IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.array.2025.100667

Khadija Shahzad , Anum Khashir , Hina Tufail , Abdul Ahad , Zahra Ali , Filipe Madeira , Ivan Miguel Pires

{"title":"Federated Convolutional Neural Networks (F-CNNs) for privacy-preserving multi-class skin lesion classification","authors":"Khadija Shahzad , Anum Khashir , Hina Tufail , Abdul Ahad , Zahra Ali , Filipe Madeira , Ivan Miguel Pires","doi":"10.1016/j.array.2025.100667","DOIUrl":"10.1016/j.array.2025.100667","url":null,"abstract":"<div><div>Skin lesions include a variety of abnormalities found on the skin. These may be benign (not cancerous) or malignant (cancerous). Every year, the number of cases of skin cancer increases globally, increasing the death rate. Medical data is scarce because people are reluctant to provide their health information due to privacy concerns. In this research, a decentralized machine learning approach, Federated learning, is the primary focus of the discipline to preserve patient data. Using this method, models are trained independently on several dispersed devices without sharing the data. To balance the data and enrich the dataset, the Synthetic Minority Over-sampling technique with Edited Nearest Neighbors (SMOTEENN) is used in this study. The HAM10000 dataset was benchmarked using a Convolutional Neural Network (CNN). Seven classes of HAM10000 include vascular skin lesions, benign keratosis, actinic keratosis, melanoma, dermatofibroma, and melanocytic nevi. A centralized method yields an accuracy of 99.39%, and f1-score, precision, and recall of 99.00%. A simulated Federated learning with three clients, ten rounds, and thirty training epochs produced 93.00% precision, 92.00% recall, 92.00% f1-score, and 91.80% accuracy, respectively. At the same time, an increase to four clients and thirty training epochs produced an accuracy, recall, precision, and f1-score of 97.00% with ten rounds.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100667"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973363","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}

引用次数: 0

Integrating feature fusion with hybrid optimization for multiple sclerosis MRI classification 融合特征融合与混合优化的多发性硬化MRI分类

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.array.2025.100677

Nandini Anam , Sharief Basha S , Chiranji Lal Chowdhary

引用次数: 0

Ret-UNet: Enhancing medical image segmentation with self-retention Ret-UNet：增强医学图像的自保留分割

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.array.2025.100653

Tianjun Guo, Weixin Zhao, Jian Peng

引用次数: 0

Exploring and identifying fine-grained accessibility issues in app store using fine-tuned deep learning 使用微调深度学习探索和识别应用商店中的细粒度可访问性问题

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.array.2025.100572

Mumrez Khan , Zhixiao Wang , Javed Ali Khan , Nek Dil Khan

{"title":"Exploring and identifying fine-grained accessibility issues in app store using fine-tuned deep learning","authors":"Mumrez Khan , Zhixiao Wang , Javed Ali Khan , Nek Dil Khan","doi":"10.1016/j.array.2025.100572","DOIUrl":"10.1016/j.array.2025.100572","url":null,"abstract":"<div><div>The Apple App Store (AAS) allows users to provide feedback on applications, offering developers insights into improving software performance. Researchers have utilized this feedback for software evolution activities, including features, issues, and nonfunctional requirements. However, end-user feedback has not been explored to identify accessibility-related challenges. This study proposes an automated approach to detect and classify accessibility issues by analyzing end-user reviews in the AAS. We crawled 178667 user reviews from 85 apps across 18 categories to represent a diverse sample. We developed a coding guideline to identify common accessibility issues, including Navigation and Interaction Problems (NAV), Input and Control Issues (INPUT), Compatibility with Assistive Technologies (CAT), Audio and visual accessibility issues (AUDIOVISUAL), and UI Accessibility Issues (UI). We manually annotated reviews using coding guidelines and content analysis to create a labeled dataset for training and evaluating deep learning(DL) algorithms to detect accessibility in user comments and classify them into categories. The experiments showed that fine-tuned DL classifiers achieved high accuracy in detecting accessibility and classifying them into specific types. For binary classification, the CNN classifier achieved 93% precision, while LSTM, BiLSTM, GRU, and BiGRU achieved accuracies from 76% to 87%. In fine-grained classification, CNN performed better with 97% accuracy, followed by BiGRU and BiLSTM at 96%. The BiLSTM and LSTM models demonstrated strong performance, with accuracies of 96% and 95%. These results show the potential of automated methods to improve identification of accessibility challenges, helping developers address these issues effectively and enhance user experience.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100572"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788379","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}

引用次数: 0

From ideation to execution: Unleashing the power of generative AI in modern digital marketing and customer engagement- A systematic literature review and case study 从构思到执行：在现代数字营销和客户参与中释放生成人工智能的力量——系统的文献回顾和案例研究

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2025-12-05 DOI: 10.1016/j.array.2025.100630

Sayeed Salih , Omayma Husain , Refan Mohamed Almohamedh , Hayfaa tajelsier , Aisha Hassan Abdalla Hashim , Hashim Elshafie , Abdelwahed Motwakel

{"title":"From ideation to execution: Unleashing the power of generative AI in modern digital marketing and customer engagement- A systematic literature review and case study","authors":"Sayeed Salih , Omayma Husain , Refan Mohamed Almohamedh , Hayfaa tajelsier , Aisha Hassan Abdalla Hashim , Hashim Elshafie , Abdelwahed Motwakel","doi":"10.1016/j.array.2025.100630","DOIUrl":"10.1016/j.array.2025.100630","url":null,"abstract":"<div><div>Generative Artificial Intelligence (GAI) is revolutionizing digital marketing by auto-content creation, personalized customer experience, and data-driven decisions. This study conducts a systematic literature review and case study analysis to explore GAI applications, benefits, and challenges in modern digital marketing. Drawing on an extensive analysis of academic journals and industry publications, the current research examines leading GAI software such as ChatGPT, DALL-E, MidJourney, Jasper.ai, and Synthesia based on how they aid in content creation, visual design, and video production. The research also provides real-world case studies in multiple industries, such as retail and fashion, food and beverages, and travel and tourism. The case findings illustrated how GAI augments marketing automation, facilitates customer engagement, and amplifies brand engagement, resulting in greater customer satisfaction, higher conversion rates, and better campaign performance. Although it has several benefits, the adoption of GAI is hampered by several critical barriers, such as data privacy, ethical risks, worker resistance, quality control issues, and infrastructure constraints. This research pinpoints these essential challenges and offers practical solutions. It provides actionable insights for businesses seeking to leverage GAI for competitive advantage in the evolving digital landscape by bridging the gap between theory and practice. The findings contribute to the growing discourse on AI-driven marketing strategies and lay the foundation for future research on GAI's long-term impact on consumer engagement and brand loyalty.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100630"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921252","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}

引用次数: 0

Weighted neural network for imbalanced information with undersampling 欠采样不平衡信息的加权神经网络

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2025-12-29 DOI: 10.1016/j.array.2025.100668

Simegnew Yihunie Alaba , Nahian A. Siddique , Emrah Benli , Dominik Enns , Yuichi Motai

引用次数: 0

Corrosion detection, localization, and severity estimation on gas pipeline surfaces using transfer learning and image processing 基于迁移学习和图像处理的天然气管道表面腐蚀检测、定位和严重程度估计

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.array.2026.100698

Danial Alvani , Gholamreza Akbarizadeh , Khalil Gheisari , Abdolnabi Kosarian

{"title":"Corrosion detection, localization, and severity estimation on gas pipeline surfaces using transfer learning and image processing","authors":"Danial Alvani , Gholamreza Akbarizadeh , Khalil Gheisari , Abdolnabi Kosarian","doi":"10.1016/j.array.2026.100698","DOIUrl":"10.1016/j.array.2026.100698","url":null,"abstract":"<div><div>This study presents a two-stage framework for corrosion detection, localization, and severity estimation in gas-pipeline imagery. In Stage 1, an optimized VGG16 backbone with squeeze-and-excitation attention and global average pooling is employed. Deep features extracted from 128 × 128 RGB images resized from the original inputs are classified using an XGBoost head, which enhances robustness under class imbalance. On a dataset of 10,000 images (6,500 corrosion; 3,500 non-corrosion), the detector achieves 98.47% accuracy, 98.18% precision, 99.49% recall, 98.83% F1-score, and 0.99 for both ROC AUC and P–R AUC. In Stage 2, operating at native resolution, contrast is normalized in Lab space using CLAHE and denoised with bilateral filtering. Three corrosion-sensitive cues—the Lab a∗ channel, a stabilized red–green (RG) index, and an HSV-based rust map—are fused into a score map and morphologically refined to produce masks. Localization evaluation on an annotated subset validates Stage-2 performance, achieving mean IoU and Dice scores of 0.742 and 0.842, respectively. After localization, a descriptor suite comprising GLCM, LBP, wavelet energies, color/score statistics, geometric features, and Zernike moments is computed. Corrosion severity is estimated as an area-coverage proxy (percentage of corroded pixels), rather than a measure of material loss or depth, and categorized as mild, moderate, or severe using calibrated area-ratio thresholds (T<sub>1</sub> = 1.5%, T<sub>2</sub> = 6.0%). A confidence score combines fused-score mean, mask coverage, and GLCM homogeneity; a priority index, defined as their product, supports maintenance prioritization. The framework achieves detection, localization, and severity estimation, offering practical decision support for gas-pipeline integrity inspection and maintenance planning.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100698"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184707","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}

引用次数: 0

An enhanced diabetic retinopathy blindness detection using deep learning and image preprocessing techniques 基于深度学习和图像预处理技术的增强糖尿病视网膜病变失明检测

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-02-21 DOI: 10.1016/j.array.2026.100716

Shourya Tyagi , Vineet Verma , Meenu Vijarania , Swati Gupta , Joseph Bamidele Awotunde , Abidemi Emmanuel Adeniyi , Oluwasegun Julius Aroba

{"title":"An enhanced diabetic retinopathy blindness detection using deep learning and image preprocessing techniques","authors":"Shourya Tyagi , Vineet Verma , Meenu Vijarania , Swati Gupta , Joseph Bamidele Awotunde , Abidemi Emmanuel Adeniyi , Oluwasegun Julius Aroba","doi":"10.1016/j.array.2026.100716","DOIUrl":"10.1016/j.array.2026.100716","url":null,"abstract":"<div><div>Diabetic retinopathy (DR) is a gradual and severe consequence of diabetes that increases the risk of visual impairment and blindness globally. Early and precise identification of DR is crucial for successful care, but availability to specialist diagnostic tools is restricted, especially in low-resource settings. This study aims to improve the automated identification of diabetic retinopathy using cutting-edge deep learning models—specifically ViT, CLIP, BLIP, ViLT, Florence-2, and ResNet50—which are applied to the APTOS Diabetic Retinopathy dataset. Diabetic Retinopathy (DR) diagnosis is often hindered by poor image quality and subtle differences across varying severity levels. The grading of DR is typically categorized into five stages: No DR, Mild, Moderate, Severe, and Proliferative, as defined by the International Clinical Diabetic Retinopathy Disease Severity Scale. To address artifacts such as uneven illumination, we employ Gaussian preprocessing to smooth noise and Wavelet transforms to enhance edge details. This study evaluates the impact of Gaussian and Wavelet preprocessing techniques on the performance of Vision-Language Models (e.g., CLIP, BLIP) and CNNs (e.g., RESNET50). Results indicate that Gaussian Filtering significantly enhances classification accuracy, yielding a 37% improvement for RESNET50 and 36% for BLIP compared to the baseline. This study demonstrates that appropriate preprocessing is critical for maximizing the potential of deep learning in medical imaging. Specifically, Gaussian Filtering proved superior to Wavelet techniques, offering substantial accuracy gains for models like BLIP and RESNET50. Future work will explore hybrid preprocessing pipelines to further distinguish between the subtle early stages of the disease.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100716"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395520","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}

引用次数: 0

MCDA4AI: A framework for managing n>2 criteria problems in decisions about artificial intelligence MCDA4AI：一个用于管理人工智能决策中2个标准问题的框架

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-02-21 DOI: 10.1016/j.array.2026.100723

Olusola Olabanjo , Phillip Honenberger

{"title":"MCDA4AI: A framework for managing n>2 criteria problems in decisions about artificial intelligence","authors":"Olusola Olabanjo , Phillip Honenberger","doi":"10.1016/j.array.2026.100723","DOIUrl":"10.1016/j.array.2026.100723","url":null,"abstract":"<div><div>Human decisions about the design, deployment, and governance of AI systems are increasingly complex, involving multiple desiderata such as accuracy, fairness, transparency, cost, and environmental impact. This paper proposes a novel and scalable framework for managing this complexity through application of resources from the field of Multi-Criteria Decision Analysis (MCDA). After an introduction to the “<span><math><mrow><mi>n</mi><mo>></mo><mn>2</mn></mrow></math></span> Criteria Problem” in decision-making about AI, an introduction to MCDA methods, and a review of prior applications of MCDA to decision support in decisions involving AI, we lay out our own novel and scalable framework for applying MCDA to such decisions. We illustrate the framework through application to four simulated scenarios involving a medical institution’s choice among AI-powered diagnostic tools with varying performance on accuracy, fairness, transparency, and cost. We apply five multi-criteria decision analysis (MCDA) methods — namely, the Weighted Sum Model (WSM), the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the VIKOR method (VlseKriterijumska Optimizacija I Kompromisno Resenje), and the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) — to rank ten AI models under four distinct weight configurations reflecting realistic deployment contexts. Sensitivity analyses are then conducted to examine how variations in criterion weight assignments affect model rankings across methods and scenarios. In concluding sections we discuss the scalability of these techniques for design, deployment, and policy decisions involving AI, as well as the likely limitations of MCDA methods for decision support involving AI.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100723"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396120","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}

引用次数: 0

An automated multi-scale and multi-contextual MobileNetv3 for malware detection based on IoT 基于物联网的自动多尺度和多上下文MobileNetv3恶意软件检测

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.array.2026.100681

Sidra Javed , Guowei Wu , Hamza Javed , Osama A. Khashan , Haseeb Hassan , Anwar Ghani

{"title":"An automated multi-scale and multi-contextual MobileNetv3 for malware detection based on IoT","authors":"Sidra Javed , Guowei Wu , Hamza Javed , Osama A. Khashan , Haseeb Hassan , Anwar Ghani","doi":"10.1016/j.array.2026.100681","DOIUrl":"10.1016/j.array.2026.100681","url":null,"abstract":"<div><div>Malware detection is a crucial aspect of cybersecurity, aimed at identifying and mitigating malicious software that poses threats to systems and networks. Traditional malware detection methods face challenges in terms of both detection accuracy and computational cost, as deep learning models can be resource-intensive and difficult to deploy in real-time environments. This paper introduces the novel MSMC-MobileNet (Multi-Scale and Multi-Contextual MobileNet) malware detection and classification model, designed to address the challenges of accuracy and computational cost. First, MobileNetv3 is used to extract features from the dataset. To enhance feature extraction, the SE (Squeeze-and-Excitation) module is integrated, focusing on the region of interest using an attention mechanism. The multiscale and multicontextual features are extracted using the ASPP (Atrous Spatial Pyramid Pooling) and FPP (Feature Pyramid Pooling) modules. Channel-wise pruning is applied to the ASPP and FPP modules, reducing computational cost. The model is evaluated on the publicly available Malimg and MaleVis datasets. The proposed MSMC-MobileNet model achieves impressive performance with 92.37% accuracy, 96.54% precision, 95.84% recall, 95.47% F1 score, and 98.59% AUC on the Malimg dataset. On the MaleVis dataset, the model yields 95.08% accuracy, 98.33% precision, 97.9% recall, 98.15% F1 score, and 96.98% AUC. When both datasets are combined, the MSMC-MobileNet achieves 98.79% accuracy, 99.84% precision, 99.73% recall, 99.89% F1 score, and 1.00 AUC. Despite its high accuracy, the model remains computationally efficient, outperforming state-of-the-art methods in both detection performance and computational cost.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100681"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034470","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}

引用次数: 0

Array最新文献