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{"title":"Research on a multi-omics prognostic model of liver cancer based on stacked supervised deep learning","authors":"Pengpeng Li ,&nbsp;Xicheng Chen ,&nbsp;Haojia Wang ,&nbsp;Tao Xu ,&nbsp;Yang Li ,&nbsp;Wei Ye ,&nbsp;Jia Chen ,&nbsp;Fang Li ,&nbsp;Ning Yao ,&nbsp;Yazhou Wu","doi":"10.1016/j.array.2025.100646","DOIUrl":"10.1016/j.array.2025.100646","url":null,"abstract":"<div><div>The high heterogeneity of Hepatocellular Carcinoma (HCC) limits prognostic prediction accuracy, and existing deep learning models often struggle to capture the complex interactions within multi-omics data. To address this limitation, this study proposes a novel Stacked Supervised Auto-Encoder (SSAE) framework. It integrates miRNA, mRNA, and DNA methylation data by embedding a Cox proportional hazards model into the hidden layers of sequentially stacked modules. Using the TCGA-LIHC dataset, we systematically evaluated Early Integration (EI-SSAE) and Late Integration (LI-SSAE) strategies while comparing them against traditional machine learning methods. The results demonstrate that the Late Integration strategy significantly outperforms both Early Integration and unsupervised variants. It achieved a high Concordance Index (CI) of 0.969 compared to 0.663 for EI-SSAE. Additionally, the LI-SSAE model exhibited superior calibration accuracy with a Brier score of 0.077 and successfully stratified patients into distinct risk groups with a P-value of 1.11e-08. Bioinformatic analysis further identified critical biomarkers such as FBXW10 and TRIP13. This study confirms that the LI-SSAE model effectively enhances prognostic prediction accuracy and offers a robust tool for clinical assessment and personalized treatment in HCC.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100646"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921255","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":"Classifying and mining design diagrams in source code repositories using transfer learning","authors":"Sergio Rodríguez,&nbsp;Daniel Benavides ,&nbsp;Héctor Cadavid ,&nbsp;Wilmer Garzón","doi":"10.1016/j.array.2025.100656","DOIUrl":"10.1016/j.array.2025.100656","url":null,"abstract":"<div><div>This paper reports on the implementation of a design diagram classifier using transfer learning and fine-tuning. For the study, we built a labeled data set with 5981 images identifying the following design diagram categories: none (no design diagram represented), activity diagram, sequence diagram, class diagram, component diagram, use case diagram, and cloud diagrams. We then used the dataset, transfer learning, and fine-tuning techniques to train a specialized DenseNet169 convolutional network, starting from a model pre-trained on ImageNet. The newly trained network achieved a prediction accuracy of 98.6% (<span><math><mrow><mn>0</mn><mo>.</mo><mn>986</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>0017</mn></mrow></math></span>) and an F1-score of 98.3% (<span><math><mrow><mn>0</mn><mo>.</mo><mn>983</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>0025</mn></mrow></math></span>). Repository mining techniques were then used to analyze 2,469,206 images, equivalent to 231 GB of data, obtained from 287,201 repositories. The analysis revealed that design diagrams are often outdated relative to the project’s evolution; on average, diagrams were last updated 554 days prior to the repository’s latest commit. This significant lag suggests that while diagrams are present, practitioners primarily rely on the source code as the living design artifact.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100656"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921322","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":"Machine learning-based prediction of maternal continuum of care completion: Evidence from Bangladesh Demographic and Health Survey 2022","authors":"Syed Toukir Ahmed Noor ,&nbsp;Raisha Binte Islam ,&nbsp;Samin Yeasar ,&nbsp;Sazid Siddique","doi":"10.1016/j.array.2025.100666","DOIUrl":"10.1016/j.array.2025.100666","url":null,"abstract":"<div><div>Despite decades of investments in maternal healthcare, the completion of the maternal continuum of care (CoC), defined as receiving at least four antenatal care visits with a medically trained provider, delivery by a skilled birth attendant at a health facility, and postnatal care within 48 h, remains low in Bangladesh. This study aimed to develop a machine learning-based prediction model to identify women most likely to complete the maternal CoC, using data from the 2022 Bangladesh Demographic and Health Survey. The analysis included 5,128 ever-married women aged 15–49 who had a live birth in the five years preceding the survey. After data pre-processing, including handling class imbalance through Synthetic Minority Oversampling Technique (SMOTE), 16 predictor variables were selected using the Boruta feature selection algorithm. Seven supervised machine learning algorithms were developed and evaluated, including Random Forest, XGBoost, support vector machines, logistic regression, and artificial neural networks. Among the 5,128 women, only 25.3 % completed the maternal CoC in Bangladesh. The random forest performed best with an accuracy of 82 %, precision of 81 %, recall of 71 %, F1-score of 0.758 and an AUC of 0.889. Feature importance and SHAP analysis identified wealth index, husband's and respondent's education, media exposure, and women's decision-making autonomy at household as the most influential predictors. Identifying women at high risk of dropout can enable healthcare providers to deliver targeted counseling and interventions. These findings offer valuable insights to inform data-driven policies and strategies to enhance maternal health service utilization and reduce preventable maternal morbidity and mortality in low-resource settings like Bangladesh.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100666"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921780","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":"Evaluating sustainability barriers for digital platform services supply chain: A study on strategic intervention through industry-academia collaboration","authors":"Sanchari Ghosh ,&nbsp;Sandeep Mondal ,&nbsp;Nishit Kumar Srivastava","doi":"10.1016/j.array.2025.100651","DOIUrl":"10.1016/j.array.2025.100651","url":null,"abstract":"<div><div>Digital Platform Services Supply Chains (DPSSC) form the operational backbone of contemporary digital economies, yet their sustainability performance is shaped by interdependent technical, organisational and behavioural constraints that remain insufficiently examined. This study addresses this gap by developing unified framework based on DEMATEL-ANP (DANP) and NSGA-II to identify, prioritise and test 7 sustainability barriers spanning from B1-B7. Expert elicitation and DANP analyses reveal a stable causal structure, in which three upstream systemic barriers B1 (ESG standardisation for digital platforms), B3 (energy-intensive infrastructure) and B2 (sustainable tech-innovation by industry-academia collaboration) emerge as system drivers with together accounting for nearly 70 % of global priority weight, whereas B4 (curricular gaps), B5 (behavioural nudges), B6 (algorithmic ESG indicators), B7 (carbon accountability) function as dependent barriers with limited leverage in the absence of upstream correction. NSGA-II is applied to quantify trade-offs between emission reduction and implementation effort under a parameterised case setting informed by publicly disclosed sustainability data. The optimisation yields stable Pareto fronts across convergence, hypervolume, spacing, multi-seed and uncertainty diagnostics. Strategies emphasising B1-B3 deliver largest marginal mitigation benefits within the efficiency zone, while interventions centred on B4-B7 rapidly encounter diminishing returns. This integrated evidence demonstrates that DPSSC sustainability follows a two-stage intervention logic one by addressing upstream structural enablers, the other followed by scaling downstream behavioural, algorithmic and logistical measures once system-level constraints are resolved. Thus, the study provides a transparent and decision-relevant basis for prioritising sustainability actions, strengthening industry-academia engagement and aligning digital platform operations with United Nations SDG 4,7,9,11–13 targets.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100651"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921781","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 eating monitoring: A cyber-physical systems approach","authors":"Angel Biskupovic ,&nbsp;Miguel A. González ,&nbsp;Fernando Huanca ,&nbsp;Mario Torres ,&nbsp;Maria Rodriguez-Fernandez ,&nbsp;Felipe Núñez","doi":"10.1016/j.array.2026.100696","DOIUrl":"10.1016/j.array.2026.100696","url":null,"abstract":"<div><div>Numerous health conditions, such as obesity, diabetes, and cardiovascular diseases, require strict adherence to nutritional guidelines and accurate reporting of eating behaviors, making effective eating monitoring essential. A common approach to eating monitoring involves maintaining a food diary, where subjects manually self-report eating events, a process inherently prone to imprecision. Recent technological advances have enabled the development of passive, automatic eating detection systems, typically relying on data from wearable devices to identify eating events. In this context, the literature is vast on efforts that use machine learning methods for this purpose, with great success. However, most existing studies focus only on eating detection mechanisms and fail to offer an integrated solution with practical use cases. To address this gap, in this work, we present a cyber–physical systems approach to eating monitoring that integrates an eating event detection module with a cloud-based service-oriented backbone where numerous services are deployed, yielding an integrated solution for real-time eating monitoring.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100696"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073733","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":"Design of cloud platform alert monitoring and automatic analysis system based on random forest algorithm","authors":"Bokai Li ,&nbsp;Mingkang Guo ,&nbsp;Yongli Jia ,&nbsp;Tianzi Zeng ,&nbsp;Xiaojing Liu","doi":"10.1016/j.array.2026.100694","DOIUrl":"10.1016/j.array.2026.100694","url":null,"abstract":"<div><div>To address the issue of alert information overload in cloud platform monitoring, where unnecessary or duplicate alerts hinder the rapid identification of problem sources by operation and maintenance personnel, an automatic analysis system for cloud platform alert monitoring based on the random forest (RF) algorithm has been proposed. In the system architecture, the infrastructure layer creates multiple virtual machines through the CloudStack cloud platform, utilizing the C8051F0403 model chip as an information collector to acquire abnormal data. The core service layer, centered around the ARM7TDMI core microprocessor, designs the hardware structure of the monitoring terminal, integrating global GSM-based SMS transmission and reception to track abnormal operational states. The user interface layer supplies alert information to the system. The alert client is functionally designed by incorporating the random forest algorithm, which is capable of processing a large volume of alert log samples from the cloud platform system while avoiding overfitting. By constructing multiple decision trees, the algorithm enhances the accuracy of classification and regression tasks, effectively identifying and filtering out unnecessary or duplicate alert information, thereby enabling automated analysis of abnormal alert monitoring. Experimental results demonstrate that the system achieves effective noise reduction in alert data, maintains a low false alert rate in alert monitoring, and supports root-cause analysis of alerts. The application of this system can significantly mitigate alert overload, ensuring that the alert information received by operation and maintenance (O&amp;M) personnel is more accurate and reliable, thereby facilitating quicker problem localization and effective resolution.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100694"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073735","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":"FORTRESS-FL: Byzantine-robust and privacy-preserving federated orchestration for next-generation networks","authors":"Quang-Vinh Dang ,&nbsp;Thi-Bich-Diem Vo ,&nbsp;Ngoc-Son-An Nguyen","doi":"10.1016/j.array.2026.100680","DOIUrl":"10.1016/j.array.2026.100680","url":null,"abstract":"<div><div>The transition to 6G and Open RAN (O-RAN) necessitates intelligent orchestration across multi-operator networks, yet this collaboration introduces severe security and privacy risks. Malicious operators may poison global models through adaptive attacks, while the exchange of raw gradients threatens data sovereignty. In this paper, we propose <strong>FORTRESS-FL</strong>, a robust and privacy-preserving federated learning framework designed for secure cross-domain orchestration. At its core is the <em>TrustChain</em> protocol, which synergizes a commit-then-reveal scheme to prevent adaptive manipulation, unsupervised spectral clustering for Byzantine detection, and a dynamic reputation system to isolate malicious actors. Furthermore, we integrate an adaptive Differential Privacy (DP) mechanism to rigorously protect operator data. Extensive evaluation on a real-world financial fraud dataset demonstrates that FORTRESS-FL achieves 100% detection accuracy against sign-flip attacks with 30% Byzantine adversaries, preventing the model divergence observed in standard baselines. Scalability tests confirm linear complexity with respect to the number of operators, validating the framework’s feasibility for large-scale, real-time network orchestration.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100680"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184709","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 novel exposine chaotic map for secure image encryption with applications in adaptive cruise control","authors":"Malik Obaid Ul Islam ,&nbsp;Shahid A. Malik ,&nbsp;Pavol Partila ,&nbsp;Jaroslav Frnda","doi":"10.1016/j.array.2026.100710","DOIUrl":"10.1016/j.array.2026.100710","url":null,"abstract":"<div><div>Chaotic maps in one dimension often fail to exhibit topological complexity, demonstrating insufficient statistical unpredictability, and deteriorate under finite-precision, producing limited dynamical characteristics, thereby limiting their applicability for lightweight, protected data transmission and control in cost-limited conditions. To address these limitations, we propose a novel one-dimensional Exposine Chaotic Map (ECM) that incorporates sinusoidal-driven variations, exponential reformations, and a dynamic scaling factor to broaden the chaotic band, enhance reactivity to initial conditions, and amplify overall nonlinearity. Moreover, we have also introduced a polynomial approximate version of the proposed ECM using Taylor's and Padé approximations, retaining its chaotic properties while further refining it for utilization in hardware-resource-effective environments. The novel ECM and AECM achieve positive Lyapunov Exponent throughout parameter space, pass all NIST SP 800-22 tests, achieve high correlation dimension, equiform bifurcation, nearly zero autocorrelation, optimum level of entropy, finite-precision adaptability, and robust sensitivity by means of indeterminate, irregular cobweb dynamics. In addition, a Look-Up Table-driven realization strategy is presented to optimize real-time complexity and advance finite-precision interoperability for cost-efficient hardware deployment. Moreover, this work demonstrates the dual applicability of the proposed Exposine map and its approximation in ensuring unpredictability, secure encryption, and cost-efficient dynamic cruise control for intelligent transportation systems. In image cryptosystem, the methodology attains a typical entropy value of 7.9973, NPCR of 99.61%, UACI of 33.47%, a correlation coefficient of 0.0016, key space <span><math><mrow><msup><mn>2</mn><mn>302.2862</mn></msup></mrow></math></span>, asymptotic intricacy of O(n), and a throughput of 1.0417 MB/s, with runtime of 0.06 s for a 256 × 256 image. In the cruise control implementation, the system acquires an average speed of 60.2915, an approximate entropy of 1.4751, a correlation dimension of 0.1639, a standard deviation of 0.6207, and Shannon entropy of 5.2977, confirming its deterministic chaos, adaptability, and compatibility for real-time adaptive control.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100710"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184901","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-driven meta-model for cybersecurity in next-generation networks using multilayer Extreme Learning Machine","authors":"Jesús Calle-Cancho ,&nbsp;Jesús Galeano-Brajones ,&nbsp;David Cortés-Polo ,&nbsp;Mercedes E. Paoletti ,&nbsp;Juan M. Haut","doi":"10.1016/j.array.2026.100707","DOIUrl":"10.1016/j.array.2026.100707","url":null,"abstract":"<div><div>Ensuring robust cybersecurity in next-generation networks has become a critical challenge due to the increasing sophistication of cyber threats, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection approaches often suffer from high computational costs, poor adaptability to evolving attack patterns, and limited interpretability. In this work, we propose an AI-driven meta-model, called Meta-Model Multilayer Extreme Learning Machines (MM-MELM), designed to enhance anomaly detection and classification in NGNs. The proposed framework integrates multiple Multilayer Extreme Learning Machine (MELM) models into a meta-learning structure, leveraging the diverse outputs of independently trained MELMs to improve robustness and generalization.</div><div>The proposed methodology is evaluated across multiple DDoS attack scenarios, demonstrating its capability to generalize across diverse threat types. Performance analysis reveals that MM-MELM achieves state-of-the-art attack detection, consistently outperforming baseline models. Moreover, MM-MELM exhibits lower variability across all evaluation metrics, ensuring robust performance regardless of the attack complexity. The results highlight that MM-MELM provides a trade-off solution among balanced accuracy, precision, recall, and <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-score, making it a highly scalable and adaptive solution for real-time network security.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100707"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395517","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 novel simulator for performance analysis in heterogeneous wormhole Network-on-Chips","authors":"Md Amirul Islam,&nbsp;Giovanni Stea","doi":"10.1016/j.array.2026.100715","DOIUrl":"10.1016/j.array.2026.100715","url":null,"abstract":"<div><div>This paper introduces <em>scNoCSim</em> (Service Curve-based NoC Simulator), a network-on-chip simulator with integrated monitoring capabilities, built on the <em>omnetpp</em> (OMNeT++) framework. The <em>scNoCSim</em> offers unique features for simulating heterogeneous wormhole Network-on-Chips (NoCs) with monitoring facilities, varying numbers of virtual channels for each unidirectional port, and flexible link capacities. The proposed simulator is extensible and scalable by adding more modules, and highly customizable through editable parameters. It is designed for Network-on-Chip modeling and offers various deterministic routing algorithms (XY, YX, XY–YX, Odd-Even, and O1Turn). It supports round-robin, weighted round-robin, and fixed-priority scheduling strategies; credit-based traffic flow control; and both centralized and distributed routing techniques, including realistic and ideal routers. This simulator offers a comprehensive set of analyses at both packet and flit levels, including, throughput, end-to-end delay, data transfer delay, required bandwidth estimation, link utilization, traffic packet success and loss rates, etc. This paper outlines the proposed architectural view, network design, functional structure, and modules, as well as the key attributes that make it ideal for progressive Network-on-Chip research. Additionally, Network Calculus is used to determine performance (delay and backlog) bounds for worst-case scenarios, estimate bandwidth, and present case studies with validation that demonstrate the capabilities of the <em>scNoCSim</em> simulator in scenarios that cannot be addressed by existing Network-on-Chip research.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100715"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395518","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}