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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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Enhancing Performances of Deep Neural Networks with Ensemble Learning Methods for Complex Human Activity Recognition Using Wearable Sensors

Enhancing Performances of Deep Neural Networks with Ensemble...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

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

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing.

关键词： Deep learning Performance evaluation Accuracy Time series analysis Bidirectional long short term memory Performance gain Human activity recognition Ensemble learning Older adults Wearable sensors

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Secure Multicast Routing Against Collaborative Attacks in FANETs with CF-mMIMO and STAR-RIS: Blockchain and Federated Learning Design

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IEEE Internet of Things Journal 2025年

作者： Pramitarini, Yushintia Perdana, Ridho Hendra Yoga Shim, Kyusung An, Beongku Hongik University Department of Software and Communications Engineering in Graduate School Sejong City30016 Korea Republic of Hankyong National University School of Computer Engineering and Applied Mathematics Gyeonggi-do17579 Korea Republic of Hongik University Department of Software and Communications Engineering Sejong30016 Korea Republic of

In this paper, we propose novel federated learning (FL) and blockchain-based secure multicast routing (FBSMR) protocol in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO) and simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS) effectively avoiding collaborative attacks. The proposed FBSMR protocol integrates FL with blockchain to enhance security and prevent collaborative attacks during the routing process. Besides, by utilizing a cross-layer design, the proposed FBSMR can enhance network security and quality of service (QoS) performance. Specifically, we implement a blockchain-based approach to support secure multicast routing, which efficiently detects and isolates malicious nodes. By using these techniques, all participating nodes achieve consensus on the validity of routing paths, thereby significantly enhancing overall network security. Besides, we address the cost-minimization problem in the proposed cross-layer design by optimizing the weight values of physical layer information, data link layer information, and network layer information subject to the minimum sequence numbers, maximum end-to-end delay, and hop count constraints. To further enhance the coverage area, improve receive signal quality, and reduce the number of hops, we leverage the capabilities of STAR-RIS technology attached to the UAV (F-STAR-RIS) to refract and reflect incident waves toward desired positions, enabling significant improvements in signal quality and transmission coverage. Additionally, the FL framework is employed for real-time prediction of the secure next node, utilizing local data from each flying access point (F-AP) to predict the optimal next node, STAR-RIS configuration, and phase shift at the STAR-RIS. Simulation results demonstrate that the proposed FBSMR protocol, combined with the FedChain-based clustering protocol, establishes a more secure route against collaborative attacks and outperforms benchmark protocol

关键词： Network layers

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TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracy

arXiv

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arXiv 2025年

作者： Valverde, Juan Miguel Koga, Motoya Otsuka, Nijihiko Dahl, Anders Bjorholm Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark A.I. Virtanen Institute University of Eastern Finland Finland Department of Architecture Faculty of Engineering Sojo University Japan

We present TopoMortar, a brick wall dataset that is the first dataset specifically designed to evaluate topology-focused image segmentation methods, such as topology loss functions. TopoMortar enables to investigate in two ways whether methods incorporate prior topological knowledge. First, by eliminating challenges seen in real-world data, such as small training set, noisy labels, and out-of-distribution test-set images, that, as we show, impact the effectiveness of topology loss functions. Second, by allowing to assess in the same dataset (TopoMortar) topology accuracy across dataset challenges, isolating dataset-related effects from the effect of incorporating prior topological knowledge. In these two experiments, it is deliberately difficult to improve topology accuracy without actually leveraging topology information, thus, permitting to attribute an improvement in topology accuracy to the incorporation of prior topological knowledge. To this end, TopoMortar includes three types of labels (accurate, pseudo-labels, noisy labels), two fixed training sets (large and small), and in-distribution and out-of-distribution test-set images. We compared eight loss functions on TopoMortar, and we found that clDice generally achieved the most topologically accurate segmentations, Skeleton Recall loss performed best particularly with noisy labels, and the relative advantageousness of the other loss functions depended on the experimental setting. Additionally, we show that simple methods, such as data augmentation and self-distillation, can elevate Cross entropy Dice loss to surpass most topology loss functions, and that those simple methods can enhance topology loss functions as well. clDice and Skeleton Recall loss, both skeletonization-based loss functions, were also the fastest to train, making this type of loss function a promising research direction. TopoMortar and our code can be found at https://***/jmlipman/TopoMortar. © 2025, CC BY.

关键词： Musculoskeletal system

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Fuzzy Logic-Based Deep Learning for Human-Machine Interaction and Gesture Recognition in Uncertain and Noisy Environments

Fuzzy Logic-Based Deep Learning for Human-Machine Interactio...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： John Anand V. Thamilarasi Ashish Rayal Himanshu Kumar Gupta Kamboji Jyothi Pramatma Vishwakarma Department of Computer Science & Engineering (AI) IIMT College of Engineering Greater Noida India Department of Computer Science & Engineering Sri Sarada College for Women(Autonomous) Salem-16 Tamilnadu India Department of Mathematics School of Applied and Life Sciences Uttaranchal University Dehradun India Department of Electrical Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India Department of Mathematics Chaitanya Bharathi Institute of Technology Proddatur AP India Department of Computer Aplication Lovely Professional University Punjab India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Conventional deep learning methods have considerable hurdles when it comes to Human-Machine Interaction (HMI) and gesture identification in noisy and uncertain situations. A Fuzzy Logic-Based Deep Learning (Fuzzy DL) method is presented in this work to improve the resilience, accuracy, and adaptability of human gesture recognition in a variety of scenarios. We test several algorithms on various datasets, lighting situations, and noise levels, such as CNN, LSTM, Transformer, and Fuzzy DL. The outcomes show that Fuzzy DL functions better than other systems, retaining 91.0% accuracy at high noise levels and reaching 97.5% accuracy under typical circumstances. In comparison to traditional structures, it also shows faster inference times and a lower mean squared error. Its superiority is further demonstrated by comparative analysis on precision, recall, and F1-score. This study presents a robust and noise-resilient solution for real-world HMI applications, including as assistive systems and virtual interfaces. The outcomes indicate that incorporating fuzzy logic with DL substantially enhances gesture recognition in difficult environments.

关键词： Deep learning Human computer interaction Fuzzy logic Accuracy Gesture recognition Transformers Noise measurement Long short term memory Noise level Resilience

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Service Caching Based Collaborative Task Offloading Algorithm in Vehicular Edge Computing

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Jisuanji Xuebao/Chinese Journal of computers 2025年第4期48卷 864-876页

作者： Tang, Chao-Gang Li, Zhao Xiao, Shuo Wu, Hua-Ming School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou221116 China Mine Digitization Engineering Research Center of the Ministry of Education Jiangsu Xuzhou221116 China The Center for Applied Mathematics Tianjin University Tianjin300072 China

Vehicular Edge Computing (VEC) extends computing resources from the cloud to the network edge, such as Roadside Units (RSUs), providing nearby vehicles with computational support and enabling data processing and analysis at the edge. Consequently, offloading tasks to the network edge, such as roadside units (RSUs), for execution rather than processing them in the cloud or on vehicles themselves, can better meet the complex requirements of vehicular tasks. Particularly, this approach not only alleviates pressure on the backhaul network but also reduces the network latency. However, several objective factors still constrain the rapid development and performance enhancement of VEC systems. For instance, the computing power and storage resources at Edge Servers (ESs) are significantly lower than those in remote cloud centers. The mobility of vehicles results in a dynamic topology for vehicular ad-hoc networks (VANETs). The real-time requirements of vehicular tasks increase the complexity of allocating network bandwidth and computing resources at the network edge. To further enhance the performance of VEC systems, it is essential to consider the aforementioned factors and develop more efficient algorithms for task offloading and resource allocation in VEC. In this paper, to make full use of the limited resources of edge servers and improve the caching benefits of application services, a service caching based collaborative offloading model for vehicular edge computing was proposed. On this basis, the research on service caching and task offloading was conducted, with the purpose of minimizing the weighted sum of delay and energy consumption. Considering the reusability of application services, the application service can be cached at the edge server such as roadside units to serve the offloading requests. On the other hand, if the application service is not cached at the network edge, the vehicles can choose to offload the blocks of the required application services to th

关键词： Edge computing

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Negative Binomial Regression Model for Predicting the Number of Tourists in National Parks of Thailand

Negative Binomial Regression Model for Predicting the Number...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sthaporn Thepsumritporn Anuchit Jitpattanakul Sakorn Mekruksavanich Wikanda Phaphan Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This article examines the use of count data models to predict the number of tourists visiting Thailand’s national parks. The dataset, encompassing observations from 2016 to 2022 across 146 national parks, exhibits characteristics such as discreteness, over dispersion, and non-negative integer values. Two models— Poisson (POI), and Negative Binomial (NB)—were compared to identify the most effective approach for modeling and prediction. Model evaluation metrics, including the Akaike Information Criterion (AIC) and log-likelihood, were employed. Results demonstrate that the Negative Binomial regression model best captures the data’s overdispersion compared to the POI model. The NB model achieved the lowest AIC and highest log-likelihood, affirming its superior performance for this application. Our findings underscore the NB model’s utility in predicting tourism-related count data and suggest its broader applicability to other domains characterized by overdispersed count data.

关键词： Natural resources Measurement Analytical models Accuracy Predictive models Data models Planning Resource management Dispersion

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LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 465-472页

作者： Krushnasamy, V.S. Al-Omari, Omaia Sundaram, Ajith Varghese, Issac K. Muniyandy, Elangovan Rao, M. V. A. L. Narasimha Dept. of EIE Dayananda Sagar College of Engineering Bangalore India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of MBA Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India

In order to estimate high-resolution air temperatures, computationally demanding procedures are required because climate models do not provide the resolution required for urban climate studies. Data-driven methods, on the other hand, provide air temperature downscaling that is quicker and more precise. Lidar technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. This research proposes a novel technique in urban–rural interface analysis in remote sensing by machine learning (ML) model. Here the input is collected as urban–rural landscape data and processed for noise removal as well as normalisation. Then these data features have been extracted and classified using a spatio extreme fuzzy gradient model with reinforcement Markov vector perceptron neural networks. Experimental analysis has been carried out for various geographical regions in terms of training accuracy, average precision, recall, RMSE, and F-1 score. The proposed model attained an F-1 score of 92%, training accuracy of 98%, average precision of 95%, recall of 93%, and RMSE of 55%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Lidar technology Machine learning model Reinforcement Markov vector Remote sensing Spatio extreme fuzzy gradient

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Optimizing Fog Computing Performance: A Machine Learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering

Optimizing Fog Computing Performance: A Machine Learning-Bas...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Ceres Dbritto Joel Lopes Sanjay Oli Maulik S Joshi Aniket Singh Ashish Rayal Reseacher St Louis USA Reseacher Redmond USA Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Department of Humanity and Science Gujarat Power Engineering and Research Institute GTU Gujarat India Department of Computer Science and Engineering Veer Madho Singh Bhandari Uttarakhand Technical University Pithoragarh Campus India Department of Mathematics School of Applied and Life Sciences Uttaranchal University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework.

关键词： Fuzzy logic Schedules Processor scheduling Clustering algorithms Machine learning Dynamic scheduling Time factors Resource management Unsupervised learning Edge computing

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TensorMD: Molecular Dynamics Simulation with Ab Initio Accuracy of 50 Billion Atoms 25

TensorMD: Molecular Dynamics Simulation with Ab Initio Accur...

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Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

作者： Yucheng Ouyang Ying Liu Honghui Shang Zhenchuan Chen Jiahao Shan Huimin Cui Xiaobing Feng Xin Chen Xingyu Gao Lifang Wang Haifeng Song Rongfen Lin Fang Li Institute of Computing Technology Chinese Academy of Sciences Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China Institute of Applied Physics and Computational Mathematics Beijing China

ISBN: (纸本)9798400714436

Molecular dynamics simulation emerges as an important area that HPC+AI helps to investigate the physical properties, with machine-learning interatomic potentials (MLIPs) being used. General-purpose machine-learning (ML) tools have been leveraged in MLIPs, but they are not perfectly matched with each other, since many optimization opportunities in MLIPs have been missed by ML tools. This inefficiency arises from the fact that HPC+AI applications work with far more computational complexity compared with pure AI scenarios. This paper has developed an MLIP, named TensorMD, independently from any ML tool. TensorMD has been evaluated on two supercomputers and scaled to 51.8 billion atoms, i.e., ~ 3× compared with state-of-the-art.

关键词： GPU

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