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arXiv 2025年

作者： Li, Zhenqi Zhong, Junhao Wang, Hewei Xu, Jinfeng Li, Yijie You, Jinjiang Zhang, Jiayi Wu, Runzhi Dev, Soumyabrata The Fu Foundation School of Engineering and Applied Science Columbia University United States Biomedical Engineering Department Pratt School of Engineering Duke University United States School of Computer Science University College Dublin Ireland Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Robotics Institute School of Computer Science Carnegie Mellon University United States Faculty of Science and Engineering University of Nottingham Ningbo China China College of Intelligence and Computing Tianjin University China The ADAPT SFI Research Centre

Rainfall prediction remains a persistent challenge due to the highly nonlinear and complex nature of meteorological data. Existing approaches lack systematic utilization of grid search for optimal hyperparameter tuning, relying instead on heuristic or manual selection, frequently resulting in sub-optimal results. Additionally, these methods rarely incorporate newly constructed meteorological features such as differences between temperature and humidity, to capture critical weather dynamics. Furthermore, there is a lack of systematic evaluation of ensemble learning techniques and limited exploration of diverse advanced models introduced in the past one or two years. To address these limitations, we propose a Robust ensemble LeArning grId search-tuNed framEwoRk (RAINER) for rainfall prediction. RAINER incorporates a comprehensive feature engineering pipeline, including outlier removal, imputation of missing values, feature reconstruction, and dimensionality reduction via Principal Component Analysis (PCA). The framework integrates novel meteorological features to capture dynamic weather patterns and systematically evaluates non-learning mathematical-based methods and a variety of machine learning models, from weak classifiers (e.g., KNN, LASSO, and Random Forest) to advanced neural networks such as Kolmogorov-Arnold Networks (KAN). By leveraging grid search for hyperparameter tuning and ensemble voting techniques, RAINER achieves state-of-the-art results within real-world dataset. Extensive experiments demonstrate the framework’s superior performance across diverse metrics, such as Accuracy, Precision, Recall, F1-score, and AUC, highlighting the pivotal role of feature engineering, grid search, and ensemble learning in rainfall prediction tasks. © 2025, CC BY.

关键词： Dimensionality reduction

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Enhanced Arithmetic Optimization Algorithm for Intrusion Detection in Wireless Sensor Networks

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International Journal of Communication Systems 2025年第10期38卷

作者： Otair, Mohammed Qawaqzeh, Faten Ali Alomari, Saleh Ali Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Alsoud, Anas Ratib Abualigah, Laith Department of Computer Science Faculty of Information Technology Middle East University Amman Jordan School of Information Engineering Sanming University Sanming China Faculty of Science and Information Technology Jadara University Irbid Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Centre for Research Impact & Outcome Institute of Engineering and Technology Chitkara University Punjab Rajpura India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq TECH Harvard John A. Paulson School of Engineering & Applied Sciences Harvard University BostonMA United States Computer Science Department Al al-Bayt University Mafraq Jordan

Wireless sensor networks (WSNs) face significant security challenges because of their resource constraints and exposure to malicious attacks. Traditional intrusion detection systems (IDSs) often suffer from low detection accuracy, high false alarm rates, and long processing times. To address these issues, this paper proposes an enhanced IDS framework based on the arithmetic optimization algorithm (AOA) for feature selection, combined with support vector machine (SVM) for classification. A new cost function is introduced to guide the feature selection process and improve model performance. The approach, named AS_IDS, is evaluated on the NSL-KDD dataset, achieving an accuracy of 96.65%, a detection rate of 98.69%, and a false alarm rate of 0.04% using only 15 features, with a significant reduction in execution time. Comparative results with state-of-the-art methods demonstrate the effectiveness and efficiency of the proposed framework in enhancing intrusion detection in WSNs. © 2025 John Wiley & Sons Ltd.

关键词： Cost functions

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

arXiv

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arXiv 2025年

作者： Al-Shareeda, Sarah Ozguner, Fusun Redmill, Keith Duong, Trung Q. Canberk, Berk Electrical and Computer Engineering Department The Ohio State University United States BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

Task offloading management in 6G vehicular networks is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1]. © 2025, CC BY.

关键词： Deep reinforcement learning

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

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IEEE Conference on Wireless Communications and Networking

作者： Sarah Al–Shareeda Fusun Ozguner Keith Redmill Trung Q. Duong Berk Canberk Electrical and Computer Engineering Department Center for Automotive Research (CAR) The Ohio State University USA BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].

关键词： 6G mobile communication Performance evaluation Identity-based encryption Authentication Deep reinforcement learning Reproducibility of results Resource management Cryptography Optimization Software development management

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Whale Optimization Algorithm Strategies for Higher Interaction Strength T-Way Testing

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Computers, Materials & Continua 2022年第10期73卷 2057-2077页

作者： Ali Abdullah Hassan Salwani Abdullah Kamal Z.Zamli Rozilawati Razali Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Computing College of Computing and Applied SciencesUniversiti Malaysia PahangPekan26600PahangMalaysia

Much of our daily tasks have been computerized by machines and sensors communicating with each other in *** is a reasonable risk that something could go wrong because there are a lot of sensors producing a lot of *** testing(CT)can be used in this case to reduce risks and ensure conformance to *** existing metaheuristic-based solutions aim to assist the test suite generation for combinatorial testing,also known as t-way testing(where t indicates the interaction strength),viewed as an optimization *** previous research,while helpful,only investigated a small number of interaction strengths up to t=*** lightweight applications,research has demonstrated good fault-finding ***,the number of interaction strengths considered must be higher in the case of interactions that generate large amounts of *** to resource restrictions and the combinatorial explosion challenge,little work has been done to produce high-order interaction *** this context,the Whale Optimization Algorithm(WOA)is proposed to generate high-order interaction *** ensure that WOA conquers premature convergence and avoids local optima for large search spaces(owing to high-order interaction),three variants of WOA have been developed,namely Structurally Modified Whale Optimization Algorithm(SWOA),Tolerance Whale Optimization Algorithm(TWOA),and Tolerance Structurally Modified Whale Optimization Algorithm(TSWOA).Our experiments show that the third strategy gives the best performance and is comparable to existing state-of-thearts based strategies.

关键词： Software testing optimization problem swarm intelligence algorithm combinatorial optimization IoT

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Increasing Power Efficiency of Spatial Cognitive Radio System by Sectorization Technique

Increasing Power Efficiency of Spatial Cognitive Radio Syste...

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IEEE Middle East and North Africa Communications Conference (MENACOMM)

作者： Zeinab Kteish Jad Abou Chaaya Abbass Nasser Koffi-Clément Yao Ali Mansour Lab-STICC UMR CNRS 6285 Université de Bretagne Occidentale Brest France Applied Physics Laboratory Faculty of Science Lebanese University Jdeideh Lebanon Lab-STICC UMR CNRS 6285 ENIB Brest Plouzané France Business Computing Department Holy-Spirit University of Kaslik (USEK) Jounieh Lebanon Lab-STICC UMR CNRS 6285 ENSTA Bretagne Brest France

ISBN: (数字)9798331519957

ISBN: (纸本)9798331519964

In this study, we examine a Cognitive Radio (CR) network comprising a Primary User (PU) and Secondary Users (SUs), all of whom are equipped with multiple antennas to take advantage of spatial characteristics for transmission. The SUs cooperate to analyze the PU’s beam of transmission, dividing the available space between the PU and the SUs. Instead of activating all SUs, we propose a sectorization technique to limit the number of active SUs. This is achieved by identifying the dominant sectors through weighted power estimates. Our study evaluates the accuracy of beam estimation and sector selection using metrics such as angular false and missed detections, the percentage of false and missed activations, and the percentage of sectorization failure. The results indicate that the sectorization technique enhances the system power efficiency by allowing a lower number of active users to achieve better beamwidth estimation accuracy, compared to using all SUs. Additionally, our sector selection process successfully identifies the dominant sectors even when the beam spans multiple sectors. We also investigate the influencing parameters impacting the selection accuracy, such as the number of sensing units per sector, the number of sectors, and the beamwidth of the PU’s signal.

关键词： Measurement Accuracy Three-dimensional displays Estimation Africa MIMO Sensors Performance analysis Cognitive radio

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RISK ATTRIBUTION AND TOBACCO-RELATED DEATHS

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AMERICAN JOURNAL OF EPIDEMIOLOGY 1993年第2期138卷 128-139页

作者： STERLING, TD ROSENBAUM, WL WEINKAM, JJ The Faculty of Applied Sciences School of Computing Science Simon Fraser University Bumaby British Columbia Canada

The number of deaths that would not have occurred had an exposure or trait been absent is generally estimated by observing mortality rates in sample populations of exposed and nonexposed persons and applying them to the population of interest. Three methods used to estimate deaths due to tobacco use are evaluated. Each method requires estimates of certain absolute and relative risks, and the published estimates based on them assume that the absolute and relative risks observed in the two large American Cancer Society prospective studies can be applied to the US population or to populations in developed countries. Computations using large representative samples of US decedents and of the entire US population for these methods result in estimated numbers of deaths for the US population that are substantially lower than those based on Cancer Prevention Survey-I or Cancer Prevention Survey-II. Computations also showed that controlling for confounding from two smoking-related variables results in still lower estimates of the number of excess deaths. Consequently, published results that ignore confounding and are based on nonrepresentative data overstate the contribution of smoking. It is imperative that estimates of excess deaths be based on representative data and control for relevant confounders.

关键词： BIAS (EPIDEMIOLOGY) EPIDEMIOLOGIC METHODS MODELS, STATISTICAL MORTALITY RISK TOBACCO

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A PRACTICAL APPROACH TO ESTIMATING THE TRUE EFFECT OF EXPOSURE DESPITE IMPRECISE EXPOSURE CLASSIFICATION

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AMERICAN JOURNAL OF INDUSTRIAL MEDICINE 1991年第5期19卷 587-601页

作者： WEINKAM, JJ ROSENBAUM, WL STERLING, TD Faculty of Applied Sciences School of Computing Science Simon Fraser University Burnaby British Columbia Canada

Accurate information on actual exposure to some possibly toxic agent usually is not available in long-term occupational studies. Any strategy for assigning exposure levels or categories necessarily results in misclassification, where some individuals classified as exposed have no real exposure and some individuals classified as not exposed have some exposure to the agent. Both misclassification errors serve to reduce the estimate of risk associated with exposure. The question arises, "How much does the true risk depart from the observed estimate given an assumed level of misclassification?" This paper quantifies the effect of such misclassification on several forms of standardized risk ratios. Our results express the true risk as a function of the apparent risk based on imprecise exposure classification and parameters representing the proportion of each of the groups that are correctly classified. In any practical situation, the apparent risk can be computed based on whatever classification scheme is being used. On the other hand, the proportions of the imprecisely classified groups actually exposed cannot. However, the investigator may have information or may make assumptions for likely ranges of values for these proportions. Given the apparent risk, estimated true risks can be calculated and plotted or represented in tabular form as a function of the proportions of actual exposure. The resulting graph or table enables the investigator to read off the range of possible true risk values based on what he is prepared to believe or what other information indicates about the range of proportions of misclassified subjects. For instance, results for a typical value of apparent risk of 1.8 show that the true risk may be twice the apparent risk with only 23% misclassification in each exposure group. The value of the true risk that would be necessary to be consistent with a given apparent risk increases rapidly as the extent of misclassification increases. We also show that,

关键词： MISCLASSIFICATION TRUE RELATIVE RISK APPARENT RELATIVE RISK BIAS CORRECTION FOR BIAS

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Artificial intelligent approach for flow stability in Eyring Powell hybrid nanofluids via porous shrinking sheets

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Journal of Molecular Liquids 2025年 422卷

作者： Fatima, Nahid Kouki, Marouan Khan, Muhammad Imran Amjad, Arslan Bin Asghar, Zaheer Zeeshan, Ahmad Ijaz, Nouman Department of Mathematics and Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Information System Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Mathematics and Statistics International Islamic University Islamabad 44000 Pakistan Centre for Mathematical Sciences Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Department of Physics and Applied Mathematics Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Department of Mathematics College of Science Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Department of Mathematics and Statistics Punjab Group of Colleges G.T. Road Jada Jhelum49600 Pakistan

This study aims to analyze flow stability using a computational intelligence technique that integrates a Levenberg-Marquardt approach utilizing artificial intelligence implemented through a backpropagation neural network. AI-based LMS-BPNN approach is utilized to examine the flow behavior of Eyring Powell hybrid nanofluid over a porous shrinking sheet. By applying skillful transformations, a set of ordinary differential equations is obtained from the partial differential equations that describe the hybrid nanofluid. Scenarios 1–5 illustrate how the built-in MATLAB function, bvp4c, solves ordinary differential equations systems by carefully modifying input parameters to get the initial or reference solution. The numerical data was split into three groups: 10 % for testing, 10 % for validation, and 80 % for training. The LMS-BPNN was used to find approximate solutions for these scenarios. To verify the effectiveness and reliability of LMS-BPNN, we employed regression analysis, error metrics, and correlation index (R)-based fitness curves. In each scenario, the temperature and velocity profiles gradually approached the boundary conditions as expected. The flow performance outcomes for the dual solution (Cfx and Nux) are also analyzed using the LMS-BPNN method. The perturbation scheme is applied to the boundary layer problem to obtain the eigenvalues problem. The unsteady solution f(η,τ) converges to steady solution fo(η) for τ→∞ when γ≥0. However, an unsteady solution f(η,τ) diverges to a steady solution fo(η) for τ→∞ when γ © 2025 Elsevier B.V.

关键词： Eigenvalues and eigenfunctions

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Chaos and proportional integral derivative (PID) control on cancer dynamics with fractal fractional operator

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Results in Engineering 2025年 26卷

作者： Farman, Muhammad Nisar, Kottakkaran Sooppy Jamil, Khadija Akgül, Ali Sambas, Aceng Bayram, Mustafa Habib, Mustafa Hassani, Murad Khan Faculty of Arts and Science Department of Mathematics Near East University Nicosia99138 Cyprus Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu 22200 Malaysia Research Center of Applied Mathematics Khazar University Baku1096 Azerbaijan Department of Mathematics College of Science and Humanities in Alkharj Prince Sattam Bin Abdulaziz University Alkharj11942 Saudi Arabia Institute of Mathematics Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Topkapi Istanbul34010 Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard Nicosia/Mersin 10 PC: 99138 Turkey Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya46196 Indonesia Department of Mathematics University of Engineering and Technology Lahore Pakistan Ghazni University Department of Mathematics Afghanistan Applied Science Research Center Applied Science Private University Amman11937 Jordan

This study presents a cancer dynamics model incorporating a fractal-fractional operator with a Mittag-Leffler kernel to capture complex interactions among cancer cells, tumor suppressor cells, immune cells, and oncolytic viruses. The model aims to enhance understanding of tumor-immune dynamics and improve treatment strategies. The existence and uniqueness of the solution are established using fixed point theory under the Lipschitz condition. Lyapunov stability of the system is also analyzed in the context of the fractal-fractional operator. To address chaotic behavior in cancer progression, chaos and Proportional-Integral-Derivative (PID) control techniques are implemented. These control methods effectively stabilize the system and regulate treatment administration. Numerical simulations illustrate the influence of fractional-order derivatives on tumor suppression and immune response, confirming the model's effectiveness in reflecting real-world cancer dynamics. © 2025 The Author(s)

关键词： Fractals

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