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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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MOAC: Multi-Objective Adaptive Clustering Optimization Algorithm Based on Cluster Adaptation

MOAC: Multi-Objective Adaptive Clustering Optimization Algor...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Dheeb Albashish Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel Multi-Objective Adaptive Clustering (MOAC) algorithm that addresses current limitations in existing multi-objective optimization methods. While current algorithms often struggle with premature convergence and diversity preservation, the MOAC algorithm proposes a novel clustering-based approach to search space by partitioning. The algorithm combines cluster evolution with an adaptive parameter control mechanism to achieve significant improvements in balancing exploration-exploitation and diversity maintenance. MOAC employs k-means clustering for population partitioning, combined with cluster-based genetic operators, and an efficient diversity preservation scheme. Extensive experiments across different ZDT, MMF, and UF test suites demonstrate that MOAC's performance is superior, which achieves outstanding hypervolume and IGD values in complex multi-modal problems. The algorithm shows strength in handling complex Pareto fronts, and comprehensive statistical analysis through the Friedman rankings test with a 2.21 rank. Moreover. The results reveal that MOAC is a robust approach for complex multi-objective optimization problems, particularly maintaining solution diversity and achieving consistent convergence across different problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Clustering algorithms Optimization methods Evolutionary computation Partitioning algorithms Maintenance Convergence Genetic operators

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Design of a Dust Cleaning Prototype for Photovoltaic Generators 4th

Design of a Dust Cleaning Prototype for Photovoltaic Generat...

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4th International Conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： Latreche, M. Medjahed, B. Mellit, A. Rabhi, H. Hajji, B. Faculty of Science and Technology University of Jijel Algiers Algeria Laboratory of Modelization Information and Systems University of Picardie Jules Verne 3 Rue Saint Leu Amiens Cedex 180039 France Embedded System and Data Processing Laboratory Renewable Energy National School of Applied Sciences Mohamed First University Oujda Morocco

ISBN: (纸本)9789819799749

Solar panels are vulnerable to accumulated dust and sand, especially in desert regions, which significantly decreases their efficiency. Therefore, the necessity of developing modern cleaning systems is a crucial task. Various types of cleaning systems have been proposed in the literature, such as electrostatic, ultrasonic, and robotic systems. However, the best methods are the ones guarantee as much efficiency as possible at the lowest cost. In this work, we propose a simple automatic system based on a rotating brush. The system is composed of two parts linked together by Bluetooth modules. The first one is a dust sensor, its main task is to send a signal to the second part, which is the cleaner, in case of detecting dirt or sand. The feasibility of the proposed prototype has been experimentally demonstrated. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cleaner production

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Analysis of Device-To-Device Communication in Iot System 1st

Analysis of Device-To-Device Communication in Iot System

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Nawaz, Usman Rehman, Faisal Ullah, Kainat Azmat Raza, Usman Ahmed Sharif, Hanan Rafique, Furqan Ali, Abid Ashiq, Masood Iqbal, Junaid Department of Computer Science Lahore Leads University Lahore Pakistan Department of Statistics and Data Science University of Mianwali Mianwali Pakistan Faculty of Information Technology University of Central Punjab Lahore Pakistan

ISBN: (纸本)9783031776199

Peer-to-Peer communication is based on an important feature of communication which is Device-to-Device (D2D) communication. IoT is a computational device that is equipped with accurate identifiers and the capacity to speak data across a network without the assistance of a human. D2D communication is referred to as "Direct verbal exchange among the taking part gadgets without the use of the centralized infrastructure". This research study examines the advantages and barriers of D2D communication as a means of meeting the IoT's requirements. Many companies and quality management organizations have demonstrated a strong interest in implementing the D2D approach in wireless networks. The thought of working without the necessary control of centralized supervision and making Wi-Fi networks wider as well as energy saving can be made possible with D2D communication in IoT. These devices’ technical limitations and the connection's support limitations provide an upward push to numerous problems that must be addressed. Some of the IoT studies Electricity production, connectivity, security, scenario technologies, and so forth are all issues. We cognizance’s on troubles that in the IoT there is an influence in D2D communication surroundings. We also discovered some important stumbling blocks to the success of smart D2D speech communication in the Internet of Things. We talked about how to deal with these difficult situations. This research explains the idea of a tool to tool verbal exchange and IoT system and defines its predominant techniques and applications imparting a subsequent era therefore as procedure strategy to the demanding situations. To boost research and development in the domains IoT problems have been studied. Every cited research work's contribution and limitations are discussed as well as any potential problem statement and open rely. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wi-Fi

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APPLYING TEXT MINING TO ANALYZE HUMAN QUESTION ASKING IN CREATIVITY RESEARCH

arXiv

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arXiv 2025年

作者： Wróblewska, Anna Korbin, Marceli Kenett, Yoed N. Dan, Daniel Ganzha, Maria Paprzycki, Marcin Faculty of Mathematics and Information Sciences Warsaw University of Technology Warsaw Poland Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Haifa Israel School of Applied Data Science Modul University Vienna Austria Systems Research Institute Polish Academy of Sciences Warsaw Poland

Creativity relates to the ability to generate novel and effective ideas in the areas of interest. How are such creative ideas generated? One possible mechanism that supports creative ideation and is gaining increased empirical attention is by asking questions. Question asking is a likely cognitive mechanism that allows defining problems, facilitating creative problem solving [15]. However, much is unknown about the exact role of questions in creativity. This work presents an attempt to apply text mining methods to measure the cognitive potential of questions, taking into account, among others, (a) question type, (b) question complexity, and (c) the content of the answer. This contribution summarizes the history of question mining as a part of creativity research, along with the natural language processing methods deemed useful or helpful in the study. In addition, a novel approach is proposed, implemented, and applied to five datasets. The experimental results obtained are comprehensively analyzed, suggesting that natural language processing has a role to play in creative research. Copyright © 2025, The Authors. All rights reserved.

关键词： Text mining

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Xi’an710072 China The University of Adelaide Australian Institute for Machine Learning AdelaideSA5000 Australia Monash University Faculty of Information Technology Department of Data Science and AI Australia

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/VerSemi. © 1982-2012 IEEE.

关键词： Self-supervised learning

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Vibration analysis of elastically supported sandwich beam with damaged core and functionally graded face sheets

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Steel and Composite Structures 2025年第1期54卷 71-83页

作者： Wang, Lulu Mohsin, Hasan F. Bains, Pardeep Singh Sharma, Rohit Bohlooli, Nasrin College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad10011 Iraq Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf54001 Iraq University Karnataka Bengaluru560069 India Department of Mechanical Engineering Vivekananda Global University Rajasthan Jaipur303012 India School of Engineering and Technology Shobhit University Uttar Pradesh Gangoh247341 India Department of Mechanical Engineering Arka Jain University Jharkhand Jamshedpur831001 India Nabi Data Science & Computational Intelligence Research Co. Tehran Iran

This paper presents the influence of carbon nanotubes (CNTs) waviness, aspect ratio and damaged core on the vibrational behavior of functionally graded nanocomposite sandwich beams resting on two-parameter elastic foundations. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness of upper and bottom layers of the sandwich beam and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes’ effective aspect ratio as the filler content increases. A damage model is introduced to provide an analytical description of an irreversible rheological process that causes the decay of the mechanical properties, in terms of engineering constants. An isotropic damage is considered for the core of the sandwich beams. The equations of motion are derived based on Timoshenko beam theory and employing Hamilton’s principle. The problem is modeled using a semi-analytical approach composed of generalized differential quadrature method (GDQM) and series solution adopted to solve the equations of motion. Detailed parametric studies are carried out to investigate the mechanical behavior of these multi-layered structures depending on the damage features, through-the-thickness distribution and boundary conditions. Also, the effects of carbon nanotubes (CNTs) waviness, CNT aspect ratio, Winkler foundation modulus, shear elastic foundation modulus and geometrical conditions on the vibrational behavior of the sandwich structure are studied. © 2025 Techno-Press, Ltd.

关键词： Aspect ratio

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Beyond Histogram Comparison: Distribution-Aware Simple-Path Graph Kernels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Ye, Wei Tang, Shuhao Tian, Hao Chen, Qijun Shanghai Institute of Intelligent Science and Technology Tongji University College of Electronic and Information Engineering Shanghai201804 China Shanghai Innovation Institute Shanghai200231 China City University of Macau Faculty of Data Science Taipa China Tongji University College of Electronic and Information Engineering Shanghai201804 China

R-convolution graph kernels are conventional methods for graph classification. They decompose graphs into substructures and aggregate all the substructure similarity as graph similarity. However, the substructure similarity is based on graph isomorphism, which not only leads to binary similarity values but also cannot be aware of the probability distribution of substructures in each graph. Moreover, the simple sum aggregation is not aware of the probability distribution differences of substructures across graphs. These drawbacks cause inaccurate graph similarity. To resolve these problems, we propose a new method called the Distribution-Aware Simple-Path (DASP) graph kernel. The neural language models are employed to capture the probability distribution of substructures (specifically, simple paths) in each graph. A new metric called Probabilistic Minkowski Distance is developed to capture the probability distribution differences of simple paths across graphs. To further improve the performance, the label alphabet is expanded to enlarge the corpus of simple paths for the neural language models and DASP. Experiments demonstrate that DASP achieves the best classification accuracy on all the selected graph benchmark datasets. © 2020 IEEE.

关键词： Knowledge graph

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Leveraging CNN-Transformer Hybrid Architecture for Accurate Breast Tumor Segmentation in Mammograms

Leveraging CNN-Transformer Hybrid Architecture for Accurate ...

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Women in data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Mudassar Ali Muhammad Zeeshan Tahir Iqra Mariam Khadija Nawaz Tariq Mahmood Samia Allaoua Chelloug College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science and Technology Zhejiang University Hangzhou China School of Electronics and Information Engineering Liaoning University of Technology Jinzhou China Faculty of Information Sciences University of Education Vehari Pakistan Artificial Intelligence and Data Analytics Lab CCIS PSU Riyadh SA Department of Information Technology CCIS PNU Riyadh SA

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Breast cancer detection through mammograms is very crucial for early diagnosis, and tumor segmentation is an important step in treatment planning. This paper proposes a novel approach by combining CNNs with transformers, which we call the Hybrid CNN + Transformer model, for the task of breast tumor segmentation. It leverages the strengths of CNN s in local feature extraction to capture fine details, such as tumor boundaries, and combines them with transformers that model long-range dependencies and contextual relationships across images for the exact delineation of tumor regions in complex tissue structures. Our methodology will be tested on the high-quality mammog-raphy dataset annotated with both benign and malignant lesions, the so-called INbreast dataset. Herein, the CNN backbone-first for instance, ResNet or EfficientNet-extracts the input mammo-gram hierarchically; features are fed into a transformer layer in order to capture global dependencies, after which the final segmentation mask is generated by using a U-Net style decoder for a pixel-wise tumor classification. The segmentation accuracies of our models demonstrated the significant improvement achieved in tumor segmentation, using Hybrid CNN + Transformer when compared with tra-ditional CNN-based models on challenging tumor boundaries and complex tissue patterns. An accurate, robust, completely automatic method for tumor segmentation will thus enable improved diagnostic feasibility on clinical grounds. Furthermore, the combination of learning at both local and global features improved generalizability to previously unseen mammogram images and therefore strengthened clinical applicability.

关键词： Image segmentation Adaptation models Accuracy Breast tumors Computational modeling Transformers Mammography Breast cancer Convolutional neural networks Tumors

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