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An Investigation Through Stochastic Procedures for Solving the Fractional Order computer Virus Propagation Mathematical Model with Kill Signals

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Neural Processing Letters 2023年第2期55卷 1783-1797页

作者： Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Mumtaz, Nadia Fathurrochman, Irwan Sadat, R. Ali, Mohamed R. Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Computer Science Iqra University Islamabad Campus Islamabad Karachi Pakistan Department of Islamic Educational Management Institute Agama Islam Negeri Curup Rejang Lebong Indonesia Department of Mathematics Zagazig Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University Cairo Egypt

In this study, the numerical investigations through the stochastic procedures for solving a class of fractional order (FO) computer virus propagation (CVP) mathematical model with kill signals (KS), i.e., CVP-KS is presented. The KS gets alert about those viruses, which can be infected through the computer system to decrease the virus propagation danger. The mathematical model of the CVP-KS is based on the SEIR-KS model. The focus of these investigations is to present the numerical solutions of the FO-SEIR-KS model using the sense of Levenberg–Marquardt backpropagation scheme (LMBS) together with the neural networks (NNs), i.e., LMBS-NNs. The use of the one dynamic of the other makes the model nonlinear. Three different FO values have been used to check the performances of the designed scheme for this FO-SEIR-KS nonlinear mathematical model. The statics used in this study is 80%, 10% and 10% for training, testing and certification for solving the FO-SEIR-KS nonlinear mathematical model. The numerical simulations are performed through the stochastic LMBS-NNs scheme for solving the FO-SEIR-KS nonlinear mathematical model. The obtained results will be compared with the design of database reference solutions based on the Adams–Bashforth–Moulton. In order to accomplish the validity, capability, consistency, competence and accuracy of the LMBS-NNs, the numerical results using the error histograms, regression, mean square error, state transitions and correlation have been provided. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Mean square error

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Spatially Constrained Hyperspectral Pigment Mapping Using Watershed Segmentation

Spatially Constrained Hyperspectral Pigment Mapping Using Wa...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Nina Eckertz Hilda Deborah Jon Y. Hardeberg Irina Crina Anca Sandu Department of Computer Science Norwegian University of Science & Technology Gjøvik Norway Conservation Section MUNCH Museum Oslo Norway

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

This article presents an algorithm to analyse and map the distribution of pigments on areas of interest on a painting surface using hyperspectral imaging (HSI) and image segmentation. Whereas previous investigations of pigments were typically on a pixel-by-pixel basis, this work presents a spatially constrained analysis. By combining independent component analysis and watershed segmentation, we provide an approach to first identify regions within the paint layer prior to pigment classification. The pigment classification itself is compared to an existing spectral library of pure pigments, providing a first analysis of the pigments' properties. The results of this investigation can be used to map the distribution of pigments, to be used in further examinations of their properties by experts in the cultural heritage field.

关键词： Image segmentation Watersheds Independent component analysis Pigments Signal processing Fluorescence Libraries Paints Hyperspectral imaging Painting

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Integrating environmental sustainability in construction Time-Cost trade-off for decision-making using hybrid NSGA-III and MOPSO approach

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Asian Journal of Civil Engineering 2025年第4期26卷 1527-1542页

作者： Nagayo, Analene Montesines Singh, Rekha Dhawan, Amit Manjunath, T.C. Qasem, Altayeb Sethi, Krushna Chandra Sharma, Kamal Department of Engineering and Technology University of Technology and Applied Sciences-Al Musanna Al Muladdah Oman Department of Civil Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Department of Civil Engineering Dr. Bhim Rao Ambedkar Polytechnic College Gwalior India Computer Science Engineering Department Rajarajeswari College of Engineering Bangalore India Department of Building Engineering College of Architecture and Planning Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Civil Engineering Centurion University of Technology and Management Gajapati India Department of Civil Engineering Madhav Institute of Technology and Science Gwalior India

Construction project management often involves optimizing time and cost while ensuring minimal environmental impact. This study presents an innovative hybrid approach combining non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective particle swarm optimization (MOPSO) to address the time-cost-environmental sustainability trade-off (TCEST) in construction projects. The proposed model aims to minimize project completion time and cost while maximizing environmental sustainability. A case study is conducted to validate the model, incorporating diverse construction activities and their respective time, cost, and environmental sustainability metrics. The results reveal Pareto-optimal solutions demonstrating significant trade-offs among the three objectives. The hybrid approach outperforms standalone algorithms in terms of solution diversity, convergence, and hypervolume indicators. Weighted sum methods are employed to select the most suitable solution from the Pareto front based on project priorities. Correlation analysis further explores interdependencies among objectives, emphasizing the feasibility of balancing these critical factors. The study contributes a robust decision-support tool for sustainable project planning, facilitating informed decision-making in modern construction management. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Hybrid Optimization MOPSO NSGA-III Sustainable Construction Time–Cost-Environmental Trade-off

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Numerical Computation of SEIR Model for the Zika Virus Spreading

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computers, Materials & Continua 2023年第4期75卷 2155-2170页

作者： Suthep Suantai Zulqurnain Sabir Muhammad Asif Zahoor Raja Watcharaporn Cholamjiak Data Science Research Center Department of MathematicsFaculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Research Group in Mathematics and Applied Mathematics Faculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlinspan64002Taiwan School of Science University of PhayaoPhayao 56000Thailand

The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., *** correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive *** outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.

关键词： SEIR nonlinear system Zika virus artificial neural networks scaled conjugate gradient statistical measures

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Optimizing Battery Management Systems For Electric Vehicles: Current Trends And Future Directions 5

Optimizing Battery Management Systems For Electric Vehicles:...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Prakash, V.S. Muthu, Annamalai Anandan, B. Velmurugan, V. Raja, T. Alex Stanley Purushothaman, V. Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Science Electrical Section Engineering Department Nizwa Oman Siddharth Institute of Engineering & Technology Department of Mechanical Engineering Andhrapradesh Puttur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Sathyamangalam India Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350376685

The rapid global shift towards electric vehicles (EVs) marks a transformative era in transportation, driven by the imperative to reduce carbon emissions and reliance on fossil fuels. Central to the performance and longevity of EVs are Battery Management Systems (BMS), which optimize battery usage, enhance safety, and extend operational life. This comprehensive review explores current trends and future directions in BMS optimization, focusing on key technological advancements and their implications for electric mobility. Battery Management Systems (BMS) serve as critical components in electric vehicles, orchestrating the efficient operation of battery packs through real-time monitoring, state-of-charge (SoC) estimation, thermal management, and fault diagnosis. These systems not only ensure optimal performance but also safeguard against potential hazards, thereby enhancing vehicle reliability and user confidence. Current advancements in BMS technology emphasize enhanced accuracy in SoC estimation, leveraging sophisticated algorithms and sensor integration to provide precise information on battery charge levels. Coupled with innovative thermal management strategies, which include active cooling and heating systems, BMS can mitigate thermal stress and optimize battery performance across diverse environmental conditions. By synthesizing technological advancements, emerging trends, and potential challenges, it offers valuable insights for researchers, engineers, and policymakers aiming to advance the efficiency, reliability, and sustainability of electric mobility solutions worldwide. © 2024 IEEE.

关键词： State of charge

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Optimizing Variable-Strength Combinatorial Test Suite Generation via Fuzzy Adaptive Sine Cosine Algorithm (FASCA) 1

Optimizing Variable-Strength Combinatorial Test Suite Genera...

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1st International Conference on Cyber Security and Computing, CyberComp 2024

作者： Zabidi, Nur Syabila Ibrahim, Noraini Rejab, Mazidah Mat Mamat, MasrulEhsan Nazir, Sumaira Mat Tuselim, Nurul Hawani Faculty of Computer Science and Information Technology Parit Raja Batu Pahat Malaysia National University of Modern Languages Department of Software Engineering Islamabad Pakistan Industrial Research Management Centre SIRIM Berhad Business Intelligence Section Shah Alam Malaysia

ISBN: (纸本)9798350387728

Combinatorial test suite generation is a critical aspect of software testing, particularly for systems with variable-strength interactions. Traditional optimization algorithms often struggle to efficiently generate minimal test suites while maintaining coverage. This research introduces the Fuzzy Adaptive Sine Cosine Algorithm (FASCA), a variant of the Sine Cosine Algorithm (SCA), tailored specifically for optimizing variable-strength combinatorial test suite generation. FASCA incorporates fuzzy adaptation mechanisms to dynamically balance exploration and exploitation, addressing the limitations of standard SCA in handling diverse interaction strengths. Experimental evaluations demonstrate that FASCA significantly reduces test suite size and execution time compared to the original SCA, offering a more efficient solution for combinatorial test optimization. These results highlight FASCA's potential as a robust tool for enhancing the efficiency of software testing processes. © 2024 IEEE.

关键词： Software testing

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Bayesian regularization knack-based intelligent networks for thermo-physical analysis of 3D MHD nanofluidic flow model over an exponential stretching surface

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European Physical Journal Plus 2023年第1期138卷 2页

作者： Awan, Saeed Ehsan Awais, Muhammad Raja, Muhammad Asif Zahoor Rehman, Saeed ur Shu, Chi-Min Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Attock 43600 Pakistan Department of Mathematics COMSATS University Islamabad Attock Campus Attock 43600 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

Artificial intelligence (AI) knacks are exploited broadly by the research community in the field of engineering and technology to enhance efficacy of the outputs for rapid development of business and society. Therefore, it looks promising to explore or investigate in the novel AI-based integrated numerical computing to study the solution dynamic of computational fluid mechanics problem of utmost significance. Boundary layer methodology is invoked for the mathematical modeling of fluidic system to study the heat and mass transport improvement of nanofluid with base water 3D-MHD flow model with an exponentially stretched surface involving three types of nanoparticles Fe₃O₄ (magnetite), Ag (silver) and C₁₄₀H₄₂O₂₀ (graphene oxide). The representative 3D PDEs for nanofluidic system are converted into equivalent nonlinear ODE-based system via suitable similarity transformation. The results of the three nanofluidic models are calculated by applying Adams numerical technique for influential outcomes of aggressing parameters including Hartmann number M, wall stretching ratio P, thermal radiation R_d and heat source/sink hs to observe the effecting profiles of temperature and velocity fields. The obtained numerical data are utilized for Bayesian regularization knack-based intelligent computing method to construct the networks for approximate solution dynamic of nanofluidic models. The worth and value of designed Bayesian regularization knack-based networks (BRKNs) is established by regression index measurements, error histogram studies and convergence curves with negligible level of mean square error (E-12 to E-10) for the exhaustive simulations. Graphical Abstract: [Figure not available: see fulltext.]. © 2023, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.

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Advancements in 5G and 6G Technologies Shaping the Future of Global Connectivity and Communication 5

Advancements in 5G and 6G Technologies Shaping the Future of...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Pachar, Sunita Reddy, Satti Sudha Mohan Kompally, Gouthami Srinivasagopalan, Lakshmi Narasimhan Sangore, Rohidas Balu Katta, Sivakoteswararao GLA University IBM Department India SRKR Engineering College Department of ECE India Department of CSE-IOT India Software Development Enterprise Architecture PlanoTX United States Computer Science and Engineering MIT Art Design Technology University India KIETW Department of Mechanical Kakinada India

ISBN: (纸本)9798331530013

The evolution of wireless communication is poised for a major shift with the transition from 5G to 6G, a leap characterized by breakthroughs in speed, ultra-reliable low-latency communication, and AI-driven network optimization. This paper examines these advancements, focusing on sub-millisecond latency, terahertz spectrum utilization, and enhanced network capabilities tailored to industries such as smart infrastructure, healthcare, and autonomous vehicles. While 6G promises revolutionary improvements, it also presents new challenges, including increased computational complexity and energy demands, data privacy concerns, and efficient spectrum allocation. The proposed model aims to address these issues by integrating AI and machine learning for optimized resource management, resulting in a high-performance, cost-effective solution for real-time, data-intensive applications. To assess the model's effectiveness, comprehensive simulations and real-world trials will be conducted, measuring critical metrics such as network throughput, latency, reliability, and energy efficiency. This study offers an all-encompassing guide to understanding and harnessing the transformative potential of 6G technology, laying the groundwork for future wireless communication systems. © 2024 IEEE.

关键词： Terahertz spectroscopy

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Real-Time Inference of Temporal Emotional and Behavioral Conditions of Learners in Problem Solving Within Mixed Reality Environments 1

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19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022

作者： Castets, Édouard Mutis, Ivan Agam, Gady Department of Mechanical Materials and Aerospace Engineering Illinois Institute of Technology ChicagoIL60616 United States Department of Civil Architecture and Environmental Engineering Illinois Institute of Technology ChicagoIL60616 United States Department of Computer Science Illinois Institute of Technology Chicago60616 United States

ISBN: (数字)9783031325151

ISBN: (纸本)9783031325144

Using new affordances of Machine Learning (ML) and Mixed Reality (MX) technology, the presented research looks at how learners’ decision-making in problem-solving can increase with the technology interaction. The approach empowers users for enhanced problem-solving outcomes by enabling them to perform adapted actions in an MX environment. For example, present an adapted user’s view of 3D design components in the immersive space for any action (locking and moving) in problem-solving (e.g., design interpretations). The aim is to incorporate ML to tailor the user’s ability to problem-solve complex 3D design components by capitalizing on the flexibility of MX immersive environments. The system uses a collection of physiological and pupillometry data to create real-time inferences of the user’s state (emotional and physiological conditions). This paper focuses on the first phase of developing the MX and ML system for the augmented technology-user interaction to allow inference of the data in real time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mixed reality

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Future connected vehicles:Communications demands,privacy and cyber-security

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Communications in Transportation Research 2022年第1期2卷 39-41页

作者： Tomas Olovsson Tommy Svensson Jiaming Wu Department of Computer Science and Engineering Chalmers University of TechnologyGothenburgSE41296Sweden Department of Electrical Engineering Chalmers University of TechnologyGothenburgSE41296Sweden Department of Architecture and Civil Engineering Chalmers University of TechnologyGothenburgSE41296Sweden

*** With the advance of data collection,data processing,telecommunication and vehicular technologies,connected vehicles(CVs)have been emerging as a crucial branch of smart mobility(Olia et al.,2015;Li et al.,2021).Its basic idea is to realize real-time exchanges and processing of essential information,such as positions and destinations,among surrounding vehicles and infrastructures.

关键词： connected branch destination

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