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Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms 4

Towards Swarm Intelligence Ontology for Formal Description o...

4th International Conference on Computing, Networking, Telecommunications and engineering sciences Applications, CoNTESA 2023

作者： Tulicic, Domagoj Horvat, Marko Lovrencic, Sandra University of Applied Science in Information Technology Department of Computer System and Networks Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia University of Zagreb Faculty of Organization and Informatics Foundations of Information Sciences Department of Theoretical and Applied Varaždin Croatia

ISBN: (纸本)9798350382754

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches. © 2023 IEEE.

关键词： Swarm intelligence

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Exploring the fixed point theory and numerical modeling of fish harvesting system with Allee effect

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Modeling Earth systems and Environment 2025年第4期11卷 1-18页

作者： Yasin, Muhammad Waqas Akhtar, Mobeen Ahmed, Nauman Akgül, Ali Al-Mdallal, Qasem Department of mathematics University of Narowal Narowal Pakistan Department of Mathematics and Statistics The University of Lahore Lahore Pakistan Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Mathematics Art and Science Faculty Siirt University Siirt 56100 Turkey Department of Mathematics Mathematics Research Center Near East University Near East Boulevard Nicosia 99138 Turkey Department of Computer Engineering Biruni University Istanbul Topkapi 34010 Turkey Department of Mathematical Sciences UAE University P.O. Box 17551 Al Ain United Arab Emirates

Fish harvesting has a major role in nutritive food that is easily accessible for human nourishment. In this article, a reaction-diffusion fish harvesting model with the Allee effect is analyzed. The study of population models is a need of this hour because by using precautionary measures, mankind can handle the issue of food better. The basic mathematical properties are studied such as equilibrium analysis, stability, and consistency of this model. The Implicit finite difference and backward Euler methods are used for the computational results of the underlying model. The linear analysis of both schemes is derived and schemes are unconditionally stable. By using the Taylor series consistency of both schemes is proved. The positivity of the Implicit finite difference scheme is proved by using the induction technique. A test problem has been used for the numerical results. For the various values of the parameters, the simulations are drawn. The dynamical properties of continuous models, like positivity, are absent from the simulations produced by the backward Euler scheme. Implicit finite difference scheme preserves the dynamical properties of the model such as positivity, consistency, and stability. Simulations of the test problem prove the effectiveness of the Implicit finite difference scheme. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Allee effect Analysis Backward Euler scheme Holling type II Implicit finite difference scheme Reaction-diffusion model Simulations

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The Influence of Pancasila Education and Religion Education on Value Education for the Students during COVID-19 Pandemic (Case Study: Bina Nusantara University Students) 22

The Influence of Pancasila Education and Religion Education ...

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7th International Conference on Big Data and Computing, ICBDC 2022

作者： Benawa, Arcadius Lusia, Erma Alwino, Alfensius Irawan, Iwan Witono, Petrus Hepi Character Building Development Center Mass Communication Program Communication Department Faculty of Economics & Communication Bina Nusantara University Jakarta Indonesia Tourism Department Faculty of Economics & Communication Bina Nusantara University Jakarta Indonesia Character Building Development Center Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Character Building Development Center Industrial Engineering Department School of Industrial Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450396097

It must be admitted that with the outbreak of the COVID-19 pandemic, conventional face-to-face learning has been disrupted. Luckily, we have to thank to advances in science and technology, so that the learning system does not have to stop, but can continue even though by online learning, either through zoom meeting or google meet or other online media. In such a context, this research was conducted with the main objective of finding out whether compulsory curriculum subjects, especially Pancasila Education and Religion Education, can still have a significant influence on character building through Value Education? Therefore, this simple research used quantitative methods by distributing questionnaires to 150 students as simple random sampling. From the data obtained, it was found the result that Pancasila Education and Religion Education still have a significant influence on Value Education which at Bina Nusantara University the values is focused to the value of SPIRIT (Striving for the excellent, Perseverance, Integrity, Respect, Innovation, and Teamwork). So, it can be concluded that the COVID-19 condition must not slacken the spirit to shape the character of students through Pancasila Education and Religion Education, so that core values of Bina Nusantara University remain embedded in students. © 2022 ACM.

关键词： Students

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Classifying Activities of Electrical Line Workers Based on Deep Learning Approaches Using Wrist-Worn Sensor 20

Classifying Activities of Electrical Line Workers Based on D...

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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350300505

The field o f human activity recognition (HAR) is a priority for cutting-edge study because of its potential to revolutionize the way we understand and improve our everyday lives. A large variety of ordinary, everyday tasks has been classified using H AR. Nevertheless, inc ontrast to basic human actions, the increasing demands of numerous real-world applications have attracted the interest of the HAR area of study. Electrical line workers (ELWs) face a variety of challenges, including long hours, working in isolated locations, and performing particularly hazardous tasks. Wearable sensor-based HAR allows for unobtrusive tracking of ELW efficiency and security. This study explores deep learning strategies for automatically categorizing ELWs' complicated actions through sensor data collected through a wrist-worn device. We propose ResNeXt, a deep residual neural network, and evaluate it with other deep learning networks for their ability to categorize ELW activities effectively. We employ a publicly available benchmark dataset that includes 10 ELW tasks. The results of the experiment demonstrate that the proposed ResNeXt achieved the highest accuracy (98.74%) and F1-score (98.81%) compared to other deep learning networks studied. © 2023 IEEE.

关键词： Wearable sensors

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Retraction Note: Enhancing low latency and high performance in clustered-based wireless sensor network routing using genetic algorithm for 5G networks

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Banu, Firdouse Udhayamoorthi, M. Ramana Gupta Nallagattla, Venkata Phaneendra Maguluri, Lakshmana Mahesh Kumar, N. B. Jayachitra, S. Department of Applied Information System Applied College King Khalid University Khamis Mushaiyt Saudi Arabia Department of Computer Science and Design Karpagam College of Engineering (Autonomous) Coimbatore India Department of CSE Prasad V. Potluri Siddhartha Institute of Technology Vijayawada India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

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Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus

Prediction model using SMOTE, genetic algorithm and decision...

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作者： Azad, Chandrashekhar Bhushan, Bharat Sharma, Rohit Shankar, Achyut Singh, Krishna Kant Khamparia, Aditya Department of Computer Applications National Institute of Technology Jamshedpur India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Electronics & Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology NCR Campus Delhi- NCR Campus Ghaziabad India Department of CSE ASET Amity University Noida201301 India Bengaluru India School of Computer Science and Engineering Lovely Professional University Punjab India

Diabetes mellitus is a well-known chronic disease that diminishes the insulin producing capability of the human body. This results in high blood sugar level which might lead to various complications such as eye damage, nerve damage, cardiovascular damage, kidney damage and stroke. Although diabetes has attracted huge research attention, the overall performance of such medical disease classification using machine learning techniques is relatively low, majorly due to existence of class imbalance and missing values in the data. In this paper, we propose a novel Prediction Model using Synthetic Minority Oversampling Technique, Genetic Algorithm and Decision Tree (PMSGD) for Classification of Diabetes Mellitus on Pima Indians Diabetes Database (PIDD) dataset. The framework of the proposed PMSGD prediction model is composed of four different layers. The first layer is the pre-processing layer which is responsible for handling missing values, detection of outlier and oversampling the minority class. In the second layer, the most significant features are selected using correlation and genetic algorithm. In the third layer, the proposed model is trained, and its effectiveness is evaluated in the fourth layer in terms of classification accuracy (CA), classification error (CE), precision, recall (sensitivity), measure (FM), and Area_Under_ROC (AUROC). The proposed PMSGD algorithm clearly outperforms its counterparts and achieves a remarkable accuracy of 82.1256%. The best outcome achieved by the proposed system in terms of CA, CE, precision, sensitivity, FM and AUROC is 82.1256%, 17.8744%, 0.8070%, 0.8598, 0.8326 and 0.8511, respectively. The obtained simulation results show the effectiveness and superiority of our proposed PMSGD model and their by reduced error rate to help in decision-making process. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision trees

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RFID-Based Student Identification Card Attendance Monitoring system 1

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6th International Conference on communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Aderinto, Muqeetat Tolulope Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (数字)9789819919833

ISBN: (纸本)9789819919826

Passive (low-frequency) radio-frequency identification (RFID) technology is being used in an increasing number of contactless systems. This project presents contactless attendance system passive radio-frequency identification (RFID)-based approach. RFID is a technology that uses radio waves to send information from an RFID tag attached to an object to a reader for the purpose of monitoring and identifying the object. This technology is one of the most widely used in the world;almost every organization uses RFID-based tracking, identification, and other functions on some level. A contactless RFID-based attendance monitoring system is a less cost-effective and efficient automated solution. The proposed system focuses on an RFID-based attendance monitoring system that uses RFID technology in connection with a programmable logic circuit (such as an Arduino) to address the problems that traditional paper and contact-based attendance systems experience. Each user (student or lecturer) will be given an RFID card. When the user puts the RFID card near the RFID reader, the RFID reader scans the user's data stored on the card, recording the current time using the real-time clock (RTC) module and the RFID tag number (UID) on the SD card. The Arduino uses the SD card module to communicate with the SD card. The buzzer beeps when a tag is scanned, and the system employs a 16 × 2 LCD module for display purposes. This system will take attendance automatically, and the attendance records will be stored in the database. The SD card serves as backup and can help to generate accurate reports when required. Therefore, the system rectifies human errors, saves time, and gives clear attendance. The system has been implemented using C# programming language. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Radio waves

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Road Surface Detection for Autonomous Smart Wheelchair 5

Road Surface Detection for Autonomous Smart Wheelchair

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5th World Symposium on communication engineering, WSCE 2022

作者： Utaminingrum, Fitri Mayena, Sri Karim, Corina Wahyudi, Slamet Huda, Fais Al Lin, Chih-Yang Shih, Timothy K. Thaipisutikul, Tipajin Computer Vision Research Groups Brawijaya University Faculty of Computer Science Malang Indonesia Brawijaya University Mathematics Departments Malang Indonesia Brawijaya University Faculty of Computer Science Malang Indonesia Yuan-Ze University Department of Electrical Engineering Taoyuan32003 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan Mohidol University Faculty of Information and Communication Technology Thailand

ISBN: (数字)9781665450577

ISBN: (纸本)9781665450577

Today's wheelchair technology is evolving, with options ranging from manual wheelchairs, electric wheelchairs to the latest smart wheelchairs. However, wheelchairs on the market today still lack of the resources to facilitate advanced security for their users, particularly from the risk of injury or death caused by user neglect to differences in the rough and uneven texture surface structure and distinctions in road height levels when crossed by wheelchairs. Which would be at risk of accidents such as the existence of descending stairs. The victim may sustain physically visible physiological effects because of the accident, such as abrasions, bruises, tears, fractures, head injuries, and even death in fatal cases. The goal of this research is to enhance the security and safety of smart wheelchairs by developing autonomous controls with the extraction of gray level cooccurrence matrix (GLCM) texture features and the Naive Bayes Classification in anticipation of irregular road conditions and the existence of levels that are at risk of endangering wheelchair users based on camera input. According to the results of 100 experiments using 2-dimensional imagery data tested at d = 1, 2, 3, 4 and θ = 0°, 45°, 90°, 135° values, the resulting d = 1 and θ = 0° levels have the highest accuracy of 87% when classifying images of descending stairs and floors. © 2022 IEEE.

关键词： Wheelchairs

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State Estimation for Integrated Energy systems: Motivations, Advances, and Future Work

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IEEE Transactions on Power systems 2024年第4期40卷 3057-3073页

作者： Zhao, Junbo Srivastava, Ankur Guo, Ye Cetenovic, Dragan Lin, Yuzhang Levi, Victor Yin, Guanxiong Huang, Manyun Zhang, Tingting Li, Zhigang Chen, Yanbo Exposito, Antonio G. Zhang, Wen Zheng, Weiye Chakrabarti, Saikat Li, Yang Sharma, Ankush Kamwa, Innocent Guo, Qinglai Terzija, Vladimir Mili, Lamine University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States Tsinghua University Tsinghua-Berkeley Shenzhen Institute Shenzhen518055 China The University of Manchester Department of Eee ManchesterM13 9PL United Kingdom University of Kragujevac Faculty of Technical Sciences Čačak Čačak32000 Serbia New York University Department of Electrical and Computer Engineering NY11201 United States Tsinghua University Department of Electrical Engineering Beijing100084 China Hohai University College of Energy and Electrical Engineering Nanjing210098 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China The Chinese University of Hong Kong-Shenzhen School of Science and Engineering Shenzhen518172 China North China Electric Power University School of Electrical and Electronic Engineering Beijing102206 China University of Sevilla Spain Sevilla41092 Spain South China University of Technology School of Electrical Power Engineering Guangzhou510641 China Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Northeast Electric Power University School of Electrical Engineering Jilin132012 China Laval University Department of Electrical and Computer Engineering QuebecQCG1V 0A6 Canada Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy systems-State Estimation (IES-SE) and more established Electric Power system-State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE. © 1969-2012 IEEE.

关键词： Power system analysis

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A Comparative Analysis of Arabic Text Summarization Techniques: Evaluating Word Frequency, K-Means Clustering, and PageRank Algorithm

A Comparative Analysis of Arabic Text Summarization Techniqu...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Abdelrhman A. Karim Manar A. Ibrahim Mahmoud M. Hosny Abdulrhman H. Ibrahim Reem A. Mohamed Magy Elbanbawy Aya M. Al-Zoghby Khaled Foad Ibrahim Fathy Moawad Artificial Intelligence Engineering Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Artificial Intelligence Science Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer and System Department Faculty of Engineering Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Damietta University Damietta Egypt Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Artificial Intelligence College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

This study explores methods to efficiently summarize extensive Arabic texts, addressing the growing need to condense large volumes of content across various fields. Three primary techniques are evaluated: Word Frequency Analysis, K-means Clustering based on Sentence Proximity, and the PageRank Algorithm. The research finds the PageRank Algorithm to be the most effective, delivering higher compression ratios while maintaining strong recall and precision metrics. In particular, the PageRank method achieved the highest compression ratio of 0.562 while maintaining a population standard deviation of 2.0, compared to other techniques. Evaluation metrics such as population standard deviation, F1 score, and compression ratio support these findings. The study also examines advanced approaches like fuzzy logic-based methods, transformers, and multi-document summarization, aiming to enhance Arabic text summarization.

关键词： Measurement Clustering algorithms Text summarization Ubiquitous computing Transformers Information retrieval Data mining Standards

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