IF 2.3

Array Pub Date : 2025-04-02 DOI: 10.1016/j.array.2025.100388

Md. Ishan Arefin Hossain, Jareen Anjom, Rashik Iram Chowdhury

{"title":"Towards walkable footpath detection for the visually impaired on Bangladeshi roads with smartphones using deep edge intelligence","authors":"Md. Ishan Arefin Hossain, Jareen Anjom, Rashik Iram Chowdhury","doi":"10.1016/j.array.2025.100388","DOIUrl":"10.1016/j.array.2025.100388","url":null,"abstract":"<div><div>One of the ongoing prevalent issues is the challenge faced by visually impaired people when crossing footpaths, especially in a densely populated geographic location such as Dhaka city in Bangladesh, where numerous accidents take place that primarily result in the demise of the affected individuals. Visually impaired people find themselves in precarious situations while navigating through these footpaths. So, having an accessible edge device like a smartphone capable of predicting walkable footpaths by detecting obstacles in real-time is a blessing. However, little work has been done on efficient obstacle detection on footpaths and their corresponding distance prediction in real-time. To address this burning issue, a U-Net-based lightweight deep learning model called QPULM along with an obstacle distance measurement technique called SODD have been proposed in this research, which is utilized in an Android application to detect walkable footpath by avoiding the obstacles via the image captured and to broadcast the directions of the walkable paths using audio feedback. The proposed novel lightweight model at the Edge showed an excellent accuracy of 99.37% with a faster prediction time in milliseconds in real-time, which is significantly better and more efficient than the existing related solutions.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100388"},"PeriodicalIF":2.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SEAGET: Seasonal and active hours guided graph enhanced transformer for the next POI recommendation 希捷：季节性和有功时数引导图增强变压器的下一个POI建议

IF 2.3

Array Pub Date : 2025-04-02 DOI: 10.1016/j.array.2025.100385

Alif Al Hasan, Md. Musfique Anwar

{"title":"SEAGET: Seasonal and active hours guided graph enhanced transformer for the next POI recommendation","authors":"Alif Al Hasan, Md. Musfique Anwar","doi":"10.1016/j.array.2025.100385","DOIUrl":"10.1016/j.array.2025.100385","url":null,"abstract":"<div><div>One of the most important challenges for improving personalized services in industries like tourism is predicting users’ near-future movements based on prior behavior and current circumstances. Next POI (Point of Interest) recommendation is essential for helping users and service providers by providing personalized recommendations. The intricacy of this work, however, stems from the requirement to take into consideration several variables at once, such as user preferences, time contexts, and geographic locations. POI selection is also greatly influenced by elements like a POI’s operational status during desired visit times, desirability for visiting during particular seasons, and its dynamic popularity over time. POI popularity is mostly determined by check-in frequency in recent studies, ignoring visitor volumes, operational constraints, and temporal dynamics. These restrictions result in recommendations that are less than ideal and do not take into account actual circumstances. We propose the Seasonal and Active hours-guided Graph-Enhanced Transformer (SEAGET) model as a solution to these problems. By integrating variations in the seasons, operational status, and temporal dynamics into a graph-enhanced transformer framework, SEAGET capitalizes on redefined POI popularity. This invention gives more accurate and context-aware next POI predictions, with potential applications for optimizing tourist experiences and enhancing location-based services in the tourism industry.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100385"},"PeriodicalIF":2.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STATOS: A portable tool for secure malware analysis and sample acquisition in low resource environments STATOS：在低资源环境中用于安全恶意软件分析和样本采集的便携式工具

IF 2.3

Array Pub Date : 2025-04-01 DOI: 10.1016/j.array.2025.100391

Alexander Cameron , Abu Alam , Nasreen Anjum , Javed Ali Khan , Alexios Mylonas

{"title":"STATOS: A portable tool for secure malware analysis and sample acquisition in low resource environments","authors":"Alexander Cameron , Abu Alam , Nasreen Anjum , Javed Ali Khan , Alexios Mylonas","doi":"10.1016/j.array.2025.100391","DOIUrl":"10.1016/j.array.2025.100391","url":null,"abstract":"<div><div>Malware poses a significant security threat to organisations worldwide, particularly in environments with limited resources. Static analysis has emerged as a crucial technique for gaining insights into malware, but it often requires specialised hardware and software, which can be a barrier for organisations facing financial or supply constraints. To address these challenges, this study presents a Static-Analysis Operating System (StatOS), a portable Linux derivative operating system designed for static malware analysis. StatOS can be executed from a USB device, allowing organisations to perform efficient, user-friendly, and secure malware analysis even on underpowered hardware. This study contributes a practical solution to field analysis of malware within low-resource environments, providing a model and requirement data for future developments in portable cybersecurity tools. The tool was validated through a combination of expert feedback using the Delphi method and security assessments, including Monte-Carlo simulations and Common Vulnerabilities and Exposures (CVE) evaluations. Results indicate that StatOS meets and exceeds key performance requirements, with 100% of surveyed cyber specialists agreeing on its effectiveness, and 80% indicating they would use StatOS in forensic investigations.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100391"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications 制造质量保证的机器学习算法：性能指标和应用的系统审查

IF 2.3

Array Pub Date : 2025-03-25 DOI: 10.1016/j.array.2025.100393

Ashfakul Karim Kausik , Adib Bin Rashid , Ramisha Fariha Baki , Md Mifthahul Jannat Maktum

{"title":"Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications","authors":"Ashfakul Karim Kausik , Adib Bin Rashid , Ramisha Fariha Baki , Md Mifthahul Jannat Maktum","doi":"10.1016/j.array.2025.100393","DOIUrl":"10.1016/j.array.2025.100393","url":null,"abstract":"<div><div>Adopting Machine Learning (ML) in manufacturing quality assurance (QA) has accelerated with Industry 4.0, enabling automated defect detection, predictive maintenance, and real-time process optimization. However, selecting the most effective ML model remains challenging due to performance variability, scalability constraints, and inconsistent evaluation metrics across manufacturing sectors. This systematic review analyzes over 300 peer-reviewed studies over the last two decades (mostly analyzing the recent works) to evaluate the effectiveness of widely used ML algorithms—Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forests (RFs), Decision Trees (DTs), and K-Nearest Neighbors (KNN)—in QA applications. Performance metrics include accuracy, precision, speed, recall, computational efficiency, scalability, and real-time processing capabilities. Findings reveal that ANNs outperform other models in image-based defect detection, while SVMs and RFs excel in predictive maintenance and process parameter optimization. DTs provide better interpretability for process control, and KNN is effective for small-scale QA implementations. In specific case scenarios, RF models showed particular strength in handling high-dimensional sensor data in fault detection in manufacturing quality assurance operations. The study presents a comparative assessment framework, guiding algorithm selection based on industry-specific requirements and operational constraints. This review provides the latest implementation of ML in QA along with quantitative evidence on which algorithm offers the most optimization in specific industrial settings, which would help in algorithm selection in manufacturing quality assurance in future for both researchers and industrial experts. Also, it offers an overview of the major and minor algorithms based on their performance metrics.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100393"},"PeriodicalIF":2.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and application of piano accompanying system based on New Fingerprint algorithm 基于新型指纹算法的钢琴伴奏系统的开发与应用

IF 2.3

Array Pub Date : 2025-03-22 DOI: 10.1016/j.array.2025.100387

Cheng Lv

{"title":"Development and application of piano accompanying system based on New Fingerprint algorithm","authors":"Cheng Lv","doi":"10.1016/j.array.2025.100387","DOIUrl":"10.1016/j.array.2025.100387","url":null,"abstract":"<div><div>In recent years, due to the rapid development of online teaching, intelligent piano accompaniment has emerged. However, the relevant applications on the market are relatively low in intelligence. It is difficult to identify the pieces played by the user, which increases operational complexity. To solve the audio retrieval when users are learning online piano, a New Fingerprint (NF) is designed based on the button behavior of users while playing the piano. Taking NF algorithm as the core, combined with music signal analysis technology, automatic piano transcription technology and alignment technology, an intelligent piano audio analysis module is constructed. Finally, a complete piano accompaniment system is established based on spectrum reading mode, practice mode, and piano performance scoring mode. The research results showed that the average precision of NF algorithm was 98.76 %, the average recall was 87.84 %, and the average F1 value was 98.53 %. The average accuracy value of the piano accompaniment system based on NF algorithm was 97.53 %, and the accuracy N value was 1.2. In practical application, 88 % of users were very willing to learn from the piano accompaniment system, and 45 % of users were very satisfied, and 28 % of users were relatively satisfied. To sum up, the proposed NF algorithm has excellent performance. The piano accompaniment system based on NF algorithm is suitable for actual piano learning.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100387"},"PeriodicalIF":2.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An explainable AI-based approach for predicting undergraduate students academic performance 一种可解释的基于人工智能的预测大学生学业成绩的方法

IF 2.3

Array Pub Date : 2025-03-15 DOI: 10.1016/j.array.2025.100384

Fatema Tuz Johora , Md Nahid Hasan , Aditya Rajbongshi , Md Ashrafuzzaman , Farzana Akter

{"title":"An explainable AI-based approach for predicting undergraduate students academic performance","authors":"Fatema Tuz Johora , Md Nahid Hasan , Aditya Rajbongshi , Md Ashrafuzzaman , Farzana Akter","doi":"10.1016/j.array.2025.100384","DOIUrl":"10.1016/j.array.2025.100384","url":null,"abstract":"<div><div>The accurate prediction of students' academic achievement has garnered considerable attention in the research community due to its importance in understanding students' progress and assisting them in achieving success. This study presents a novel approach for predicting undergraduate student's performance in the context of Bangladesh. The dataset contains 872 student records from multiple institutions. Initially the dataset was produced utilizing data-preprocessing techniques such as one-hot encoding, column remaining, and managing missing values. SMOTE (Synthetic Minority Oversampling Technique) and normalizing algorithms were employed to attain data balance and feature scaling, respectively. Afterwards, a total of seven distinct machine learning (ML) classifiers, with hyperparameter tuning, were employed to train and test in order to achieve the prediction of students' academic performance. Furthermore, a custom stacking ensemble classifier was utilized, which attained an accuracy of 86.38 %. This classifier outperformed the machine learning classifiers based on the four performance evaluation metrics. Two eXplainable Artificial Intelligence (XAI) algorithms, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), were integrated to provide a comprehensible prediction of the best model and determine the significant factors. This approach provided transparency, fairness and reliability on prediction that improved student performance in the classroom and anticipation.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100384"},"PeriodicalIF":2.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAB-RSP: Data pricing based on Stackelberg game in MCS 基于Stackelberg博弈的MCS数据定价

IF 2.3

Array Pub Date : 2025-03-12 DOI: 10.1016/j.array.2025.100380

Yongjiao Sun, Xueyan Ma, Anrui Han

引用次数: 0

Investigating diverse loss functions for myocardium ring segmentation in Cardiac Magnetic Resonance images using fuzzy pooling 利用模糊池化方法研究不同损失函数在心脏磁共振图像心肌环分割中的应用

IF 2.3

Array Pub Date : 2025-03-08 DOI: 10.1016/j.array.2025.100382

Riandini , Eko Mulyanto Yuniarno , I. Ketut Eddy Purnama , Masayoshi Aritsugi , Mauridhi Hery Purnomo

{"title":"Investigating diverse loss functions for myocardium ring segmentation in Cardiac Magnetic Resonance images using fuzzy pooling","authors":"Riandini , Eko Mulyanto Yuniarno , I. Ketut Eddy Purnama , Masayoshi Aritsugi , Mauridhi Hery Purnomo","doi":"10.1016/j.array.2025.100382","DOIUrl":"10.1016/j.array.2025.100382","url":null,"abstract":"<div><div>Cardiovascular disease, a leading cause of mortality, underscores the critical need for precise diagnostic methods. Cardiac Magnetic Resonance (CMR) imaging is pivotal for diagnosing heart conditions, yet accurately segmenting the myocardium ring (MYO) remains a significant challenge. This study enhances the U-Net model with fuzzy pooling and evaluates the effects of different loss functions: cross-entropy loss, which evaluates the disparity between predicted and actual probability distributions; focal loss, which tackles class imbalance by reducing the weight of easily classified examples; dice loss, which emphasizes the overlap between predicted and actual segments; Lovász-Softmax loss, which is optimized for Intersection over Union (IoU); and CrossLov, which merges cross-entropy and Lovász-softmax, using the ACDC 2017 dataset. Focal loss achieved the lowest train loss scores of 0.0011% at epoch 95 and 0.0012% at epoch 96. Cross-entropy showed high dice scores but did not excel in boundary delineation. Dice loss showed moderate performance. Lovász-softmax excelled in IoU with an average score of 90.68%, while CrossLov exhibited balanced performance, achieving robust general segmentation results with an IoU score of 93.691%. Additionally, CrossLov attained the lowest Hausdorff Distance (HD), with an overall score of 2.816 mm and 1.309 mm for the MYO, indicating superior boundary precision. These findings highlight the role of loss function selection used together with fuzzy pooling for enhancing the robustness and precision of MYO segmentation, thereby contributing to improved diagnostic accuracy in cardiovascular care.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100382"},"PeriodicalIF":2.3,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AggreGait: Automatic gait feature extraction for human age and gender classification with possible occlusion AggreGait：自动步态特征提取，用于可能闭塞的人类年龄和性别分类

IF 2.3

Array Pub Date : 2025-03-05 DOI: 10.1016/j.array.2025.100379

Timilehin B. Aderinola , Tee Connie , Thian Song Ong , Andrew Beng Jin Teoh , Michael Kah Ong Goh

{"title":"AggreGait: Automatic gait feature extraction for human age and gender classification with possible occlusion","authors":"Timilehin B. Aderinola , Tee Connie , Thian Song Ong , Andrew Beng Jin Teoh , Michael Kah Ong Goh","doi":"10.1016/j.array.2025.100379","DOIUrl":"10.1016/j.array.2025.100379","url":null,"abstract":"<div><div>The growing interest in smart surveillance and automated public access control necessitates robust age and gender classification (AGC) techniques that can operate effectively in unconstrained environments. While model-based gait obtained via pose estimation offers a promising approach, its performance can be hindered by occlusions commonly encountered in real-world videos. In this work, we propose a custom Graph Neural Network (GNN) architecture, AggreGait, for robust AGC under occlusions. AggreGait integrates upper and lower body features with whole-body information for age and gender prediction. We train AggreGait on pose sequences from the gait-in-the-wild (GITW) dataset, simulating different types of occlusions. AggreGait performs comparably to existing methods, achieving an overall accuracy of 91% in unobstructed conditions. Notably, AggreGait maintains reasonable accuracy using only upper limb (or upper and lower limb) features, suggesting its potential for real-time surveillance applications despite occlusions. This work paves the way for practical gait-based AGC in unconstrained environments, enhancing the effectiveness of surveillance systems and facilitating automated access control.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100379"},"PeriodicalIF":2.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GuardianAI: Privacy-preserving federated anomaly detection with differential privacy 基于差分隐私保护的联邦异常检测

IF 2.3

Array Pub Date : 2025-03-05 DOI: 10.1016/j.array.2025.100381

Abdulatif Alabdulatif

{"title":"GuardianAI: Privacy-preserving federated anomaly detection with differential privacy","authors":"Abdulatif Alabdulatif","doi":"10.1016/j.array.2025.100381","DOIUrl":"10.1016/j.array.2025.100381","url":null,"abstract":"<div><div>In the rapidly evolving landscape of cybersecurity, privacy-preserving anomaly detection has become crucial, particularly with the rise of sophisticated privacy attacks in distributed learning systems. Traditional centralized anomaly detection systems face challenges related to data privacy and scalability, making federated learning a promising alternative. However, federated learning models remain vulnerable to several privacy attacks, such as inference attacks, model inversion, and gradient leakage. To address these threats, this paper presents GuardianAI, a novel federated anomaly detection framework that incorporates advanced differential privacy techniques, including Gaussian noise addition and secure aggregation protocols, specifically designed to mitigate these attacks. GuardianAI aims to enhance privacy while maintaining high detection accuracy across distributed nodes. The framework effectively prevents attackers from extracting sensitive data from model updates by introducing noise to the gradients and securely aggregating updates across nodes. Experimental results show that GuardianAI achieves a testing accuracy of 99.8 %, outperforming other models like Logistic Regression, SVM, and Random Forest, while robustly defending against common privacy threats. These results demonstrate the practical potential of GuardianAI for secure deployment in various network environments, ensuring privacy without compromising performance.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"26 ","pages":"Article 100381"},"PeriodicalIF":2.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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