Dakshnamoorthy Manivannan^*

Journal of Cyber Security, Vol.8, pp. 397-468, 2026, DOI:10.32604/jcs.2026.082741 - 06 July 2026

Abstract Ransomware has emerged as one of the most disruptive and financially damaging forms of cybercrime, affecting individuals, enterprises, and critical infrastructures worldwide. Over the past decade, ransomware attacks have evolved from simple file-encryption malware to sophisticated, multi-stage campaigns involving data exfiltration, double extortion, and ransomware-as-a-service (RaaS) ecosystems. In response, a large body of research has proposed diverse techniques for detecting, preventing, mitigating, and recovering from ransomware attacks. This paper presents a comprehensive survey of ransomware research spanning behavioral and runtime detection, machine learning and deep learning-based approaches, network and SDN-based detection, platform-specific defenses for mobile… More >

Gaziza Zhussipnazarova¹, Reshmy Rajasekharan², Sachin Kalumkumvathukkal Sajeev³, Jijo Thomas Koshy³, Dhanaraj Sangeetha³, Rekha Unni⁴, Raveendran Sindhu⁵, Akmaral Darmenbayeva¹, Mohammed Kuddus^6,7,*

Journal of Polymer Materials, Vol.43, No.2, 2026, DOI:10.32604/jpm.2026.075396 - 30 June 2026

Abstract It takes centuries for chemically reinforced, short-term designed plastics to decompose naturally. Despite this, there has been a significant surge in plastics production recently, accounting for a considerable part of the total historical output. Forecasts indicate that plastics production could reach unprecedented levels if this trend continues. However, increasing environmental concerns and stricter waste regulations have intensified research into biodegradable alternatives. As a result, there is a growing shift toward sustainable polymeric systems capable of replacing conventional petroleum-based plastics. One such class of promising and renewable materials is cellulose-chitosan bioplastic. This review provides an in-depth… More > Graphic Abstract

Raquel Sanchez-Baltasar¹, Nerea Castañeda-Fernández¹, Jorge Olivares-Arancibia², Carlos Torres-Villar^3,4, Julio Plaza-Diaz^5,6,7,8,*, Lourdes Herrera-Quintana^1,*

Oncology Research, Vol.34, No.7, 2026, DOI:10.32604/or.2026.081924 - 16 June 2026

Abstract Breast cancer (BC) is the most frequently diagnosed malignancy in women worldwide and remains one of the leading causes of cancer-related mortality, with substantial international disparities in incidence, stage at diagnosis, access to treatment, and survival. In recent years, BC management has evolved rapidly through advances in molecular characterization, imaging, pathology, targeted therapies, immunotherapy, and survivorship care. Nevertheless, important gaps persist in early and accurate detection, biomarker standardization, equitable access to care, and patient-specific treatment selection. These advances require timely, evidence-based, and context-specific clinical frameworks to support appropriate implementation, and to avoid the use of… More >

Said Benkachcha^1,*, Mustapha Adar¹, Mohamed Maniana², Youssef Najih¹, Mourad Kaddiri¹, Mutapha Mabrouki¹

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.075406 - 27 May 2026

Abstract Accurate forecasting of solar photovoltaic (PV) power generation is essential for enabling reliable integration of renewable energy into modern power systems. Variability in solar production, driven by meteorological fluctuations and inherent nonlinear dynamics, presents significant challenges for grid stability, operational planning, and energy management. This study investigates and compares the performance of classical statistical forecasting techniques and advanced deep learning approaches using real PV production data from a Moroccan solar plant. The analysis focuses on accuracy, robustness, computational efficiency, and suitability for short-term operational applications. Among statistical approaches, the Holt–Winters model demonstrated strong capability in… More > Graphic Abstract

Kirill V. Chernov^1,#, Artemii M. Savin^1,#, Daria E. Otvodnikova¹, Oleg A. Kuchur^1,2, Sergey A. Tsymbal^1,2,*

Oncology Research, Vol.34, No.6, 2026, DOI:10.32604/or.2026.077445 - 21 May 2026

Abstract The formation of drug resistance poses the ultimate threat in modern oncology. Targeted therapy lacks versatility, while conventional therapy is famous for its side effects. However, for the new therapeutics to address the challenge of drug resistance, such compounds should combine properties of both modalities. In this review, we argue that metal-based therapeutics are paramount substances for achieving this goal. The unique physico-chemical properties and metabolism of these compounds, as well as metals themselves, allow to realize unique activities in normal and cancer cells, including precise targeting, non-apoptotic cell death, and disruption of critical signaling More > Graphic Abstract

Akash Garg^*

Journal on Artificial Intelligence, Vol.8, pp. 273-298, 2026, DOI:10.32604/jai.2026.079401 - 12 May 2026

Abstract As cyber threats continue to evolve in scale and sophistication, the need for intelligent and adaptive security mechanisms has become increasingly urgent. Intrusion Detection Systems (IDS) are critical components in safeguarding computer networks from malicious activities. This review paper presents a comprehensive analysis of recent advancements in deep learning-based IDS, examining various architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs). The study compares traditional intrusion detection techniques with modern deep learning approaches, highlighting their strengths, limitations, and suitability for real-world deployment. Special attention is given to… More >

Minhyeok Choi¹, Imran Ahsan², Hyunwook Yu¹, Taeyoung Choe¹, Mucheol Kim^1,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079504 - 08 May 2026

Abstract Large language models (LLMs) are increasingly integrated into recommender systems to support semantic reasoning, natural language understanding, and user-adaptive personalization. However, their reliance on static parametric knowledge and fixed representations limits robustness in dynamic environments, particularly under long-tail and cold-start conditions. Retrieval-augmented architectures have emerged to address these limitations by grounding LLMs in external, non-parametric knowledge sources. This systematic literature review synthesizes 138 peer-reviewed studies published between 2023 and 2025 in conferences and journals, focusing on retrieval-augmented and LLM-enhanced recommendation. We analyze these works through a three-dimensional framework covering: (i) domain application, (ii) semantic feature… More >

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 >