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International Conference on Smart Green technology in Electrical and Information Systems (ICSGTEIS)

作者： Fumiyoshi Kuwashima Mona Jarrahi Semih Cakmakyapan Kenji Wada Masanobu Haraguchi Yuki Kawakami Takeshi Moriyasu Osamu Morikawa Kazuyoshi Kurihara Hideaki Kitahara Takashi Furuya Makoto Nakajima Masahiko Tani Dep. of Electr. and Electron. Eng. Fukui University of Technology Fukui Japan Electrical and Computer Eng. UCLA Los Angeles CA USA Dep. of Physics and Electronics Osaka Metropolitan University Osaka Japan Industrial and Social Science Grad. Sch. of Tech. Tokushima Univ. Tokushima Japan Dep. of Elec. and Inf. Eng. Natl. Inst. of Tech. Fukui Col. Fukui Japan Faculty of Engineering University of Fukui Fukui Japan Chair of Liberal Arts Japan Coast Guard Academy Kure Japan School of Education University. of Fukui Fukui Japan FIR-UF University of Fukui Fukui Japan Inst. of Laser Engineering Osaka Univ. Osaka Japan

We apply the chaos supremacy of laser chaos system to the high stabilization of THz waves. Stability was demonstrated by down converting the optical beats between laser longitudinal modes in the THz domain to the radio frequency domain and comparing a conventional free running CW laser diode to a chaotically oscillating laser diode. Because of their exceptional stability, chaotically oscillating lasers are attractive options for optical pump sources in terahertz time-domain spectroscopy systems.

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Cognitive Inspired Sound-Based Automobile Problem Detection: A Step Toward Xai

SSRN

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SSRN 2024年

作者： Hamad, Abdulsattar Abdullah Nasim, Muhammad Fawad Jaffar, Arfan Khalaf, Osamah Ibrahim Ouahada, Khmaies Hamam, Habib Akram, Sheeraz Siddique, Abubakar Faculty of Computer Science and Information Technology The Superior University Lahore54000 Pakistan Lahore55150 Pakistan University of Samarra College of Education Department of physics Iraq Lahore 54000 Lahore55150 Pakistan Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Baghdad Jadriya Iraq School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Faculty of Engineering Uni de Moncton NBE1A3E9 Canada Hodmas University College Somalia Bridges for Academic Excellence Taleh Area Mogadishu Centre-Ville Tunis Tunisia School of Electrical Engineering University of Johannesburg South Africa Riyadh12571 Saudi Arabia Wellington Institute of Technology Te Pukenga Wellington6022 New Zealand

The automobile has become an essential part of modern human life, and one of the critical factors that determine a vehicle’s value is its reliability. eng.ne breakdowns are not only hazardous but also financially burdensome. Early detection of eng.ne failure can help save costs and improve the average lifespan of a car. Traditionally, manual inspection of eng.ne sounds by expert mechanics has been used as an effective approach for the early detection of eng.ne problems. There have been recent attempts to develop sound detection-based methods for the automatic diagnosis of eng.ne problems. However, the majority of these techniques are neither robust nor explainable. These systems act like black boxes, making their decision-making process difficult to *** Learning Classifier System, also known as LCS, is a machine learning technique that operates on a set of rules. It has shown promise in producing robust, explainable, and generalizable solutions for many problem domains. The objective of this work is to develop a novel LCS-based system for the automatic detection of eng.ne problems. The developed system’s decision-making process is interpretable, which is a step toward eXplainable artificial intelligence. Using time domain, frequency domain, and time-frequency domain features, the innovative system’s generalizability is assessed. While the developed system’s robustness is evaluated using noisy sound data collected under various normal and abnormal conditions, The experimental results indicate that the novel approach outperformed conventional state-ofthe-art techniques by 2.6% − 6.0% and achieved the highest performance accuracy of 98.6%. © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Investigation the Performance Effect of QOS in MPLS-TE Network

Investigation the Performance Effect of QOS in MPLS-TE Netwo...

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Signal, Control and Communication (SCC), International Conference on

作者： Kenz A. Bozed Ahlam Mohammed Elbeskri Jazia R. Ali Zreg Amer R. Zerek Department of Computer System Design Faculty of Information Technology Benghazi University Benghazi Libya Higher institute of Technical Water Affairs Sabratha Libya Sabratha University College of Science Sabratha Libya Electrical and Electronic Eng. Department Faculty of Engineering Zawia University Zawia Libya

ISBN: (数字)9781665402705

ISBN: (纸本)9781665402712

Due to the need for different communication types (Voice, video, and data) over the network, vendors improving enterprise networks for this purpose to minimize delays in order to support the better result, some applications like voice considered real-time applications which suffer from delay, jitter and packet losses, This paper discusses comparison of QoS performance between three QoS algorithms (PQ, FIFO, and WFQ), when using MPLS-TE technology considering the VoIP QoS parameters (end to end delay, jitter, and packet loss, as simulation done by OPNET modeler 14.5.

关键词： Packet loss Quality of service Switches Jitter Streaming media Data models Routing protocols

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challeng.s in the healthcare domain. The primary goals of this study are to review various XAI methods, their challeng.s, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

关键词： Decision making

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Towards Secure Integration of Wireless Sensor Networks and Cloud Computing

Towards Secure Integration of Wireless Sensor Networks and C...

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2019 IEEE Global Conference on Internet of Things, GCIoT 2019

作者： El Zouka, Hesham A. Hosni, Mustafa M. College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Computer Eng. Dept. Alexandria Egypt Faculty of Engineering OMIKRON Technologies Electrical Engineering Dept. Alexandria Egypt

ISBN: (纸本)9781728148731

In this paper, the system presents an integrated wireless sensor network (WSN) to monitor the information regarding the environment using cloud technology. The purpose is to guarantee a fast and a sage platform for information coming from the sensor nodes. The WSN will perform this task by collecting data from the environment and compressing and transmitting it back to the cloud gateway which will decompress the data and store it in a large cloud server. Not only the tasks that could be performed the WSN will presented here, but also the challeng.s facing it regarding security issues and cloud computing services. Therefore, a hierarchical structure of symmetric and asymmetric cryptosystems has been implemented regarding the context of data aggregation in WSN and a protocol that can handle the limited resources of the nodes and the real time needed for them to be applied to the cloud has been demonstrated. By the end of the paper it will be quite clear how the security protocols presented here can keep the whole energy of the network by saving the energy of the nodes when providing secure and reliable communication between the WSN and the cloud platform. © 2019 IEEE.

关键词： Internet of things

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Artificial Intelligence Based Cooperative Spectrum Sensing Algorithm for Cognitive Radio Networks 16

Artificial Intelligence Based Cooperative Spectrum Sensing A...

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16th International Learning and technology Conference, L and T 2019

作者： Mabrook, M. Mourad Khalil, Hussein A. Hussein, Aziza I. Faculty of Navigation Science and Space Technology Beni-Suef University Egypt Faculty of Engineering Electrical Dept. Sohag University Egypt Electrical and Computer Eng. Dept. Effat University Jeddah Saudi Arabia

Cognitive Radio (CR) technology is regarded as a key network technology used to manage the limitation of the available spectrum in wireless communication networks. Spectrum Sensing (SS) is the core process in CR eng.ne based on detecting the free channels and sharing it among other users. In wideband spectrum, many algorithms are proposed to sense the available free channels. Cooperative sensing is mainly considered as an effective solution of signal fading and shadowing problems in CR networks. From another side, CR networks can utilize Artificial Intelligence (AI) techniques for dynamically sensing and decision making processes. In this paper, a blind adaptive spectrum sensing algorithm based over centralized cooperative sensing platform is proposed. Then, an Adaptive Neuro-Fuzzy Interference System (ANFIS) technique is applied in decision-making process to achieve the optimum and accurate decisions. The simulation process and the output results showed that, the proposed technique outperforms both a standalone sensing techniques and other cooperative sensing with conventional decision making algorithms regarding to the probability of false alarms, probability of detection and probability of missed detection especially in low Signal to Noise Ratio (SNR). The results obtained by the proposed algorithm based on ANFIS decision making technique outperformed the other conventional decision making techniques. © 2019 The Authors. Published by Elsevier B.V.

关键词： Cognitive radio

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Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach

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Scientific Reports 2025年第1期15卷 1-19页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Ali, Tariq Ayaz, Muhammad Ghadi, Yazeed Yasin Aggoune, EL-Hadi M. Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Artificial Intelligence and Sensing Technologies (AIST) Research Centre University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk 71491 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 12555 United Arab Emirates School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada International Institute of Technology and Management (IITG) Av. Grandes Ecoles BP Libreville 1989 Gabon College of Computer Science and Eng. (Invited Prof) University of Ha’il Ha’il 55476 Saudi Arabia

This study uses Quantum Particle Swarm Optimization (QPSO) optimized Recurrent Neural Networks (RNN), standard RNN, and autoregressive integrated moving average (ARIMA) models to anticipate educational building power demand accurately. Energy efficiency, cost reduction, and resource allocation depend on accurate load forecasts. The study evaluates model performance using year-long load data from seasonal, daily, and hourly fluctuations. Performance indicators, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), were used to assess the models. The QPSO-optimized RNN outperformed traditional RNN and ARIMA models with the lowest MAE of 15.2, MSE of 520.15, and RMSE of 22.8. Comparative investigation shows the QPSO-RNN’s capacity to capture complicated load data patterns, especially during peak demand. This study shows that hybrid optimization can improve forecasting accuracy, making it a powerful tool for energy management in dynamic contexts. © The Author(s) 2025.

关键词： Educational buildings Energy management Load demand forecasting Seasonal adjustment Time series forecasting

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Mitigating urban heat islands: Enhancing outdoor comfort with retro-reflective building materials through computational-fluid-dynamics analysis

Next Research

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Next Research 2025年第2期2卷

作者： Jihui Yuan Da Yan Xiong Xiao Yasuhiro Shimazaki Yupeng.Wang Zhichao Jiao Zheng.ong Lin Marko Bizjak Craig Farnham Kazuo Emura Department of Living Environment Design Grad. School of Human Life and Ecology Osaka Metropolitan University Osaka Japan Department of Architecture and Civil Eng. Grad. School of Eng. Toyohashi University of Technology Aichi Japan Building Energy Research Center School of Architecture Tsinghua University Beijing China School of Human Settlements and Civil Engineering Xi'an Jiaotong University No.28 Xianning West Road Xi'an Shaanxi Province China College of Art & Design Nanjing Forestry University Nanjing 210037 China Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 SI 2000 Maribor Slovenia

This study aims to delineate the origin, purpose, process, and results of the research, focusing on investigating the impact of different exterior wall materials on outdoor thermal indicators in Nagoya, Japan. The ultimate goal is to mitigate the Urban Heat Island (UHI) effect and improve urban outdoor thermal comfort. Computational Fluid Dynamics (CFD) is employed to evaluate two types of exterior wall materials: diffuse highly reflective (DHR) and retro-reflective (RR) materials. The research process involves assessing various outdoor thermal indices, such as wet-bulb globe temperature (WBGT), standard effective temperature (SET*), and universal thermal climate index (UTCI), in order to comprehend the thermal performance of each material. The results of the study indicate that the RR material consistently outperforms the DHR material in terms of outdoor thermal comfort, despite having slightly lower solar reflectance. This is supported by lower surface temperatures and surface heat flow associated with the RR material, suggesting reduced indoor cooling loads and significant energy savings for cooling purposes. These findings highlight the potential of RR material in mitigating the UHI effect and enhancing urban outdoor thermal conditions. By establishing a critical relationship between exterior wall materials and outdoor thermal indicators, this study proposes the adoption of RR materials for urban building exteriors as a sustainable and economically viable solution to improve urban outdoor thermal comfort and enhance the quality of life for residents.

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Adversarial bandit approach for stand alone RIS operation

TechRxiv

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TechRxiv 2021年

作者： Ahmed Ouameur, Messaoud Lê, Dương Tun Anh Jeon, Gwanggil de Figueiredo, Felipe A.P. Massicotte, Daniel The Université du Québec à Trois-Rivières Department of Electrical and Computer Eng. 3351 Boul. des Forges Trois-RivièresQCG9A 5H7 Canada Faculty of Information Technology VNU-HCM University of Science Viet Nam Department of Embedded Systems Engineering College of Information Technology Incheon National University Incheon Korea Republic of National Institute of Telecommunications Minas Gerais Santa Rita do Sapucaí Brazil

Even though, reconfigurable intelligent surfaces (RISs) are adopted in various scenarios to enable the implementation of a smart radio environment, there are still challeng.ng issues for its real-time operation due to the need for a costly full dimensional channel estimation with offline exhaustive search or online exhaustive beam-training. The application of the deep learning (DL) tools is favored to enable feasible solutions. In this work, we propose two low training overhead and energy efficient adversarial bandit-based schemes with outstanding performance gains compared to reference DL based reflection beamforming methods. The resulting deep learning models are also discussed using state-of-the art model quality prediction trends. © 2021, CC BY.

关键词： Beamforming

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