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An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling

computers, Materials & Continua 2022年第7期72卷 1995-2014页

作者： Mutasem K.Alsmadi Ghaith M.Jaradat Malek Alzaqebah Ibrahim A.Lmarashdeh Fahad A.Alghamdi Rami Mustafa A.Mohammad Nahier Aldhafferi Abdullah Alqahtani Department of MIS College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Department of Computer Science Faculty of Computer Science and InformaticsAmman Arab UniversityAmmanJordan Department of Mathematics College of ScienceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Basic and Applied Scientific Research CenterImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Computer Information Systems Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

Timetabling problem is among the most difficult operational tasks and is an important step in raising industrial productivity,capability,and *** tasks are usually tackled using metaheuristics techniques that provide an intelligent way of suggesting solutions or *** intelligence techniques including Particle Swarm Optimization(PSO)have proved to be effective *** recent experiments showed that the PSO algorithm is reliable for timetabling in many applications such as educational and personnel timetabling,machine scheduling,***,having an optimal solution is extremely challenging but having a sub-optimal solution using heuristics or metaheuristics is *** research paper seeks the enhancement of the PSO algorithm for an efficient timetabling *** algorithm aims at generating a feasible timetable within a reasonable *** enhanced version is a hybrid dynamic adaptive PSO algorithm that is tested on a round-robin tournament known as ITC2021 which is dedicated to sports *** competition includes several soft and hard constraints to be satisfied in order to build a feasible or sub-optimal *** consists of three categories of complexities,namely early,test,and middle *** showed that the proposed dynamic adaptive PSO has obtained feasible timetables for almost all of the *** feasibility is measured by minimizing the violation of hard constraints to *** performance of the dynamic adaptive PSO is evaluated by the consumed computational time to produce a solution of feasible timetable,consistency,and *** dynamic adaptive PSO showed a robust and consistent performance in producing a diversity of timetables in a reasonable computational time.

关键词： Sports timetabling particle swarm optimization ITC2021 roundrobin tournament dynamic adaptive

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Proof of Activity Protocol for IoMT Data Security

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computer Systems science & Engineering 2023年第1期44卷 339-350页

作者： R.Rajadevi K.Venkatachalam Mehedi Masud Mohammed A.AlZain Mohamed Abouhawwash Department of Information Technology Kongu Engineering CollegeErode638060TamilnaduIndia Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time ***,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical *** the use of sensors,IoMT applications are used in healthcare *** such applications,one of the most important factors is data security,given that its transmission over the network may cause *** data security in IoMT systems,blockchain is used due to its numerous blocks for secure data *** this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare *** main aim is to enhance the data security over the *** PoA protocol enhances high security of data from the literature *** replacing the malicious node from the block,the PoA can provide high security for medical data in the *** with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.

关键词： Blockchain IoMT malicious code detection security secure data management framework data management PoA

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Asymptotic Analysis of a Stochastic Model of Mosquito-Borne Disease with the Use of Insecticides and Bet Nets

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Journal of applied mathematics and Physics 2024年第1期12卷 305-329页

作者： Boubacar Sidiki Kouyaté Modeste N’zi Department of Mathematics and Computer Science University of Science Technical and Technologies of Bamako Bamako Mali Laboratory of Applied Mathematics and Computer Science University Felix Houphouet-Boigny Abidjan C&#244te d’Ivoire

Ross’ epidemic model describing the transmission of malaria uses two classes of infection, one for humans and one for mosquitoes. This paper presents a stochastic extension of a deterministic vector-borne epidemic model based only on the class of human infectious. The consistency of the model is established by proving that the stochastic delay differential equation describing the model has a unique positive global solution. The extinction of the disease is studied through the analysis of the stability of the disease-free equilibrium state and the persistence of the model. Finally, we introduce some numerical simulations to illustrate the obtained results.

关键词： Vector-Borne Disease Epidemic Model Stochastic Delay Differential Equations Stochastic Stability Lyapunov Functional Technique

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Effect of Credit Financing on the Supply Chain for Imperfect Deteriorating Items with Carbon Emission 11

Effect of Credit Financing on the Supply Chain for Imperfect...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mittal, Mandeep Chauhan, Sudhir Kumar Rastogi, Shivangi School of Computer Science Engineering and Technology Bennett University Department of Mathematics Uttar Pradesh Greater Noida India Amity University Uttar Pradesh Aias Department of Mathematics Noida India Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

The sustainable inventory model is a framework designed to ensure manufacturing processes' sustainability while maintaining profitability. During manufacturing, some items may experience degradation, resulting in imperfect goods. Nevertheless, a screening process is implemented to eliminate such items before they reach the consumers. Therefore, it is critical to scrutinize items prone to deterioration thoroughly. Suppliers prioritize trade credit in payments when supplying retailers, necessitating the development of an inventory model that caters to the needs of retailers dealing with imperfect and deteriorating quality items while considering carbon emissions and trade credit as a payment. The study presents numerical illustrations and sensitivity analysis for a better understanding of the model. © 2024 IEEE.

关键词： Supply chains

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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2 9th

An Exploratory Computational Approach for Elucidating a Nons...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University 11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings. © 2024 The Authors.

关键词： Molecular docking

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Bounds on the Rate of Convergence for a Nonstationary Two - Class Priority Queuing System 8

Bounds on the Rate of Convergence for a Nonstationary Two - ...

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8th International Conference on Information, Control, and Communication Technologies, ICCT 2024

作者： Usov, Ilya Zeifman, Alexander Satin, Yacov Shilova, Galina Sipin, Alexander Department of Applied Mathematics Vologda State University Vologda Russia Moscow Center for Fundamental and Applied Mathematics Lomonosov Moscow State University Moscow Russia Federal Research Center Computer Science and Control of the Russian Academy of Sciences Moscow Russia Vologda Research Center of the Russian Academy of Sciences Vologda Russia Department of Mathematics and Informatics Vologda State University Vologda Russia

ISBN: (纸本)9798331517564

In this article, an approach to studying two-dimensional processes of birth and death was considered using the example of a non-stationary two - class priority queuing system. An estimate of the rate of convergence of the original process was obtained, with the help of which one can study, first of all, the projections of process Z(t). Estimates for the projection of process Z(t) = (X(t),Y (t)) were obtained using the logarithmic norm method. A numerical example illustrates the behavior of projections X(t) and Y (t) of the original two-dimensional process Z(t);graphs are constructed for the probability of an empty queue, the average number of customers in the system for specific intensity functions λ1(t),μ1(t),λ2(t),μ2(t). An estimate of the rate of convergence of the original process Z(t) and an estimate of the limiting average were also obtained. © 2024 IEEE.

关键词： Markov chains

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Analysis of Student Behaviour on Large Learning Management Systems

Analysis of Student Behaviour on Large Learning Management S...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Lars Mehnen Thomas Mandl Birgit Pohn Matthias Blaickner Isabel Dregely Department of Computer Science Faculty Computer Science & Applied Mathematics University of Applied Sciences Technikum Wien

This paper presents a comprehensive analysis of student behaviour on Moodle, a widely used Learning Management System (LMS), in a large university setting with over 2000 students. The study investigates the distribution of student access to the LMS relative to the deadlines of quizzes (self-checked). Further, it explores the correlation between student engagement with course materials and their academic performance, finding that students with higher grades tend to access course materials earlier and more frequently before quiz deadlines. The paper also examines the distribution of two different student engagement with quizzes across different weekdays, highlighting distinct patterns between weekdays and weekends, and a unique trend on Saturdays due to lecture schedules for two study types - full-time and part-time. These findings provide valuable insights into student learning behaviours on large LMS, with potential implications for enhancing teaching strategies and improving student outcomes. The paper contributes to the growing body of research on learning analytics and offers a foundation for future studies in this area.

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Refining Blockchain-Powered Industrial Internet-of-Things Architecture for Improved Performance 5

Refining Blockchain-Powered Industrial Internet-of-Things Ar...

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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Gambo, Ishaya Ogundare, Timilehin Magreola, Michael Ajayi, Temitope Obafemi Awolowo University Department of Computer Science and Engineering Ile-Ife Nigeria University of the Witwatersrand Computer Science and Applied Mathematics Johannesburg South Africa University of Arkansas Little Rock Department of Computer Science 2801 S. University Ave Little RockAR72204 United States

ISBN: (纸本)9798350339239

This paper introduces a high-performance blockchain solution to enhance Industrial Internet of Things (IIoT) operations, effectively addressing the challenge of reconciling blockchain's computational demands with the limited capabilities of IIoT devices. Through quantitative research, we present an IIoT blockchain-powered deep reinforcement learning performance optimization protocol. Our evaluation covers key aspects such as latency, scalability, security, and decentralization, demonstrating enhanced scalability without compromising security or latency. We establish a scalable and robust blockchain architecture tailored for high-processing IIoT systems, fine-tuning its components using deep reinforcement learning. Our simulations illustrate the framework's capacity to significantly boost the performance of blockchain-powered IIoT systems while adapting to their diverse operational dynamics. © 2023 IEEE.

关键词： Reinforcement learning

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Extracting Commuters from Automated Road Traffic Counters: A Gaussian Mixture Approach

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Data science for Transportation 2025年第2期7卷 1-28页

作者： Brustad, Hilde Kjelgaard Midtbø, Jørgen E. Tomba, Gianpaolo Scalia Kivelä, Mikko Gregersen, Fredrik Alexander Alessandretti, Laura Oslo Center for Biostatistics and Epidemiology Oslo University Hospital Oslo Norway Department of Method Development and Analytics Norwegian Institute of Public Health Oslo Norway Department of Mathematics Stockholm University Stockholm Sweden Universita’ di Roma Tor Vergata Rome Italy Department of Computer Science Aalto University Espoo Finland Institute of Transportation economics Gaustadalleén 21 Oslo 0349 Norway Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby 2800 Denmark

Assessing traffic patterns is important for many applications such as rush hour traffic management, cross-border commuting statistics, transportation disruption assessment, and crisis management. We present a method for detecting commuting patterns from time-detailed traffic sensor data. Our method uses Gaussian mixture models to identify morning peaks that also exhibit expected variation patterns over weekends and holidays as corresponding to commuting. We apply the method to detect the variation in commuting between countries in the Nordics during the disruptions caused by the COVID-19 pandemic. Results show that the commuting traffic experienced a smaller decrease (42–71%) than the total traffic (87–92%) during the pandemic. For Finland and Sweden, both types of traffic have in 2023 returned to approximately the same level as before the pandemic, while the traffic between Norway and Sweden has only recovered to about 73% of the pre-pandemic level. Our methods can be applied in real-time to provide useful information for applications. © The Author(s) 2025.

关键词： Automated traffic counters Commuting patterns COVID-19 Cross-border commuting Gaussian mixture model Nordic countries Road traffic data

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Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

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Intelligent Automation & Soft Computing 2023年第5期36卷 2111-2128页

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research CenterFaculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingﬁrm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new *** techniques for identifying and detecting worker performance in industrial applications are based on computer vision *** widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is *** the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction *** suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker *** proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work *** addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed *** an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalﬁndings revealed that the suggested model could accurately recognize the actions of construction ***,we examined the impact of window size and sensor position on the identiﬁcation efﬁciency of the proposed method.

关键词： Complex human activity recognition wearable inertial sensors deep learning construction workers automatic recognition

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