Moein Tosan^1,*, Vahid Nourani^2,3, Ozgur Kisi^4,5,6, Yongqiang Zhang⁷, Sameh A. Kantoush⁸, Mekonnen Gebremichael⁹, Ruhollah Taghizadeh-Mehrjardi¹⁰, Jinhui Jeanne Huang¹¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074768 - 29 January 2026

Abstract The integration of machine learning (ML) into geohazard assessment has successfully instigated a paradigm shift, leading to the production of models that possess a level of predictive accuracy previously considered unattainable. However, the black-box nature of these systems presents a significant barrier, hindering their operational adoption, regulatory approval, and full scientific validation. This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence (XAI) as applied to geohazard science (GeoXAI), a domain that aims to resolve the long-standing trade-off between model performance and interpretability. A rigorous synthesis of 87 foundational… More >

Jihoon Moon^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071052 - 29 January 2026

Abstract Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data, a challenge that becomes especially pronounced when commissioning new facilities where operational records are scarce. This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such “cold-start” forecasting problems. It primarily covers three interrelated domains—solar photovoltaic (PV), wind power, and electrical load forecasting—where data scarcity and operational variability are most critical, while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective. To this end, we examined… More >

Kwabena Owusu-Mensah^*, Edward Danso Ansong , Kofi Sarpong Adu-Manu, Winfred Yaokumah

Journal of Cyber Security, Vol.8, pp. 33-92, 2026, DOI:10.32604/jcs.2026.074265 - 20 January 2026

Abstract The rapid growth of digital payment systems and remote financial services has led to a significant increase in Card-Not-Present (CNP) fraud, which is now the primary source of card-related losses worldwide. Traditional rule-based fraud detection methods are becoming insufficient due to several challenges, including data imbalance, concept drift, privacy concerns, and limited interpretability. In response to these issues, a systematic review of twenty-four CNP fraud detection frameworks developed between 2014 and 2025 was conducted. This review aimed to identify the technologies, strategies, and design considerations necessary for adaptive solutions that align with evolving regulatory standards.… More >

Muhammad Adil¹, Nadeem Javaid^1,*, Imran Ahmed², Abrar Ahmed³, Nabil Alrajeh^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071215 - 10 November 2025

Abstract Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention. However, existing Deep Learning (DL) approaches often face several limitations, including inefficient feature extraction, class imbalance, suboptimal classification performance, and limited interpretability, which collectively hinder their deployment in clinical settings. To address these challenges, we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture. The preprocessing stage involves label encoding and feature scaling. To address the issue of… More >

Asma Batool¹, Fahad Ahmed¹, Naila Sammar Naz¹, Ayman Altameem², Ateeq Ur Rehman^3,4, Khan Muhammad Adnan^5,*, Ahmad Almogren^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4129-4152, 2025, DOI:10.32604/cmes.2025.073149 - 23 December 2025

Abstract Early and accurate cancer diagnosis through medical imaging is crucial for guiding treatment and enhancing patient survival. However, many state-of-the-art deep learning (DL) methods remain opaque and lack clinical interpretability. This paper presents an explainable artificial intelligence (XAI) framework that combines a fine-tuned Visual Geometry Group 16-layer network (VGG16) convolutional neural network with layer-wise relevance propagation (LRP) to deliver high-performance classification and transparent decision support. This approach is evaluated on the publicly available Kaggle kidney cancer imaging dataset, which comprises labeled cancerous and non-cancerous kidney scans. The proposed model achieved 98.75% overall accuracy, with precision, More >

Deema Alzamil^1,2,*, Bader Alkhamees², Mohammad Mehedi Hassan^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2971-3027, 2025, DOI:10.32604/cmes.2025.070867 - 23 December 2025

Abstract Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images, ultrasound scans, patient records, and genetic tests—but most AI tools look at only one of these at a time, which limits their ability to produce accurate and comprehensive decisions. In recent years, multimodal learning has emerged, enabling the integration of heterogeneous data to improve performance and diagnostic accuracy. However, doctors cannot always see how or why these AI tools make their choices, which is a significant bottleneck in their reliability, along with adoption in clinical settings. Hence, people are adding… More >

Jihoon Moon¹, Jiyoung Woo^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1383-1425, 2025, DOI:10.32604/cmc.2025.067784 - 29 August 2025

Abstract Improving early diagnosis of autism spectrum disorder (ASD) in children increasingly relies on predictive models that are reliable and accessible to non-experts. This study aims to develop such models using Python-based tools to improve ASD diagnosis in clinical settings. We performed exploratory data analysis to ensure data quality and identify key patterns in pediatric ASD data. We selected the categorical boosting (CatBoost) algorithm to effectively handle the large number of categorical variables. We used the PyCaret automated machine learning (AutoML) tool to make the models user-friendly for clinicians without extensive machine learning expertise. In addition,… More >

Qamar H. Naith^1,*, H. Mancy^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 387-405, 2025, DOI:10.32604/cmes.2025.066917 - 31 July 2025

Abstract Effective water distribution and transparency are threatened with being outrightly undermined unless the good name of urban infrastructure is maintained. With improved control systems in place to check leakage, variability of pressure, and conscientiousness of energy, issues that previously went unnoticed are now becoming recognized. This paper presents a grandiose hybrid framework that combines Multi-Agent Deep Reinforcement Learning (MADRL) with Shapley Additive Explanations (SHAP)-based Explainable AI (XAI) for adaptive and interpretable water resource management. In the methodology, the agents perform decentralized learning of the control policies for the pumps and valves based on the real-time… More >

Jitendra Kumar Samriya¹, Virendra Singh², Gourav Bathla³, Meena Malik⁴, Varsha Arya^5,6, Wadee Alhalabi⁷, Brij B. Gupta^8,9,10,11,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3893-3910, 2025, DOI:10.32604/cmc.2025.063242 - 03 July 2025

Abstract The integration of the Internet of Things (IoT) into healthcare systems improves patient care, boosts operational efficiency, and contributes to cost-effective healthcare delivery. However, overcoming several associated challenges, such as data security, interoperability, and ethical concerns, is crucial to realizing the full potential of IoT in healthcare. Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems. In this context, this paper presents a novel method for healthcare data privacy analysis. The technique is based on the identification of anomalies in… More >

Ali Hamid Farea^1,*, Iman Askerzade^1,2, Omar H. Alhazmi³, Savaş Takan⁴

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1457-1484, 2025, DOI:10.32604/cmc.2025.064872 - 09 June 2025

Abstract Feature selection (FS) is a pivotal pre-processing step in developing data-driven models, influencing reliability, performance and optimization. Although existing FS techniques can yield high-performance metrics for certain models, they do not invariably guarantee the extraction of the most critical or impactful features. Prior literature underscores the significance of equitable FS practices and has proposed diverse methodologies for the identification of appropriate features. However, the challenge of discerning the most relevant and influential features persists, particularly in the context of the exponential growth and heterogeneity of big data—a challenge that is increasingly salient in modern artificial… More >