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作者： Hammad, Mohamed Plawiak, Pawel Wang, Kuanquan Acharya, Udyavara Rajendra School of Computer Science and Technology Harbin Institute of Technology Harbin China Faculty of Computers and Information Menoufia University Menoufia Egypt Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore School of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan

Authentication is the process of verifying the claimed identity of the user. Recently, traditional authentication methods such as passwords, tokens, and so on are no longer used for authentication as they are more prone to theft and different types of violations. Therefore, new authentication approaches based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals are considered. Unlike other biometrics, ECG provides the assurance that the person is alive, and is considered as one of the most accurate recent methods for authentication. In this article, two end-to-end deep neural network models for ECG-based authentication are proposed. In the first model, a convolutional neural network (CNN) is developed and in the second model, a residual convolutional neural network (ResNet) with attention mechanism called ResNet-Attention is designed for human authentication. We have used 2-s duration ECG signals obtained from two ECG databases (Physikalisch-Technische Bundesanstalt [PTB] and Check Your Bio-signals Here initiative [CYBHi]) for authentication. Our proposed ResNet-Attention algorithm achieved an accuracy of 98.85 and 99.27% using PTB and CYBHi, respectively. The results obtained by our developed model show that the performance is better than existing algorithms and can be used in real-time authentication systems after the validation with more diverse ECG data. © 2020 John Wiley & Sons, Ltd

关键词： Electrocardiograms

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An Optimized Sensors Associated Forest Fire Prediction and Reporting System Based on Internet of Things technology

An Optimized Sensors Associated Forest Fire Prediction and R...

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

作者： P. Deepak Nidamanuri Srinu Benisha Maria Xavier R. Sravani Nandurkar Y Suha Afaneh Dept of Computer Science and Engineering Einstein Engineering college Tirunelveli Department of CSE QIS College of Engineering and Technology AndhraPradesh Department of ECE Jeppiaar Institute of Technology Kunnam Chennai Tamilnadu India Department of S&H QIS College of Engineering and Technology AndhraPradesh Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of cybersecurity faculty of information technology Zarqa university Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Forest fires pose a significant threat to the environment, leading to the destruction of ecosystems and contributing to climate change. This paper presents an optimized sensor-based forest fire prediction and reporting system leveraging Internet of Things (IoT) technology. The system integrates Wireless Sensor Networks (WSNs) and IoT-enabled devices to monitor environmental parameters, including temperature, CO2, and smoke levels. The system demonstrated high sensor accuracy, with temperature sensors showing a maximum deviation of ±0.5°C and gas sensors displaying an accuracy of ±2 ppm for CO2. Smoke detection accuracy was within ±0.05 ppm, making the system reliable for early fire detection. The system's response time improved significantly with increasing temperatures, detecting fire events within 10 seconds for temperatures above 100°C. The Wireless Sensor Network maintained a packet delivery ratio (PDR) of over 99% and an average latency of 200ms under high node density, ensuring timely transmission of critical data. With power consumption optimized for long-term deployment, sensor nodes were estimated to operate for up to 24 months. Overall, this system offers a cost-effective, accurate, and efficient solution for real-time forest fire monitoring and response, with potential applications in large, remote forest areas.

关键词： Temperature sensors Temperature measurement Wireless sensor networks Accuracy Forestry Real-time systems Internet of Things Time factors Reliability Monitoring

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Deep Learning: Classification and Automated Detection Earlier of Alzheimer's Disease Using Brain MRI Images

Deep Learning: Classification and Automated Detection Earlie...

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International Laser technology and Optics Symposium in Conjunction with Photonics Meeting 2020, ILATOSPM 2020

作者： Kadhim, Karrar A. Mohamed, Farhan Khudhair, Zaid Nidhal Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam ja'Afar Al-sadiq University Baghdad Iraq UTM-IRDA MaGICX Institute of Human Centered Engineering Universiti Teknologi Malaysia

Alzheimer's disease (AD) progression can be avoided by conducting diagnosis beforehand. This diagnosis acquired quick preventive care which could be possibly done by specialists. Fast and accurate evaluation at the earliest and most challenging stage were required to detect in the diagnosis of AD. In this paper, previous studies were reviewed into a better approach that recognizes the presence of disease in sagittal magnetic resonance automatically (MRI) images that are unusually used. The MRI brain images were used to identify and distinguish characteristics using a range of characteristics recognition techniques. The review of research papers on Alzheimer's Disease published in reputable journals from 2017 to 2020 were presented and discussion of various strategies related to the latest tools used in early diagnosis is our main focus in this study, which could enable researchers to understand current algorithms and techniques in this area, and eventually develop new and more effective algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Magnetic resonance

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The Low-Cost IoT Mornitoring the Quality of River: A Case Study of Wang River in Lampang Municipality

The Low-Cost IoT Mornitoring the Quality of River: A Case St...

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International computer Science and engineering Conference (ICSEC)

作者： Narong Krueganta Oran Chieochan Anukit Saokaew Ekkachai Yana Chaowoot Singkaew Weerachai Sawangtook Ekkarat Boonchieng Warakhom Wongchai Sakchai Srimakorn Faculty of Industrial Technology School of Industrial Technology Lampang Rajabhat University Lampang Thailand Faculty of Sciences School of Information Technology Lampang Rajabhat University Lampang Thailand Faculty of Science School of Environmental innovation Lampang Rajabhat University Lampang Thailand Faculty of Industrial Technology School of Computer Engineering Lampang Rajabhat University Lampang Thailand Department of Computer Sciences Faculty of Sciences Chiang Mai University Chiang Mai Thailand Faculty of Industrial Technology School of Industrial Electrical Engineering Lampang Rajabhat University Lampang Thailand Faculty of Industrial Technology School of Software Engineering Lampang Rajabhat University Lampang Thailand

The research project focuses on prototyping an IoT (Internet of Things) system for measuring and monitoring the quality of the Wang River in Lampang Municipality. The system utilizes EC (Electrical Conductivity), pH, and temperature sensors connected to an ESP32 microcontroller with a custom-developed board. Additionally, the IoT system is supported by a solar cell system to provide power. The software programming is implemented using Arduino C++ on the PlatformIO platform, which is integrated with Visual Studio code. The IoT platform chosen for this project is ThingsBoard, specifically the community version. The research findings indicate that the utilization of IoT technology combined with the sensors has improved the prototype of a low-cost IoT monitoring system for assessing the quality of the Wang River in Lampang Municipality.

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Energy Management for PV-Powered EV Charging With Grid Integration and Battery Energy Storage System using Dung Beetle Optimizer

Energy Management for PV-Powered EV Charging With Grid Integ...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Neelashetty K Ruchira Rawat Doradla Raja Ramesh Gordhan Jethava Praful V. Nandanka Natrayan L Department of Electrical and Electronics Engineering Guru Nanak Dev Engg College Bidar Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Mechanical Engineering Sri Vasavi Institute of Engineering and Technology Nandamuru Andhra Pradesh India Information Technology Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat India Department of Electrical Engineering Government College of Engineering Nagpur Maharashtra India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

An Energy Management (EM) System for Photovoltaic (PV)-powered Electric Vehicle (EV) charging with grid integration and Battery Energy Storage System (BESS) optimizes energy consumption by integrating renewable energy, storage, and grid capabilities. However, drawbacks include the high initial costs associated with installation and infrastructure, as well as on-going operational costs for managing and maintaining both the storage and charging systems. Furthermore, the intermittency of solar energy can lead to inefficiencies in utilizing Renewable Energy Sources (RESs), requiring additional storage capacity to ensure a reliable power supply, while emissions from battery production, operation, and disposal may counteract some of the system’s environmental benefits. To overcome these drawbacks, this manuscript proposes an approach for EM for PV powered EV charging with BESS. The suggested method is Dung Beetle Optimizer (DBO). The major objective of this work is to reduce operational costs, improve energy efficiency, and lower carbon emissions by improving the use of renewable energy, minimizing grid reliance, and optimizing battery storage operations. The DBO optimizes the scheduling of EV charging, the operation of battery storage (charging and discharging cycles), and the interaction with the grid. Artificial Neural Network (ANN), Genetic Algorithm (GA), Adaptive Interaction Artificial Neural Network (AI-ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) are some of the existing methods that are compared with the suggested method once it is implemented in MATLAB. By achieving the lowest operational cost of 0.121M$/year and reducing carbon emissions to 505.73ktons, the suggested DBO method outperforms traditional approaches while maintaining high energy efficiency and environmental sustainability in PV-powered EV charging with BESS.

关键词： Photovoltaic systems Renewable energy sources Costs Battery energy storage system Carbon dioxide Artificial neural networks Electric vehicle charging Energy efficiency Batteries Genetic algorithms

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Metaheuristic Clustering Protocol for Healthcare DataCollection in MobileWireless Multimedia Sensor Networks

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computers, Materials & Continua 2021年第3期66卷 3215-3231页

作者： G G.Kadiravan P.Sujatha T.Asvany R.Punithavathi Mohamed Elhoseny Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of Computer Science Pondicherry UniversityPuducherry605014India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Faculty of Computers and Information Mansoura UniversityEgypt Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudiIndia

Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare *** in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia *** compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia ***,clustering techniques are deployed to achieve low amount of energy *** current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency *** DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node *** addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)*** supports multi-hop data transmission that assists in effectively balancing the available *** simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse *** simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.

关键词： Smart sensor environment healthcare data multimedia big data processing clustering mobility energy efficiency

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One-for-all: bridge the gap between heterogeneous architectures in knowledge distillation 23

One-for-all: bridge the gap between heterogeneous architectu...

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Proceedings of the 37th International Conference on Neural information Processing Systems

作者： Zhiwei Hao Jianyuan Guo Kai Han Yehui Tang Han Hu Yunhe Wang Chang Xu School of information and Electronics Beijing Institute of Technology and Huawei Noah's Ark Lab. School of Computer Science Faculty of Engineering The University of Sydney Huawei Noah's Ark Lab. School of information and Electronics Beijing Institute of Technology

Knowledge distillation (KD) has proven to be a highly effective approach for enhancing model performance through a teacher-student training scheme. However, most existing distillation methods are designed under the assumption that the teacher and student models belong to the same model family, particularly the hint-based approaches. By using centered kernel alignment (CKA) to compare the learned features between heterogeneous teacher and student models, we observe significant feature divergence. This divergence illustrates the ineffectiveness of previous hint-based methods in cross-architecture distillation. To tackle the challenge in distilling heterogeneous models, we propose a simple yet effective one-for-all KD framework called OFA-KD, which significantly improves the distillation performance between heterogeneous architectures. Specifically, we project intermediate features into an aligned latent space such as the logits space, where architecture-specific information is discarded. Additionally, we introduce an adaptive target enhancement scheme to prevent the student from being disturbed by irrelevant information. Extensive experiments with various architectures, including CNN, Transformer, and MLP, demonstrate the superiority of our OFA-KD framework in enabling distillation between heterogeneous architectures. Specifically, when equipped with our OFA-KD, the student models achieve notable performance improvements, with a maximum gain of 8.0% on the CIFAR-100 dataset and 0.7% on the ImageNet-1K dataset. PyTorch code and checkpoints can be found at https://***/Hao840/OFAKD.

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A Three-Step Procedural Paradigm for Domain-Specific Social Media Slang Analytics

A Three-Step Procedural Paradigm for Domain-Specific Social ...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Aishwarya Sundaram Hema Subramaniam Siti Hafizah Ab Hamid Azmawaty Mohamad Nor Institute for Advanced Studies Universiti Malaya Kuala Lumpur Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Educational Psychology and Counselling Faculty of Education Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Social media is a crucial aspect of modern society, shaping global communication and information exchange. Social media slang encompasses informal language expressions, abbreviations, and unconventional words on these platforms, serving purposes such as concise communication and identity establishment. Data analytics from social media slang plays a vital role in providing real-time trends, enhancing sentiment analysis, and improving the accuracy of predictive models by capturing evolving linguistic trends. This study aims to address gaps in existing social media slang analytics by proposing a three-step process for extracting and validating domain-specific social media slang terms, specifically focusing on the domain of anxiety prediction in the context of mental health. The proposed approach involves the identification of relevant users, extraction of social media slang terms, and validation by subject matter experts. A pilot study is conducted in the domain of anxiety prediction, employing a self-prepared questionnaire and involving participants aged 13-14 from schools in Selangor, Malaysia. The pilot study identifies ten social media slang terms associated with anxiety, validated by subject matter experts who are licensed mental health practitioners. The slang terms exhibit varying prevalence levels among adolescents, with some terms absent from popular slang dictionaries like Urban Dictionary. The findings highlight the need for extracting slang terms from pertinent users and subject matter expert validation in incorporating social media slang into more appropriate predictive analytics.

关键词： Sentiment analysis Dictionaries Social networking (online) Soft sensors Anxiety disorders Mental health Predictive models

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Application of Evolutionary Computing for Optimal Design of Base Station Antenna

Application of Evolutionary Computing for Optimal Design of ...

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IEEE International Conference on Electronic information and Communication technology (ICEICT)

作者： Naveed Hamid Nawaz Khan Botao Feng Xiao Ding Li Deng Wenzhe Gu Kwok L. Chung College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering Huizhou University Huizhou P. R. China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

This study outlines the development and refinement of an innovative base station antenna for contemporary wireless communication systems, which operates within the frequency range of 1.9 to 4.9 GHz. The antenna is made of copper and has a radiating patch thickness of 0.2 mm. The ground patch measures 38×38 mm and the total height of the antenna is 16 mm. The performance of the system relies on four reflecting walls that have been carefully carved to reduce out-of-band radiations and minimize interference. The antenna attains a peak gain of 6 dBi throughout the operational range, while keeping the |S 11 | parameters below -10 dBi, ensuring effective signal transmission. The process of optimization involves the utilization of evolutionary computers, more specifically a Genetic Algorithm, to meticulously adjust the design parameters. This approach guarantees that the antenna fulfills the demanding criteria of high gain and wide bandwidth, offering a strong and reliable solution for modern communication requirements.

关键词： Wireless communication Antenna measurements Base stations Transmitting antennas Interference Bandwidth Reliability

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Rubber Buddy: A Mobile Application to Empower Rubber Planters of Sri Lanka

Rubber Buddy: A Mobile Application to Empower Rubber Planter...

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Advancements in Computing (ICAC), International Conference on

作者： Aparna Jayawardena Kasuni Ganegoda Sakuni Imbulana Gavin Gunapala Nuwan Kodagoda Thilini Jayasinghe Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

This research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. The application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries; leaf disease identification; cover crop identification; and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology.

关键词： Image recognition Crops Machine learning computer architecture Mobile handsets Mobile applications Rubber

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