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Combining decomposition and graph capsule network for multi-objective vehicle routing optimization

Intelligent Data Analysis 2025年第1期29卷 116-140页

作者： Zhang, Haifei Ge, Hongwei Li, Ting Zhou, Lujie Su, Shuzhi Tong, Yubing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi China Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University) Ministry of Education Jiangsu Wuxi China College of Automobile and Transportation Tianjin University of Technology and Education Tianjin China School of Computer Science and Engineering Anhui University of Science and Technology Anhui Huainan China Medical Image Processing Group Department of Radiology University of Pennsylvania Philadelphia PA United States

In order to alleviate urban congestion, improve vehicle mobility, and improve logistics delivery efficiency, this paper establishes a practical multi-objective and multi constraint logistics delivery mathematical model based on graphs, and proposes a solution algorithm framework that combines decomposition strategy and deep reinforcement learning (DRL). Firstly, taking into account the actual multiple constraints such as customer distribution, vehicle load constraints, and time windows in urban logistics distribution regions, a multi constraint and multi-objective urban logistics distribution mathematical model was established with the goal of minimizing the total length, cost, and maximum makespan of urban logistics distribution paths. Secondly, based on the decomposition strategy, a DRL framework for optimizing urban logistics delivery paths based on Graph Capsule Network (G-Caps Net) was designed. This framework takes the node information of VRP as input in the form of a 2D graph, modifies the graph attention capsule network by considering multi-layer features, edge information, and residual connections between layers in the graph structure, and replaces probability calculation with the module length of the capsule vector as output. Then, the baseline REINFORCE algorithm with rollout is used for network training, and a 2-opt local search strategy and sampling search strategy are used to improve the quality of the solution. Finally, the performance of the proposed method was evaluated on standard examples of problems of different scales. The experimental results showed that the constructed model and solution framework can improve logistics delivery efficiency. This method achieved the best comprehensive performance, surpassing the most advanced distress methods, and has great potential in practical engineering. © 2024 – IOS Press. All rights reserved.

关键词： attention mechanism decomposition strategy deep reinforcement learning graph capsule network multi objective optimization Urban logistics distribution

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Self Supervised Prediction of Genetic Associations in Comorbid Diseases With Masked Autoencoder Using Hypergraph Representations

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 628-639页

作者： Saikat Biswas Vibhanshu Ranjan Pabitra Mitra Krothapalli Sreenivasa Rao Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Department of Electrical Engineering Indian Institute of Technology Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

Comorbid disease association refers to the simultaneous occurrence of a disease with the coexistence of another primary disease. Due to the complex traits of these co-occurring multi-diseases, it is crucial to know the underlying genetic molecular basis of the prevalent diseases. The inference of common genetic association based on gene co-expression data helps to unveil the pathogenesis of comorbid diseases. There exist a few disease-specific gene co-expression-based analyses to predict the hub genes causing these diseases. However, works lack multi-relational biological data integration. In addition, there still does not exist any unified method to predict the common genetic associations from the co-expression graph across comorbid diseases. Hence, we introduce a generalized and novel approach to predict overlapping genetic associations from disease-specific gene co-expression networks with a self-supervised edge-masking technique catapult with a hypergraph-based pre-embedding learning approach. The advantage of hypergraph learning is that it induces higher-order rich biological information of candidate genes. In addition, we use the self-supervised-based edge masking strategy to attain model training over only a few numbers of edge labels. Our proposed approach outperforms the six baseline models for our case-study datasets and also predicts novel genetic associations across comorbid disease pairs.

关键词： Diseases Genetics Gene expression Databases Biological system modeling Bioinformatics Analytical models Proteins Phenotypes Drugs

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MKMGCN-DDI: Predicting Drug-Drug Interactions via Magnetic Graph Convolutional Network With Multiple Kernels

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 469-480页

作者： Ming Chen Yunhan Pan Xiujuan Lei Chunyan Ji Yinglong Dai Yi Pan Department of Artificial Intelligence Hunan Normal University Changsha Hunan China School of Computer Science Shaanxi Normal University Xi’an China Computer Science Department BNU-HKBU United International College Zhuhai China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have demonstrated encouraging performance in predicting DDIs. However, most studies overlook the comprehensive aspects of DDIs, such as the coexistence of types of pharmacological changes and the asymmetric roles of drugs. In this article, we define new prediction tasks, taking into account both enhancive or depressive changes and the roles of drugs, and then establish spectral GNNs to predict comprehensive information of DDIs. First, we formally define several tasks, including joint prediction tasks designed to leverage both types and directions. These tasks deduce to sub-tasks in previous studies. Then, we propose a unified framework, the MKMGCN-DDI, via introducing two Magnetic Laplacian matrices to encode comprehension information within DDIs, defining multiple graph filters, and designing multiple-kernel based Magnetic graph convolutional networks (MKMGCN). Experiments across three datasets show that it not only has good adaptability to multiple tasks but also significantly improves results on simple tasks. Case studies on breast neoplasms and lung neoplasms verify its feasibility, as over half of top-10 items are supported.

关键词： Drugs Predictive models Magnetic separation Laplace equations Bioinformatics Sodium Market research Graph convolutional networks computer science Computational modeling

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MIN: Multi-stage Interactive Network for Multimodal Recommendation 25th

MIN: Multi-stage Interactive Network for Multimodal Recomme...

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Mo, Minghao Lu, Weihai Xie, Qixiao Lv, Xiang Xiao, Zikai Yang, Hong Zhang, Yanchun Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China School of Computer Science and Technology Zhejiang Normal University Zhejiang China Peking University Beijing China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom China

ISBN: (纸本)9789819605699

With the surge in multimedia applications and the explosion of multimedia information, multimodal recommendation has garnered increasing attention. The core idea of multimodal recommendation is to effectively utilize the multimodal information of items to enhance the performance of recommendation systems. Previous work has primarily focused on integrating item ID embeddings and multimodal representations. However, there are two main drawbacks: first, a lack of interaction between multimodal features, which neglects the inherent semantic relationships within these features. Second, the distribution gap between ID embeddings and multimodal representations adversely affects recommendation performance. In this paper, we propose a novel Multi-stage Interactive Network for multimodal recommendation, named MIN. Specifically, we first discarded the ID embeddings, retaining only the multimodal features to represent the items. Then, we designed a three-stage interaction network to model the multimodal representations and the latent structural semantics between users and items. Additionally, to obtain more general and robust representations, we applied graph enhancement techniques in the final stage to enhance the representations of users and items. Finally, contrastive learning techniques were employed to address the inconsistency in user and item representations. A series of comprehensive experiments on three publicly available datasets have validated the efficacy of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms

A Music Recommender System to Identify the Emotional State o...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Muruganantham A C. Malarvizhi Divya Vahini. S D. Saranya A. Uthiramoorthy Department of Computer Science (PG) Kristu Jayanti College Bengaluru Karnataka India Department of Computer Science PSG Institute of Advanced Studies Coimbatore India Department of Computer Applications Dr.Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India Department of Information Technology Sri Eshwar College of Engineering Coimbatore Department of Computer Science Rathinam College of Arts and Science

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods--with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be--make tailored entertainment paths in the digital era.

关键词： Support vector machines Emotion recognition Recurrent neural networks Machine learning algorithms Accuracy Mood Face recognition Computational modeling Convolutional neural networks Recommender systems

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Toward Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年第2期7卷 768-781页

作者： Lei, Long Hu, Yingbai Jiang, Zixing Miao, Juzheng Luo, Xiao Zhang, Yu Wang, Qiong Wang, Shujun Li, Zheng Heng, Pheng-Ann The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Chinese University of Hong Kong Multi-Scale Medical Robotics Centre Ltd. Hong Kong The Chinese University of Hong Kong Department of Surgery Hong Kong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen518055 China The Hong Kong Polytechnic University Department of Biomedical Engineering Hong Kong Chow Yuk Ho Technology Centre for Innovative Medicine Li Ka Shing Institute of Health Sciences Multiscale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Department of Surgery Hong Kong

Lung ultrasound scanning is essential for diagnosing lung diseases. The scan effectiveness critically depends on both longitudinal and transverse scans through intercostal spaces to reduce rib shadowing interference, as well as maintaining the probe perpendicular to pleura for pathological artifact generation. Achieving this level of scan quality often depends heavily on the experience of doctors. Robotic ultrasound scanning shows promise, but currently lacks a direct path planning method for intercostal scanning, and probe orientation does not consider imaging differences between lungs and solid organs. In this paper, we aim to fully automate two fundamental operations in lung ultrasound scanning: longitudinal and transverse scans. We propose pioneering path planning methods along intercostal spaces and innovative solutions for adaptive probe posture adjustment using real-time pleural line feedback, specifically addressing the unique characteristics of lung ultrasound scanning. This ensures the acquisition of high-quality, diagnostically meaningful ultrasound images. In addition, we develop a robotic lung ultrasound system to validate the proposed methods. Extensive experimental results on two volunteers and a chest phantom confirm the efficacy of our methods, and demonstrate the system's feasibility in automated lung ultrasound examinations. Our work lays a solid foundation for automated robotic complete lung scanning. © 2018 IEEE.

关键词： Motion planning

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MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers

MPEC: Manifold-Preserved EEG Classification via an Ensemble ...

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International CSI computer Conference, CSICC

作者： Shermin Shahbazi Mohammad–Reza Nasiri Majid Ramezani Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Accurate classification of EEG signals is crucial for brain-computer interfaces (BCls) and neuroprosthetic applications, yet many existing methods fail to account for the non-Euclidean, manifold structure of EEG data, resulting in suboptimal performance. Preserving this manifold information is essential to capture the true geometry of EEG signals, but traditional classification techniques largely overlook this need. To this end, we propose MPEC (Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers), that introduces two key innovations: (1) a feature engineering phase that combines covariance matrices and Radial Basis Function (RBF) kernels to capture both linear and non-linear relationships among EEG channels, and (2) a clustering phase that employs a modified K-means algorithm tailored for the Riemannian manifold space, ensuring local geometric sensitivity. Ensembling multiple clustering-based classifiers, MPEC achieves superior results, validated by significant improvements on the BCI Competition IV dataset 2a.

关键词： Manifolds Geometry Support vector machines Technological innovation Accuracy Sensitivity Brain modeling Electroencephalography Brain-computer interfaces Reliability

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Two-stage effective attentional generative adversarial network

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Applied Intelligence 2025年第8期55卷 1-11页

作者： Jin, Mingyu Yu, Qinkai Zhang, Chong Xue, Haochen Zhao, Shuliang College of Computer and Cyber Security Hebei Normal University No. 20 Nan’erhuan East Road Hebei Shijiazhuang050024 China Electrical and Computer Engineering Northwestern University Clark Street EvanstonIL60208 United States Department of Mathematics Science University of Liverpool Brownlow Hill Merseyside LiverpoolL35UE United Kingdom School of Advanced Technology Xi’an Jiaotong-Liverpool University Ren’ai Road Jiangsu Suzhou215123 China

Although GAN models have succeeded in relevant tests, text-to-image modelling using GANs to synthesize high-quality images is still challenging. Existing multi-stage models face several problems: first, the scale is too large, and the model has a large number of redundant structures. Second, the model often generates duplicate images without progress and cannot update the parameters efficiently. In this paper, we propose a two-stage model to solve the above problem. 1)We remove the redundancy structure and use an improved network structure that reduces the scale of the model size. 2)Our method employs a model trained in two stages instead of simultaneously, which shortens the training time and ensures that the model does not have vanishing gradients or mode collapse. In addition, we added an attention mechanism to the model to help optimize details. Experimental results show that our model saw excellent results in terms of generation quality and reduced model size on CUB(IS 4.83, FID 15.13) and COCO dataset(FID 33.74). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Attention mechanism GAN Multi-stage mode Text-to-image

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Enhancing Chest X-ray Pathology Prediction through Data-Driven Approaches and Transfer Learning

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Procedia computer science 2025年 258卷 3447-3456页

作者： Rekha R. Nair Kishore S Tina Babu Vishnu Vilashini S Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte (Deemed to be University) Nitte Institute of Professional Education Mangalore India Analyst Capgemini Bellandur Karnataka India

Chest X-ray pathology prediction play a very important role in early disease detection, enabling timely intervention and improving patient outcomes. This project undertakes a comprehensive exploration of medical imaging analysis, focusing on chest X-rays for pathological identification. Starting this inquiry is thorough getting of information and exploratory information assessment (EDA), which demonstrates the significant elements of the dataset’s people, disease characteristics, and intrinsic predicaments. By making use of sophisticated image processing techniques, the study uses a Super-Resolution Convolutional Neural Network (SRCNN) in refining the resolution of chest X-ray scans, leading to improvement in diagnostic accuracy. Dealing with natural data imbalance found in medical data sets, this project proposed a new method by including a weighted loss function during training. Thus designed, this system allows the model to practically learn from uneven class distributions ensuring strong efficiency throughout different pathologies. The neural architecture for chest X-ray classification is built upon transfer learning principles, utilizing pretrained ResNet18, Alex net, Custom Neural Network models. The models are designed to meet the characteristics of the medical dataset in particular way, having replaced last fully connected layer with one that gives outputs across 14 different pathologies for predictions. The training process involves fine-tuning the models with a meticulously crafted weighted loss function and adaptive learning rate schedules, ensuring optimal convergence.

关键词： MQ2 Sensor NodeMCU Data Logging Analytics Real-time Alerts

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Enhancing Solar Photovoltaic Efficiency:A Computational Fluid Dynamics Analysis

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Energy engineering 2025年第1期122卷 153-166页

作者： Rahool Rai Fareed Hussain Mangi Kashif Ahmed Sudhakar Kumaramsay Faculty of Mechanical and Automotive Engineering Universiti Malaysia Pahang Al-Sultan AbdullahPekan26600PahangMalaysia Faculty of Engineering Science&Technology Hamdard UniversityKarachi74600Pakistan Electrical Engineering Department SUKKUR IBA UniversitySukkur65200Pakistan Berlin School of Business and Innovation BSBI Faculty of Computer Science and InformaticsBerlin12043Germany Automotive Engineering Centre University Malaysia Pahang Al Sultan AbdullahPekan26600PahangMalaysia Centre for Research in Advanced Fluid&Processes(Fluid Centre) Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)Paya BesarKuantan26300PahangMalaysia

The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable *** energy,among the various renewable sources,is particularly appealing due to its abundant ***,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight *** study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat *** CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of *** specific parameters were found to maximize the heat transfer and temperature reduction *** finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar *** cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.

关键词： PV module efficiency water film thickness reynolds number CFD analysis PV/T renewable energy

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