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Modeling Price-Aware Session-Based Recommendation Based on Graph Neural Network

computers, Materials & Continua 2023年第7期76卷 397-413页

作者： Jian Feng Yuwen Wang Shaojian Chen College of Computer Science&Technology Xi’an University of Science and TechnologyXi’an710600China

Session-based Recommendation(SBR)aims to accurately recom-mend a list of items to users based on anonymous historical session *** methods for SBR suffer from several limitations:SBR based on Graph Neural Network often has information loss when constructing session graphs;Inadequate consideration is given to influencing factors,such as item price,and users’dynamic interest evolution is not taken into account.A new session recommendation model called Price-aware Session-based Recommendation(PASBR)is proposed to address these *** constructs session graphs by information lossless approaches to fully encode the original session information,then introduces item price as a new factor and models users’price tolerance for various items to influence users’*** addition,PASBR proposes a new method to encode user intent at the item category level and tries to capture the dynamic interest of users over ***,PASBR fuses the multi-perspective features to generate the global representation of users and make a ***,the intent,the short-term and long-term interests,and the dynamic interests of a user are *** on two real-world datasets show that PASBR can outperform representative baselines for SBR.

关键词： Session-based recommendation graph neural network price-aware intention dynamic interest

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Principle of subtraction ghost imaging in scattering medium

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Chinese Physics B 2023年第6期32卷 250-254页

作者：付芹白艳锋谭威黄贤伟南苏琴傅喜泉 College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China School of Computer and Information Engineering Hunan University of Technology and BusinessChangsha 410205China

Scattering medium in light path will cause distortion of the light field,resulting in poor signal-to-noise ratio(SNR)of ghost *** disturbance is usually eliminated by the method of *** deduce the intensity fluctuation correlation function of the ghost imaging with the disturbance of the scattering medium,which proves that the ghost image consists of two correlated results:the image of scattering medium and the target *** effect of the scattering medium can be eliminated by subtracting the correlated result between the light field after the scattering medium and the reference light from ghost image,which verifies the theoretical *** research may provide a new idea of ghost imaging in harsh environment.

关键词： ghost imaging image reconstruction techniques scattering medium

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model 22

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Zhi Zhang, Shiwen Chen, Shuang Liang, Wei Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798331509712

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients' potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning. © 2024 IEEE.

关键词： Federated learning

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A self-learning human-machine cooperative control method based on driver intention recognition

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CAAI Transactions on Intelligence technology 2024年第5期9卷 1101-1115页

作者： Yan Jiang Yuyan Ding Xinglong Zhang Xin Xu Junwen Huang College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina Department of Computer Science Technical University of MunichGarchingGermany

Human-machine cooperative control has become an important area of intelligent driving,where driver intention recognition and dynamic control authority allocation are key factors for improving the performance of cooperative decision-making and *** this paper,an online learning method is proposed for human-machine cooperative control,which introduces a priority control parameter in the reward function to achieve optimal allocation of control authority under different driver intentions and driving safety ***,a two-layer LSTM-based sequence prediction algorithm is proposed to recognise the driver's lane change(LC)intention for human-machine cooperative steering ***,an online reinforcement learning method is developed for optimising the steering authority to reduce driver workload and improve driving *** driver-in-the-loop simulation results show that our method can accurately predict the driver's LC intention in cooperative driving and effectively compensate for the driver's non-optimal driving *** experimental results on a real intelligent vehicle further demonstrate the online optimisation capability of the proposed RL-based control authority allocation algorithm and its effectiveness in improving driving safety.

关键词： Self-leaming Control Hmman-machine Cooperation Intelligent Vehicles Reinfocement Learning

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Improved YOLOv8n Models for Object Detection in Remote Sensing Images

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IEEE Access 2025年 13卷 97889-97900页

作者： Chen, Young-Long Hung, Kai-Chun Zhang, Jia-Yun Lin, Ling-Wei National Taichung University of Science and Technology Department of Computer Science and Information Engineering China National Taichung University of Science and Technology College of Information and Distribution Science China

In recent years, numerous efficient object detectors have emerged in computer vision. However, applying these models to remote sensing images remains challenging due to complex backgrounds, high object scale variation, and the difficulty of detecting small objects. Many existing approaches prioritize accuracy but overlook the trade-off between precision and efficiency, limiting their usability in resource-constrained environments. To address these challenges, we propose a lightweight model based on the original YOLOv8n, culminating in the Transformer with Bi-directional and Coordinate Attention-YOLOv8n (TBC-YOLO8n) model. Our approach introduces a weighted bidirectional feature pyramid network that adaptively assigns importance to different feature maps, enhancing multi-scale object detection. Additionally, we integrate transformer blocks to improve global feature representation and capture long-range dependencies, alongside a coordinate attention block before each detection head to enhance localization by incorporating spatial information. Unlike previous YOLO variants, our method optimizes feature fusion strategies and integrates advanced attention mechanisms specifically tailored for remote sensing images, effectively addressing small-object detection, large-scale variations, and complex backgrounds. Compared to the original YOLOv8n, our proposed TBC-YOLO8n model improves mAP@50 from 90.2% to 94.5%, with a slight increase in inference time from 3.0 ms to 4.0 ms. Experimental results confirm the robustness and effectiveness of our model, making it a practical choice for real-time remote sensing applications with limited computational resources. © 2013 IEEE.

关键词： Remote sensing

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The Impact of the Internet of Everything on Green Cloud Computing 3rd

The Impact of the Internet of Everything on Green Cloud Comp...

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3rd International Conference on Internet of Everything, IoECon 2024

作者： Ma, Zhaoyang Chen, Juan Yuan, Yuan Xu, Tao College of Computer Science and Technology National University of Defense Technology Hunan410073 China

ISBN: (纸本)9783031844256

In today’s world, the demand for computing power and the need for environmental protection and energy saving have made Green Cloud Computing (GCC) popular in various fields. Customers from all over the world can access processing power and resources through cloud computing. It offers excellent performance and cost savings compared to specialized HPC devices. However, cloud computing will use many data centers, resulting in considerable energy consumption and a large amount of carbon dioxide emissions. Green Cloud Computing was created to solve this problem using recycled energy and sustainable and energy-efficient methods. For Green Cloud Computing, IoE allocates energy and computing resources more reasonably. At the same time, IoE promotes the concepts and techniques of Green Cloud Computing to reduce carbon emissions further. The Internet of Everything will more deeply influence the application of Green Cloud Computing and various fields by connecting people, data, and machines. This paper provides an overview of the history of the Internet of Everything and Green Cloud Computing. The Internet of Everything is widely used and has been shown to impact Green Cloud Computing profoundly. There is great potential for developing the Internet of Everything and Green Cloud Computing in the short future and significantly promoting sustainable computing development. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Cloud platforms

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Social Recommendation via Graph-Level Counterfactual Augmentation 39

Social Recommendation via Graph-Level Counterfactual Augment...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Yinxuan Liang, Ke Huang, Yanyi Zeng, Xiang Chen, Kai Zhou, Bin College of Computer Science and Technology National University of Defense Technology Hunan Changsha China

ISBN: (纸本)157735897X

Traditional recommendation system focus more on the correlations between users and items (user-item relationships), while research on user-user relationships has received significant attention these years, which is also known as social recommendation. Graph-based models have achieved a great success in this task by utilizing the complex topological information of the social networks. However, these models still face the insufficient expressive and overfitting problems. Counterfactual approaches are proven effective as information augmentation strategies towards above issues in various scenarios, but not fully utilized in social recommendations. To this end, we propose a novel social recommendation method, termed SR-GCA, via a plug-and-play Graph-Level Counterfactual Augmentation mechanism. Specifically, we first generate counterfactual social and item links by constructing a counterfactual matrix for data aug- mentation. Then, we employ a supervised learning strategy to refine data both factual and counterfactual links. Thirdly, we enhance representations learning between users via an alignment and self-supervised optimization techniques. Extensive experiments demonstrate the promising capacity of our model from five aspects: superiority, effectiveness, transferability, complexity, and robustness. In particular, the transferability is well-proven by extending our GCA module to three typical social recommendation models. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

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Parallelism Everywhere: The Internet of Everything and Parallel Computing 3rd

Parallelism Everywhere: The Internet of Everything and Paral...

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3rd International Conference on Internet of Everything, IoECon 2024

作者： Zhou, Kexing Chen, Juan College of Computer Science and Technology National University of Defense Technology Hunan410073 China

ISBN: (纸本)9783031844256

In today’s computing power era, AI model training, weather forecasting, aircraft design, and so on are inseparable from parallel computing. Parallel computing is everywhere. However, effectively tackling parallel computing tasks in real-world applications demands a deep connection of devices, people, processes, and data. This notion aligns with the core principle of the Internet of Everything (IoE), which posits that connections generate value. Moreover, the continuing flourish of IoE relies on the parallel computing. This paper aims to reveal the connection and interaction between the IoE and parallel computing. It provides an overview of their development and outlines the profound influence of IoE on parallel computing. The three primary communication channels of IoE—people-to-people (P2P), machine-to-machine (M2M), and people-to-machine (P2M)—are embodied in parallel computing. The potential is substantial to research and develop IoE and parallel computing in years to come, and combining them will navigate multi-domain requirements. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Weather forecasting

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Using the Internet of Everything for Machine-Learning-Based computer System Design and Optimization 3rd

Using the Internet of Everything for Machine-Learning-Based ...

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3rd International Conference on Internet of Everything, IoECon 2024

作者： Feng, Shaojun Zhong, Jiaqing Chen, Juan College of Computer Science and Technology National University of Defense Technology Hunan410073 China

ISBN: (纸本)9783031844256

Machine learning (ML) has been widely used in computer system development and optimization levels, boosting computer design and optimization improvement. With the increase of computer system design complexity and the boost of the demand for software and application optimization, the difficulties of computer system problem solving are increasing, stimulating the development of ML methods. ML models represent computer system design and development approaches;combining these with the Internet of Everything (IoE) cannot be ignored. For ML models, all kinds of computer system sample data are the fundamental prerequisite for quantifying the computer system characteristics;the connection (internet) of all computer devices is critical for predicting computer system behaviors. The IoE enables more computer devices and ML models to be connected, extending how computer systems can be well developed. The IoE will affect the applications between computer system development and various ML models more deeply by connecting people, data, and machines. This work provides an overview of the history of the IoE and ML-based computer system development. It shows the interaction between ML models and computer system design and optimization. It also emphasizes the people-to-people (P2P), people-to-machine (P2M), and machine-to-machine (M2M) in the ML-based computer system design and optimization. It discusses the interpretability of ML-based computer system development. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Application programs

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Brain Tumor Classification in MRI Images Using VGG19 with Type-2 Fuzzy Logic

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 163-174页

作者： Dihin, Rasha Ali Hamza, Nesreen Readh Department of Computer Science College of Education for Women University of Kufa Najaf Iraq Department of Computer Science College of Computer Science and Information Technology University of Al-Qadisiyah Diwania

A Brain Tumors are highly dangerous illnesses that significantly reduce the life expectancy of patients. The classification of brain tumors plays a crucial role in clinical diagnosis and effective treatment. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients Precisely diagnosing brain tumors is of utmost importance for devising suitable treatment plans that can effectively cure and improve the quality of life for patients afflicted with this condition. To tackle this challenge, present a framework that harnesses deep convolutional layers to automatically extract crucial and resilient features from the input data. Systems that use computers and with the help of convolutional neural networks have provided huge success stories in early detection of tumors. In our framework, utilize VGG19 model combined with fuzzy logic type-2 where used fuzzy logic type-2 that applied to enhancement the images brain where Type-2 fuzzy logic better handles uncertainty in medical images, improving the interpretability of image enhancement by managing noise and subtle differences with greater precision than Type-1 fuzzy logic for MRI images often contain ambiguous or low-contrast areas where noise, lighting conditions different and greatly improve accuracy. while used the VGG19 architecture to feature extraction and classify Tumor and non- Tumor. This approach enhances the accuracy of tumors classification, aiding in the development of targeted treatment strategies for patients. The method is trained on the Br35H dataset, resulting in a training accuracy of 0.9983 % and Train loss of 0.2118 while the validation accuracy of 0.9953 % validation loss of 0.2264. This demonstrates effective pattern learning and generalization capabilities. The model achieves outstanding accuracy, with a best accuracy for the model of 0.9983 %, While the test accuracy of the model reached of 99 %, and both of sensitivity and specificity at 0.9967

关键词： Convolutional neural networks

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