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The integrated lot-sizing problem of production, remanufacturing and transportation, considering recovery targets

Journal of Remanufacturing 2025年第1期15卷 127-149页

作者： Vidal, Luciana Piñeyro, Pedro Viera, Omar Department of Operations Research Institute of Computer Science (InCo) Faculty of Engineering Universidad de la República Montevideo Uruguay

This paper considers a hybrid production-remanufacturing system of a single product, composed of an original equipment manufacturer (OEM) and a set of customers/retailers, operating under collection and remanufacturing targets. For this kind of production system, we address the problem of simultaneously determining the sizes of the lots of production, remanufacturing and transportation to and from customers, in order to meet their demand on time and collect used products to be remanufactured. Set-up and unit costs are incurred for the three activities, and unit costs for holding inventory for both used and serviceable items in the OEM but also in the customers. Recovery targets are stated as lower bounds on the number of used products that must be collected and remanufactured by the OEM. We provide a mixed integer-linear programming formulation for the problem and suggest a solution procedure based on the metaheuristic of Tabu Search. We propose and evaluate several variants of the heuristic procedure by extending a well-known set of instances from the literature. The suggested procedures are compared against a state-of-the-art optimization solver, under different parameter settings of interest such as set-up costs, inventory holding costs and recovery targets. The results obtained from the numerical experimentation allow us to conclude that at least one of the variants of the proposed heuristic is effective both in cost and running time, even outperforming the optimization solver for some of the large instances. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Closed-loop supply chain Integrated production and transportation Lot-sizing Metaheuristics Recovery targets Remanufacturing

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Adopting Graph Neural Networks to Understand and Reason About Dynamic Driving Scenarios

IEEE Open Journal of Intelligent Transportation Systems

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IEEE Open Journal of Intelligent Transportation Systems 2025年 6卷 579-589页

作者： Su, Peng Xiang, Conglei Chen, Dejiu KTH Royal Institute of Technology Faculty of Engineering Design Department Stockholm100 44 Sweden KTH Royal Institute of Technology Electrical Engineering and Computer Science School Stockholm100 44 Sweden

With advances in Deep Neural Networks (DNN), Automated Driving Systems (ADS) enable the vehicle to perceive their surroundings in dynamic driving scenarios and perform behaviors by collecting operational data from sensors such as LiDAR and cameras. Current DNN typically detect objects by analyzing and classifying unstructured data (e.g., image data), providing critical information for ADS planning and decision-making. However, advanced ADS, particularly those required to perform the Dynamic Driving Task (DDT) autonomously, are expected to understand driving scenarios across various Operational Design Domains (ODD). This capability requires the support for a continuous comprehension of driving scenarios according to operational data collected by sensors. This paper presents a framework that adopts Graph Neural Networks (GNN) to describe and reason about dynamic driving scenarios via analyzing graph-based data based on collected sensor inputs. We first construct the graph-based data using a meta-path, which defines various interactions among different traffic participants. Next, we propose a design of GNN to support both the classification of the node types of objects and predicting relationships between objects. As results, the performance of the proposed method shows significant improvements compared to the baseline method. Specifically, the accuracy of node classification increases from 0.77 to 0.85, while that of relationships prediction rises from 0.74 to 0.82. To further utilize graph-based data constructed from dynamic driving scenarios, the proposed framework supports reasoning about operational risks by analyzing the observed nodes and relationships in the graph-based data. As a result, the model achieves a MRR of 0.78 in operational risks reasoning. To evaluate the practicality of the proposed framework in real-world systems, we also conduct a real-time performance evaluation by measuring the average process time and the Worst Case Execution Time (WCET). Com

关键词： Graph neural networks

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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Optimized ensemble deep learning approach for accurate breast cancer diagnosis using transfer learning and grey wolf optimization

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Evolving Systems 2025年第2期16卷 1-17页

作者： Hassan, Esraa Saber, Abeer El-Sappagh, Shaker El-Rashidy, Nora Faculty of Artificial Intelligence Kafrelsheikh University Kafr Elsheikh Egypt Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt

Breast cancer (BC) is one of the most significant threats to women’s health worldwide, affecting one in eight women and causing over 42,250 deaths in 2024. Early detection plays a crucial role in improving patient outcomes, with mammography being the primary screening method most health organizations recommend. This paper highlights the potential of deep learning (DL) in enhancing diagnostic Accuracy and speed. However, despite the promising results of DL models, the increasing complexity of breast cancer data presents a significant challenge in selecting the most relevant features. We propose a robust DL model incorporating transfer learning (TL) and grey wolf optimization (GWO) to enhance BC diagnosis. The architecture is developed by leveraging multiple deep neural networks, including ResNet and Inception, based on convolutional neural networks (CNNs). These networks are fine-tuned using a mammographic image dataset to optimize model weights and parameters for higher classification accuracy. Additionally, the Wisconsin Breast Cancer Dataset (WBCD) is utilized with GWO to refine feature selection further. Experimental results demonstrate that the proposed ensemble method improves robustness, generalization, and detection rates while minimizing false positives and negatives. Large-scale dataset evaluation yielded a precision of 0.942, sensitivity of 0.982, Accuracy of 0.965, and an area under the curve (AUC) value of 0.971. These findings suggest that the proposed framework could significantly enhance patient care and improve healthcare service organization and management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Diseases

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An Analytic Policy Gradient-Based Deep Reinforcement Learning Motion Cueing Algorithm for Driving Simulators

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IEEE Access 2025年 13卷 81507-81523页

作者： Huang, Xiaowei Shi, Xuhua Wang, Peiyao Xu, Hongzan Tang, Xiaojun Zhang, Gaoran Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China CO.Ltd Ningbo315800 China

The proposed motion cueing algorithm (MCA), based on a reinforcement learning algorithm using gradient information to directly update the control policy, introduces three significant enhancements. First, transform the black-box simulator environment into a differentiable simulator environment that provides gradient information at each time step and use this gradient information to directly update the control policy. Second, the network architecture is reconfigured into a concurrent controller format, similar to Model Predictive Control (MPC). This controller processes a sequence of vehicle motion reference signals over a future period, utilizing a multi-layer perceptron to generate the simulator’s motion reference control signal sequences for the same duration. Unlike the online optimization employed in MPC, this algorithm as an offline optimization method, providing substantial computational advantages when integrated into the driving simulator. As the prediction horizon increases, the algorithm demonstrates superior computational efficiency, which helps reduce the incidence of motion sickness during the use of the driving simulator. Third, a loss function specifically designed for the motion simulator is proposed. This function incorporates constraints derived from the MPC framework to address workspace limitations and applies them to workspace management. These constraints restrict the platform’s acceleration and speed near the workspace boundaries, allowing for better utilization of the available space. The algorithm is validated using Carla’s autonomous driving simulation software as the dataset generator. During the training process, the proposed algorithm in this paper achieves an order-of-magnitude improvement in convergence speed compared to conventional training methods of PPO and DDPG. Simulations with a 10-step prediction horizon indicate that the Root Mean Square Error (RMSE) produced by this algorithm is comparable to that of the MCA based on MPC (MPC-

关键词： Automobile driver simulators

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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The influence of fear effect and phase of chaos control of discrete predator–prey system with mixed Holling types

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Alexandria engineering Journal 2025年 127卷 296-308页

作者： Madian, A.M. Zheng, Rong El-sheikh, M.M.A. Elettreby, M.F. El-Gaber, A.A. Mathematics and Computer Science Department Faculty of Science Menofia University Menofia Egypt New Engineering Industry College Putian University Fujian Putian351100 China School of Information Engineering Sanming University Fujian Sanming365004 China Mathematics Department Faculty of Science Mansoura University Mansoura35516 Egypt

In this paper, a discrete-time predator–prey system with mixed Holling types I and III functional responses and fear effect is discussed. Three fixed points are determined and their stability is established. Analytical criteria for Neimark Sacker bifurcation (NSB) and flip bifurcation (FB) are proposed. Applying the technique of state feedback control, we introduce the corresponding controlled system. At an unstable fixed point the chaotic orbits can be stabilized. We show that the fear effect of prey species has a negative effect on the density of predator species in our model. Furthermore, fear effect may increase the possibility that prey species will go extinct, which implies that Allee effect may occur in this case. These analytical studies are illustrated with some numerical simulations. © 2025 The Authors

关键词： State feedback

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Enhancing Image Search Engine Accuracy Using Psychological Intention Diagrams 3

Enhancing Image Search Engine Accuracy Using Psychological I...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Aswathi, K.P. Yadwad, Sunita Raj, D. Nirmal Rastogi, Shailesh Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Pune India Pune India

ISBN: (纸本)9798331508456

As the Internet becomes increasingly image-centric, users face substantial challenges in efficiently locating desired images due to the inherent limitations of image search engines in interpreting visual content and keyword ambiguity. This paper introduces an innovative solution employing psychological intention diagrams to enhance search accuracy. These diagrams harness historical data to depict the search intentions associated with specific keywords, reflecting both user and web designer perspectives. By integrating these diagrams, our proposed search engine significantly refines its ability to predict and retrieve the most relevant images. The implementation of this system involves constructing two types of intention diagrams: one based on past user queries and another derived from the intentions of web designers for cases without historical user data. This dual approach not only captures the semantic essence of images more effectively but also addresses the multi-faceted meanings of search keywords. Our experimental results confirm that incorporating psychological dimensions into image search mechanisms substantially improves precision, facilitating more accurate and user-aligned image retrieval. This pioneering approach is the first to apply psychological profiling in enhancing image search engine functionalities, marking a significant advancement in digital image retrieval technologies. © 2025 IEEE.

关键词： Content based retrieval

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The Utilization of Google Cloud in the Healthcare Industry 2

The Utilization of Google Cloud in the Healthcare Industry

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kapse, Rishikesh Gourshettiwar, Palash Faculty of Engineering and Technology Dept. of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Dept. of Computer Science And Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, 'artificial intelligence,' and 'machine learning' applications in diagnostics, diagnostics, and personalized medicine. Data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare. © 2025 IEEE.

关键词： Theranostics

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Multimodal Brain Tumor Segmentation Using a Dual-Branch Vision Transformer Integrated with Region-Attention Fusion Network 3

Multimodal Brain Tumor Segmentation Using a Dual-Branch Visi...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Yadwad, Sunita Rajeswari, C. Raj, D. Nirmal Dange, Pravin Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Gujarat India Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur India Pune India Pune India

ISBN: (纸本)9798331508456

Accurate segmentation of brain tumors is a critical task in medical imaging, aiding diagnosis, treatment planning, and prognosis. This paper introduces a novel framework for brain tumor segmentation that leverages a Dual-Branch Vision Transformer (DB-ViT) integrated with a Region-Attention Fusion Network (RAFN) to address the limitations of conventional convolutional neural networks. The DB-ViT processes multimodal MRI sequences, including T1, T1c, T2, and FLAIR, to extract global contextual features while preserving modality-specific information. Simultaneously, the RAFN enhances segmentation precision by refining local features and focusing on critical regions of interest through an attention mechanism. The proposed method effectively combines global and local feature representations, ensuring accurate delineation of tumor boundaries. Extensive experiments were conducted on the BraTS dataset, demonstrating the superiority of our approach over existing methods such as U-Net, Attention U-Net, and TransUNet. Quantitative evaluation metrics, including Dice Score and Intersection over Union (IoU), indicate significant improvements in segmentation accuracy. The framework also achieves computational efficiency and robustness across diverse tumor morphologies. Visual results further validate its ability to enhance boundary delineation, particularly in heterogeneous tumor regions. The proposed methodology represents a significant advancement in automated medical image analysis, offering potential for real-time clinical applications. Future work aims to explore broader clinical validation and real-time deployment of this framework. © 2025 IEEE.

关键词： Convolutional neural networks

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