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Leveraging Deep Learning for Precise Chronic Bronchitis Identification in X-Ray Modalities

computers, Materials & Continua 2025年第4期83卷 381-405页

作者： Fahad Ahmad Saad Awadh Alanazi Kashaf Junaid Maryam Shabbir Asim Ali School of Computing Faculty of TechnologyUniversity of PortsmouthWinston Churchill Ave SouthseaPortsmouthPO12UPUK Portsmouth Artificial Intelligence and Data Science Center University of PortsmouthWinston Churchill AveSouthseaPortsmouthPO12UPUK Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAljouf72341Saudi Arabia School of Biological and Behavioural Sciences Queen Mary University of LondonLondonE14NSUK Department of Computer Sciences Bahria UniversityLahorePunjab54700Pakistan

Image processing plays a vital role in various fields such as autonomous systems,healthcare,and cataloging,especially when integrated with deep learning(DL).It is crucial in medical diagnostics,including the early detection of diseases like chronic obstructive pulmonary disease(COPD),which claimed 3.2 million lives in ***,a life-threatening condition often caused by prolonged exposure to lung irritants and smoking,progresses through *** diagnosis through image processing can significantly improve survival *** encompasses chronic bronchitis(CB)and emphysema;CB particularly increases in smokers and generally affects individuals between 50 and 70 years *** damages the lungs’air sacs,reducing oxygen transport and causing symptoms like coughing and shortness of *** such as beta-agonists and inhaled steroids are used to manage symptoms and prolong lung ***,COVID-19 poses an additional risk to individuals with CB due to its impact on the respiratory *** proposed system utilizes convolutional neural networks(CNN)to diagnose *** this system,CNN extracts essential and significant features from X-ray modalities,which are then fed into the neural *** network undergoes training to recognize patterns and make accurate predictions based on the learned *** leveraging DL techniques,the system aims to enhance the precision and reliability of CB *** research specifically focuses on a subset of 189 lung disease images,carefully selected for model *** further refine the training process,various data augmentation and noise removal techniques are *** techniques significantly enhance the quality of the training data,improving the model’s robustness and *** a result,the diagnostic accuracy has improved from 98.6%to 99.2%.This advancement not only validates the efficacy of our proposed model but also represents a significant improvement over existing *** h

关键词： Deep learning chronic obstructive pulmonary disease chronic bronchitis convolutional neural network X-ray images

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An Interpretable Neural Network for Vegetation Phenotyping with Visualization of Trait-Based Spectral Features 14

An Interpretable Neural Network for Vegetation Phenotyping w...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Basener, William Basener, Abigail Luegering, Michael School of Data Science University of Virginia United States College of Computer Mathematical and Natural Sciences University of Marlyand United States School of Architecture University of Virginia United States

ISBN: (纸本)9798331513139

Plant phenotyping is the assessment of a plant's traits and plant identification is the process of determining the category such as genus and species. In this paper we present an interpretable neural network trained on the UPWINS spectral library which contains spectra with rich metadata across variation in species, health, growth stage, annual variation, and environmental conditions for 13 selected indicator species and natural common background species. We show that the neurons in the network learn spectral indicators for chemical and physiological traits through visualization of the network weights, and we show how these traits are combined by the network for species identification with an accuracy around 90% on a test set. While neural networks are often perceived as 'black box' classifiers, our work shows that they can be in fact more explainable and informative than other machine learning methods. We show that the neurons learn fundamental traits about the vegetation, for example the composition of different types of chlorophyll present which indicates species as well as response to illumination conditions. There is clear excess training capacity in our network, and we expect that as the UPWINS spectral library continues to grow the approach in this paper will provide further foundational insights in understanding plant traits. This provides a methodology for designing and interpreting neural networks on spectral data in general, and provides a framework for using neural networks with hyperspectral imagery for understanding vegetation that is extendable to other domains. © 2024 IEEE.

关键词： Plant diseases

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Improving Low-Resource Sequence Labeling with Knowledge Fusion and Contextual Label Explanations

arXiv

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arXiv 2025年

作者： Lai, Peichao Gan, Jiaxin Ye, Feiyang Wang, Yilei Cui, Bin School of Computer Science Peking University China College of Computer and Data Science Fuzhou University China

Sequence labeling remains a significant challenge in low-resource, domain-specific scenarios, particularly for character-dense languages like Chinese. Existing methods primarily focus on enhancing model comprehension and improving data diversity to boost performance. However, these approaches still struggle with inadequate model applicability and semantic distribution biases in domain-specific contexts. To overcome these limitations, we propose a novel framework that combines an LLM-based knowledge enhancement workflow with a span-based Knowledge Fusion for Rich and Efficient Extraction (KnowFREE) model. Our workflow employs explanation prompts to generate precise contextual interpretations of target entities, effectively mitigating semantic biases and enriching the model’s contextual understanding. The KnowFREE model further integrates extension label features, enabling efficient nested entity extraction without relying on external knowledge during inference. Experiments on multiple Chinese domain-specific sequence labeling datasets demonstrate that our approach achieves state-of-the-art performance, effectively addressing the challenges posed by low-resource settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Adapting Contextual Embedding to Identify Sentiment of E-commerce Consumer Reviews with Addressing Class Imbalance Issues

Adapting Contextual Embedding to Identify Sentiment of E-com...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Rakib Mollah, Md. Abdur Jahangir Kabir, Mir Md. Reza, Md. Sazid Kabir, Monika Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh Td School University of Technology Sydney Data Science and Innovation Sydney Australia Murdoch University Information Technology Perth Australia

ISBN: (纸本)9798350350289

Understanding consumer attitudes toward specific products is crucial for boosting sales in the e-commerce industry. To effectively target customers with popular products based on reviews, the classification of consumer feedback becomes imperative. However, classifying product reviews can be challenging, particularly when dealing with imbalanced data labels, which often result in suboptimal classification performance. This study builds upon previous efforts that utilized the Amazon Fine Food Reviews dataset for classification tasks. While these prior attempts showed promise, they were hindered by either poor embeddings or the prevalent class imbalance issue. In response, this research tries to solve these problems by using word embeddings with RoBERTa, a pre-trained transformer-based language model, to classify reviews. Additionally, the XGBoost classifier was implemented, along with embeddings from the language model. Losses were first calculated with equal weights for all class labels, and a re-weighted loss was subsequently adopted to balance the impact of each class on the loss function during training. The incorporation of RoBERTa and XGBoost, along with the class label re-weighting, contributed to improved capturing of intricate word relationships within reviews. As a result, this approach achieves significantly improved accuracy in both binary and multiclass classifications compared to earlier endeavors. Notably, it attained an impressive accuracy of 83.84% in multiclass classification and 93.29% in binary classification tasks, marking a substantial advancement in the field of consumer review analysis. © 2024 IEEE.

关键词： Embeddings

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Faster Local Solvers for Graph Diffusion Equations

arXiv

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arXiv 2024年

作者： Bai, Jiahe Zhou, Baojian Yang, Deqing Xiao, Yanghua The School of Data Science Fudan University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

Efficient computation of graph diffusion equations (GDEs), such as Personalized PageRank, Katz centrality, and the Heat kernel, is crucial for clustering, training neural networks, and many other graph-related problems. Standard iterative methods require accessing the whole graph per iteration, making them time-consuming for large-scale graphs. While existing local solvers approximate diffusion vectors through heuristic local updates, they often operate sequentially and are typically designed for specific diffusion types, limiting their applicability. Given that diffusion vectors are highly localizable, as measured by the participation ratio, this paper introduces a novel framework for approximately solving GDEs using a local diffusion process. This framework reveals the suboptimality of existing local solvers. Furthermore, our approach effectively localizes standard iterative solvers by designing simple and provably sublinear time algorithms. These new local solvers are highly parallelizable, making them well-suited for implementation on GPUs. We demonstrate the effectiveness of our framework in quickly obtaining approximate diffusion vectors, achieving up to a hundred-fold speed improvement, and its applicability to large-scale dynamic graphs. Our framework could also facilitate more efficient local message-passing mechanisms for GNNs. © 2024, CC BY-NC-SA.

关键词： Iterative methods

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Three Preemption Approaches towards EDF Scheduling for Homogeneous Multiprocessors 29

Three Preemption Approaches towards EDF Scheduling for Homog...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wu, Peng Li, Zhi Zhang, Zhicai Yan, Tao Chen, Lu Shanxi University Institute of Big Data Science and Industry Taiyuan030006 China Shanxi University School of Computer and Information Technology Taiyuan030006 China Hainan University School of Computer Science and Technology Haikou570228 China

ISBN: (纸本)9798350330717

When scheduling a set of real-time tasks, researchers can choose between preemptive and non-preemptive algorithms. However, these algorithms each have their own advantages and drawbacks, necessitating specific analysis in different contexts. In contrast to these two extremes, researchers have proposed the concept of limited-preemption algorithms. Such limited-preemption algorithms serve as feasible alternatives to the former two approaches. Currently, research on this algorithm is based on a homogenous computing *** this paper, we investigate the fixed-preemption point method in limited-preemption algorithms. We compare the impact of limited-preemption EDF algorithm on the performance of homogenous multiprocessors under different fixed-preemption strategies. We introduce a preemptive strategy where high-priority tasks can only be preempted at the most suitable fixed-preemption points, and we provide an effective schedulability analysis for this strategy. The EDF scheduling algorithm based on the best-fit fixed preemption strategy is called the Best Preemptive EDF(BP-EDF). We have introduced an effective schedulability analysis for BP-EDF. Experimental results indicate that the strategy with the most suitable fixed-preemption points demonstrates superior schedulability compared to the other two strategies. Furthermore, its performance in terms of task preemption frequency closely approaches that of the strategy employing fixed-preemption at the lowest priority points. © 2023 IEEE.

关键词： EDF scheduling algorithm fixed preemption points homogeneous multiprocessors limited-preemption Scheduling

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Stochastic Block Models for Complex Network Analysis: A Survey

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ACM Transactions on Knowledge Discovery from data 2025年第3期19卷 1-35页

作者： Liu, Xueyan Song, Wenzhuo Musial, Katarzyna Li, Yang Zhao, Xuehua Yang, Bo College of Computer Science and Technology Jilin University Changchun China School of Information Science and Technology Northeast Normal University Changchun China Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Sydney Australia Aviation University of the Air Force Changchun China School of Digital Media Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Changchun China

Complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Complex Networks Structural Pattern Discovery Stochastic Block models Learning Methods of SBMs

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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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A Timed Calculus with Mobility for Wireless Networks 23

A Timed Calculus with Mobility for Wireless Networks

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Xie, Wanling Zhu, Huibiao Wu, Xi School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software ChangZhou University ChangZhou China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China School of Computer Science The University of Sydney Australia

ISBN: (纸本)9798400700705

Unreliability of communication links is a very common problem in wireless networks, and node mobility as one of significant features may importantly affect the reliability of communication links. In this paper, we present a Timed Calculus with Mobility for Wireless Networks called MTCWN to capture mobility in wireless networks. In our calculus, we introduce a random direction mobility model to depict node movement. With time passing, the mobility function and its timeout will be changed by this mobility model. Our calculus also describe local broadcast communication in wireless networks. Moreover, we study the operational semantics of the MTCWN calculus and illustrate the applications of the semantics rules by examples. © 2023 ACM.

关键词： Wireless networks

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Multi-head Hashing with Orthogonal Decomposition for Cross-modal Retrieval 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Liu, Wei Li, Jun Wu, Zhijian Xu, Jianhua Yang, Bo School of Computer and Electronic Information Nanjing Normal University Nanjing210023 China School of Data Science and Engineering East China Normal University Shanghai200062 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (数字)9783031533082

ISBN: (纸本)9783031533075

Recently, cross-modal hashing has become a promising line of research in cross-modal retrieval. It not only takes advantage of complementary multiple heterogeneous data modalities for improved retrieval accuracy, but also enjoys reduced memory footprint and fast query speed due to efficient binary feature embedding. With the boom of deep learning, convolutional neural network (CNN) has become the de facto method for advanced cross-model hashing algorithm. Recent research demonstrates that dominant role of CNN is challenged by increasingly effective Transformer architectures due to their advantages of long-range modeling by relaxing local inductive bias. However, the absence of inductive bias shatters the inherent geometric structure, which inevitably leads to compromised neighborhood correlation. To alleviate this problem, in this paper, we propose a novel cross-modal hashing method termed Multi-head Hashing with Orthogonal Decomposition (MHOD) for cross-modal retrieval. More specifically, with the multi-modal Transformers used as the backbones, MHOD leverages orthogonal decomposition for decoupling local cues and global features, and further captures their intrinsic correlations through our designed multi-head hash layer. In this way, the global and local representations are simultaneously embedded into the resulting binary code, leading to a comprehensive and robust representation. Extensive experiments on popular cross-modal retrieval benchmarking datasets demonstrate the proposed MHOD method achieves advantageous performance against the other state-of-the-art cross-modal hashing approaches. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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