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8th International Conference on electronics, communication and Aerospace Technology, ICECA 2024

作者： Kannappan, A. Kolagani, Nagesh Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The condition of the ocean is monitored through a network of acoustic sensor nodes. After the transmitter and receiver nodes are formed, they are placed in a shallow sea environment. Underwater communication takes more time, so optimization of it is a top priority. Cat swarm optimization method and sea lion optimization algorithms are two examples of currently used bio-inspired algorithms. By analyzing the output parameters of both algorithms, we can see which one is more optimized. When comparing cat swarm optimization techniques to, the sea lion algorithm provides superior energy efficiency by lowering the amount of power required to process acoustic sensor node data. Existing cat swarm optimization Algorithms compared with sea lion optimization algorithm. The amount of energy difference that occurs in the two algorithms is nearly up to 18 % more than energy efficiency. Sea lions and cat swarm based upon condition need the individual or group performance, these recreate the bio inspire algorithm nodes. © 2024 IEEE.

关键词： Underwater acoustic communication

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Development Of An Image Restoration Algorithm Utilizing Generative Adversarial Networks (GAN’s) For Enhanced Performance In engineering Applications: A Comprehensive Approach To Improving Image Quality And Clarity Through Advanced Machine Learning Techniques

Development Of An Image Restoration Algorithm Utilizing Gene...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Manjunath, T.C. Pavithra, G. Samyama Gunjal, G.H. Ninawe, Swapnil S. Dept. of Electronics & Communication Engineering Rajrajeswari College of Engineering Bangalore India Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Computer Science & Engineering University Visvesvaraya College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331505134

Image restoration, a critical task in computer vision and image processing, focuses on recovering degraded or damaged images to their original, high-quality state. This paper introduces an innovative approach to image restoration using Generative Adversarial Networks (GANs). GANs, a prominent deep learning framework, consist of two neural networks—a generator and a discriminator—that compete to produce and evaluate realistic images. The generator creates images, while the discriminator distinguishes between real and generated ones, refining the generator's capability through adversarial training. Leveraging GANs' ability to learn complex image features, the proposed algorithm restores degraded images affected by noise, blur, and low resolution, producing high-quality, realistic results. Simulation outcomes demonstrate significant advancements in image restoration, showcasing GANs as a powerful tool for addressing challenges in this domain. The study underscores the potential of GANs in generating visually appealing restorations and advancing the state-of-the-art in image processing and restoration tasks. © 2024 IEEE.

关键词： Generative adversarial networks

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A Dual-Action Approach to Mosquito Control: Harnessing Mosquitoes Affinity for Light with Metofluthrin's Lethality 18

A Dual-Action Approach to Mosquito Control: Harnessing Mosqu...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Mittal, Manisha Singh, Mahender Dewan, Prachi Singh, Prabhmeet Dept. of Electronics and Communication Engineering New Delhi India Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9789380544519

Amidst rapid urbanization and fluctuating climatic patterns, the world witnessed a surge in mosquito-borne diseases, escalating global health concerns. Mosquitoes, often dismissed as mere irritants, are carriers for a spectrum of conditions, including malaria, dengue, and the Zika virus, with WHO reporting large numbers of dengue cases annually. This review delves deep into the observation of mosquito behavior and the challenges in eradicating these pests, emphasizing the inefficacy of singular sensory targeting repellents. This paper introduces a pioneering device, combining the mosquito's affinity towards light with the suffocating effects of metofluthrin, offering a holistic solution. By the combination of advanced engineering with a comprehensive understanding of mosquito behavioral dynamics, this device promises not only to repel but to substantially reduce these vectors, fostering safer indoor spaces. Through a detailed exploration of current methodologies and the introduction of this integrated strategy, this paper provides insights into creating a healthier, mosquito-free environment. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： fluorescent light metofluthrin mosquito control mosquito-borne diseases multifaceted sensory system

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LGDF-Net: Local and Global Feature Based Dual-Branch Fusion Networks for Deepfake Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5489-5500页

作者： Long, Min Liu, Zhenyu Zhang, Le-Bing Peng, Fei Guangzhou University School of Electronics and Communication Engineering Guangdong Guangzhou510006 China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China Huaihua University School of Computer and Artificial Intelligence Huaihua418000 China Guangzhou University School of Artificial Intelligence Guangdong Guangzhou510006 China

With the rapid development of Deepfake technology, social security is facing great challenges. Although numerous Deepfake detection algorithms based on traditional CNN frameworks perform well on specific datasets, they still suffer from overfitting due to an over-reliance on localized artifact information. This limitation leads to degraded detection performance across diverse datasets. To address this issue, this study proposes a dual-branch fusion network called LGDF-Net. LGDF-Net uses a dual-branch structure to process the local artifact features and global texture features generated by Deepfake separately, preserving their unique characteristics. Specifically, the local compression branch utilizes a specially designed local compression module (LCM) that allows the network to focus more accurately on key regions of localized artifacts in Deepfake faces. The global expansion branch enhances the analysis of the global facial context through a global expansion module (GEM), which captures image context information and subtle texture features more comprehensively. Additionally, the proposed multi-scale feature extraction module (MSFE) delves into image features at various scales, enriching the extraction of detailed information. Finally, the multi-level feature fusion strategy (MLFF) improves the integration of local and global features through multiple layers, enabling the network to learn the intrinsic connections between these two types of features. A series of experimental validations demonstrate that the proposed scheme outperforms many existing detection networks in terms of accuracy and generalization ability. © 1991-2012 IEEE.

关键词： Feature extraction

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Food Classification using Machine Learning 1

Food Classification using Machine Learning

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1st International Conference on Computational Intelligence for Security, communication and Sustainable Development, CISCSD 2024

作者： Santhosh Krishna, B.V. Mai, P. Kiran Muthumanickam, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350365405

The use of machine learning techniques in food categorization has received a lot of interest since it has the potential to transform a variety of industries. Food categorization has developed as a critical application of machine learning techniques in recent years, providing potential answers to a variety of problems in the food business, health monitoring, and nutritional evaluation. This paper presents a comprehensive review of methodologies, datasets, and challenges associated with food classification using machine learning. We will have an overview of linear regression. The publicly available datasets are commonly used for training and evaluation purposes, along with challenges such as data imbalance, handling missing values, and limited annotated data. We will perform EDA, finding products by categories and checking missing values and duplicates from the dataset. Finally, we identify future research directions aimed at improving the accuracy, scalability, and real-world applicability of machine learning-based food classification systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Power and Temperature Analysis in FPGA: Nine's Complement Operation on Artix Platform 1

Power and Temperature Analysis in FPGA: Nine's Complement Op...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Singh, Gurdeep Mittal, Sonam Chitkara University Department of Electronics and Communication Engineering Rajpura India Chitkara University Department of Computer Science and Engineering Rajpura India

ISBN: (纸本)9798331518943

Reducing power consumption in communication circuits will help to reach sustainable, green communication in the era of increasing need for energy-efficient digital systems. By prioritizing the execution of a special one-of-a-kind operation on an ArtixFPGA board, this work aims to tackle the energy limit problem in digital circuits. First prioritizing power economy, the proposed design offers the best performance in the execution of basic mathematical operations. Reflecting the energy-conscious attitude of the system, the design exhibited a 1.479 watts overall power consumption over testing. Moreover under control was thermal management;a thermal margin discovered at 57.2°C enabled to maintenance of the system within safe operating limits free from too much heat generation. The board was efficiently distributing its resources using only 2.0% I/O to create the required capacity. Emphasizing the need for hardware optimization for building sustainable digital and communication systems, this work shows the opportunities of basic operations, including nine's complement, to help support energy-efficient design methodologies on FPGA platforms. © 2024 IEEE.

关键词： Energy efficiency

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Convolutional Neural Network in Combination with Multiple Machine Learning Models to Recognize the Spoken Digits 2

Convolutional Neural Network in Combination with Multiple Ma...

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

作者： Soundarya, M. Anusuya, S. Kumar, R. Dinesh Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

The classification of audio signals plays a crucial role in various applications including speech recognition, analysis of environmental audio, and music classification. Digit recognition, as a subset of audio classification, plays an essential role in domains comprising automated systems, telecommunications, transport systems, and security. A review of recent research papers in the field of audio classification, focusing on the use of Artificial Intelligence such as Neural Networks, and ML - Machine Learning, along with its hybrid models, has been conducted. The objective is to enhance training accuracy using the Mel Spectrogram in CNN - Convolutional Neural Networks and improve prediction accuracy with ML models. Various hybrid approaches, including CNN combined with Logistic Regression, K-Nearest Neighbor, Random Forest, Decision Tree, Naive Bayes, Support Vector Machine, Gaussian Mixture Model, and Linear Discriminant Analysis, have been evaluated. The proposed method, CNN combined with Logistic Regression, establishes the highest accuracy compared to the other models. Experimentation was conducted using the Audio MNIST dataset, which includes 10 distinct classes of digits. Numerical results reveal that the CNN + Logistic Regression accomplished the highest Accuracy value of about 99%, and CNN + K-Nearest Neighbor achieved 98%, making CNN + Logistic Regression the most effective model for digit recognition within the audio classification field. © 2024 IEEE.

关键词： Convolutional neural networks

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Utilizing Blockchain Technology for Health Insurance in Healthcare Sector 1

Utilizing Blockchain Technology for Health Insurance in Heal...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Buvana, J. Gayathri, R. Sri Venkateswara College of Engineering Department of Computer Science & Engineering Sriperumbudur India Sri Venkateswara College of Engineering Department of Electronics and Communication Engineering Sriperumbudur India

ISBN: (纸本)9798350377002

This project delves into the integration of blockchain technology within the healthcare sector, with a focus on managing health insurance processes. Blockchain, renowned for its decentralized and immutable characteristics, emerges as a promising solution to tackle diverse challenges in health insurance, ranging from fraud prevention to ensuring data security and streamlining claims processing. Through harnessing blockchain's distributed ledger technology, stakeholders in the healthcare ecosystem can securely store and exchange sensitive health insurance data while upholding transparency and integrity throughout the entire process. This research endeavour will explore the practical implementation of blockchain-based solutions in healthcare insurance, examining their potential advantages, obstacles, and ramifications for enhancing efficiency and trust in the healthcare insurance domain. By conducting thorough research and analysis, this project aims to shed light on the real-world applications of blockchain technology in reshaping health insurance systems, thereby contributing to the advancement of accessibility, affordability, and reliability of healthcare services for both individuals and organizations. © 2024 IEEE.

关键词： Blockchain Technology Byzantine Fault Tolerance (BFT) Data Security Decentralized Systems Fraud Prevention Public Key Infrastructure (PKI)

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Exploring SVM Kernel and Voting Classifiers for Malware Classification in Android Permission Systems 1

Exploring SVM Kernel and Voting Classifiers for Malware Clas...

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1st International Conference on Innovations in communications, Electrical and computer engineering, ICICEC 2024

作者： Singh, Gurdeep Mittal, Sonam Singh, Preet Chitkara University Department of Electronics and Communication Engineering Rajpura India Chitkara University Department of Computer Science and Engineering Rajpura India

ISBN: (纸本)9798350376517

Sometimes referred to as hostile software, malware is a subset of programming codes aimed to exploit weaknesses in computer system security. Not only through data theft but also by letting Distributed Denial of Service (DDoS) attacks, malware seriously threatens companies. Nowadays, machine learning is a required technique for spotting these threats since the diversity and volume of malware keep increasing. This study aims to determine whether an application device's requested rights are benign or questionable. In performance analysis, the Voting Classifier (VC) ensemble approach was better than the Support Vector Machine (SVM) algorithm with an accuracy rating of 97.9%. Anti-malware preventive measures support the guarantee of safe digital surroundings. © 2024 IEEE.

关键词： Android malware

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Leveraging EfficientNet-B3 with Advanced Fine-Tuning for Precise Breast Cancer Classification 16

Leveraging EfficientNet-B3 with Advanced Fine-Tuning for Pre...

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16th IEEE International Conference on Computational Intelligence and communication Networks, CICN 2024

作者： George, Geomol Anusuya, S. Simats School of Engineering Department of Electronics and Communication Chennai India Simats School of Engineering Department of Computer Science Chennai India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505264

This study explores the use of the pre-trained EfficientNet-B3 model for classifying breast cancer images into benign, malignant, and normal categories. The model was finetuned with layer-specific adjustments, advanced regularization techniques, and comprehensive layer fine-tuning to enhance its ability to detect complex patterns. The results demonstrated exceptional performance, with benign cases achieving a precision of 98%, recall of 94%, and F1 score of 96%;malignant cases with precision of 90%, recall of 96%, and F1 score of 93%;and normal cases showing precision of 94%, recall of 96%, and F1 score of 95%. The model also achieved 99.84% training accuracy, 0.2964 training loss, and 95.25% test accuracy with a loss of 0.4333. These findings underscore the potential of this advanced EfficientNet-B3-based method to improve diagnostic accuracy and support timely, reliable treatment decisions in breast cancer care. © 2024 IEEE.

关键词： Ultrasonic imaging

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