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Receiver-Based Data Communication for Wireless Sensor Networks 2

Receiver-Based Data Communication for Wireless Sensor Networ...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Chitra Kiran, N. Parkash, Jai Cheerla, Sreevardhan Shanmugaraja, P. Manohara, M. Madan, Parul Alliance University Department of Electronics and Communication Engineering Karnataka Bangalore560068 India Gulzar Group of Institutes Department of Computer Science and Engineering Punjab Khanna141401 India Koneru Lakshmaiah Education Foundation Department of Electronics and Communications Engineering Andhra Pradesh Vaddeswaram522302 India Sona College of Technology Department of Information Technology Tamil Nadu Salem636005 India Mohan Babu University School of Engineering Andhra Pradesh Tirupati517102 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350315905

As the Web of Things (IoT) has created throughout recent years, unlicensed radio range has become more clogged with brilliant wireless gadgets that are utilized in an assortment of use spaces, prominently the 2.4 GHz ISM band. One of the easiest and most productive answers for energy-compelled networks is the wake-up receiver (WuRx). Rather than utilizing a conventional radio, this innovation empowers energy-independent on-request communication for persistent observing. One of the WSN methods that utilizes the most investment is directing. Consequently, planning an energy-effective steering procedure is fundamental. This exploration proposes Grouped WuRx based on Multicast wake-up (CWM), a progressive Wake-up Receiver-based directing convention that all the while streamlines energy use and time productivity for indoor conditions. In our proposed technique, bunches are made inside the organization. Estimation discoveries show that, in contrast with past directing conventions, our proposed arrangement has much better execution as far as conveyance delay and further developed energy effectiveness. © 2023 IEEE.

关键词： Wireless sensor networks

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Device-to-Device Communications aided Integrated Sensing and Communication Networks: A Joint design of Bandwidth and Power Allocations

Device-to-Device Communications aided Integrated Sensing and...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Dou, Chenglong Huang, Ning Wu, Yuan Qian, Liping Quek, Tony Q.S. University of Macau State Key Laboratory of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519301 China Zhejiang University of Technology College of Information Engineering Hangzhou310023 China Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

Integrated sensing and communication (ISAC) networks constitute a crucial paradigm for facilitating numerous advanced services in future wireless networks. This paper investigates the joint bandwidth and power allocations for device-to-device (D2D) communications aided ISAC in which D2D pairs complete their data transmissions by using the bandwidth allocated by the base station (BS) while providing sensing services for the BS. To this end, we formulate a joint optimization of bandwidth allocation and power allocations for both the target sensing and data transmission of each D2D pair, with the objective of maximizing a system-wise gain that accounts for both performances of target sensing and D2D data transmission. Despite the formulated optimization problem is strictly non-convex, we develop an efficient algorithm based on Lagrangian duality and sequential convex programming for solving it. Simulation results demonstrate that our proposed D2D communications aided ISAC is both accurate and efficient over several benchmark schemes. © 2024 IEEE.

关键词： Bandwidth

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Skin Disease Detection and Diagnosis Using Deep Neural Network

Skin Disease Detection and Diagnosis Using Deep Neural Netwo...

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International Conference on Computing and Networking technology (ICCNT)

作者： K Kavitha K Gopalakrishnan M Parvathi M Indumathi S Kavitha S Balaji Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Information Technology Velalar College of Engineering Thindal India Department of Artificial Intelligence and DataScience Nandha Engineering College Erode India Department of Computer Science and Design Erode Sengunthar Engineering College Perundurai Road Erode India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep learning stands poised to revolutionize the field of dermatology, offering innovative solutions to pressing challenges in skin disease management. By harnessing the capabilities of these advanced models, we can address crucial aspects such as lesion classification, severity assessment, and early detection, paving the way for more effective patient care. Consider the scenario of deep learning models trained on extensive repositories of dermatological images. These sophisticated algorithms can meticulously scrutinize skin lesions, examining nuances in color, texture, and vascular patterns with unparalleled precision. This granular analysis empowers the model to categorize lesions accurately, distinguishing between conditions like eczema, psoriasis, and potentially malignant melanomas. Armed with this invaluable information, dermatologists can expedite diagnoses with greater confidence, facilitating prompt intervention for improved patient outcomes. Yet, the application of deep learning extends beyond mere classification, extending to the nuanced evaluation of disease severity. By scrutinizing parameters such as lesion size, color variations, and surrounding tissue health, these models can predict the progression of skin conditions. Such insights not only inform treatment strategies, guiding medication intensity and surgical interventions but also enable clinicians to monitor patient progress, ensuring adaptive and personalized care plans.

关键词： Deep learning Image color analysis Psoriasis Malignant tumors Transfer learning Eczema Predictive models Feature extraction Skin Lesions

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

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

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Women in Computing (InCoWoCo), International Conference for

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

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

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

关键词： Deep learning Image quality Image segmentation Recurrent neural networks Accuracy Magnetic resonance imaging Feature extraction Alzheimer's disease Random forests Long short term memory

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A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis 50th

A Multi-Scale Decomposition MLP-Mixer for Time Series Analys...

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50th International Conference on Very Large Data Bases, VLDB 2024

作者： Zhong, Shuhan Song, Sizhe Zhuo, Weipeng Li, Guanyao Liu, Yang Chan, S.-H. Gary Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangdong Provincial Key Laboratory IRADS and Department of Computer Science BNU-HKBU United International College China Guangdong Enterprise Key Laboratory for Urban Sensing Monitoring and Early Warning Guangzhou Urban Planning and Design Survey Research Institute China

Time series data, including univariate and multivariate ones, are characterized by unique composition and complex multi-scale temporal variations. They often require special consideration of decomposition and multi-scale modeling to analyze. Existing deep learning methods on this best ft to univariate time series only, and have not sufciently considered sub-series modeling and decomposition completeness. To address these challenges, we propose MSD-Mixer, a Multi-Scale Decomposition MLP-Mixer, which learns to explicitly decompose and represent the input time series in its diferent layers. To handle the multi-scale temporal patterns and multivariate dependencies, we propose a novel temporal patching approach to model the time series as multi-scale patches, and employ MLPs to capture intra- and inter-patch variations and channel-wise correlations. In addition, we propose a novel loss function to constrain both the mean and the autocorrelation of the decomposition residual for better decomposition completeness. Through extensive experiments on various real-world datasets for fve common time series analysis tasks, we demonstrate that MSD-Mixer consistently and signifcantly outperforms other state-of-the-art algorithms with better efciency. © 2024, VLDB Endowment. All rights reserved.

关键词： Mixers (machinery)

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Cybersecurity in Internet of Things Networks using Deep Learning Models

Cybersecurity in Internet of Things Networks using Deep Lear...

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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： V Anitha C.G. Naveen Kumar Ramu Kuchipudi Khushboo Sharma J Muralidharan R. Rajagopal Department of Computer Science and Engineering Imayam College of Engineering Kannanur Thuraiyur Trichy Tamil Nadu India Department of Studies and Research in Computer Science Karnataka State Open University Mysuru-06 Karnataka India Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering JECRC University Jaipur Rajasthan Department of ECE KPR Institute of Engineering and technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering Alliance College of Engineering & Design Alliance University Bangalore Karnataka India

The Internet of Things and global grid that connects millions of sensors and devices have made distributed software and services ubiquitous. IoT’s vast reach and economic value make it a natural target for hackers, making security the industry’s top worry. Privacy has always been studied, but IoT design and new intimidations make most standard safeguards ineffective. Deep learning’s data, odd case technique, and ability to detect unexpected threats may help detect IoT intrusions. The “Internet of Things” includes computers, sensors, network infrastructure, software, networks, users, gateways, and services (IoT). Due to the increasing importance of Internet of Things (IoT) innovations, growth and management are huge. Data protection and privacy have recently caught users’ attention. As social media grows, more people connect. As connections grow, more safe spaces are needed. This article discusses data protection, including deep learning models, Machine Learning (ML) concepts, safety and security, and cybersecurity management. To illustrate IoT network vulnerability, convolutional neural networks (CNN), Long Short-Term Memory (LS TM), and CNN combinations have been studied and compared. We can detect IoT cyberattacks with over 99% accuracy using Modbus network traffic data.

关键词： Deep learning Biological system modeling Data protection Telecommunication traffic Software Data models Sensors

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Enhanced Multi-Scale Convolutional Neural Network with Attention Mechanism for Accurate and Efficient Automated Hematological Diagnostics from Blood Smear

Enhanced Multi-Scale Convolutional Neural Network with Atten...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Santosh Kumar B CH Hussaian Basha Jini Kumari M Pradeep Kumar S V. Sathya K Rajaram Department of Computer Science and Engineering New Horizon College of Engineering Bengaluru Department of Electrical and Electronics Engineering SR University Warangal Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Department of Computer Science Design Department Rajalakshmi Engineering College Thandalam Chennai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Biomedical Engineering Velalar College of Engineering and Technology Thindal Erode

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Hematological disorders require accurate and timely diagnosis for effective treatment and patient management. This research develops a new framework that uses deep learning to rapidly identify blood disorders from peripheral blood pictures. Our system uses an AECM and MSCNN combination to diagnose anemia, infections, and leukemia with greater accuracy. The MSCNN captures image details at multiple scales while the AECM detects meaningful areas in blood images which boosts detection results. Our system reached 98.5% total accuracy after validating its performance on public blood smear images. The detection method delivered 97.8% precision and 98.2% recall values during testing. Our diagnostic framework makes blood test results quicker and more affordable while reducing patient labs demands in healthcare environments that face resource constraints.

关键词： Deep learning Accuracy Attention mechanisms Medical services computer architecture Convolutional neural networks Blood Medical diagnostic imaging Diseases Testing

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Enhanced Honey Badger Algorithm for Resource Allocation and Task Scheduling in Cloud Environment

Enhanced Honey Badger Algorithm for Resource Allocation and ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： R Rajagopal AR Arunarani A Arivarasi Anup Ingle Ravichandran T R. Vijaya Prakash Department of Computer Science & Engineering Alliance College of Engineering & Design Alliance University Bengaluru Karnataka India Department of Compuational Intelligence School of Computing College of Engineering and Technology SRM Instutite of Science and technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru Karnataka India Department of Electronics and Telecommunications Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science and Engineering Karpagam College of engineering Coimbatore Tamilnadu India School of Computer Science and Artificial Intelligence SR University Warangal India

Cloud computing is a technology that offers dynamic resources to the users with enhanced scalability and flexibility. The major concerns in cloud environment that has direct impact on the throughput of cloud system and contentment of cloud users is the problem of task scheduling and resource allocation. The time taken to execute the tasks and cost incurred for the computation are the significant objectives that affect the performance of the cloud system. This work proposes a multi-objective task scheduling and resource allocation technique using meta-heuristic optimization algorithm. Enhanced Honey Badger algorithm (EHBA) is employed to schedule the tasks and allocate computing resources effectively while minimizing the time and cost objectives. The performance of the proposed technique is assessed in a simulation environment, CloudSim, which mimics the settings of real cloud computing system. Various measures such as Time-to-Execute, Cost-to-Compute, Task-to-Resource utilization and Time-to-Respond are used to assess the performance of the suggested EHBA method for efficient task scheduling and resource allocation. The experimental results produced by the proposed method is also compared against the state-of-the-art studies that employ meta-heuristic optimization algorithms. The outcomes revealed that EHBA outperformed other methods by executing the tasks in a minimum time with reduced cost and maximum utilization of the computing resources.

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A Time-Dependent Mathematical Model for COVID-19 Transmission Dynamics and Analysis of Critical and Hospitalized Cases with Bed Requirements

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International Conference on Machine Vision and Augmented Intelligence, MAI 2022

作者： Singh, Avaneesh Bajpai, Manish Kumar Gupta, Shyam Lal Department of Computer Science and Engineering PDPM Indian Institute of Information Technology Design and Manufacturing Madhya Pradesh Jabalpur482005 India Department of Physics Government College Bassa Himachal Pradesh University Gohar Himachal Pradesh Mandi175029 India

ISBN: (纸本)9789819901883

A time-dependent SEAIHCRD model is the extension of the SEIR model, which includes some new compartment that is asymptomatic infectious people, hospitalized people, critical people, and dead compartments. In this article, we analyzed six countries, namely the United States, Brazil, India, South Africa, Russia, and Mexico. A time-dependent SEAIHCRD model calculates the magnitude of peaks for exposed people, asymptomatic infectious people, symptomatic infectious people, hospitalized people, the number of people admitted to ICUs, and the number of COVID-19 deaths over time. It also computes the spread scenario and endpoints of disease. The proposed model also involves asymptomatic infectious individuals. To estimate the various parameters, we first collect the data and fit that using the Levenberg–Marquardt model for death cases. Then, we calculate infection rate, recovery rate, case fatality rate, and the basic reproduction number over time. We calculate two types of case fatality rates: one is the daily case fatality rate, and the other is the total case fatality rate. The proposed model includes the social distance parameter, various age classes, hospital beds for severe cases, and ICU beds or ventilators for critical cases. This model will be useful to determine various essential parameters such as daily hospitalization rate, daily death rates, including the requirement of normal and ICU beds during peak days of infection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using computer Vision and Deep Learning

Multi-Modal Emotion and Gesture Recognition System for Human...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Rekha R Nair Tina Babu Kishore S Smitha Shivaswamy Aruna S Manchikalapudi Bhargavi Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Application Nitte Institute of Professional Education Nitte(Deemed to be University) Mangaluru Karnataka India Department of Computer Applications Acharya Institute of Technology Bengaluru India School of Science and Computer studies CMR University Bangalore India Department of Computer Science and Engineering(AIML) Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like “need help” or “need water.” An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare.

关键词： Hands Human computer interaction Emotion recognition computer vision Accuracy Animals Face recognition Computational modeling Gesture recognition Mental health

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