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Enhancing COVID-19 vaccination and medication distribution routing strategies in rural regions of Morocco: A comparative metaheuristics analysis

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Informatics in Medicine Unlocked 2024年 46卷

作者： Mzili, Toufik Mzili, Ilyass Riffi, Mohammed Essaid Kurdi, Mohamed Ali, Ali Hasan Pamucar, Dragan Abualigah, Laith Department of Computer Science Faculty of Science Chouaib Doukkali University EI Jadida Morocco Department of Business Management Al-imam University College Balad Iraq Department of Computer Science Faculty of Economics and Management Hasan 1st University Settat Morocco Faculty of Informatics Engineering Idlib University Idlib Syrian Arab Republic Institute of Mathematics University of Debrecen Pf. 400 Debrecen H-4002 Hungary College of Engineering Technology National University of Science and Technology Dhi Qar 64001 Iraq Technical Engineering College Al-Ayen University Dhi Qar 64001 Iraq Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia College of Engineering Yuan Ze University Taiwan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan

The optimization of the vaccination campaign and medication distribution in rural regions of Morocco conducted by the Ministry of Health can be significantly improved by employing metaheuristic algorithms in conjunction with a tour planning system. This research proposes the utilization of six metaheuristic algorithms: genetic algorithm, rat swarm optimization, whale optimization, spotted hyena optimizer, penguins search optimization, and particle swarm optimization, to determine the most efficient routes for equipped trucks carrying vaccines and medications. These algorithms consider critical field constraints, such as operating hours of vaccination centers, vaccine availability, and distances between centers while minimizing the overall journey duration. In addition, a comprehensive tour planning system is integrated into the optimization framework accounting for transportation costs such as fuel expenses and truck maintenance costs. By incorporating these factors, the Ministry of Health aims to achieve the maximum efficiency while minimizing the financial burden associated with the vaccination campaign in rural areas. The integration of metaheuristics and the tour planning system presents a robust and data-driven solution for the Ministry of Health to enhance the effectiveness of their vaccination and medication distribution campaigns in rural regions of Morocco. This approach not only minimizes costs but also improves overall efficiency by ensuring timely access to vaccines and medications for the rural population. The findings of this research contribute to the growing body of knowledge in the field of healthcare logistics optimization and provide valuable insights for policymakers and practitioners involved in similar campaigns worldwide. © 2024 The Authors

关键词： Healthcare logistics Medication distribution Metaheuristics Route optimization Tour planning system Transportation Vaccination campaign

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Software requirements modeling: A systematic literature review

Software requirements modeling: A systematic literature revi...

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2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020

作者： Arif, Mohd. Mohammad, Chaudhary Wali Sadiq, Mohd. Faculty of Engineering and Technology Department of Applied Sciences and Humanities New Delhi110025 India Indian Institute of Science Department of Computer Science and Automation Bangalore560012 India

ISBN: (纸本)9781728150703

Software requirements modeling (SRM) is a subprocess of requirements engineering (RE) which is used to elicit and represent the need of the stakeholders. Different systematic literature reviews (SLR) have been performed in different areas of RE like requirements elicitation, stakeholder identification, requirements prioritization, use case models, etc. Despite the availability of different SRM techniques, less attention is given to synthesize the existing SRM techniques in the context of the unified modeling language (UML) and goal oriented techniques like 'Knowledge Acquisition for Automated Specifications' (KAOS), I∗ framework, non-functional requirements (NFR) framework, and Tropos, etc. Therefore, to address this issue, in this paper we present the SLR by analysing the existing SRM techniques based on the following formulated research questions (RQs): (a) how UML and goal oriented techniques were evolved? (b) which modeling techniques are appropriate for modeling the NFRs? (c) what are the tools available for modeling the different types of the software requirements, i.e., functional and nonfunctional requirements? Search items were extracted from the RQs to identify the primary studies from the Journals, Conferences, Workshops, and Symposium. Our SLR has identified 56 distinct studies which have been published from 2008 to 2019. Selected studies were assessed according to the formulated RQs for their quality and coverage to specific SRM technique thus identifying some gaps in the literature. We observed that there is need to develop the SRM techniques for representing the different types of the NFRs;and also to strengthen the UML by integrating the NFRs and multi-criteria decision making techniques. © 2020 IEEE.

关键词： Requirements engineering

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Cross-Game Generalization Approaches for General Video Game Playing using Deep Reinforcement Learning 2

Cross-Game Generalization Approaches for General Video Game ...

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2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Ojha, Rajesh Kumar Rao, S Janardhana Goel, Pankaj Srivastava, Sandeep Kumar, K. Hareesh Prasad, Chitturi Sri Sri University Faculty of Science Odisha Cuttack753001 India Koneru Lakshmaiah Education Foundation Department of CSE AP Vaddeswaram522502 India GL Bajaj Institute of Technology Management Department of Applied Science and Humanities UP Greater Noida201306 India Galgotias University School of Computer Science Engineering UP Greater Noida201306 India Seshadri Rao Gudlavalleru Engineering College Department of CSE AP Gudlavalleru521356 India Koneru Lakshmaiah Education Foundation Department of CSE Department of CSE AP Vaddeswaram522502 India

ISBN: (纸本)9781665433686

This paper presents a number of novel approaches to help facilitate Deep Reinforcement Learning (DRL) for Neural Networks based agents in the domain of General Video Game Playing (GVGP). Using common processing methods, the NN can retain a fixed predetermined input and output shape while having the flexibility to play a variety of games. Training can also be made more efficient via reward normalization. Our results show that these modifications negligibly impact the learning time and performance of the model when testedupon games in the GVGAI framework. Furthermore analysis of these approaches within the parameters of the GVGAI learning competition was also performed. This research work attempts to use the adaptations presented to train an agent thatwill compete in the CEC2019 GVGAI Learning Competition. © 2021 IEEE.

关键词： Reinforcement learning

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On a Cyclometric Modification of the Chua System

On a Cyclometric Modification of the Chua System

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Bálint Nagy Antal Joós Attila Kovari Department of Mathematics Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Natural Sciences Kandó Kálmán Faculty of Electrical Engineering Institute of Electrophysics Obuda University Budapest Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eger Hungary Department of Natural Sciences Institute of Engineering University of Dunaújváros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary

Chaos is not only a unique chapter in the theory of dynamical systems but also a useful one with many applications in the field of communications. In this work a cyclometric modification of the well-known example of chaotic systems, the modified differential equations describing the behavior of the Chua-circuit is investigated. The results obtained can be applied in the encryption methods of the implementation of different communication channels like machine-machine or cognitive one.

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An interactive web-based tool for predicting and exploring brain cancer survivability

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Healthcare Analytics 2023年 3卷 100132-100132页

作者： Nath, Gopal Coursey, Austin Li, Yang Prabhu, Srikanth Garg, Harish Halder, Shaymal C. Sengupta, Saptarshi Murray State University Department of Mathematics & Statistics Murray 42071 KY United States Vanderbilt University Department of Computer Science Nashville 37235 TN United States Montclair State University Feliciano School of Business Montclair 07043 NJ United States Manipal Institute of Technology Department of Computer Science and Engineering Manipal India School of Mathematics Thapar Institute of Engineering & Technology (Deemed University) Punjab Patiala 147004 India Department of Mathematics Graphic Era Deemed to be University Uttarakhand Dehradun 248002 India Applied Science Research Center Applied Science Private University Amman 11931 Jordan Grand Valley State University The Department of Statistics Allendale 49401 MI United States San Jose State University Department of Computer Science San Jose 95192 CA United States

Brain cancer is one of the most deadly cancers, with a very low survival rate. By understanding the factors that lead to cancer spreading, practitioners can concentrate their efforts on providing the most effective treatment, and they can modify the treatment plan as necessary. Also, knowing the likelihood of a patient's survival over a specified time period can enable them to make informed decisions about adjusting their routines, future investments, and other health-related decisions. The use of data-driven models in cancer research has gained increased popularity over the past several decades. Moreover, there is still much uncertainty surrounding the factors that contribute to survival of cancer, making it difficult to develop a model. The existing literature on brain cancer contains a variety of machine learning models. However, many of them lack a high degree of accuracy, and, in medical research, accuracy is essential to the proper guidance of treatment decisions. Therefore, we have proposed a framework comprising multiple phases of classical statistics and machine learning methods to find a parsimonious model with a high degree of accuracy (98.9%) for predicting brain cancer survivability. Furthermore, we develop a prototype web-based interactive tool to facilitate the practical implementation of the proposed model and provide a deeper understanding of how a particular factor affects survival when other factors remain unchanged. By integrating this tool into healthcare settings, medical professionals can rapidly detect potentially vulnerable patients, and it can also be useful in determining the most effective treatment plan. © 2022 The Author(s)

关键词： brain cancer Cook's distance cross-validation data mining machine learning predictive analytics

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Electrocatalytic Reduction of Nitrate to Ammonia on Low-Cost Manganese-Incorporated Co3o4 Nanotubes

SSRN

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SSRN 2022年

作者： Liu, Di Qiao, Lulu Chen, Yuyun Zhou, Pengfei Feng, Jinxian Leong, Chon Chio Ng, Kar Wei Peng, Shengjie Wang, Shuangpeng Ip, Weng Fai Pan, Hui Institute of Applied Physics and Materials Engineering University of Macau China Department of Electrical and computer Engineering Faculty of Science and Technology University of Macau China Department of Physics and Chemistry Faculty of Science and Technology University of Macau China College of Materials Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China

Spinel oxides, especially Co3O4, have been considered as ideal electrocatalysts for electrocatalytic nitrate reduction reaction (e-NO3RR). However, their application in e-NO3RR is still limited because of relatively low activity and selectivity under high potentials. Herein, we present that the incorporation of manganese (Mn) into nanoscale Co3O4 lattice can solve the issue, which shows high activity and selectivity in e-NO3RR. The catalyst demonstrates a remarkable e-NO3RR activity with a high ammonia yield rate of 35 mg h-1 cm-2 and excellent selectivity with a Faraday efficiency for ammonia up to 99.5% in neutral media, which are much better than those of transition-metal oxides catalysts. Our calculations further show that the replacement of Co by Mn can tune the adsorption behavior of intermediates, and thus reduces the limiting potential of e-NO3RR. We believe that the findings provide an insightful guidance to engineer the spinel oxides for enhanced activity and selectivity towards ideal products. © 2022, The Authors. All rights reserved.

关键词： Ammonia

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Prediction of FinalAcademic Performance of Secondary School Students Based on Alcohol Consumptions and Diverse Backgrounds

Prediction of FinalAcademic Performance of Secondary School ...

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作者： Yu Xiong Yuchen Ding Jiayi Li Wenting Zhang Shutong Ni School of Computer Science and Engineering The Ohio State University Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute School of Banking and Finance University of International Business and Economics

The power of machine learning is prompting more educators to consider applying the technology to education. Also, alcoholism among students has become an important problem in society. Based on data from two US secondary schools, this paper investigates the impact of attributes including alcohol consumption, on students' grades, and aims to predict students' final grades in Portuguese. The normal distribution index was used as a reference to analyze and clean the data. The visualization of the data to explore which attributes were most correlated with final grade has been achieved. In this paper, ridge regressor, decision tree regressor, decision tree classifier, and KNN classifier were used as training models, with MSE, MAE, r2, and accuracy as testing indexes and cross validation to verify results. The final results show that alcohol consumption has a significant negative impact on students' academic performance.

关键词： student education alcohol consumption machine learning decision tree regressor decision tree classifier

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Physical Layer Security for NOMA Systems: Requirements, Issues, and Recommendations

arXiv

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arXiv 2023年

作者： Pakravan, Saeid Chouinard, Jean-Yves Li, Xingwang Zeng, Ming Hao, Wanming Pham, Quoc-Viet Dobre, Octavia A. The Department of Electric and Computer Engineering Laval University Quebec CityQCG1V 0A6 Canada The School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The School of Computer Science and Statistics Trinity College Dublin Dublin Ireland The Faculty of Engineering and Applied Science Memorial University St. John’s Canada

Non-orthogonal multiple access (NOMA) has been viewed as a potential candidate for the upcoming generation of wireless communication systems. Comparing to traditional orthogonal multiple access (OMA), multiplexing users in the same time-frequency resource block can increase the number of served users and improve the efficiency of the systems in terms of spectral efficiency. Nevertheless, from a security viewpoint, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential. In this context, physical layer security (PLS) has been introduced as a supplement of protection to conventional encryption techniques by making use of the random nature of wireless transmission media for ensuring communication secrecy. The recent years have seen significant interests in PLS being applied to NOMA networks. Numerous scenarios have been investigated to assess the security of NOMA systems, including when active and passive eavesdroppers are present, as well as when these systems are combined with relay and reconfigurable intelligent surfaces (RIS). Additionally, the security of the ambient backscatter (AmB)-NOMA systems are other issues that have lately drawn a lot of attention. In this paper, a thorough analysis of the PLS-assisted NOMA systems research state-of-the-art is presented. In this regard, we begin by outlining the foundations of NOMA and PLS, respectively. Following that, we discuss the PLS performances for NOMA systems in four categories depending on the type of the eavesdropper, the existence of relay, RIS, and AmB systems in different conditions. Finally, a thorough explanation of the most recent PLS-assisted NOMA systems is given. © 2023, CC0.

关键词： Efficiency

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： Artificial intelligence Big data Data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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Quantum analog of Landau-Lifshitz-Gilbert dynamics

arXiv

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arXiv 2024年

作者： Liu, Yuefei Miranda, Ivan P. Johnson, Lee Bergman, Anders Delin, Anna Thonig, Danny Pereiro, Manuel Eriksson, Olle Azimi-Mousolou, Vahid Sjöqvist, Erik Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology AlbaNova University Center StockholmSE-10691 Sweden Department of Physics and Astronomy Uppsala University Box 516 UppsalaSE-751 20 Sweden Department of Physics and Electrical Engineering Linnaeus University KalmarSE-39231 Sweden KTH Royal Institute of Technology StockholmSE-10044 Sweden KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Science and Technology Örebro University ÖrebroSE-701 82 Sweden WISE-Wallenberg Initiative Materials Science Uppsala University Box 516 UppsalaSE-751 20 Sweden Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan81746-73441 Iran

The Landau-Lifshitz-Gilbert (LLG) and Landau-Lifshitz (LL) equations play an essential role for describing the dynamics of magnetization in solids. While a quantum analog of the LL dynamics has been proposed in [Phys. Rev. Lett. 110, 147201 (2013)], the corresponding quantum version of LLG remains unknown. Here, we propose such a quantum LLG equation that inherently conserves purity of the quantum state. We examine the quantum LLG dynamics of a dimer consisting of two interacting spin- 12 particles. Our analysis reveals that, in the case of ferromagnetic coupling, the evolution of initially uncorrelated spins mirrors the classical LLG dynamics. However, in the antiferromagnetic scenario, we observe pronounced deviations from classical behavior, underscoring the unique dynamics of becoming a spinless state, which is non-locally correlated. Moreover, when considering spins that are initially entangled, our study uncovers an unusual form of revival-type quantum correlation dynamics, which differs significantly from what is typically seen in open quantum systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum entanglement

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