IF 2.3

Array Pub Date : 2025-07-03 DOI: 10.1016/j.array.2025.100443

Giovanni Mazzuto, Ilaria Pietrangeli, Marco Ortenzi, Filippo Emanuele Ciarapica, Maurizio Bevilacqua

{"title":"Leveraging Digital Twin for operational resilience in the oil and gas industry","authors":"Giovanni Mazzuto, Ilaria Pietrangeli, Marco Ortenzi, Filippo Emanuele Ciarapica, Maurizio Bevilacqua","doi":"10.1016/j.array.2025.100443","DOIUrl":"10.1016/j.array.2025.100443","url":null,"abstract":"<div><h3>Motivation</h3><div>The oil and gas industry is a highly complex and interconnected environments, where system failures or cyber threats can lead to severe operational and safety risks. Digital Twin enables real-time monitoring and predictive analysis to enhance resilience and decision-making, and existing studies often lack a comprehensive integration of system components and interdependencies, limiting the effectiveness of DT applications in critical scenarios.</div></div><div><h3>Methodology</h3><div>This paper presents the development of a DT for an experimental oil and gas transportation system at Università Politecnica delle Marche. The DT integrates the same digital PID controllers used in the real plant to replicate system dynamics and predict anomalous behaviours. Developed within the EU- and MUR-funded RESIST project, the DT is part of a broader digital environment that includes both the plant and operator models, aimed at enhancing system resilience.</div><div>A Gradient Boosted Tree algorithm, selected through comparative analysis, proved the most reliable technique for predictive modelling. The model was validated using real data, focusing on two key components: the ejector-pump system and the vertical tank.</div></div><div><h3>Results</h3><div>The findings demonstrate the potential of DTs as decision-support tools for plant operators, enabling proactive risk management and enhanced operational resilience despite, the presence of air phase variability introduces fluctuations in some critical conditions, requiring further data acquisition and refinement.</div></div><div><h3>Future work</h3><div>Future improvements will focus on refining the air flow modelling and expanding the dataset to improve predictive performance in extreme conditions providing an efficient anomaly detection system based on the DT prediction.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100443"},"PeriodicalIF":2.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570994","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

Metric-based defect prediction from class diagram 来自类图的基于度量的缺陷预测

IF 2.3

Array Pub Date : 2025-07-01 DOI: 10.1016/j.array.2025.100438

Batnyam Battulga, Lkhamrolom Tsoodol, Enkhzol Dovdon, Naranchimeg Bold, Oyun-Erdene Namsrai

{"title":"Metric-based defect prediction from class diagram","authors":"Batnyam Battulga, Lkhamrolom Tsoodol, Enkhzol Dovdon, Naranchimeg Bold, Oyun-Erdene Namsrai","doi":"10.1016/j.array.2025.100438","DOIUrl":"10.1016/j.array.2025.100438","url":null,"abstract":"<div><div>A software defect refers to a fault, failure, or error in software. With the rapid development and increasing reliance on software products, it is essential to identify these defects as early and easily as possible, given the efforts and budget invested in their creation and maintenance. In the literature, various approaches such as machine learning (ML) and deep learning (DL), have been proposed and proven effective in detecting defects in source code during the implementation or testing phases of the software development life cycle (SDLC). A promising approach is crucial for predicting defects at earlier stages of the SDLC, particularly during the design phase, with the goal of enhancing software quality while reducing time, effort, and costs. Meanwhile, software metrics provide a quantifiable way to analyze the software, making it easier to identify defects. Many researchers have leveraged these metrics to predict defects using ML and DL methods, achieving state-of-the-art performance. The objective of this paper is to present a novel approach to predict defects in class diagram (i.e., at design stage) using ML and DL with software metrics. Due to a lack of defect datasets extracted from class diagram, firstly, we created a model-based metric dataset using reverse engineering from a code-based dataset. Then, we apply various ML and DL techniques to the newly created dataset to predict defects in classes by classifying them as either defective or clean. The study utilizes a large dataset called the Unified Bug Dataset, which comprises five publicly available sub-datasets. We compare ML and DL models in terms of accuracy, precision, recall, F-measure, AUC and provide a performance comparison against code-based methods. Finally, we conducted a cross-dataset experiment to evaluate the generalizability of our approach.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100438"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536200","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

Proactive detection of cyber-physical grid attacks: A pre-attack phase identification and analysis using anomaly-based machine learning models 网络物理网格攻击的主动检测：使用基于异常的机器学习模型进行攻击前阶段识别和分析

IF 2.3

Array Pub Date : 2025-06-30 DOI: 10.1016/j.array.2025.100441

Shaharier Kabir, Nasif Hannan, Abu Shufian, Md Saniat Rahman Zishan

{"title":"Proactive detection of cyber-physical grid attacks: A pre-attack phase identification and analysis using anomaly-based machine learning models","authors":"Shaharier Kabir, Nasif Hannan, Abu Shufian, Md Saniat Rahman Zishan","doi":"10.1016/j.array.2025.100441","DOIUrl":"10.1016/j.array.2025.100441","url":null,"abstract":"<div><div>Cyber-physical power systems (CPPS), such as smart grids, are essential to modern infrastructure but are increasingly vulnerable to sophisticated cyber-attacks. Traditional security approaches often detect threats only after damage occurs, underscoring the need for proactive solutions. This research introduces a proactive anomaly detection framework that focuses on identifying pre-attack behaviors—an underexplored area in current literature. We investigate the effectiveness of machine learning models for early detection of cyber-attacks in smart grids, emphasizing the identification of pre-attack phases. Several unsupervised learning algorithms were applied to time series data simulating normal operations and attack scenarios. Models include Isolation Forest, K-Means Clustering, DBSCAN, and One-Class SVM. Among them, Isolation Forest outperformed others, achieving 100 % accuracy, 100 % sensitivity, and an AUC of 1.0. DBSCAN followed with an AUC of 0.79 and 97.3 % accuracy but showed a higher false positive rate. A key contribution of this study is the use of anomaly scores from Isolation Forest to detect subtle deviations before full-scale attacks. A threshold of 0.3 effectively balanced detection and false positives, capturing multiple pre-attack phases. A higher threshold (0.97) reduced false positives but missed early warning signs, indicating that some attacks may begin abruptly. These findings demonstrate the potential of machine learning, particularly Isolation Forest, in enhancing CPPS security by enabling early warnings and minimizing cyber-attack impact. The proposed framework lays the foundation for proactive threat detection strategies in smart grids and other critical infrastructure systems.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100441"},"PeriodicalIF":2.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536179","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

Cybersecurity of remote work migration: A study on the VPN security landscape post Covid-19 outbreak 远程工作迁移的网络安全：新冠疫情爆发后VPN安全格局研究

IF 2.3

Array Pub Date : 2025-06-28 DOI: 10.1016/j.array.2025.100437

Kushtrim Qollakaj, Lukas Einler Larsson, Suejb Memeti

{"title":"Cybersecurity of remote work migration: A study on the VPN security landscape post Covid-19 outbreak","authors":"Kushtrim Qollakaj, Lukas Einler Larsson, Suejb Memeti","doi":"10.1016/j.array.2025.100437","DOIUrl":"10.1016/j.array.2025.100437","url":null,"abstract":"<div><div>The Covid-19 pandemic led to an unprecedented reliance on Virtual Private Networks (VPNs) for remote work, exposing critical vulnerabilities in global cybersecurity infrastructures. As organizations rapidly transitioned to remote operations, many lacked the necessary security measures to protect their VPN systems, making them prime targets for cybercriminals. This study synthesizes findings from 106 studies (2020–2023) to analyze the evolution of VPN-targeted cyberattacks, the tactics employed by threat actors, and effective mitigation strategies.</div><div>Our analysis reveals that the widespread adoption of remote work triggered a 238% surge in VPN-targeted attacks between 2020 and 2022, as adversaries exploited vulnerabilities, misconfigurations, and inadequate security policies. Both independent cybercriminals and state-sponsored actors leveraged phishing, ransomware, and advanced persistent threats (APTs) to gain unauthorized access to corporate networks. In many cases, organizations struggled with outdated VPN protocols, weak authentication mechanisms, and insufficient network segmentation, allowing attackers to infiltrate systems with minimal resistance.</div><div>To address these challenges, we propose a VPN Hardening Framework incorporating strong authentication, robust encryption, secure configurations, and continuous monitoring, expected to significantly reduce breach risks and enhance VPN resilience in the post-pandemic era. Additionally, we highlight emerging cybersecurity trends, including the role of zero-trust architectures, quantum-resistant encryption, and AI-driven intrusion detection in fortifying VPN security against evolving threats.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100437"},"PeriodicalIF":2.3,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517670","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

Intelligent detection methods for miniature defects in metallic materials 金属材料微小缺陷的智能检测方法

IF 2.3

Array Pub Date : 2025-06-27 DOI: 10.1016/j.array.2025.100430

Chuan-Hao Liu , Wei-Lun Lin , Fan-Shuo Tseng

引用次数: 0

Neighborhood Information Aggregation and Multi-View Feature Extraction-Based Contrastive Graph Clustering 基于邻域信息聚合和多视图特征提取的对比图聚类

IF 2.3

Array Pub Date : 2025-06-25 DOI: 10.1016/j.array.2025.100427

Liulong Yao , Jinrong Cui , Yazi Xie , Chengli Sun

{"title":"Neighborhood Information Aggregation and Multi-View Feature Extraction-Based Contrastive Graph Clustering","authors":"Liulong Yao , Jinrong Cui , Yazi Xie , Chengli Sun","doi":"10.1016/j.array.2025.100427","DOIUrl":"10.1016/j.array.2025.100427","url":null,"abstract":"<div><div>In recent years, graph contrastive clustering has received increasing attention in the field of graph deep clustering and achieved very excellent performance. Although graph contrast clustering has shown significant results in this field, most of the existing methods rely on manually designed data enhancement strategies. While these strategies perform well on image data, they often tend to lead to semantic drift when used on graph-structured data, thus limiting the performance of the model. In addition, existing methods mainly rely on the original graph topology information and fail to fully utilize the neighborhood information hidden in the node attribute features. To address the above problems, we proposes a Neighborhood Information Aggregation and Multi-View Feature Extraction-Based Contrastive Graph Clustering (NIA-MVFE-CGC) framework, which improves the existing methods from the perspectives of network architecture, feature redundancy and neighborhood information. First, We directly use multiple multilayer perceptrons (MLPs) to generate multiple views instead of using data augmentation methods. Secondly we utilize mutual information to reduce the redundancy between feature dimensions. Then, we design a neighborhood information aggregation module for mining the neighborhood information relationships of the samples. This module not only considers the explicit structures in the data, but also generates a new neighborhood relationship graph by combining the learned potential relationship structures. In addition, we design a weight graph that allows the model to adaptively adjust the proximity between samples during the learning process. Extensive experiments on five benchmark datasets show that our proposed method outperforms most other clustering algorithms.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100427"},"PeriodicalIF":2.3,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548966","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

RiceKernelEngine: Benchmarking transfer learning models for microscopic images of rice kernel rice kernelengine：稻米显微图像的基准迁移学习模型

IF 2.3

Array Pub Date : 2025-06-24 DOI: 10.1016/j.array.2025.100429

Mehrab Islam Arnab , Anika Tabassum Nafisa , Md Tahsin , Md Monjor Morshed , Maksura Binte Rabbani Nuha , Md Sawkat Ali , Mahamudul Hasan , Maheen Islam , Taskeed Jabid , Mohammad Rifat Ahmmad Rashid , Mohammad Manzurul Islam

{"title":"RiceKernelEngine: Benchmarking transfer learning models for microscopic images of rice kernel","authors":"Mehrab Islam Arnab , Anika Tabassum Nafisa , Md Tahsin , Md Monjor Morshed , Maksura Binte Rabbani Nuha , Md Sawkat Ali , Mahamudul Hasan , Maheen Islam , Taskeed Jabid , Mohammad Rifat Ahmmad Rashid , Mohammad Manzurul Islam","doi":"10.1016/j.array.2025.100429","DOIUrl":"10.1016/j.array.2025.100429","url":null,"abstract":"<div><div>Rice serves as a principal dietary staple food nationwide. The high demand for this cereal grain has led to extensive research, resulting in the frequent development of new grain varieties. Proper cultivation time, region, and nurturing techniques specific to each rice variety can significantly boost production. However, the subtle differences distinguishing one rice grain from another make the classification of rice kernels a resource-intensive and exhaustive task. Our proposed approach explores the potential for automated classification of five different rice varieties developed in Bangladesh: BINADHAN-8, BINADHAN-23, BRRI-67, BRRI-74, and BRRI-102. These varieties are highly similar in appearance, and state-of-the-art transfer learning models have been employed to assess their practical feasibility. The dataset comprises 3155 images, with classes containing 605, 578, 642, 695, and 635 images, respectively. The implemented models—VGG19, MobileNetV3, EfficientNet, and ConvNeXt—achieved accuracies of 85%, 94%, 96%, and 87%, respectively, with an average accuracy of 90.50%. The results indicate the practical applicability of these models in this field, with EfficientNet providing the highest accuracy rate for classifying rice grains. This study utilized an exclusive, self-obtained dataset of microscopic rice grain images. An autonomous and efficient classification system will benefit rice farmers and open new research opportunities for agriculturists and other stakeholders.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100429"},"PeriodicalIF":2.3,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548338","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

CNN-ViT: A multi-feature learning based approach for driver drowsiness detection CNN-ViT：一种基于多特征学习的驾驶员困倦检测方法

IF 2.3

Array Pub Date : 2025-06-24 DOI: 10.1016/j.array.2025.100425

Madduri Venkateswarlu, Venkata Rami Reddy Chirra

{"title":"CNN-ViT: A multi-feature learning based approach for driver drowsiness detection","authors":"Madduri Venkateswarlu, Venkata Rami Reddy Chirra","doi":"10.1016/j.array.2025.100425","DOIUrl":"10.1016/j.array.2025.100425","url":null,"abstract":"<div><div>Driver drowsiness remains a critical contributor to road accidents, frequently resulting in severe injuries and fatalities. To address this issue, the present study proposes an advanced drowsiness detection system that combines the competencies of Convolutional Neural Networks (CNNs) — namely DenseNet121, VGG16, VGG19, and ResNet50 — with a Vision Transformer (ViT). This hybrid framework is designed to harness the complementary strengths of CNNs and transformers: CNNs excel at capturing fine-grained local features, while ViT effectively models global dependencies within images. The input images are processed simultaneously through both branches, and their extracted features are merged and used to classify the driver’s state into one of four categories: Closed, Open, no_yawn, or yawn. The proposed system was evaluated on two separate datasets, named Dataset-1 and Dataset-2. Results demonstrated that the ResNet50_ViT hybrid attained a high accuracy of 99.76% on Dataset-1, while the VGG19_ViT model attained 98.21% on Dataset-2. Performance was assessed using metrics such as accuracy, precision, F1-score, and recall. The strong results, supported by optimized hyperparameters, highlight the reliability and effectiveness of the hybrid model for real-time driver drowsiness detection.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100425"},"PeriodicalIF":2.3,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144488959","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

Novel delayed binary time-series pattern based machine learning techniques for stock market forecasting 基于延迟二值时间序列模式的股票市场预测机器学习新技术

IF 2.3

Array Pub Date : 2025-06-24 DOI: 10.1016/j.array.2025.100426

Zeqiye Zhan, Song-Kyoo Kim

引用次数: 0

A hybrid approach for cervical cancer detection: Combining D-CNN, transfer learning, and ensemble models 宫颈癌检测的混合方法：结合D-CNN、迁移学习和集成模型

IF 2.3

Array Pub Date : 2025-06-24 DOI: 10.1016/j.array.2025.100434

Abu Hanzala, Tanjila Akter, Md. Sadekur Rahman

{"title":"A hybrid approach for cervical cancer detection: Combining D-CNN, transfer learning, and ensemble models","authors":"Abu Hanzala, Tanjila Akter, Md. Sadekur Rahman","doi":"10.1016/j.array.2025.100434","DOIUrl":"10.1016/j.array.2025.100434","url":null,"abstract":"<div><div>Cervical cancer is the leading cause of cancer-related death among women worldwide, although it is easily preventable through early detection and treatment. This paper proposed deep learning techniques, specifically transfer learning, deep convolutional neural networks (D-CNNs), and ensemble learning for automating cervical cancer detection and classification. Specifically, we evaluate the performance of four deep convolutional neural networks architectures: AlexNet, ZfNet, HighwayNet, and LeNet-5, as well as four transfer learning architectures: EfficientNetB0, ResNet50, MobileNetV2, and DenseNet201. The dataset was preprocessed from the beginning. For this, we performed error level analysis (ELA) on the dataset to ensure that no patterns were missed within each image. We also performed augmentation on the dataset (resizing, rescaling, flipping, rotation, zooming, and contrasting). It is possible to achieve improved diagnostic accuracy using deep learning on the multi-cancer dataset. Comparative studies were conducted in a very short time to investigate the accuracy of these architectures. Based on performance comparisons, we propose a novel hybrid ensemble model AZL which combines AlexNet, ZfNet, and LeNet to overcome individual model limitations. We compared all these models in an experimental format. Our experimental results show that the AZL ensemble model achieved a classification accuracy of 99.92 %, outperforming individual D-CNN and transfer learning models in terms of precision, recall, and F1-score. These findings highlight the effectiveness of ensemble deep learning approaches in improving cervical cancer diagnosis. Our developed method holds promise to help pathologists diagnose this disease in a timely manner, especially given the limited resources. Ultimately, it is capable of accurately detecting and classifying cervical cancer, which contributes to reducing mortality.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"27 ","pages":"Article 100434"},"PeriodicalIF":2.3,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480942","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