Ali Hamidoğlu^1,2, Ali Elghirani^3,4, Ömer Melih Gül^5,6,7, Seifedine Kadry^8,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077060 - 09 April 2026

Abstract Multi-Robot Task Allocation (MRTA) has proven its importance in the current and near-future era, wherein in every aspect of life, there will be robots to handle tasks effectively and efficiently. While there has been a growing interest in MRTA problems in the robotics industry, the question arises of how to make robots more decentralized and intelligent through rational decision-makers rather than ones that are centralized and filled with black boxes. This survey aims to address that question by examining recent MRTA literature and exploring topics including MRTA taxonomy, centralized and decentralized controls, static and dynamic… More >

Raed Alotaibi^1,*, Muhammad Atta Othman Ahmed², Omar Reyad^3,4,*, Nahla Fathy Omran⁵

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076156 - 09 April 2026

Abstract The widespread use of social media has made assessing users’ tastes and preferences increasingly complex and important. At the same time, the rapid dissemination of misinformation on these platforms poses a critical challenge, driving significant efforts to develop effective detection methods. This study offers a comprehensive analysis leveraging advanced Machine Learning (ML) techniques to classify news articles as fake or true, contributing to discourse on media integrity and combating misinformation. The suggested method employed a diverse dataset encompassing a wide range of topics. The method evaluates the performance of five ML models: Artificial Neural Networks… More >

Ehsan Momeni¹, Mohammad Dehghannezhad¹, Fereydoon Omidinasab¹, Danial Jahed Armaghani^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077300 - 30 March 2026

Abstract In the last decade, the importance of sustainable construction and artificial intelligence (AI) in civil engineering has been underlined in many studies. Numerous studies highlighted the superiority of AI techniques over simple and mathematical regression analyses, which suffer from relatively poor generalization and an inability to capture highly non-linear relationships among inputs and output(s) parameters. In this study, to evaluate the compressive strength of concrete with glass powder (GP) and recycled aggregates, 600 concrete samples were tested in the laboratory, and their results were evaluated. For intelligent assessment of concrete compressive strength (CCS), the study… More >

Mahboobeh Attaei^1,2, Maria Vieira¹, Cinthia Maia Pederneiras^3,4,*, Filipa Clara Coimbra¹, David Bastos¹, Rosário Veiga³

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2026.074246 - 27 March 2026

Abstract The construction industry is a significant contributor to global CO₂ emissions, and urgent innovation is needed to mitigate its environmental impact. This paper provides a comprehensive review of scalable approaches for CO₂ uptake in construction materials, including the injection of CO₂ into fresh concrete, the CO₂ curing of precast concrete, and the use of ceramics as CO₂ sinks. Among these three approaches, CO₂ curing methods for concrete represent the most advanced and widely adopted strategies within industrial practice, with substantial research supporting their effectiveness and scalability. The comparison of carbonation mineralisation across three distinct material groups reveals that… More > Graphic Abstract

Eshita Dhar^1,2, Muhammad Ashad Kabir³, Divyabharathy Ramesh Nadar⁴, Li-Jen Kuo⁵, Jitendra Jonnagaddala^6,7, Yaoru Huang¹, Mohy Uddin^8,*, Shabbir Syed-Abdul^1,2,9,*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.074385 - 23 March 2026

Abstract Objectives: Decisions regarding CT after nCCRT for locally advanced rectal cancer (LARC) are challenging due to limited evidence guiding treatment. This study aimed to (i) evaluate the predictive performance of machine learning (ML) models in patients treated with neoadjuvant concurrent chemoradiotherapy (nCCRT) alone vs. those receiving nCCRT plus chemotherapy (CT), (ii) identify features associated with treatment improvement, and (iii) derive ML-based thresholds for treatment response. Methods: This retrospective study included 409 patients with LARC treated at three affiliated hospitals of Taipei Medical University. Patients were categorised into two groups: nCCRT alone followed by surgery (n =… More >

Sophiette G. Hong^1,2, George F. Murphy², Christine G. Lian^2,*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.073894 - 23 March 2026

Abstract Malignant melanoma (MM) is a highly aggressive skin cancer known for its rapid progression, potential for metastasis, and resistance to treatment. Despite advances in targeted therapies and immunotherapy, the prognosis for metastatic melanoma remains unfavorable. Recent research has shed light on the significance of epigenetic modifications in the pathogenesis of melanoma, revealing critical mechanisms of melanoma development and progression. Epigenetic modifications, including DNA and RNA modifications, histone modifications, chromatin remodeling, and non-coding RNA regulation, disrupt normal gene expression without modifying the DNA sequence, leading to cellular transformation, invasion, immune evasion, and therapeutic resistance. The reversible… More >

Adam Khorasanchi, Merve Hasanov, Richard Wu, Hisham Alsharif, Kari Kendra, Claire Verschraegen^*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.069012 - 23 March 2026

Abstract Cutaneous squamous cell carcinoma (CSCC) is the second most common type of skin cancer and typically involves the head and neck. Systemic therapy is often required for patients with advanced CSCC to achieve optimal disease control. Immune checkpoint inhibitors (ICIs) are now the standard of care for these patients, with a 50%–60% response rate and sustainable remission for at least 30% of patients. Given the activity of ICIs in advanced head and neck CSCC, ICIs are being studied in early-stage disease or neoadjuvant situations. The purpose of this review is to provide an overview of More >

Mohammad Alshinwan¹, Radwan M. Batyha^1,2, Walaa Alayed^3,*, Saad Said Alqahtany⁴, Suhaila Abuowaida⁵, Hamza A. Mashagba⁶, Azlan B. Abd Aziz^6,*, Samir Salem Al-Bawri⁷

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.072806 - 12 March 2026

Abstract Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics. Although numerous machine-learning-based intrusion detection systems (IDS) have been developed, their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency. This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization (PDO) with the exploitation behavior of Ant Colony Optimization (ACO). The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSL-KDD dataset and evaluates them using a Support Vector Machine (SVM) More >

Kirubavathi Ganapathiyappan¹, Chahana Ravikumar¹, Raghul Alagunachimuthu Ranganayaki¹, Ayman Altameem², Ateeq Ur Rehman^3,*, Ahmad Almogren^4,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072840 - 10 February 2026

Abstract Android smartphones have become an integral part of our daily lives, becoming targets for ransomware attacks. Such attacks encrypt user information and ask for payment to recover it. Conventional detection mechanisms, such as signature-based and heuristic techniques, often fail to detect new and polymorphic ransomware samples. To address this challenge, we employed various ensemble classifiers, such as Random Forest, Gradient Boosting, Bagging, and AutoML models. We aimed to showcase how AutoML can automate processes such as model selection, feature engineering, and hyperparameter optimization, to minimize manual effort while ensuring or enhancing performance compared to traditional… More >

Lingyun Xiang^1,*, Nian Li², Yuling Liu³, Jiayong Hu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073347 - 12 January 2026

Abstract The rapid advancement of large language models (LLMs) has driven the pervasive adoption of AI-generated content (AIGC), while also raising concerns about misinformation, academic misconduct, biased or harmful content, and other risks. Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information. This survey reviews recent progress in detection methods, categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals. Passive detection is further divided into surface linguistic feature-based and language model-based methods, whereas active detection encompasses watermarking-based and semantic retrieval-based More >