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2nd International Conference on Electronic and Electrical engineering and Intelligent system, ICE3IS 2022

作者： Ardiyanto, Yudhi Adi, Kusworo Kurnianingsih Putra, Karisma Trinanda Utomo, Pradityo System School of Postgraduate Studies Universitas Diponegoro Doctoral Program of Information Semarang Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia Universitas Diponegoro Faculty of Science and Mathematics Department of Physics Semarang Indonesia Politeknik Negeri Semarang Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Universitas Merdeka Madiun Faculty of Engineering Department of Informatics Management Madiun Indonesia

ISBN: (纸本)9781665465410

The decline in physical, cognitive, and sensory functions for most elderly people leads to the development of several health issues such as memory loss, a tendency to fall, dizziness, and reduced ability to care for oneself independently. These can be mitigated using devices designed to monitor vital signs and human activity such as a smart band. However, the application of a smart band is characterized by a disadvantage which involves storing user data in the vendor's cloud and only one device can access the client applications. Therefore, nurses, doctors, and family members cannot simultaneously analyze data and receive alerts when abnormal conditions occur in elderly patients. This study aims to develop a system capable of monitoring the health conditions of multiple elderly individuals and sending alerts during abnormal conditions based on the heart rate value read by the smart band sensor. The process involved installing a database on the Raspberry Pi to store data while a monitoring system was built on the Node-RED platform to display elderly health data and made it accessible locally. The detection of an abnormal heart rate condition in the elderly patient leads the system to send notifications via telegram to multiple receivers registered in the system using their telegram identity. © 2022 IEEE.

关键词： Heart

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Virtual Reality Semiconductor Laboratory: Usability Testing to Improve User Experience (UX) in Virtual Environments 3

Virtual Reality Semiconductor Laboratory: Usability Testing ...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Ahmed Alnagrat, Ahmed Jamah Ismail Alkhallas, Muhannad Imhmed Mohamed, Abdalslam S Che Ismail, Rizalafande Syed Idrus, Syed Zulkarnain Salem Saed, Halifah Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya Perlis Kangar01000 Malaysia Faculty of Engineering Technology in Zuwarah Department of Software Engineering Zuwarah Libya Aljufra University Faculty of Information Technology Aljufra Libya Arau02600 Malaysia Department of Communication Perlis Kangar01000 Malaysia Higher Institute of Science and Technology Wadi Al-Shati Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya

ISBN: (纸本)9798350319897

The Virtual Reality (VR) Semiconductor Laboratory, a computer-based virtual lab designed for training engineering students in semiconductor concepts, was evaluated to enhance user experience (UX) in virtual environments. A sample of 97 participants, including undergraduate, Master's, and PhD students, participated in the study. The User Experience Questionnaire (UEQ) was developed to collect data from users to evaluate their experience with the VR lab. The results of the study showed that the participants had a positive experience with the VR learning environment. They found the experience to be enjoyable, easy to understand, and helpful in completing learning tasks. The majority of participants rated the experience as very easy or easy in terms of learning and understanding the material, enjoyment, ease of using the interface, comfort with body movement during learning, and assistance with tasks. Additionally, most participants rated the experience as very easy in terms of task completion and comfort with the VR experiment. The study found high reliability in terms of usability, indicating that the UEQ is a valid and reliable tool for assessing virtual laboratory usability. These findings suggest that virtual laboratories can be effective learning and training tools in various fields, such as science and engineering. This research highlights the potential of VR technology in improving the training and learning experience for students. © 2023 IEEE.

关键词： Virtual reality

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Modeling and Simulation of Three-Phase Grid-Connected Solid-Oxide Fuel Cell systems

Modeling and Simulation of Three-Phase Grid-Connected Solid-...

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Energy Efficiency and Agricultural engineering (EE&AE), International Conference on

作者： Ioan Viorel Banu Roxana Grigore George Culea Sorin Gabriel Vernica Sorin Eugen Popa Department of Power Engineering and Computer Science Faculty of Engineering “Vasile Alecsandri” University of Bacau Marasesti 157 Bacau Romania

ISBN: (数字)9798350369380

ISBN: (纸本)9798350369397

This paper presents a method for modeling and simulating three-phase grid-connected solid-oxide fuel cell systems in the Matlab/Simulink environment. The approach is utilized to analyze the interaction between solid-oxide fuel cells and the electrical power system, focusing on understanding the dynamics of the electrochemical reaction and its impact on the performance of grid-connected fuel cell systems. The results highlight the effectiveness of the proposed method in accurately modeling and operating three-phase grid-connected solid-oxide fuel cell systems.

关键词： Electrodes Solid modeling Gases Simulation Electricity Power system dynamics Fuel cells

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A Reliable and Stable V2V Routing Scheme for Warning Message Dissemination in VANETs using Bi-Directional Clustering 12

A Reliable and Stable V2V Routing Scheme for Warning Message...

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12th International Conference on Communication and Network Security, ICCNS 2022

作者： Khan, Mansoor Abbas, Ghulam Abbas, Ziaul Haq Alasmary, Hisham Tu, Shanshan Waqas, Muhammad Faculty of Computer Science and Engineering Gik Institute of Engineering and Technology Topi23640 Pakistan Faculty of Electrical Engineering Gik Institute of Engineering and Technology Topi23640 Pakistan Department of Computer Science College of Computer Science King Khalid University Information Security and Cybersecurity Unit King Khalid University Abha Saudi Arabia Faculty of Information Technology Beijing University of Technology Beijing100124 China Computer Engineering Department College of Information Technology University of Bahrain 32038 Bahrain School of Engineering Edith Cowan University PerthWA6027 Australia

ISBN: (纸本)9781450397520

Road traffic injuries cause considerable life and economic losses to individuals, their families, and nations as a whole. To avoid road accidents, Vehicular Ad-hoc Networks (VANETs) play a vital role by enhancing transportation systems to make them intelligent. To enhance road safety, emergency message dissemination schemes are indispensable in VANET-based applications. This paper proposes a routing protocol for emergency message dissemination, which uses stable clustering for the bi-directional highway scenario. To assure the long-term sustainability of a cluster, a median technique for selecting a CH is devised. Further, an extended form of the K-medoids algorithm for a new node joining a cluster is employed, which ensures that both node and cluster have the same direction for bi-directional road traffic. Simulation results reveal that the proposed protocol proffers enhanced throughput and lowers the packet loss rates and end-to-end delay compared with state-of-the-art routing schemes. © 2022 ACM.

关键词： Vehicular ad hoc networks

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Fake Reviews Detection with Hybrid Features Using Time-Sequential Deep Learning Model 3

Fake Reviews Detection with Hybrid Features Using Time-Seque...

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3rd IEEE International Conference on Human-Machine systems, ICHMS 2022

作者： Weng, Yu-Liang Chien, Shih-Yi Lin, Szu-Yin National Chengchi University Department of Management Information System Taipei Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan Taiwan

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

This study aims to train a time-sequential deep learning model to classify reviews as genuine or fake. The real-life dataset from Yelp contains review text and user behavior, which are taken together as features within the LSTM model. The results suggest that a time-sequential model such as LSTM is promising to predict fake reviews with an 89% accuracy score. Besides, the textual features represented in word embedding vectors perform better than user behavior features. The results provided by deep learning models are expected to extend further to explainable AI (XAI) in future work. © 2022 IEEE.

关键词： Long short-term memory

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Monitoring energy losses of a residential building through thermographic assessments

Monitoring energy losses of a residential building through t...

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International Conference on Development and Application systems

作者： Iavorschi Eugen Milici Dan Laurențiu Department of Electrical Engineering Faculty of Electrical Engineering and Computer Science "Stefan cel Mare" University of Suceava Suceava Romania

ISBN: (数字)9798350349290

ISBN: (纸本)9798350349306

One of the significant opportunities for reducing energy waste and carbon emissions is the renovation, modernization, and rehabilitation of existing buildings. Replacing the windows of existing residential buildings with new energy-efficient ones is an easy way to upgrade an existing home into a nearly zero-energy building, NZEB (nearly zero-energy building). There is a wide variety of methods to discover the thermal weak points of a building, and the continuous innovation of construction materials provides solutions to maximize energy savings and minimize heat transfer to the exterior. This document presents a methodology for visualizing and recording the temperature distribution on the investigated surface of the building envelope to identify and locate vulnerable points where heat losses to the exterior are substantial. The objective was to highlight the need for upgrading the insulation system and replacing the existing windows of the analyzed building based on a multi-objective approach to maximize the building’s energy performance and increase thermal comfort indoors. Using a Fluke Ti300+ Thermal Camera, energy losses were visualized through thermographic assessments, which could provide an adequate picture for accurately assessing the building’s condition before renovation, to evaluate the need for partial renovation. For the case study, a residential building with one floor and mansard roof located in a region with hot summers and cold winters in Romania was chosen. The results showed that there are several options for significantly reducing the building’s energy consumption. Thus, multiple energy efficiency solutions were proposed for the building to reduce heat losses.

关键词： Temperature measurement Energy loss Visualization Insulation Climate change Buildings Energy efficiency

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LPBSA: Enhancing Optimization Efficiency through Learner Performance-based Behavior and Simulated Annealing

arXiv

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arXiv 2024年

作者： Hamad, Dana Rasul Rashid, Tarik A. Computer Science Department Faculty of Science Soran University Erbil Soran Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq

This study introduces the LPBSA, an advanced optimization algorithm that combines Learner Performance-based Behavior (LPB) and Simulated Annealing (SA) in a hybrid approach. Emphasizing metaheuristics, the LPBSA addresses and mitigates the challenges associated with traditional LPB methodologies, enhancing convergence, robustness, and adaptability in solving complex optimization problems. Through extensive evaluations using benchmark test functions, the LPBSA demonstrates superior performance compared to LPB and competes favorably with established algorithms such as PSO, FDO, LEO, and GA. Real-world applications underscore the algorithm's promise, with LPBSA outperforming the LEO algorithm in two tested scenarios. Based on the study results many test function results such as TF5 by recording (4.76762333) and some other test functions provided in the result section prove that LPBSA outperforms popular algorithms. This research highlights the efficacy of a hybrid approach in the ongoing evolution of optimization algorithms, showcasing the LPBSA's capacity to navigate diverse optimization landscapes and contribute significantly to addressing intricate optimization challenges. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Evaluating the Wear Characteristics of Al 5024 Alloy with Varying SiC Nano Particle Percentages and Predictive Analysis using Artificial Neural Networks 1

Evaluating the Wear Characteristics of Al 5024 Alloy with Va...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Boddepalli, Eshwararao Jotiram, Gujar Anantkumar Thilagham, K.T. Al Ansari, Mohammed Saleh Baburao Desai, Anil Bajaj, K.K. Virginia Tech Department of Ece Blacksburg24060 United States D y Patil College of Engineering and Technology Department of Mechanical Engineering Kolhapur India Government College of Engineering Department of Metallurgical Engineering Tamilnadu Salem11 India University of Bahrain Department of Chemical Engineering Bahrain Graphic Era Hill University Dehradun Department of Computer Science and Engineering Uttarakhand India Rnb Global University Faculty of Basic and Applied Sciences Rajasthan Bikaner India

ISBN: (纸本)9798350359756

This study analyses wear and tear characteristics of EN 24 alloy better suited for optimum proportions of nanosilicon carbide (Nano SiC) particles. The study employed an intensive Taguchi layout of experiments to take a look at the influence of three crucial elements, particularly the friction coefficient (Fc) and the specific wear rate (Ks),. The three variables are the Nano SiC composition, the load, and the rotating speed of the disc. The results of the take a look at show that an increase in the Nano SiC concentration greatly reduces the specific, underlining the crucial function that Nano SiC composition performs in improving put on resistance. The test in addition reveals that the sliding distance and implemented force are the number one component that effect friction behaviour. These insights are particularly useful, notably for engineering projects wherever the management of friction and the development of wear resistance are vital. This study applies Artificial Neural Networks (ANN) to enhance our understanding and predicting skills by simulating the complicated interconnections among input factors and answers. The artificial neural network (ANN) model properly estimates an ordinary performance of 85.229 %. This prediction achievement improves the usefulness of the observe by supplying a sturdy foundation for increasing the performance and durability of material in actual-world circumstances. © 2023 IEEE.

关键词： Neural networks

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Geometry of Autometrized Lattice Ordered Monoids

arXiv

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arXiv 2024年

作者： Regasa, Tekalign Aklilu, Girum Venkateswarlu, Kolluru Department of Mathematics Addis Ababa University Ethiopia Department of Computer Science and System Engineering Andhra University Visakhapatnam India

In this paper, we study the geometry of Autometrized lattice ordered monoid(short as AL-monoids) by introducing the concept of metric betweeness and its properties t1,t2, B-linearity, D-linearity, lattice betweeness, B-linearity, and D-linearity, segments and equilateral triangles. It is proved that there do not exist equilateral triangles in AL-monoids. It is also proved that any AL-monoid is ptolemaic. This result subsumes all the geometry of commutative *** Codes 47H09, 47H10 © 2024, CC BY.

关键词： Algebra

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Enhancing IoT Network Security: A Double Decker Convolutional Neural Network with Brown-Bear Optimization for Intrusion Detection 8

Enhancing IoT Network Security: A Double Decker Convolutiona...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Chaturvedi, Abhay Suhas, S. Divya Shivani, J.L. Raja, Ch. Soni, Umang Natrayan, L. Department of Electronics & Communication Engineering GLA University Uttar Pradesh Mathura 281406 India Department of Computer Science and Engineering (CSE) SJCE JSS Science and Technology University Karnataka Mysore India Department of Electronics and Communication Engineering CMR College of Engineering & Technology Telangana Hyderabad 501401 India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad 500075 India Department of Electronics & Communication Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Post Limda Gujarat 391760 India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai 602105 India

ISBN: (纸本)9798331512248

The extensive adoption of Internet of Things (IoT) networks has exponentially increased cybersecurity threats, necessitating effective intrusion detection mechanisms to secure connected devices. Traditional security mechanisms fail to detect sophisticated cyberattacks as IoT environments are dynamic in nature. Existing intrusion detection techniques suffer from high false alarms and low computational efficiency, making them impractical to apply in resource-constrained IoT networks. To overcome these challenges, this paper presents Enhancing IoT Network Security: A Double Decker Convolutional Neural Network with Brown-Bear Optimization for Intrusion Detection (BrBO-DD-CNN), which yields high detection efficiency and accuracy. This paper utilizes data from the NSL-KDD and BoT-IoT datasets, which are IoT network traffic and attack types. Raw data is preprocessed by Min-Max Zscore Normalization to remove duplicates and complete missing values. Planet Optimization selects significant features, reducing data complexity without losing key information. Preprocessed data is input to a Double Decker Convolutional Neural Network that detects low-level and high-level features to differentiate normal and malicious traffic. Brown-Bear Optimization optimizes model parameters, enhancing convergence and detection performance. This research yields an accuracy of 99.98% for NSL-KDD and 99.95% for BoT-IoT. Detection rate is 99.97% and 99.93%, and F1-score is 99.96% and 99.92%, respectively, proving its effectiveness. The results guarantee that BrBO-DD-CNN performs better than existing intrusion detection techniques, offering a highly accurate, efficient, and scalable intrusion detection mechanism to secure IoT networks against emerging cyberattacks. © 2025 IEEE.

关键词： Attack BoT-IoT Internet of Things Intrusion Detection NSL-KDD

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