Array最新文献

{"title":"Breast cancer detection using BCDNet convolutional neural network","authors":"Aneesha Jose,&nbsp;Zoheir Ezziane","doi":"10.1016/j.array.2026.100711","DOIUrl":"10.1016/j.array.2026.100711","url":null,"abstract":"<div><div>Breast cancer detection from histopathological images has benefited significantly from advances in deep learning; however, many high-performing models rely on deep architectures and substantial computational resources, limiting their applicability in resource-constrained clinical settings. Recent studies have demonstrated that convolutional neural networks (CNNs) can effectively support invasive ductal carcinoma (IDC) classification, but often at the cost of increased training time and memory usage [1, 13].</div><div>In this study, we propose BCDNet_Updated, an optimized lightweight CNN derived from the reference BCDNet architecture [1] for IDC detection. The proposed model introduces targeted architectural refinements aimed at improving classification performance while preserving computational efficiency. Experiments conducted on the Kaggle IDC dataset demonstrate that BCDNet_Updated achieves improved accuracy, recall, and F1-score compared to the reference BCDNet, while requiring significantly less GPU memory and comparable training time.</div><div>While the evaluation is limited to a single dataset and does not include formal statistical significance testing or explainability analysis, the results indicate that careful optimization of lightweight CNN architectures can yield meaningful performance improvements without increasing computational cost. Future work will incorporate multi-dataset validation, statistical rigor through k-fold cross-validation, and explainability mechanisms (Grad-CAM, attention visualization) to support clinical adoption. This work contributes to ongoing efforts toward efficient deep learning models for histopathological image analysis and provides a foundation for future studies involving expanded validation and model interpretability.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100711"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient crowd anomaly detection using C3D-LSTM networks with enhanced attention mechanisms","authors":"Sarah Altowairqi ,&nbsp;Suhuai Luo ,&nbsp;Peter Greer ,&nbsp;Shan Chen","doi":"10.1016/j.array.2025.100625","DOIUrl":"10.1016/j.array.2025.100625","url":null,"abstract":"<div><div>The rising deployment of surveillance systems in urban environments necessitates efficient automated anomaly detection methods. While showing promise, current deep learning approaches struggle with computational complexity and real-time performance in processing spatiotemporal information. This paper presents a hybrid framework integrating Convolutional 3D Networks (C3D), Long Short-Term Memory (LSTM) networks, and attention mechanisms for anomaly detection. Through a systematic evaluation of four attention mechanisms—self-attention, multi-head attention, Bahdanau attention, and Luong attention—we demonstrate their operational differences and their differential impact on feature extraction and classification performance across three diverse benchmark datasets. Our multi-head attention variant achieves state-of-the-art results with 99.40 % accuracy and 99.96 % Area Under the Curve (AUC) on Violent Flows, while maintaining robust performance across varying dataset complexities, achieving 91.87 % accuracy on the ShanghaiTech Campus and 79.7 % accuracy on the UCF-Crime dataset. Comprehensive cross-dataset evaluation demonstrates consistent improvements of 2.4 %–3.5 % over baseline approaches, with all attention mechanisms outperforming traditional spatiotemporal models. The proposed architecture effectively balances computational requirements with detection performance, maintaining real-time processing capabilities suitable for operational deployment. This framework advances the technical capabilities of anomaly detection systems while providing a validated foundation for practical deployment in diverse surveillance environments, from controlled scenarios to challenging real-world conditions.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100625"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of lightweight methods for object detection networks","authors":"Jing He,&nbsp;Jianfei Jiang,&nbsp;Changfan Zhang","doi":"10.1016/j.array.2025.100589","DOIUrl":"10.1016/j.array.2025.100589","url":null,"abstract":"<div><div>As social production technologies develop, object detection becomes vital in sectors such as agriculture, industry, and healthcare. It decreases dependence on manual labour and enhances accuracy and efficiency. However, edge devices confront limitations in computational power, storage, and energy, creating a trade-off between accuracy and model size. To tackle this, academia and industry have proposed solutions including hardware-coordinated acceleration, adaptive task lightweighting, and hybrid compression. This paper reviews research from 2020 to 2025 on lightweight object detection, providing a systematic overview of efficient architecture and model compression techniques, explaining their mechanisms, challenges, and future directions to support ongoing progress.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100589"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KeepUp: A unified framework fusing knowledge extraction, social platform engagement, and user profiling for fake news detection","authors":"Muhammad Wasim ,&nbsp;Sehrash Safdar ,&nbsp;Abdur Rehman ,&nbsp;Zahoor Ur Rehman ,&nbsp;Osama A. Khashan ,&nbsp;Naif Alzahrani ,&nbsp;Anwar Ghani","doi":"10.1016/j.array.2026.100687","DOIUrl":"10.1016/j.array.2026.100687","url":null,"abstract":"<div><div>Approximately half of the global population relies on social media platforms such as Facebook, Twitter, and Instagram for news consumption. The vast volume and rapid dissemination of information on these platforms pose substantial challenges for the timely and accurate detection of fake news. Academics are working harder to develop AI-based automated systems to check news accuracy because of the detrimental effects of misinformation on public health, social trust, and political stability. But the majority of false news detection methods currently in use focus primarily on content-based features, often ignoring essential factors such as user profiling, social context, and knowledge extraction. The knowledge-based features necessary for effective document retrieval, position identification, social engagement analysis, and user profile integration are often absent from datasets, even though some of them contain elements of social context and user behavior. This work offers a thorough, fully annotated dataset that integrates user profiles, stance information, social engagements, knowledge extraction, and content elements into a single resource to overcome these limitations. Building on this dataset, this study creates KeepUp, a unified system that integrates user profiles, social media activity, and knowledge extraction to detect bogus news. KeepUp outperforms all baseline models, achieving a detection accuracy of 0.78, demonstrating the effectiveness of this combined approach.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100687"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-clinic fusion: Multimodal deep learning redefines IBD diagnostics via synergistic CT-clinical feature integration","authors":"Yong Huang ,&nbsp;Pan Li ,&nbsp;Xiao-N Zhong ,&nbsp;DongPu Luo ,&nbsp;Yin Zhou ,&nbsp;Yong-J Li ,&nbsp;Ming-hong Sun ,&nbsp;Li-J Zhang ,&nbsp;Li-H Yang ,&nbsp;JunLong Li ,&nbsp;Yi Tao","doi":"10.1016/j.array.2026.100702","DOIUrl":"10.1016/j.array.2026.100702","url":null,"abstract":"<div><h3>Background</h3><div>Traditional methods like endoscopy and imaging are important for IBD management, which are limited by operator dependency, complexity, and high costs, making standardization and routine monitoring difficult.</div></div><div><h3>Methods</h3><div>In this study, seven machine learning algorithms were applied to the UK Biobank database to optimize the selection of clinical features and identify key variables associated with IBD. The nnUNetv2 model was then used for CT gut segmentation and validated by a local dataset. Next, the YOLOv10 algorithm was employed to accurately detect IBD lesion regions in the intestine. Finally, an IBD diagnostic model was developed by integrating the selected clinical features with the YOLOv10 identification results, thereby demonstrating the advantages of multimodal data integration.</div></div><div><h3>Results</h3><div>A machine learning-driven feature selection identified chronic abdominal pain, LDL-C, gender, TC, RDW, WBC, ALB, HDL-C, and neutrophil counts as clinical predictors. The nnUNetv2 model achieved high-precision intestinal segmentation, while YOLOv10 demonstrated robust lesion detection. A multimodal nomogram combining clinical features with YOLOv10-derived imaging biomarkers significantly enhanced IBD diagnosis, achieving an AUC of 0.967 that surpassed the clinical-only model (AUC = 0.730). This improvement highlights the synergistic value of deep learning-augmented diagnostics for classification accuracy.</div></div><div><h3>Conclusion</h3><div>Integrating clinical features with AI-driven image analysis improves IBD diagnostic precision, enabling better clinical decision-making and patient outcomes.</div></div><div><h3>Novelty</h3><div>To our knowledge, this is the first multimodal nomogram that combines the predictive clinical features of the UK Biobank scale with CT-based YOLOv10 lesion detection for IBD diagnosis. Different from prior models that solely rely on clinical data or radiomics, our approach achieves integration of population-level epidemiology and deep learning-based imaging, and was validated through external datasets.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100702"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic literature review on deep learning approaches for small object detection","authors":"Javed Sayyad,&nbsp;Khush Attarde","doi":"10.1016/j.array.2025.100615","DOIUrl":"10.1016/j.array.2025.100615","url":null,"abstract":"<div><div>Object detection is essential in several industries, including defense, autonomous vehicles, and surveillance. These applications rely on various devices equipped with cameras, such as vehicles, drones, and satellites; primarily operating in the visible spectral domain rather than infrared or other spectral ranges. Deep Learning (DL) techniques have significantly advanced the field of object detection, enabling the identification of various objects. However, detecting tiny objects remains a challenging task. Despite its difficulty, identifying small objects in images captured by these devices in the visible spectrum is crucial. It is essential to explore hybrid techniques and modifications in feature architectures to address the challenge of detecting tiny objects. Simple architectures often fall short in this regard, necessitating more sophisticated approaches. This paper systematically reviews different DL-based approaches researchers have previously employed to tackle this issue. A systematic literature review on SOD and DL techniques uses the ”Preferred Reporting Items for Systematic Reviews and Meta-Analysis” (PRISMA) methodology. It discusses various DL-based theoretical frameworks, including Reinforcement Learning and Generative Adversarial Networks, specifically for Small Object Detection (SOD) in visible spectral images. The review begins by defining a small object and identifying the datasets available for various applications, such as remote sensing and autonomous vehicles. It then examines the implementation of models according to these datasets and analyzes the findings from other researchers. The analysis reveals that, for most datasets, the average precision (AP) for SOD ranges from 20% to 40% and showcases the need for the advancement and focus.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100615"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive load classification during online shopping using deep learning on time series eye movement indices","authors":"Sunu Wibirama ,&nbsp;Muhammad Ainul Fikri ,&nbsp;Iman Kahfi Aliza ,&nbsp;Kristian Adi Nugraha ,&nbsp;Syukron Abu Ishaq Alfarozi ,&nbsp;Noor Akhmad Setiawan ,&nbsp;Ahmad Riznandi Suhari ,&nbsp;Sri Kusrohmaniah","doi":"10.1016/j.array.2025.100669","DOIUrl":"10.1016/j.array.2025.100669","url":null,"abstract":"<div><div>Cognitive load classification during online shopping activities is important to understand the user experience of e-commerce. Traditional classification methods that rely on proprietary software and obtrusive physiological measures often result in inconsistent performance. To address this research gap, we propose a novel approach that leverages deep learning to analyze raw eye movement data during online shopping tasks with low and high cognitive load. The Attention-based Long Short-Term Memory Fully Convolutional Network (ALSTM-FCN) model outperformed other machine learning and deep learning models with an average accuracy and F1 score of 97.70% and 97.69%, respectively. Cognitive load was also measured using the NASA TLX questionnaire, which showed significantly higher scores in high cognitive load tasks for all dimensions: “Mental Demand” (39.37, <span><math><mrow><mi>p</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>), “Performance” (46.12, <span><math><mrow><mi>p</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>004</mn></mrow></math></span>), “Effort” (51.92, <span><math><mrow><mi>p</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>002</mn></mrow></math></span>), and “Frustration Level” (60.53, <span><math><mrow><mi>p</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>). Based on the analysis of eye movement features used in cognitive load classification, we found that the variability in eye movement during tasks with low and high cognitive loads was predominantly spatial rather than temporal (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>). Our findings indicate a strong correlation between the deep learning-based classification of raw eye movement data and subjective cognitive load assessments. This study demonstrates the potential of using an affordable eye tracking sensor to classify cognitive load without being constrained by the capability of proprietary software.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100669"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time adaptive neural network-based state of charge prediction of battery pack in a digital twin framework","authors":"Shaik Farooq ,&nbsp;M. Harshith ,&nbsp;Aneeshsingh Bhatkhande ,&nbsp;N. Kumaresan ,&nbsp;B. Karthikeyan ,&nbsp;A. Rammohan","doi":"10.1016/j.array.2025.100638","DOIUrl":"10.1016/j.array.2025.100638","url":null,"abstract":"<div><div>The accurate and real-time prediction of state of charge (SoC) in battery pack is crucial for the safe and efficient operation of electric vehicles. Traditional estimation methods often suffer from reduced accuracy under sensor errors, battery aging, and dynamic load conditions. This study presents a real-time adaptive neural network (ANN)-based SoC prediction model integrated within a digital twin (DT) framework, designed, and validated using MATLAB/Simulink. The proposed algorithm continuously updates its parameters using real-time current, SoC, and voltage data of battery pack, enabling adaptive learning under varying load and ambient conditions. Compared with traditional methods such as Extended Kalman Filter and particle-filter based estimators, the proposed algorithm reduces the prediction error by 18–22 % and it shortens the response time by 30 %. The simulation results confirm that a strong correlation between the predicted and actual SoC values (R = 0.9999) with a maximum deviation of ±1.5 %. The proposed algorithm demonstrates robust convergence, improved generalization through Bayesian regularization, and high stability during real-time adaptation. This adaptive DT-integrated ANN framework enhances the BMS reliability, supports predictive maintenance, and provides a scalable, and intelligent solution for next-generation electric mobility applications.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100638"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A parallel particle swarm optimization for improving wireless sensor networks longevity-based dynamic clustering method","authors":"Ahmed Abdelaziz ,&nbsp;Alia Nabil Mahmoud ,&nbsp;Vitor Santos","doi":"10.1016/j.array.2025.100633","DOIUrl":"10.1016/j.array.2025.100633","url":null,"abstract":"<div><div>Determining the optimal configuration for wireless sensor networks (WSNs) can be challenging due to the multitude of possible setups. To address this issue, our team has developed the Parallel Particle Swarm Optimization-based Self-Organizing Network Clustering (PPSOPM) method. By taking into account variables like remaining node energy, predictable energy usage, proximity to the base station, and number of nearby nodes, PPSOPM dynamically enhances wireless sensor node clusters. Achieving a balance between these factors is crucial to effectively organize nodes into clusters and select a surrogate node as the cluster's head. In comparison to alternative methods, PPSOPM significantly improves network structure by 44.39 % and extends network lifespan. However, node density may impact network longevity by increasing the distance between nodes. Also, when the base station is far from the sensor area, creating additional clusters can help conserve energy. On average, PPSOPM requires 0.57 s to complete, with a standard deviation of 0.04.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100633"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bi-term association based on fuzzy logic","authors":"Wensu Liu ,&nbsp;Jing Wan ,&nbsp;Na Lv ,&nbsp;Xiaobei Zhou","doi":"10.1016/j.array.2026.100704","DOIUrl":"10.1016/j.array.2026.100704","url":null,"abstract":"<div><div>Traditional Boolean search strategies identify co-occurrence but fail to capture semantic relationships. Current relation extraction (RE) frameworks, which rely on domain-specific training, struggle with adaptability to emerging research topics. To address these limitations, we propose a novel framework integrating fuzzy logic with zero-shot large language models (LLMs), enabling the quantification of bi-term associations without explicit training data. Bi-term associations are defined as contextually grounded relationships that extend co-occurrence analysis while avoiding the data annotation requirements of traditional RE. Key contributions include: a fuzzy logic framework for graded membership assignment to capture uncertainty and relationship strength, design of complex fuzzy rules within a lightweight zero-shot LLM, and detailed demonstration of four conceptual case studies in diverse fields, ranging from simple to complex rule design. Benchmark comparisons on RE tasks show that the fuzzy logic method outperforms the baseline model (Qwen3:30b-a3b): F1 scores improve by 0.05 (n2c2 dataset: 0.82 vs. 0.77) and 0.17 (GAD dataset: 0.52 vs. 0.35). However, the fuzzy method is computationally intensive. This work introduces a bi-term association approach based on fuzzy logic to bridge co-occurrence analysis and RE, demonstrating its potential for biomedical knowledge discovery while addressing critical computational challenges in real-world deployment.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100704"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}