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Multi-trial Vector-based Whale Optimization Algorithm

Journal of Bionic engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms Optimization engineering design problems Whale optimization algorithm

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A Study on 1D-Point Cloud Using Through-Wall mm Wave Radar for Human Pose Estimation 12

A Study on 1D-Point Cloud Using Through-Wall mm Wave Radar f...

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12th International Electrical engineering Congress, iEECON 2024

作者： Phaisiri, Kritthee Kittiyanpunya, Chainarong Shutimarrungson, Nattakarn Boonpoonga, Akkarat Akkaraekthalin, Prayoot Kaemarungsi, Kamol King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Bangkok Thailand Rajamangala University Of Technology Rattanakosin Faculty Of Engineering Department Of Mechatronics Engineering Nakhon Pathom Thailand Mahasarakham Business School Mahasarakham University Department Of Business Computer Maha Sarakham Thailand Faculty Of Engineering Department Of Electrical And Computer Engineering Bangkok Thailand National Electronics And Computer Technology Center National Science And Technology Development Agency Pathum Thani Thailand

ISBN: (纸本)9798350383591

This paper presents human pose estimation using millimeter-wave (mmWave) radar technology capable of penetrating obstacles. Operating within the frequency modulated continuous wave (FMCW) at 60 - 64 GHz, our radar system generates signals conducive to accurate human pose representation. Our data processing framework focuses on producing point cloud data, effectively capturing each human movement. Results are visualized through a 2D graph, with the z-axis representing time. Experimental data, collected from various activities conducted behind different walls, underwent thorough analysis, comparing point cloud positions with actual human postures. The outcomes affirm the effectiveness of mmWave radar systems, positioned behind obstacles, in accurately extracting human postures. © 2024 IEEE.

关键词： Frequency modulation

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Infodeslib: Python Library for Dynamic Ensemble Learning using Late Fusion of Multimodal Data 4

Infodeslib: Python Library for Dynamic Ensemble Learning usi...

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4th International Workshop on Knowledge-Infused Learning, KiL 2024

作者： Juraev, Firuz El-Sappagh, Shaker Abuhmed, Tamer College of Computing and Informatics Sungkyunkwan University Korea Republic of Faculty of Computer Science and Engineering Galala University Egypt

There has been a notable increase in research focusing on dynamic selection (DS) techniques within the field of ensemble learning. This leads to the development of various techniques for ensembling multiple classifiers for a specific instance or set of instances during the prediction phase. Despite this progress, the design and development of DS approaches with late fusion settings and their explainability remain unexplored. This work proposes an open-source Python library, Infodeslib, to address this gap. The library provides an implementation of several DS techniques, including four dynamic classifier selections and seven dynamic ensemble selection techniques, all of which are integrated with late data fusion settings and novel explainability features. Infodeslib offers flexibility and customization options, making it a versatile tool for various complex applications that require the fusion of multimodal data and various explainability features. Multimodal data, which integrates information from diverse sources or sensor modalities, is a common and essential setting for real-world problems, enhancing the robustness and depth of data analysis. These data can be fused in two main ways: early fusion, where different modalities are combined at the feature level before model training, and late fusion, where each modality is processed separately and the results are combined at the decision level. The library is fully documented following the Read the Docs standards. The documentation, code, and examples are available anonymously on GitHub at https://***/InfoLab-SKKU/infodeslib. © 2024 Copyright for this paper by its authors.

关键词： Python

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A Review of IoT-Driven Innovations in Supply Chain Management 3

A Review of IoT-Driven Innovations in Supply Chain Managemen...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chandankhede, Arpit Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Meghe Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Meghe Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Supply Chain Management (SCM) worldwide makes major advancements through the Internet of Things (IoT) by enabling real-time monitoring and prediction analytics and automatic decision capabilities in worldwide deliver chains. Through IoT technology integration with Artificial Intelligence (AI) and blockchain and cloud computing the supply chain solves major operational problems including monitoring difficulties and waste reduction and regulatory compliance requirements and operation leadership deficits. The implementation of IoT for supply chain management encounters major adoption barriers from high installation costs combined with security risks and operability complications as well as limited scalability capacity that affects especially small to medium enterprises (SMEs) and developing markets. The evaluation paper examines the shifting impact of IoT in supply chain management which enhances performance metrics and brings greater clarity and strengthens supply chain resilience. A scientist studied existing papers and case studies to reveal several essential advantages which include better inventory management, predictive analysis systems and more effective supply-chain collaborations. The analysis emphasizes effective solutions for security privacy and moral and environmental issues to guarantee sustainable expanded IoT adoption. This paper delivers practical knowledge alongside best practices to help organizations adopt IoT systems which create adaptable efficient deliver chains with sustainability at their core thus achieving long-term market benefits in today's complex global marketplace. © 2025 IEEE.

关键词： Inventory control

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The Effect of Solar Radiation on Solar Panels in Aeroponic Plant Systems

The Effect of Solar Radiation on Solar Panels in Aeroponic P...

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2023 International Conference on Networking, Electrical engineering, computer science, and Technology, IConNECT 2023

作者： Putri, Novia Utami Pranita, Elka Sembiring, Jaka Persada Jayadi, Akhmad Aryanto, Aryanto Muzamil Suat, Fatul K Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Lampung Indonesia Universitas Lampung Faculty of Engineering Lampung Indonesia

ISBN: (纸本)9798350331172

The sun is a large, continuous source of energy available to nature. Therefore, one of the uses of solar energy is to use a technology called solar panels or photovoltaic which can convert solar energy into electrical energy to meet human needs. The use of solar panels can encourage technological advances, one of which is in agriculture in order to produce an agricultural system that is environmentally friendly and has efficiency in the use of electrical energy. The model and further analyze the relationship between the effect of solar radiation intensity on the output power of solar panels as a source of electrical energy in aeroponic plants. The method used in this study uses a systematic observation method that is used to measure sunlight into solar radiation by making direct observations of the intensity of sunlight at a certain time using a light intensity meter which can then be converted to solar radiation intensity using a formula or conversion tool that has been determined. The results of the study are that solar radiation greatly influences the power generated in solar panels because solar radiation is a process of propagation of energy (heat) in the form of electromagnetic waves. So, when the photovoltaic process occurs, the higher the solar radiation value, the higher the output power generated by the solar panel. In the overall results of the research conducted for 7 days, when the solar radiation is high at 941.6 W/m2, the resulting output power is also high at 27.75 Watt. Conversely, when the solar radiation value is low, around 558.38 W/m2, the output power is also low at 18.83 Watt. © 2023 IEEE.

关键词： Solar panels

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Navigating online learning challenges and opportunities: Insights from small group of lecturers during pandemic 7

Navigating online learning challenges and opportunities: Ins...

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7th Information Systems International Conference, ISICO 2023

作者： Faza, Ahmad Santoso, Harry Budi Hadi Putra, Panca O. Universitas Multimeia Nusantara Faculty of Engineering and Informatics Tangerang15810 Indonesia Universitas Indonesia Faculty of Computer Science Depok16424 Indonesia

The implementation of e-learning platforms in higher education offers opportunities to enhance education quality and increase access to flexible learning. However, challenges arise, including limited guidance for students, high dropout rates, and obstacles faced by instructors. This qualitative case study explores the challenges and opportunities of online learning from instructors' perspectives. Data was collected through a survey administered to 23 instructors, and thematic analysis was conducted to identify the themes of challenges and opportunities. Findings reveal challenges in interaction, practical session delivery, monitoring, assessment, and identity verification. Institutional support challenges include insufficient training and socialization. Individual Challenges encompass digital skills, health concerns, and fatigue. Technological challenges involve availability, integration, capacity, and network-related issues. Despite these challenges, online learning provides opportunities for time effectiveness, flexibility, personal development, innovative teaching methods, and collaboration with external experts. © 2023 The Authors. Published by Elsevier B.V.

关键词： E-learning

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A Framework for Output-Feedback Symbolic Control

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IEEE Transactions on Automatic Control 2023年第9期68卷 5600-5607页

作者： Khaled, Mahmoud Zhang, Kuize Zamani, Majid Minia University Faculty of Engineering Minia61519 Egypt University of Surrey Department of Computer Science GuildfordGU2 7XH United Kingdom University of Colorado Boulder Department of Computer Science BoulderCO80309 United States Ludwig Maximilian University of Munich Department of Computer Science Munich80539 Germany

Symbolic control is an abstraction-based controller synthesis approach that provides, algorithmically, certifiable-by-construction controllers for cyber-physical systems. Symbolic control approaches usually assume that full-state information is available, which is not suitable for many real-world applications with partially observable states or output information. This article introduces a framework for output-feedback symbolic control. We propose relations between original systems and their symbolic models based on outputs. They enable designing symbolic controllers and refining them to enforce complex requirements on original systems. We provide an example methodology to synthesize and refine output-feedback symbolic controllers. © 1963-2012 IEEE.

关键词： Controllers

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm With Particle Swarm Optimization K-Means for Banking Data 6

Performance Comparison of the Invasive Weed Optimization K-M...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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Diabetes Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Diabetes Prediction Using Machine Learning Techniques: A Cas...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Batyha, Radwan Samara, Ghassan Al Henawi, Esraa Al-Safarini, Maram Y. Samhan, Ahmad A. Elrashidi, Ali Alluwaici, Mo'Ath Al-Omari, Deana It Zarqa University Department of Data Science Ai Faculty Zarqa Jordan Private University Department of Computer Science Faculty of It Applied Science Amman Jordan Zarqa University Department of Computer Science Faculty of It Zarqa Jordan Zarqa University Department of Software Engineering Faculty of It Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Diabetes is one of the most common diseases in Jordan. It is the main reason of death among Jordanian adult citizens. Worldwide, 48% of all deaths are due to Diabetes occurred before the age of 70 years. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that BayesNet and NaiveBayes are the best classification models to handle the task of the early prediction of Diabetes in Jordan © 2024 IEEE.

关键词： Adversarial machine learning

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BIOPHYSICAL SIGNAL PROCESSING FOR AUTOMATIC ANXIETY CLASSIFICATION IN A VIRTUAL REALITY EXPOSURE THERAPY SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第2期86卷 73-84页

作者： Jinga, Nicolae Petrescu, Cătălin-Dumitru Mitruț, Oana Moldoveanu, Alin Moldoveanu, Florica Petrescu, Livia Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Biology University of Bucharest Romania

This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were recorded, pre-processed and passed through a feature extraction procedure that provided input for the real-time anxiety level classification algorithm. We showcase the mathematical and engineering techniques behind the procedures and conclude with the challenges encountered in our research and future development ideas. The proposed method provides a good estimate of the level of anxiety while having at the same time a reduced level of complexity, allowing implementation on equipment with limited computing resources. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Classification (of information)

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