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Jellyfish Search Algorithm for UWSNs Energy Management

Jellyfish Search Algorithm for UWSNs Energy Management

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Senthilkumar, K.K. Manhas, Saloni Gnanapriya, K.Mythili Alsalami, Zaid Rai, Avinash Kumar Praveenkumar, C. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Department of Computer Application Chandigarh Engineering College Punjab Mohali India Nehru Arts and Science College Department of Mathematics Coimbatore India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Ies College of Technology Department of Electrical & Electronics Engineering M.P. Bhopal India Sri Ramakrishna Engineering College Department of Eee Coimbatore India

ISBN: (纸本)9798350390759

The results of this research measures the efficacy of JSA in managing energy resources of UWSNs. Thus, it has been observed that JSA has better performance than the LEACH algorithm with to some extent since the energy used by cluster heads by JSA is approximately 27. 8J and a network lifespan of 810 rounds, whereas LEACH's protocol only succeeds in 31 rounds. 5J and 750 rounds. This demonstrates that JSA can also achieve better clustering with less electrical power for the clusters to handle and for the cluster heads consuming 16J of energy. Based on the outcomes presented in this paper, we can conclude that JSA is a beneficial approach for increasing the energy efficiency and network durability in the UWSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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An Effective Detection of Ransomware Using Improved Long Short-Term Memory

An Effective Detection of Ransomware Using Improved Long Sho...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： V. Vijayalakshmi Muhamed Fallahhusein D Balamurugan T. Suresh K Muthu krishnan Department of Computer Science with Artificial Intelligence Lakshmi Bangaru Arts and Science College Melmaruvathur India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Sona College of Technology Salem India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Ransomware detection focuses on recognizing the unusual system activities which indicate an unauthorized data access and restriction. It continuously monitors the system behavior continuously to detect potential threats before significant damage occurs. By identifying anomalies and patterns, it effectively distinguishes ransomware attacks from normal operation. However, Ransomware attacks cause a serious threat because of far-reaching effects and its zero-day variants are even more hazardous. This research proposes Improved Long Short-Term Memory (LSTM) to detect the ransomware effectively and accurate detection of Ransomware. The proposed improved LSTM learns the sequential patterns in system behavior and detects subtle abnormalities over time. The enhanced model improves detection accuracy by learning past data and adapts to new attack variants including zero-day threats. By identifying anomalies, the proposed method enables timely detection and reduces the impact of ransomware attacks. To select the appropriate features, the Dung Beetle Optimization (DBO) is used to optimize the feature subset for better detection performance. The proposed improved LSTM obtains a better accuracy of 95% when compared to existing method like cost sensitive Pareto Ensemble classifier (CSPE-R).

关键词： Deep learning Accuracy Heuristic algorithms Feature extraction Ransomware Security Integrated circuit modeling Long short term memory Optimization Monitoring

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A Study on Battery Storage Exploitation using Reinforcement Learning 2

A Study on Battery Storage Exploitation using Reinforcement ...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Narayana, Patel Badari Mahalakshmi, Geetha Yadi Reddy, M. Kumar, B. Suresh Prakash, R.B.R. Suganthi, D. Dept of Mechanical Engg Hyderabad India Eee Easwari Engineering College Chennai India Chaitanya Bharathi Institute of Technology Department of Eee Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Electrical and Electronics Engineering Vaddeswaram522302 India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Computer Science Chennai India

ISBN: (纸本)9781665456302

An integral part of the new smart grid, battery storage is becoming an increasingly common requirement. Batteries are considered as an attractive alternative for other distributed intelligent energy resources because of their speed in responding to events like changes in renewable power or grid interruptions. Various research studied exist to explain about making money out of this talent. Any solution must take into account both the fast-paced nature of electrical phenomena and the more gradual changes in the relevant power markets. An area of AI called reinforcement learning has demonstrated promising results in optimizing difficult tasks with high degrees of uncertainty. As a result, the need for first-of-its-kind smart buildings is growing in tandem with the complexity of decentralized power systems and the widespread use of renewable energy sources. The initial portion of the algorithm is based on deep learning using recurrent neural networks to deal with the unknowns of power pricing and load needs in the future. The other method uses reinforcement learning to determine the best time to charge or discharge a battery bank based on profit, load, and grid peak conditions. This research study presents a reinforcement learning approach for battery scheduling, which is considered as a crucial component to meet the consumer objectives. The framework relies on a customer making a multi-criteria choice in order to maximize local energy production from batteries during peak demand. This will save costs by reducing the amount of power used by the grid. The reinforcement learning system used to choose the best battery scheduling actions uses predictions of available wind power. Consumers' understanding of what to do to maximize battery life in a variety of time-dependent settings is expanded thanks to the built-in learning mechanism. The established framework enables smart consumers to acquire knowledge of the stochastic environment and apply it to the process of making de

关键词： Reinforcement learning

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Our Seventh IMS Microwave Week 3MT Competition [IMS2023]

Our Seventh IMS Microwave Week 3MT Competition [IMS2023]

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作者： Hester, Jimmy G.D. Eid, Aline Bandler, John W. Kiley, Erin M. Tajik, Daniel Atheraxon AtlantaGA30308 United States Media Lab Massachusetts Institute of Technology CambridgeMA02139 United States McMaster University and Bandler Corporation HamiltonONL8S 4K1 Canada Massachusetts College of Liberal Arts North AdamsMA01247 United States Department of Electrical and Computer Engineering McMaster University HamiltonONL8S 4K1 Canada

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A Dynamic Model for Pauses in the Synthesized Speech of Mathematical Expressions in MathML 22

A Dynamic Model for Pauses in the Synthesized Speech of Math...

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Proceedings of the 14th International Conference on Education technology and computers

作者： Adriana Souza Diamantino Freitas Bahia Federal Institute of Education Science and Technology Brazil and Doctoral Program in Digital Media Faculty of Engineering Faculty of Engineering of University of Porto Portugal Department of Electrical and Computer Engineering Faculty of Engineering of University of Porto Portugal

ISBN: (纸本)9781450397766

Voice synthesizers still present several challenges in the speech of mathematical content, as spoken mathematics has quite peculiar rules. In the synthesized speech, pauses help blind and visually impaired students identify the limits of mathematical operators and subexpressions. However, most studies on pauses define them uniformly or use simple punctuation marks to force the synthesizer to introduce pauses in certain parts of the expression. Speech is a dynamic process and pauses in expressions also need to be dynamic to make the synthetic speech of expressions more natural, as this can help in memorizing this type of content. This work proposed a dynamic model of pauses for mathematical expressions. Collected math expressions spoken by teachers were used to create the model. These expressions were useful for identifying patterns and creating a linear regression model. Blind and visually impaired students evaluated the model. Some improvements were observed when we compared the synthesized mathematical expressions with the model and Audiomath, the parameter tool used in this study.

关键词： Accessibility Synthesized Speech Mathematics Visual Impairment

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ECBA-MLI: Edge Computing Benchmark Architecture for Machine Learning Inference

ECBA-MLI: Edge Computing Benchmark Architecture for Machine ...

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IEEE International Conference on Edge Computing (EDGE)

作者： Mathias Schneider Ruben Prokscha Seifeddine Saadani Alfred Hö ß Department of Electrical Engineering Media Technology and Computer Sciences Ostbayerische Technische Hochschule Amberg-Weiden Amberg Germany

ISBN: (数字)9781665481403

ISBN: (纸本)9781665481410

Recent developments in Artificial Intelligence (AI) research enable new strategies for running Machine Learning (ML) models. Evaluating application data on a remote server used to be common practice. However, novel AI accelerators herald a paradigm shift by moving the inference step from the cloud closer to the application in the edge. This approach increases service availability while significantly reducing latency. Nevertheless, choosing the right target platform and model for inference is a challenge that depends on the use case and its non-functional requirements. In this work, we present an Edge Computing Benchmark Architecture for Machine Learning Inference (ECBA-MLI) which provides a universal, reproducible, and comparable solution for evaluating non-functional criteria such as latency and energy consumption for edge deployment scenarios. It further evaluates results for six state-of-art object detection models deployed on twenty-one different platform configurations.

关键词： Runtime Computational modeling Image edge detection computer architecture Machine learning Object detection Benchmark testing

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A Novel Approach on Advanced Fruit Disease Identification Using RCNN

A Novel Approach on Advanced Fruit Disease Identification Us...

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

作者： Kumar R P. Boopathi M. Sundar Rajan Department of Computer Science (PG) Kristu Jayanti College (Autonmous) Bangalore India Department of Computer Science Shri Nehru College of Arts and Science Coimbatore Tamil Nadu India Faculty of Electrical and Computer Engineering Arba Minch Institute of Technology Arba Minch University Ethiopia

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The early diagnosis of diseases in fruits holds immense importance for agricultural industries, as it directly impacts production quality and quantity. This study introduces a novel approach utilizing Recursive Convolutional Neural Network (RCNN) for automated citrus plant disease detection. Through a comprehensive methodology involving data collection, image pre-processing, segmentation, feature extraction using Linear Principal Component Analysis (LPCA), and classification, the proposed RCNN algorithm showcases promising results. Experimental evaluation demonstrates the efficacy of the RCNN-based approach, with precision averaging at 0.929, recall at 0.946, accuracy at 0.945, F-measure at 0.943, and minimal time complexity. Compared to traditional manual inspection methods, RCNN offers a more accurate and efficient solution, significantly reducing the burden on farmers and experts while enhancing fruit disease detection capabilities. Overall, the proposed RCNN-based approach presents a significant advancement in fruit disease identification, offering precise and timely detection to mitigate agricultural losses. With its superior performance metrics and streamlined process, RCNN stands as a promising tool for revolutionizing fruit disease diagnosis and improving agricultural productivity.

关键词： Productivity Plant diseases Accuracy Sensitivity Robustness Convolutional neural networks Medical diagnosis Time complexity Diseases Principal component analysis

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A Design of an Effective Methodology to Detect Glaucoma Disease using Intelligent Learning Strategy 9

A Design of an Effective Methodology to Detect Glaucoma Dise...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Shobana, D. Akilarasu, G. Sangeetha, J. Priya, V. Banu Karkuzhali, S. Malathi, P. Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Lovely Professional University Department of Computer Science and Engineering Punjab India S. A. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Mathematics Tamil Nadu Chennai India R.M.K. Engineering College Department of General Engineering Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350384208

Globally, glaucoma is now among the leading causes of blindness. It is a chronic, irreversible illness. Screening for glaucoma and detecting it early are crucial in the fight against this illness. Here, deep learning shows great promise. Two deep learning architectures, the Convolutional Neural Network (CNN) model and the Improved Learning Strategy to Predict Glaucoma (ILSPG) model, have been evaluated in this article. In order to diagnose glaucoma, the DCNN method is utilized. Ultimately, this area of study aims to help doctors diagnose glaucoma by developing methods to spot the disease in retinal fundus pictures. Because glaucoma can have both beneficial and bad effects on the model, the result of the model might be good or bad. Several measures are utilized in the process of evaluating models, including accuracy, precision, specificity, and reliability. In order to determine the accuracy of the proposed model, an additional comparison study is conducted. The results are contrasted with a deep learning-based classification system that uses convolutional neural networks. The results demonstrate, without a doubt, that the proposed system is effective. © 2023 IEEE.

关键词： Convolutional neural networks

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NeRF-enabled Analysis-Through-Synthesis for ISAR Imaging of Small Everyday Objects with Sparse and Noisy UWB Radar Data

NeRF-enabled Analysis-Through-Synthesis for ISAR Imaging of ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Md Farhan Tasnim Oshim Albert Reed Suren Jayasuriya Tauhidur Rahman Manning College of Information and Computer Sciences University of Massachusetts Amherst MA USA Kitware INC NC USA Schools of Arts Media and Engineering and Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA Halıcıoğlu Data Science Institute University of California San Diego CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Inverse Synthetic Aperture Radar (ISAR) imaging presents a formidable challenge when it comes to small everyday objects due to their limited Radar Cross-Section (RCS) and the inherent resolution constraints of radar systems. Existing ISAR reconstruction methods including backprojection (BP) often require complex setups and controlled environments, rendering them impractical for many real-world noisy scenarios. In this paper, we propose a novel Analysis-through-Synthesis (ATS) framework enabled by Neural Radiance Fields (NeRF) for high-resolution coherent ISAR imaging of small objects using sparse and noisy Ultra-Wideband (UWB) radar data with an inexpensive and portable setup. Our end-to-end framework integrates ultra-wideband radar wave propagation, reflection characteristics, and scene priors, enabling efficient 2D scene reconstruction without the need for costly anechoic chambers or complex measurement test beds. With qualitative and quantitative comparisons, we demonstrate that the proposed method outperforms traditional techniques and generates ISAR images of complex scenes with multiple targets and complex structures in Non-Line-of-Sight (NLOS) and noisy scenarios, particularly with limited number of views and sparse UWB radar scans. This work represents a significant step towards practical, cost-effective ISAR imaging of small everyday objects, with broad implications for robotics and mobile sensing applications.

关键词： Radar cross-sections Imaging Radar imaging Reconstruction algorithms Robot sensing systems Neural radiance field Rendering (computer graphics) Sensors Noise measurement Ultra wideband radar

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Deep Learning Approach to Predict Alzheimer's Disease through Magnetic Resonance Images

Deep Learning Approach to Predict Alzheimer's Disease throug...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Ramadhan, Gilang Titah Pusparani, Yori Farady, Isack Furqon, Elvin Nur Lin, Chih-Yang Chao, Wen-Hung Rani Alex, John Sahaya Aparajeeta, Jeetashree Lung, Chi-Wen Asia University Department of Computer Science and Information Engineering Taichung Taiwan Budi Luhur University Department of Visual Communication Design Jakarta Indonesia Asia University Department of Digital Media Design Taichung Taiwan Mercu Buana University Electrical Engineering Jakarta Indonesia Department of Electrical Engineering Yuan Ze University Chung-Li Taiwan National Central University Department of Mechanical Engineering Taoyuan Taiwan Innovation and Research Vellore Institute of Technology Division of Healthcare Advancement Tamil Nadu India Vellore Institute of Technology School of Electronics Engineering Tamil Nadu India Asia University Department of Creative Product Design Taichung Taiwan

ISBN: (纸本)9798350324174

Alzheimer's disease is the most common type of dementia that causes many of the functions of the human brain to be severely weakened. To date, there has not been a cure for Alzheimer's disease. Therefore, early diagnosis is needed using MRI images with the help of a classification program. Deep learning using the Convolutional Neural Network (CNN) method is receiving increasing attention because of its excellent performance. Its architecture can be modified according to user needs based on the data to be processed and the approach for classifying, detecting, and segmenting visual objects. In this paper, we offer a classification of Alzheimer's disease using one of the architectures on CNN, namely Visual Geometry Group-19 (VGG-19), with a sagittal view of MRI images with an image size of 229 x 229 pixels. The classification accuracy of the described method is 94% for the validation set. © 2023 IEEE.

关键词： Magnetic resonance imaging

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