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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Residual diverse ensemble for long-tailed multi-label text classification

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science China(Information sciences) 2024年第11期67卷 92-105页

作者： Jiangxin SHI Tong WEI Yufeng LI National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of Education

Long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set, where the training datasets usually follow long-tailed label distributions. Many of the previous studies have treated head and tail labels equally, resulting in unsatisfactory performance for identifying tail labels. To address this issue, this paper proposes a novel learning method that combines arbitrary models with two steps. The first step is the “diverse ensemble” that encourages diverse predictions among multiple shallow classifiers, particularly on tail labels, and can improve the generalization of tail *** second is the “error correction” that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels. Thus, it enables the “diverse ensemble” to focus on optimizing the tail label performance. This overall procedure is called residual diverse ensemble(RDE). RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels. We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets, especially with respect to the propensity-scored evaluation ***, RDE converges in less than 30 training epochs without increasing the computational overhead.

关键词： multi-label learning extreme multi-label learning long-tailed distribution multi-label text classification ensemble learning

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Design and implementation of vehicle type recognition system based on language large model 6

Design and implementation of vehicle type recognition system...

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6th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Liu, Xiang Zhang, Yikuan Software Engineering Institute of GuangZhou Computer Science Department Guangzhou China Software Engineering Institute of GuangZhou Department of Electronics Guangzhou China

ISBN: (纸本)9798350386974

Using large language model to generate vehicle type recognition algorithm can reduce the burden of developers and realize the rapid development of projects. In this paper, LangChain large model interface provided by Baidu Qianfan large-scale platform is used to realize the auxiliary development of vehicle type recognition. With the support of large model, the training and testing model of vehicle type recognition is constructed by using deep learning method, and the fine-grained vehicle image classification and recognition system is realized. At present, the ResNet50 Enhance MEAL2 model is used for classification recognition, supporting 1748 types of vehicle classification and recognition. In the Car-Dataset of vehicle data, the Accuracy of the training set is about 95.9%, and the Accuracy of the test set is about 84.3%. The backbone network can support Googlenet. ResNet [18, 34, 50], Inception-v3, Mobilenet-v2 and other common models. The use of language large model in this project improves the efficiency of development. © 2024 IEEE.

关键词： Convolutional neural networks

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MixSSC: Forward-Backward Mixture for Vision-based 3D Semantic Scene Completion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5684-5696页

作者： Wang, Meng Ding, Yan Liu, Yumeng Qin, Yunchuan Li, Ruihui Tang, Zhuo The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing100190 China

Vision-based semantic scene completion task aims to predict dense geometric and semantic 3D scene representations from 2D images. However, 3D modeling from a single view is an ill-posed problem, limited by the field of view and occlusion problems caused by image input. Moreover, existing methods tend to produce erroneous scene hallucinations and overly smooth boundary segmentation due to a lack of information. To address this problem, we propose MixSSC, which mixes the sparsity of forward projection with the denseness of depth-prior backward projection. The aim is to use sparse features to fill information-poor regions and dense features to enhance visible regions. Specifically, we develop the forward-backward mixture module, which enables the generation of scene mixture voxel representation by leveraging the benefits of both forward and backward projection. Subsequently, we design the semantic-spatial fusion module, which utilizes a coarse-to-fine approach to process mixture voxel features at the semantic-spatial level. Extensive experimental results on the SemanticKITTI, SSCBench-KITTI-360 and nuScenes datasets demonstrate the superiority of MixSSC. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

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computer Modeling in engineering & sciences 2024年第9期140卷 2425-2448页

作者： Md.Atiqur Rahman Mustavi Ibne Masum Khan Md Hasib M.F.Mridha Sultan Alfarhood Mejdl Safran Dunren Che Department of Computer Science and Engineering Ahsanullah University of Science and TechnologyDhaka1208Bangladesh Department of Computer Science and Software Engineering The University of Western AustraliaPerthWA 6009Australia Department of Computer Science American International University-BangladeshDhaka1229Bangladesh Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia School of Computing Southern Illinois UniversityCarbondale62901USA

Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global *** study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more *** research introduces an innovative custom convolutional neural network(CNN)model,*** stands out as a lightweight CNN model compared to its *** research utilized the BraTS 2020 dataset for its *** with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification *** challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.

关键词： Deep learning magnetic resonance imaging convolutional neural networks explainable AI boosting algorithm ablation

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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective

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science China(Information sciences) 2025年第4期68卷 284-299页

作者： Gang HUANG Fuqi LIN Yun MA Haoyu WANG Qingxiang WANG Gareth TYSON Xuanzhe LIU Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Institute for Artificial Intelligence Peking University School of Cyber Science and Engineering Huazhong University of Science and Technology Alibaba Group Hong Kong University of Science and Technology (Guangzhou)

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect(or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the i OS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.

关键词： mobile app recommendation complex network user behavior policy-violating app

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Low-shot Video Object Segmentation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5538-5555页

作者： Yan, Kun Wei, Fangyun Dai, Shuyu Wu, Minghui Wang, Ping Xu, Chang Peking University School of Computer Science Beijing100871 China University of Sydney School of Computer Science DarlingtonNSW2008 Australia Peking University School of Software and Microelectronics Beijing100871 China Shanghai Artificial Intelligence Laboratory and Mininglamp Technology China Peking University National Engineering Research Center for Software Engineering Beijing100871 China Peking University School of Software and Microelectronics China China

Prior research in video object segmentation (VOS) predominantly relies on videos with dense annotations. However, obtaining pixel-level annotations is both costly and time-intensive. In this work, we highlight the potential of effectively training a VOS model using remarkably sparse video annotations - specifically, as few as one or two labeled frames per training video, yet maintaining near equivalent performance levels. We introduce this innovative training methodology as low-shot video object segmentation, abbreviated as low-shot VOS. Central to this method is the generation of reliable pseudo labels for unlabeled frames during the training phase, which are then used in tandem with labeled frames to optimize the model. Notably, our strategy is extremely simple and can be incorporated into the vast majority of current VOS models. For the first time, we propose a universal method for training VOS models on one-shot and two-shot VOS datasets. In the two-shot configuration, utilizing just 7.3% and 2.9% of labeled data from the YouTube-VOS and DAVIS benchmarks respectively, our model delivers results on par with those trained on completely labeled datasets. It is also worth noting that in the one-shot setting, a minor performance decrement is observed in comparison to models trained on fully annotated datasets. Code and models are available at https://***/yk-pku/Low-shot-VOS. © 1979-2012 IEEE.

关键词： Video analysis

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Human Action Recognition in Multi-Level Convolutional Temporal Attention Network

Human Action Recognition in Multi-Level Convolutional Tempor...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Chen, ZhongQi Li, Chang Qian, WeiWen College of Computer Science and Software Engineering Hohai University NanJing China

ISBN: (纸本)9798350368741

Human Action Recognition (HAR) has widespread applications in areas such as human-computer interaction, elderly care, and home healthcare. However, current sensor-based HAR faces challenges of low fine-grained recognition performance and difficulty in distinguishing similar actions. To solve this problem, this paper proposes a model based on Multilevel Convolutional Time Series Attention Network (MCTSANet). By Multi-ResCNN to pay attention to different levels of features and using Time Series Attention (TSA) to pay attention to the more important data in the channel, so as to improve the ability of confusable action *** on three public datasets show that the proposed method outperforms state-of-the-art sensor-based HAR approaches. © 2025 IEEE.

关键词： Attention Mechanism Deep Learning Human Action Recognition Wearable Sensors

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software approaches for resilience of high performance computing systems:a survey

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Frontiers of computer science 2023年第4期17卷 43-56页

作者： Jie JIA Yi LIU Guozhen ZHANG Yulin GAO Depei QIAN School of Computer Science and Engineering Beihang UniversityBeijing 100191China Sino-German Joint Software Institute Beihang UniversityBeijing 100191China

With the scaling up of high-performance computing systems in recent years,their reliability has been descending ***,system resilience has been regarded as one of the critical challenges for large-scale HPC *** techniques and systems have been proposed to ensure the correct execution and completion of parallel *** paper provides a comprehensive survey of existing software resilience ***,a classification of software resilience approaches is presented;then we introduce major approaches and techniques,including checkpointing,replication,soft error resilience,algorithmbased fault tolerance,fault detection and *** addition,challenges exposed by system-scale and heterogeneous architecture are also discussed.

关键词： resilience high-performance computing fault tolerance challenge

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Bias analysis of AI models for undergraduate student admissions

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Neural Computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the School of science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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