shafai Sameh Ibrahim Mohamed G.Egila H.Shawkey Mohamed K.H.Elsaid Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Microelectronics Electronics Research InstituteAlnozhaEgypt Department of Electronics and Electrical Communications Engineering Ain Shams UniversityCairoEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh 11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Biometric security is a growing trend,as it supports the authentication of persons using conﬁdential biometric *** of the transmitted data in multi-media systems are susceptible to attacks,which affect the security of these *** systems provide sufﬁcient protection and privacy for *** recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of *** addition,they have not been studied on different and large biometric *** point that deserves consideration is the hardware implementation of cancellable biometric recognition *** paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic *** rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates,and hence enhance the security and privacy of *** suggested cryptosystem adds signiﬁcant permutation and diffusion to the encrypted biometric *** introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against *** addition,a Field Programmable Gate Array(FPGA)implementation of the pro-posed system is *** obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes,such as Double Random Phase Encoding(DRPE)to reveal superiority,and hence high recogni-tion performance of the proposed cancellable biometric recognition *** obtained results prove that the proposed cryptosystem enhances the security and leads to better efﬁciency of the cancellable biometric recognition system in the presence of different types of attacks.

关键词： Information security cancellable biometric recognition systems cryptanalysis 3D chaotic map encryption FPGA

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Improving the Performance of Network-on-Chip by Utilizing Fault Zone Tiling to Reuse Trace Buffers 5

Improving the Performance of Network-on-Chip by Utilizing Fa...

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

作者： Suseela, R. Valli Sabu, Neethu Anna Andrews, S.Kevin Revathy, S. Revathy, L. Narayanan, K.Lakshmi Francis Xavier Engineering College Department of ECE Tamil Nadu Tirunelveli India K J Somaiya College of Engineering Department of Electronics and Telecommunication Mumbai India Dr. MGR Educational Educational and Research institute Department of Computer Applications Chennai-95 India Francis Xavier Engineering College ECE Department Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331530013

Network-on-Chip (NoC) is employed in multi-core systems to enhance the data transfer capacity and adaptability. The Network-on-Chips (NoCs) experience dependability issues due to software faults. Tiling is a procedure used to enhance the accuracy of data in NoCs (Networks-on-Chip). The fault-masking technique prevents the need for rechecking by the block. At this location, the presence of X tracer and the process of tiling are ongoing. The proposed work is implemented by simulation using Xilinx 12.1. © 2024 IEEE.

关键词： Network-on-chip

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An Improved Bat Optimization Algorithm for Knowledge Discovery for Routing and Energy Efficiency in the Internet of Things Environment

An Improved Bat Optimization Algorithm for Knowledge Discove...

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

作者： Ramachandran, R. Kannagi, L. Maria Rubiston, M. Karthikeyan, Govindaraj Ratheesh, R. Abijith, G.R. Rajalakshmi Engineering College Department of Biomedical Engineering Chennai India Sri Sairam Institute of Technology Department of Computer and Communication Engineering West Tambaram Chennai44 India S.A. Engineering College Department of Ece Chennai India Department of Vlsi Microelectronics Chennai India Agni College of Technology Department of Electronics and Communication Engineering Chennai600130 India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331543617

An improved optimization algorithm, IBA, enhances routing and energy efficiency in IoT environments. As energy conservation becomes increasingly vital in our technological landscape, managing resource consumption is crucial for sustainability. The Internet of Things (IoT) has revolutionized connectivity, enabling diverse applications across various sectors, from smart homes to industrial systems. The impending 5G technology promises to accelerate this transformation further, offering unprecedented speed and coverage. However, with the exponential growth in mobile data transmission, network operators face the challenge of meeting escalating demands. Bio-inspired optimization techniques, like the Bat Algorithm (BA), have emerged as promising solutions due to their computational efficiency and applicability to IoT challenges. The proposed IBA builds upon BA principles, introducing novel enhancements to address specific limitations. Through methodical testing, IBA shows better results in packet delivery ratio, network throughput, energy efficiency, average delay, and routing overhead than current protocols. These findings highlight the capability of optimized algorithms to improve IoT system performance and address changing application needs. © 2024 IEEE.

关键词： Bat Algorithm (BA) Improved Routing and Knowledge Discovery Internet of Things (IoT)

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