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Multiple lung diseases detection using advanced deep learning model with attention mechanisms and upsampling features

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Zhu, Jie Al-qaness, Mohammed A.A. AL-Alimi, Dalal Tao, Heng Zhi Alsamhi, Saeed Hamood College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Faculty of Engineering Sana'a University Sana'a12544 Yemen Insight Centre for Data Analytics University of Galway Galway Ireland School of ICT Bahrain Polytechnic Isa Town Bahrain Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of

Lung diseases represent a major health challenge, necessitating accurate and timely diagnostic methods. X-ray imaging plays a critical role in diagnosing various lung conditions, with physicians traditionally relying on these images for disease identification. However, with rapid advancements in artificial intelligence and imaging technologies, the volume of medical image data has grown significantly, spurring the development of machine learning techniques, particularly deep learning, to improve diagnostic accuracy. Unlike previous studies that focused on binary or three-class classification tasks, this paper presents an efficient deep learning model capable of classifying six types of lung X-ray images. The model employs an efficient feature extraction approach by combining the Visual Geometry Group Network (VGG16) with a Convolutional Neural Network (CNN) feature fusion, further enhanced by a Squeeze-and-Excitation (SE) block and self-attention modules. This comprehensive framework enables effective classification of tuberculosis, pneumonia, COVID-19, normal lungs, lung opacity, and pneumothorax. To validate the model, we constructed a robust dataset consisting of 45,241 X-ray images sourced from multiple databases. The proposed model was evaluated in various conditions to verify its performance. It achieves a remarkable validation accuracy of 93% in detecting the six lung diseases from X-ray images. © 2025 Elsevier Ltd

关键词： Lung cancer

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Multi-Label Movie Genre Classification with Attention Mechanism on Movie Plots

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computers, Materials and Continua 2025年第3期83卷 5595-5622页

作者： Faheem Shaukat Naveed Ejaz Rashid Kamal Tamim Alkhalifah Sheraz Aslam Mu Mu Department of Computing and Technology Iqra University H-9 Campus Islamabad 04436 Pakistan School of Computing Queens University Kingston ON K7L2N8 Canada School of Computing Ulster University Belfast BT15 1ED Northern Ireland UK Faculty of Arts Science and Technology University of Northampton Waterside Campus Northampton Northamptonshire NN1 5PH UK Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science CTL Eurocollege Limassol 3077 Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol 3036 Cyprus

Automated and accurate movie genre classification is crucial for content organization, recommendation systems, and audience targeting in the film industry. Although most existing approaches focus on audiovisual features such as trailers and posters, the text-based classification remains underexplored despite its accessibility and semantic richness. This paper introduces the Genre Attention Model (GAM), a deep learning architecture that integrates transformer models with a hierarchical attention mechanism to extract and leverage contextual information from movie plots for multi-label genre classification. In order to assess its effectiveness, we assess multiple transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT), A Lite BERT (ALBERT), Distilled BERT (DistilBERT), Robustly Optimized BERT Pretraining Approach (RoBERTa), Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA), eXtreme Learning Network (XLNet) and Decoding-enhanced BERT with Disentangled Attention (DeBERTa). Experimental results demonstrate the superior performance of DeBERTa-based GAM, which employs a two-tier hierarchical attention mechanism: word-level attention highlights key terms, while sentence-level attention captures critical narrative segments, ensuring a refined and interpretable representation of movie plots. Evaluated on three benchmark datasets Trailers12K, Large Movie Trailer Dataset-9 (LMTD-9), and MovieLens37K. GAM achieves micro-average precision scores of 83.63%, 83.32%, and 83.34%, respectively, surpassing state-of-the-art models. Additionally, GAM is computationally efficient, requiring just 6.10 Giga Floating Point Operations Per Second (GFLOPS), making it a scalable and cost-effective solution. These results highlight the growing potential of text-based deep learning models in genre classification and GAM’s effectiveness in improving predictive accuracy while maintaining computational efficiency. With it

关键词： Multi-label classification artificial intelligence movie genre classification hierarchical attention mechanisms natural language processing content recommendation text-based genre classification explainable AI (Artificial Intelligence) transformer models BERT

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Decision-analytics-based green performance evaluation in the railway transportation industry – An integrated hesitant fuzzy approach

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Transportation Research Interdisciplinary Perspectives 2025年 31卷

作者： Norouzi, Ashraf Pamucar, Dragan Simic, Vladimir Department of Industrial Engineering Faculty of Engineering Arak University Arak Iran Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department Sarıyer İstanbul 34396 Turkey Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 South Korea

Green performance evaluation at the industry level is of great importance in logistics literature. This evaluation can assist in more effectively allocating government resources to support companies with superior environmental performance. Many criteria used to evaluate the environmental performance of companies involve uncertainty and ambiguity inherent in expert judgments. To address this issue, the present research aims to develop a new hybrid method for group decision-making in a hesitant fuzzy environment. The proposed method investigates companies’ green performance in Iran's passenger rail transport industry. For this purpose, by combining the field experts’ knowledge and literature review, the framework of the decision-making problem is determined. The paper's main contribution is to extend a hybrid MADM technique into interval type-2 hesitant fuzzy (IT2HF) environment. In this hybrid approach, the IT2HF SWARA (Stepwise Weight Assessment Ratio Analysis) is utilized to determine the weight of criteria, and IT2HF MARCOS (Measurement of Alternatives and Ranking according to the Compromise Solution) is applied to evaluate the alternatives. Moreover, the proposed approach provides a systematic way to aggregate the opinions of a group of experts through the use of hesitant fuzzy sets. At the same time, each of these experts can explain their opinions in hesitant terms. The data used in this research is obtained from a group of qualified field experts. According to their opinions, the performance of eight major companies that own more than 80% of Iran's private railway fleet has been evaluated. To validate the results, sensitivity and comparative analysis are carried out. The results indicate the stability of the ranking achieved with the proposed approach. © 2025 The Author(s)

关键词： Group decision-making Hesitant fuzzy MARCOS Hesitant fuzzy sets Hesitant fuzzy SWARA Interval type-2 fuzzy sets

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ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis

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computer Methods and Programs in Biomedicine Update 2025年 7卷

作者： Sheakh, Md. Alif Azam, Sami Tahosin, Mst. Sazia Karim, Asif Montaha, Sidratul Fahim, Kayes Uddin Shafiabady, Niusha Jonkman, Mirjam De Boer, Friso Health Informatics Research Laboratory (HIRL) Department of Computer Science and Engineering Daffodil International University Dhaka 1341 Bangladesh Faculty of Science and Technology Charles Darwin University Darwin 0909 NT Australia Department of Computer Science and Engineering University of Calgary Calgary Canada Department of Information Technology Australian Catholic University North Sydney 2060 NSW Australia

Endometrial cancеr is the fourth fastеst-growing cancеr among women worldwide, affecting the uterus's lining. This research proposes a novel approach called ECgMLP for the automated diagnosis of endometrial cancer by analyzing histopathological images. Several preprocessing techniques are employed to increase the quality of the images, including normalization, Non-Local Means denoising, and alpha-beta enhancement. Effective segmentation is achieved through a combination of Otsu thresholding, morphological operations, distance transformations, and the watershed approach to identify major regions of interest. Through a sequence of blocks, the ECgMLP architecture processes input images to remove unimportant patterns. Model hyperparameters are improved via ablation research. The evaluations show a maximum accuracy of 99.26 % for identifying multi-class histopathological categories of endometrial tissue, which is higher than the previous best technique. The proposed model offers an automated, correct diagnosis, enhancing clinical processes. This proposition could be added to the current tools for finding endometrial cancer early, leading to better patient outcomes. © 2025

关键词： Deep learning Endometrial cancer gMLP Histopathological Watershed

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Data Dams: A Novel Framework for Regulating and Managing Data Flow in Large-Scale Systems

arXiv

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arXiv 2025年

作者： Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Ivković, Nikola Mihaljević, Ivan Mohamud, Mudathir Ahmed Kowrina, Ahmed Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Department of Electrical-Electronics Engineering Biruni University Istanbul Turkey Faculty of Organization and Informatics University of Zagreb Pavlinska 2 Varaždin42000 Croatia Faculty of Computing SIMAD University Mogadishu252 Somalia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia Département d’Informatique et Mathématiques Faculté des Sciences et Techniques Université de Nouakchott Al Aasriya Nouakchott880 Mauritania

In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congestion while maximizing resource utilization. Inspired by physical dam mechanisms, the framework employs intelligent sluice controls and predictive analytics to regulate data flow based on system conditions such as bandwidth availability, processing capacity, and security constraints. Simulation results demonstrate that the Data Dam significantly reduces average storage levels (371.68 vs. 426.27 units) and increases total outflow (7999.99 vs. 7748.76 units) compared to static baseline models. By ensuring stable and adaptive outflow rates under fluctuating data loads, this approach enhances system efficiency, mitigates overflow risks, and outperforms existing static flow control strategies. The proposed framework presents a scalable solution for dynamic data management in large-scale distributed systems, paving the way for more resilient and efficient real-time processing architectures. © 2025, CC BY.

关键词： Queueing theory

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Highly-Compact Wideband High-Gain Four-Element MIMO Antenna for 5G New Radio IoT

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IEEE Internet of Things Journal 2025年

作者： Khan, Shahid Khan, Owais Shah, Syed Ahson Ali Nasir, Jamal Malik, Bilal Tariq Khan, Salahuddin Koziel, Slawomir Department of Electrical and Computer Engineering COMSATS University Islamabad Abbottabad-Campus Islamabad Pakistan Gwangju Institute of Science and Technology Graduate School of Energy Convergence Gwangju Korea Republic of King Saud University College of Engineering P.O. Box 800 Riyadh11421 Saudi Arabia Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland Reykjavik University Engineering Optimization and Modeling Center Reykjavik101 Iceland

Wideband millimeter-wave (mm-wave) coverage is essential for the high-speed, low-latency communication required in next-generation 5G New Radio (NR) Internet of Things (IoT) systems. This study develops a T-shaped four-element, highly compact wideband multiple-input multiple-output (MIMO) antenna covering the mm-wave n260 (37.40 GHz) and n259 (42.43.5 GHz) bands for 5G NR IoT applications. The antenna is designed on a 0.76 mm-thick Rogers RO4350B substrate with overall dimensions of 28 28 mm2 (3.3 × 3.3 λ2). For design simplicity, a T-shaped patch backed by a full-ground plane is devised to serve as the radiator of a single-element antenna with the dimensions of (Ls × Ws) mm2, optimized through a systematic three-step design process for improved performance. Moreover, the design has evolved into a 4× 4 orthogonal MIMO configuration, achieving improved gain, polarization diversity, and higher data rates, with each element exhibiting wideband characteristics across 11.5 GHz (36.5 to 48 GHz) with high gains of 9.8 dBi and 6.6 dBi at n260 and n259 5G mm-wave bands, respectively. Additionally, arc-shaped complementary split-ring resonators (CSRR) are integrated into the ground plane to significantly enhance the gain while effectively reducing mutual coupling, remarkably achieving a maximum gain of 9.8 dBi. Furthermore, the MIMO antenna exhibits an envelope correlation coefficient of less than 0.10 between any two MIMO elements encountering the required condition of © 2014 IEEE.

关键词： Microwave antennas

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Explainable AI chatbots towards XAI ChatGPT: A review

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Heliyon 2025年第2期11卷 e42077页

作者： Kovari, Attila Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eszterhazy ter 1 Eger 3300 Hungary Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Tancsics M. 1/A Dunaujvaros 2400 Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki u. 10 Kecskemét 6000 Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Óbuda University Bécsi street 96/B Budapest 1034 Hungary

Advances in artificial intelligence (AI) have had a major impact on natural language processing (NLP), even more so with the emergence of large-scale language models like ChatGPT. This paper aims to provide a critical review of explainable AI (XAI) methodologies for AI chatbots, with a particular focus on ChatGPT. Its main objectives are to investigate the applied methods that improve the explainability of AI chatbots, identify the challenges and limitations within them, and explore future research directions. Such goals emphasize the need for transparency and interpretability of AI systems to build trust with users and allow for accountability. While integrating such interdisciplinary methods, such as hybrid methods combining knowledge graphs with ChatGPT, enhancing explainability, they also highlight industry needs for explainability and user-centred design. This will be followed by a discussion of the balance between explainability and performance, then the role of human judgement, and finally the future of verifiable AI. These are the avenues through which insights can be used to guide the development of transparent, reliable and efficient AI chatbots. © 2025 The Author

关键词： AI chatbots ChatGPT Controllable AI Explainable AI (XAI) Natural language processing (NLP) Transparency

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Explainable machine learning versus known nomogram for predicting non-sentinel lymph node metastases in breast cancer patients: A comparative study

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computers in Biology and Medicine 2025年 184卷 109412-109412页

作者： Fattahi, Asieh Sadat Hoseini, Maryam Dehghani, Toktam Nezhad Noor Nia, Raheleh Ghouchan Naseri, Zeinab Ebrahimzadeh, Amirali Mahri, Ali Eslami, Saeid Department of Surgery Faculty of Medicine Endoscopic and Minimally Invasive Research Center Mashhad University of Medical Sciences Mashhad Iran General Surgeon Department of Surgery Endoscopic and Minimally Invasive Research Center Mashhad University of Medical Sciences Mashhad Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran Computer Engineering Sharif University of Technology Tehran Iran Endoscopic and Minimally Invasive Research Center Mashhad University of Medical Sciences Mashhad Iran Pharmaceutical Research Center Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran Department of Medical Informatics UMC-Location AMC University of Amsterdam Amsterdam Netherlands

Introduction: Axillary lymph node dissection (ALND) is the standard of care for breast cancer patients with positive sentinel lymph nodes (SLN), which are the first lymph nodes that drain the breast. However, many patients with positive SLNs may not have additional positive nodes, making the prediction of non-sentinel lymph node (NSLN) metastasis challenging. Reliable prognostic tools are essential for accurately assessing NSLN metastasis. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram has demonstrated effectiveness in this context, but it requires further evaluation within the Iranian breast cancer population. While ALND remains the gold standard, its unnecessary application in patients without evidence of additional positive nodes raises concerns due to potential complications such as lymphedema, nerve injury, and shoulder joint dysfunction. Furthermore, integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques presents an opportunity to enhance the precision of NSLN metastasis predictions. Method: This study conducts an extensive comparative analysis between the MSKCC nomogram and various ML models to predict NSLN metastasis, utilizing a dataset of Iranian breast cancer patients. Employing eXplainable Artificial Intelligence (XAI) methodologies, we analyzed 16 clinical features across a cohort of 183 patients. Our methodology includes rigorous statistical evaluations and the training and validation of ML models to assess the precision and robustness of these models compared to the MSKCC nomogram. Results: Our analysis revealed that the Random Forest (RF) model outperformed the MSKCC nomogram, achieving an accuracy of 72.2 % and an AUC of 0.77, compared to the nomogram's AUC of 0.73. Logistic Regression (LR) also demonstrated competitive performance with an accuracy of 65 % and an AUC of 0.73. The RF model exhibited high sensitivity (75 %) and precision (73 %), effectively identifying critical predictors of NSLN metastasis, includi

关键词： Diseases

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Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models

arXiv

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arXiv 2025年

作者： Khalila, Zahra Nasution, Arbi Haza Monika, Winda Onan, Aytug Murakami, Yohei Radi, Yasir Bin Ismail Osmani, Noor Mohammad Department of Informatics Engineering Universitas Islam Riau Pekanbaru28284 Indonesia Department of Library Information Universitas Lancang Kuning Riau28266 Indonesia Department of Computer Engineering College of Engineering and Architecture Izmir Katip Celebi University Izmir35620 Turkey Faculty of Information Science and Engineering Ritsumeikan University Shiga Kusatsu525-8577 Japan Perlis Kuala Perlis02000 Malaysia Department Of Qur’an And Sunnah Studies Ahas Kirkhs International Islamic University Malaysia Malaysia

Accurate and contextually faithful responses are critical when applying large language models (LLMs) to sensitive and domain-specific tasks, such as answering queries related to quranic studies. General-purpose LLMs often struggle with hallucinations, where generated responses deviate from authoritative sources, raising concerns about their reliability in religious contexts. This challenge highlights the need for systems that can integrate domain-specific knowledge while maintaining response accuracy, relevance, and faithfulness. In this study, we investigate 13 open-source LLMs categorized into large (e.g., Llama3:70b, Gemma2:27b, QwQ:32b), medium (e.g., Gemma2:9b, Llama3:8b), and small (e.g., Llama3.2:3b, Phi3:3.8b). A Retrieval-Augmented Generation (RAG) is used to make up for the problems that come with using separate models. This research utilizes a descriptive dataset of Quranic surahs including the meanings, historical context, and qualities of the 114 surahs, allowing the model to gather relevant knowledge before responding. The models are evaluated using three key metrics set by human evaluators: context relevance, answer faithfulness, and answer relevance. The findings reveal that large models consistently outperform smaller models in capturing query semantics and producing accurate, contextually grounded responses. The Llama3.2:3b model, even though it is considered small, does very well on faithfulness (4.619) and relevance (4.857), showing the promise of smaller architectures that have been well optimized. This article examines the trade-offs between model size, computational efficiency, and response quality while using LLMs in domain-specific applications. © 2025, CC BY.

关键词： Semantics

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