检索结果-内蒙古大学图书馆

Dual-Channel Multiplex Graph Neural Networks for Recommendation

IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3327-3341页

作者： Li, Xiang Fu, Chaofan Zhao, Zhongying Zheng, Guangjie Huang, Chao Yu, Yanwei Dong, Junyu Ocean University of China Faculty of Information Science and Engineering Qingdao266005 China Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China The University of Hong Kong Department of Computer Science Hong Kong

Effective recommender systems play a crucial role in accurately capturing user and item attributes that mirror individual preferences. Some existing recommendation techniques have started to shift their focus towards modeling various types of interactive relations between users and items in real-world recommendation scenarios, such as clicks, marking favorites, and purchases on online shopping platforms. Nevertheless, these approaches still grapple with two significant challenges: (1) Insufficient modeling and exploitation of the impact of various behavior patterns formed by multiplex relations between users and items on representation learning, and (2) ignoring the effect of different relations within behavior patterns on the target relation in recommender system scenarios. In this work, we introduce a novel recommendation framework, Dual-Channel Multiplex Graph Neural Network (DCMGNN), which addresses the aforementioned challenges. It incorporates an explicit behavior pattern representation learner to capture the behavior patterns composed of multiplex user-item interactive relations, and includes a relation chain representation learner and a relation chain-aware encoder to discover the impact of various auxiliary relations on the target relation, the dependencies between different relations, and mine the appropriate order of relations in a behavior pattern. Extensive experiments on three real-world datasets demonstrate that our DCMGNN surpasses various state-of-the-art recommendation methods. It outperforms the best baselines by 10.06% and 12.15% on average across all datasets in terms of Recall@10 and NDCG@10 respectively. The source code of our paper is available at https://***/lx970414/TKDE-DCMGNN. © 1989-2012 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Multi-feature Fusion Deep Network for Skin Disease Diagnosis

引用

Multimedia Tools and Applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

引用

Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

MSMFT: Multi-Stream Multimodal Factorized Transformer for Human Activity Recognition

引用

IEEE Sensors Journal 2025年第6期25卷 10402-10416页

作者： Zhou, Xianfa Yuan, Jiabin Fan, Lili Niu, Xuewei Zha, Keke Liu, Xu Nanjing University of Aeronautics and Astronautics Department of Computer Science and Technology Nanjing210016 China Jinling Institute of Technology Department of Computer Engineering Nanjing211169 China

Existing video-based human activity recognition (HAR) methods are susceptible to challenges such as lighting variations and occlusions in complex environments. Wearable sensors can effectively mitigate these issues. The fusion of the two can reduce environmental impacts, thus enhancing accuracy. However, the heterogeneity between the two data types presents a challenge for fusion. To address this, we propose an architecture called multi-stream multimodal factorized transformer (MSMFT), which leverages the aggregation capabilities of transformers to achieve alignment and fusion of multimodal features without relying on specific feature extractors. First, we designed a statistical feature projection (SFP) method to effectively tokenize wearable sensor data for transformer models. Second, we introduced temporal embedding (TE) to capture local temporal feature variations. Then, we incorporated a factorized transformer architecture that includes temporal and spatial transformer encoders (STEs) to extract global spatiotemporal features fully. Finally, to avoid the issue of modality entanglement caused by deep fusion, we constructed a multi-stream architecture comprising a video stream, wearable sensors stream, and fusion stream. This design allows for the extraction of both independent and interactive modality features. The performance of the MSMFT method was evaluated on three multimodal datasets: UP-Fall, UTD-MHAD, and MMAct. The results show that MSMFT performs better than the state-of-the-art, with a 16.83% improvement in cross-scene F1-score on the MMAct dataset. © 2001-2012 IEEE.

关键词： Wearable sensors

来源：评论

学校读者我要写书评

暂无评论

Leveraging BERT, DistilBERT and TinyBERT for Rumor Detection

引用

IEEE Access 2025年 13卷 72918-72929页

作者： Qazi, Aijazahamed Goudar, R.H. Patil, Rudragoud Hukkeri, Geetabai S. Kulkarni, Dhanashree Angadi Institute of Technology and Management Department of Artificial Intelligence and Data science Belagavi591108 India Visvesvaraya Technological University Department of Computer Science and Engineering Belagavi590018 India KLS Gogte Institute of Technology Department of Computer Science and Engineering Belagavi590008 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Bengaluru Karnataka Manipal576104 India Angadi Institute of Technology and Management Department of Computer Science and Engineering Belagavi591108 India

The rapid spread of false information on social media has become a major challenge in today's digital world. This has created a need for an effective rumor detection system that can identify and control the spread of false information in real-time. The proposed work introduces a rumor detection system by integrating transformer-based models such as BERT, DistilBERT, and TinyBERT with traditional Machine Learning (ML) techniques. The proposed method involves fine-tuning transformer models to recognize the contextual meaning of social media posts. The classifiers include Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB), which help in categorizing content as either rumor or non-rumor based on the patterns. The proposed work evaluated BERT, DistilBERT, TinyBERT combined with ML models (SVM, DT, RF, NB) across PHEME dataset using 70:30, 60:40, and 80:20 splits. Overall, BERT+DT and TinyBERT+SVM provided significant results, with BERT+RF and DistilBERT+NB demonstrating better classification capabilities across various events and split ratios on the dataset. © 2013 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

NC-Load: On-Demand Program Loading and Running for Computing Sharing Among IoT Devices

引用

IEEE Internet of Things Journal 2025年第9期12卷 12242-12256页

作者： Dong, Yanhao Duan, Sijing Lyu, Feng Zhao, Peng Zhang, Yongmin Ren, Ju Zhang, Yaoxue Central South University School of Computer Science and Engineering Hunan Changsha410083 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

The number of Internet of Things (IoT) devices has increased rapidly in recent years, but lack effective methods to integrate their computational power. In this article, we propose NC-Load, which couples IoT devices into a multiprocessor system, allowing process scheduling across different devices to share their computing power and improve overall throughput. Specifically, NC-Load consists of three key designs, i.e., remote page fault (RPF), lightweight program cropping, and identical memory layout migration, contributing to three merits compared to existing systems: 1) high storage efficiency: the target device launches the program with a locally stored lightweight icon and leverages RPFs to retrieve the required code/data from the source device;2) on-demand memory loading: only the required memory portions are transmitted when scheduling programs across different devices, which ensures quick recovery of the program;and 3) consistent memory layout: to ensure consistency of addresses after program offloading, the virtual memory area layout of the source device is migrated to the target device. We implement NC-Load on Linux 6.1 and conduct performance evaluation using unmodified programs and the N-Queens cases. The results demonstrate that NC-Load can achieve superior performance in terms of storage efficiency, program performance, memory usage, and throughput. © 2014 IEEE.

关键词： Multiprocessing systems

来源：评论

学校读者我要写书评

暂无评论

An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

引用

computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of LIME and SHAP Interpreters With Explainable ML-Based Diabetes Predictions

引用

IEEE Access 2025年 13卷 37370-37388页

作者： Ahmed, Shamim Kaiser, M. Shamim Shahadat Hossain, Mohammad Andersson, Karl Jahangirnagar University Institute of Information Technology Savar Dhaka1342 Bangladesh University of Chittagong Department of Computer Science and Engineering Chattogram4331 Bangladesh Luleå University of Technology Electrical and Space Engineering Department of Computer Science Skellefteå931 87 Sweden

Explainable artificial intelligence is beneficial in converting opaque machine learning models into transparent ones and outlining how each one makes decisions in the healthcare industry. To comprehend the variables that affect decision-making regarding diabetes prediction that can be accounted for by model-agnostic techniques. In this project, we investigate how to generate local and global explanations for a machine-learning model built on a logistic regression architecture. We trained on 253,680 survey responses from diabetes patients using the explainable AI techniques LIME and SHAP. LIME and SHAP were then used to explain the predictions produced by the logistic regression and Random forest-based model on the validation and test sets. With a discussion of future work, the comparative analysis and discussion of various experimental findings between LIME and SHAP are provided, along with their strengths and weaknesses in terms of interpretation. With a high accuracy of 86% on the test set, we used LR architecture with a spatial attention mechanism, demonstrating the possibility of merging machine learning and explainable AI to improve diabetes prediction, diagnosis, and treatment. We also focus on various applications, difficulties, and probable future directions of machine learning models for LIME and SHAP interpreters. © 2013 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Double Averaging and Gradient Projection: Convergence Guarantees for Decentralized Constrained Optimization

引用

IEEE Transactions on Automatic Control 2025年第5期70卷 3433-3440页

作者： Shahriari-Mehr, Firooz Panahi, Ashkan Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering SE-412 96 Gothenburg Sweden

We consider a generic decentralized constrained optimization problem over static, directed communication networks, where each agent has exclusive access to only one convex, differentiable, local objective term and one convex constraint set. For this setup, we propose a novel decentralized algorithm, called double averaging and gradient projection (DAGP). We achieve global optimality through a novel distributed tracking technique we call distributed null projection. Further, we show that DAGP can be used to solve unconstrained problems with nondifferentiable objective terms with a problem reduction scheme. Assuming only smoothness of the objective terms, we study the convergence of DAGP and establish sublinear rates of convergence in terms of feasibility, consensus, and optimality, with no extra assumption (e.g., strong convexity). For the analysis, we forego the difficulties of selecting Lyapunov functions by proposing a new methodology of convergence analysis, which we refer to as aggregate lower-bounding. To demonstrate the generality of this method, we also provide an alternative convergence proof for the standard gradient descent algorithm with smooth functions. © 1963-2012 IEEE.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

引用

Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：