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 >

Hüseyin Bilgin^*, Bredli Plaku

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071007 - 08 January 2026

Abstract Nonlinear static procedures are widely adopted in structural engineering practice for seismic performance assessment due to their simplicity and computational efficiency. However, their reliability depends heavily on how the nonlinear behaviour of structural components is represented. The recent earthquakes in Albania (2019) and Türkiye (2023) have underscored the need for accurate assessment techniques, particularly for older reinforced concrete buildings with poor detailing. This study quantifies the discrepancies between default and user-defined component modelling in pushover analysis of pre-modern reinforced concrete structures, analysing two representative low- and mid-rise reinforced concrete frame buildings. The lumped plasticity approach… More > Graphic Abstract

Virna Margarita Martín Giménez¹, SebastiáN GarcíA MenéNdez^2,3, Luiz Gustavo A. Chuffa⁴, Vinicius Augusto SimãO⁴, Russel J. Reiter⁵, Ramaswamy Sharma⁶, Walter Balduini⁷, Carla Gentile⁸, Walter Manucha^2,3,*

BIOCELL, Vol.49, No.12, pp. 2245-2282, 2025, DOI:10.32604/biocell.2025.071830 - 24 December 2025

Abstract Neurodegenerative diseases (NDs) such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS) are characterized by progressive neuronal loss, which is closely linked to mitochondrial dysfunction. These pathologies involve a complex interplay of genetics, protein misfolding, and cellular stress, culminating in impaired energy metabolism, an increase in reactive oxygen species (ROS), and defective mitochondrial quality control. The accumulation of damaged mitochondria and dysregulation of pathways such as the Integrated Stress Response (ISR) are central to the pathogenesis of these conditions. This review explores the critical relationship between mitochondrial stress… More >

Somayah Albaradei^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2937-2970, 2025, DOI:10.32604/cmes.2025.072584 - 23 December 2025

Abstract Cancer deaths and new cases worldwide are projected to rise by 47% by 2040, with transitioning countries experiencing an even higher increase of up to 95%. Tumor severity is profoundly influenced by the timing, accuracy, and stage of diagnosis, which directly impacts clinical decision-making. Various biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, contribute to cancer development. The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers. This integrative approach supports the notion that cancer is fundamentally driven by such alterations, enabling the discovery of molecular signatures… More > Graphic Abstract

Vardhelli Soukya¹, Soumen Maitra^1,*, Nandita Sahana², Saikat Das³, Rupsanatan Mandal³, Arpita Mandal Khan¹, Arindam Das⁴, Ashok Choudhury⁵, Prodyut Kumar Paul⁶, Ahmed Gaber⁷, Mohammed M. Althaqafi⁸, Akbar Hossain^9,*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3493-3508, 2025, DOI:10.32604/phyton.2025.071450 - 01 December 2025

Abstract The present study investigated the genetic diversity of 24 germplasms of Polianthes tuberosa L. via 16 inter simple sequence repeat (ISSR) marker techniques. The research findings revealed that the ISSR markers presented higher levels of band reproducibility and were more efficient at clustering germplasms. Among the 16 markers examined in this study, 12 had a complete polymorphism rate of 100%. The molecular analysis revealed a PIC ranging from 0.079 to 0.373, with a mean value of 0.30, whereas the range of the marker index was from 0.0001 to 0.409, with an average value of 0.03, and More >

Neethu Rose Thomas^1,2, J. Anitha², Cristina Popirlan³, Claudiu-Ionut Popirlan³, D. Jude Hemanth^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4407-4440, 2025, DOI:10.32604/cmc.2025.070689 - 23 October 2025

Abstract Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors. Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations. In this review, we discuss computer-based image processing methods using deep learning, convolutional neural networks (CNNs), radiomics, and transformer-based methods for kidney tumors. These techniques hold significant potential for automated segmentation, classification, and prognostic estimation with high accuracy, enabling more precise and personalized treatment planning. Special focus More >

Xi Chen^1,3, Wanru Lin¹, Yuefu Liu^2,*

Journal of Psychology in Africa, Vol.35, No.5, pp. 661-669, 2025, DOI:10.32604/jpa.2025.068787 - 24 October 2025

Abstract Accurate assessment of climate anxiety is crucial, yet the cross-cultural transportability of existing instruments remains an open question. This study translated and validated the Hogg Climate Anxiety Scale for the Chinese context. A total of 959 students (females = 69.7%; M age = 19.60 years, SD = 1.40 years) completed the Hogg Climate Anxiety Scale, with the Climate Change Anxiety Scale and the Anxiety Presence Subscale served as criterion measures for concurrent validity. Test–retest reliability was evaluated with a subset after one month. Confirmatory factor analysis supported the original four-factor structure and measurement invariance across genders.… More >

Muhammad Salman Qamar^1,2,*, Muhammad Fahad Munir², Athar Waseem²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3557-3584, 2025, DOI:10.32604/cmes.2025.067447 - 30 September 2025

Abstract Air pollution, specifically fine particulate matter (PM2.5), represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems. Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks; however, the inherent nonlinearity and dynamic variability of air quality data present significant challenges. This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and the hybrid CNN-LSTM as well as statistical models, AutoRegressive Integrated Moving Average (ARIMA) and Maximum Likelihood Estimation (MLE) for hourly PM2.5 forecasting. Model performance is… More >

Puranam Revanth Kumar¹, Gouse Baig Mohammad², Pallati Narsimhulu³, Dharnisha Narasappa⁴, Lakshmana Phaneendra Maguluri⁵, Subhav Singh^6,7,8, Shitharth Selvarajan^9,10,11,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2779-2818, 2025, DOI:10.32604/cmes.2025.066365 - 30 September 2025

Abstract Blockchain Technology (BT) has emerged as a transformative solution for improving the efficacy, security, and transparency of supply chain intelligence. Traditional Supply Chain Management (SCM) systems frequently have problems such as data silos, a lack of visibility in real time, fraudulent activities, and inefficiencies in tracking and traceability. Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues; it facilitates trust, security, and the sharing of data in real-time among all parties involved. Through an examination of critical technologies, methodology, and applications, this paper delves deeply into computer modeling based-blockchain framework… More >