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A Decade of Soft Computing Approaches in Power system Investment Planning

Journal of Physics: Conference Series 2021年第1期1878卷

作者： S B Yaakob A S F Rahman N M Mukhtar M N Yaakob N Arbaiy P-C Lin Control System Group (CSG) Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Kampus Alam UniMAP Pauh Putra 02600 Arau Perlis Malaysia Institut Pendidikan Guru Malaysia Kampus Darul Aman Bandar Darul Aman Kedah 06000 Jitra Kedah Malaysia Faculty of Computer Science and Information Technology University Tun Hussein Onn Malaysia 86400 Batu Pahat Johor Malaysia Department of Information Engineering and Computer Science Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan

This paper opens up an issue regarding to power system investment planning. In order to obtain a sustainable electricity supply, the power outage should be minimized. Maintenance cost plays as an essential element in investment planning, therefore misdirect investment should be avoided. Since soft computing can solved this problem, this paper offers a review in solving investment planning problem through such approaches. The main objective of this paper is to examine the concept and findings by the researchers in power system investment planning problem. Besides, this paper will highlight the most relevant contributions and limitations that can be improve in the other research. Finally, this paper will be pointed out some trends in power system investment planning problem.

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Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking

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Optik 2023年 272卷

作者： Almuhannad Sulaiman Alorfi Saud Yonbawi Sultan Alahmari Abdullaev Abror Bozorboevich Mahendran Arumugam Pham Quang Huy Department of Information System Faculty of Computing and Information Technology King Abdulaziz University City Rabigh Saudi Arabia Department of Software Engineering College Of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia The Department of Acconting Economic Analysis and Audit Tashkent Institute of Finance Tashkent Uzbekistan Center for Transdisciplinary Research Saveetha Dental College Saveetha institute of Medical and Technical Science Chennai India University of Economics Ho Chi Minh City (UEH) Vietnam

Recently, biometric technology has been extensively embedded in mobile devices to enhance security of mobile devices. With rise of financial technology (FinTech) that employs mobile applications as well as devices as promotional platforms, biometrics has a significant role in strengthening the detection of this privacy application. This manuscript offers the design of salp swarm optimization with auto-encoder based biometric authentication (SSOAE-BMA) model for the recognition of abnormal activities in the Fintech banking applications based on wireless communication. The major aim of the SSOAE-BMA model lies in the proper authentication of persons via biometric matching process. Initially, the presented SSOAE-BMA model makes use of stacked ResNet-50 model for deriving feature vectors. Next, the SSOAE-BMA model utilizes AE for biometric verification and the performance of the AE model is adjusted using the Social Spider Optimization (SSO) Algorithm which in turn enhances the recognition outcomes. To demonstrate the improved performance of SSOAE-BMA model, a series of simulations were carried out. The experimental outcomes signified the enhancements of the SSOAE-BMA model over existing models.

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Retraction notice to “Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking” [Optik 272 (2023) 170294]

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Optik 2023年 288卷

作者： Almuhannad Sulaiman Alorfi Saud Yonbawi Sultan Alahmari Abdullaev Abror Bozorboevich Mahendran Arumugam Pham Quang Huy Department of Information System Faculty of Computing and Information Technology King Abdulaziz University City Rabigh Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia The Department of Acconting Economic Analysis and Audit Tashkent Institute of Finance Tashkent Uzbekistan Center for Transdisciplinary Research Saveetha Dental College Saveetha institute of Medical and Technical Science Chennai India University of Economics Ho Chi Minh City (UEH) Viet Nam

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Content-Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

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Computational intelligence and neuroscience 2023年第1期2023卷 7735846页

作者： Amani K Samha Ghalib H Alshammri Stephen Jeswinde Nuagah Madhu Sharma R Mekala Management Information System Department College of Business Administration King Saud University Riyadh 28095 Saudi Arabia. Department of Computer Science Community College King Saud University Riyadh 11437 Saudi Arabia. Department of Electrical Engineering Tamale Technical University Tamale Ghana. School of Engineering University of Petroleum and Energy Studies Dehradun Uttarakhand 248001 India. Department of Information Technology M.Kumarasamy College of Engineering Thalavapalayam Karur Tamil Nadu India.

Audio classification and retrieval has been recognized as a fascinating field of endeavor for as long as it has existed due to the topic of identifying and choosing the most useful audio attributes. The categorization of audio files is significant not only in the area of multimedia applications but also in the disciplines of medicine, sound analysis, intelligent homes and cities, urban informatics, entertainment, and surveillance. This study introduces a new algorithm called the modified bacterial foraging optimization algorithm (MBFOA), which is based on a method that retrieves and classifies audio data. The goal of this algorithm is to reduce the computational complexity of existing techniques. Along with the combination of the peak estimated signal, the enhanced mel-frequency cepstral coefficient (EMFCC) and the enhanced power normalized cepstral coefficients (EPNCC) are used. These are then optimized using the fitness function utilizing MBFOA. The probabilistic neural network is used to differentiate between a music signal and a spoken signal from an audio source (PNN). It is next necessary to extract and list the characteristics that correspond to the class that was arrived at as a consequence of the categorization. When compared to other approaches that are somewhat similar, MBFOA demonstrates superior levels of sensitivity, specificity, and accuracy.

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第Sup1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

Shaoqing Wang1, Xiancun Yang2, Meixia Su1, Qiang Liu1 1department of MRI, Shandong Medical Imaging Research Institute Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China; 2department of Interventional Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China Correspondence: Qiang Liu (2002md@***) Aims To evaluate the diagnostic value of three- dimensional rotational angiography (3D-RA) of intracranial micro-aneurysms (diameter ≤ 3 mm) and provide guidance on the value of endovascular treatment. Materials and methods 43 patients with intracranial micro-aneurysms were analyzed retrospectively, all patients had undergone angiography with both conventional 2D-DSA(Two-Dimensional Digital Subtraction Angiography) and rotational angiography with three-dimensional reconstruction; the frequency of detection of aneurysms, depiction of aneurysm neck, radiation dose, and the dosage of contrast agent were recorded respectively. Results 55 pieces of aneurysms were detected out from the 43 cases with intracranial micro-aneurysms by 3D-RA. But only 39 cases were detected out using 2D-DSA from the 55 samples, there were significant differences with regards to detection rate (P < 0.05). There were significant differences in radiation dose and dosage of contrast agent (P < 0.05) between the two methods of using 3D-RA can improve the detection rate of micro-aneurysms, which bestows obvious advantages on displaying the shape of aneurysms, the aneurysm neck at the best angle, and the relationship with the parent artery, at the same time, the amount of contrast agent and radiation dose are reduced in 3D-RA compared to 2D-DSA.

关键词： Convolutional Neural Network High Resolution Compute Tomography Intensity Inhomogeneity Kernel Support Vector Machine Clonal Selection Algorithm

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Digital Twin for Digital Health: Body Joint Modeling and 3D Pose Reconstruction

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ACM Transactions on Computing for Healthcare 1000年

作者： Huan Chen Xiaoye Qian Yi Cai Tianyu Lu Xiaowei Xu Feng Lin Shih-Ching Yeh Ming-Chun Huang Department of Electrical Computer and Systems Engineering Case Western Reserve University USA Department of Computer Science Northeastern University USA Department of Cardiovascular Surgery Guangdong Cardiovascular Institute Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) and Southern Medical University China School of Cyber Science and Technology ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University China Department of Photonic System National Yang Ming Chiao Tung University and Department of Computer Science and Information Engineering National Central University Taiwan Department of Data and Computational Science Duke Kunshan University China

Body joint modeling and human pose reconstruction provide precise motion and quantitative geometric information about human dynamics. The rich motion information obtained from human pose estimation plays important roles in a wide range of digital twin and connected health applications. However, current related researches have difficulties in extracting the joints’ spatial-temporal correlations from different levels. This is due to the poses being at various complexities in moving various joints differently. Hence, the typical conventional transformer method is non-adaptable and barely meets the aforementioned requirement. In this paper, we propose the Body Joint Interactive transFormers (BJIFormer) to extract the multi-level joints’ spatial-temporal information. The design enables the model to learn the inner joints’ correlation inside the body parts across frames and propagate the extracted information across the body parts with shared joints. The multi-level body joint interactive scheme has greater efficiency improvement by restricting the self-attention computation to partial body parts and connecting each body part by torso. The proposed interactive approach explores the spatial-temporal correlation following the hierarchical paradigm and effectively estimates and reconstructs 3D human poses.

关键词： Digital Twin Digital Health Body Joint Modeling Pose Reconstruction

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Prediction of traffic congestion based on time series dataset number of vehicles using neural network algorithm

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AIP Conference Proceedings 2024年第1期2982卷

作者： Prasetyo Wibowo Yunanto Rahmat Gernowo Oky Dwi Nurhayati Doctoral Program of Information System Diponegoro University Jl. Imam Bardjo SH No.5 Semarang Indonesia Information Systems and Technology Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta Indonesia Department of Physics Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia Computer Engineering Department Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia

The increasing number of vehicles that was not accompanied by an adequate road infrastructure readiness causes traffic jams. Traffic jams often occur repeatedly every day, especially at certain times, such as when people are going to and from work. The same traffic jams can also occur at the beginning or the end of the week; this usually repeats every week. As a result, if the congestion dataset is known, the repeating traffic congestion for daily and weekly congestion can be anticipated. In this study, traffic congestion predictions are modeled using the Neural Network Algorithm based on traffic congestion data collected within 24 hours for one to two weeks. The parameters used in optimizing Neural Network performance are learning rate, momentum, and epochs (training cycles). Based on these experiments, the Neural Network Algorithm successfully modeled traffic density patterns which are recurring congestion patterns, with quite good results.

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Front Cover: International Journal of Intelligent systems, Volume 37 Issue 11 November 2022

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International Journal of Intelligent systems 2022年第11期37卷

作者： Xianjia Meng Yong Yang Ximeng Liu Nan Jiang School of Information Science and Technology Northwest University Xi'an People's Republic of China Fujian Province Key Laboratory of Information Security of Network System and College of Computer and Data Science Fuzhou University Fuzhou People's Republic of China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen People's Republic of China Department of Internet of Things School of Information Engineering East China Jiao Tong University Nanchang People's Republic of China

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Design Monitoring Electrical Power Consumtion at computer Cluster

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Journal of Physics: Conference Series 2020年第1期1445卷

作者： Alfianto, Enggar Agustini, Siti Muharom, Syahri Rusydi, Febdian Puspitasari, Ira Department of Computer System Institut Teknologi Adhi Tama Surabaya Jl. Arief Rachman Hakim No 100 Surabaya Indonesia Electrical Engineering Department Institut Teknologi Adhi Tama Surabaya Jl. Arief Rachman Hakim No 100 Surabaya Indonesia Theoretical Physics Research Group Dep. of Physics Fac. of Science and Technology Airlangga University Jl. Mulyorejo Surabaya Indonesia 60115 Dep. of the information system Fac. of Science and Technology Airlangga University Jl. Mulyorejo Surabaya Indonesia 60115

Electricity is one of the basic needs in the era of technological development, where all equipment must use electricity to operate such as computer, so that it requires a system that can monitor power consumption at computer cluster. To monitoring power consumption using WCS1800 to current sensor and microcontroller Atmega32 to data sensor process, and serial communication to send data to display at personal computer. From test system having two result, first is power consumption at computer cluster starting, where current value range is 0 to 38A with power consumption is 0 to 8360 watt. And second is power consumtion at computer cluster execution progran, current value is 27 to 40 A, with power consumption 5940 to 8800 watt. From this system has been design, the power consumption at computer cluster can be monitored and known value of energy consumption.

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基于任务空间的协同学习环境的本体建模

基于任务空间的协同学习环境的本体建模

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第20届全国计算机新科技与计算机教育学术大会

作者： ZHANG Rong 张荣 WU Junxiu 吴军秀赵素蕊 Faculty of Information Science &Technology NingBo University Ningbo 315211 宁波大学信息科学与工程学院宁波315211 Xinhua News Agency Technology Bureau Computer System Department Bejing 100089 新华社通讯社技术局计算机系统部北京100089 河北经贸大学计算机中心石家庄050061

本文在任务空间模型的基础上，提出了协同学习环境的本体建模。基于该模型设计和开发协同学习系统，不仅可以在多个不同层次上解决协同学习系统的资源管理问题，同时解决了现有协同学习系统开发中存在的知识描述不规范、知识重用性低、... 详细信息

本文在任务空间模型的基础上，提出了协同学习环境的本体建模。基于该模型设计和开发协同学习系统，不仅可以在多个不同层次上解决协同学习系统的资源管理问题，同时解决了现有协同学习系统开发中存在的知识描述不规范、知识重用性低、重复性开发等问题，提高了系统的互操作性，以及灵活性和可扩缩性。给出了学科知识本体的设计。

关键词：协同学习环境本体建模任务空间模型资源管理

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