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 >

Zehao Wei^1,#, Xuejian Liu^2,#, Zheng Zhang¹, Yimin Ma^2,*, Min Xu^1,*

Oncology Research, Vol.34, No.7, 2026, DOI:10.32604/or.2026.078793 - 16 June 2026

Abstract Pancreatic cancer is one of the most lethal malignancies, characterized by difficulties in early diagnosis, limited therapeutic options, and generally poor patient prognosis. In recent years, immunotherapy has provided new opportunities for the treatment of pancreatic cancer; however, its clinical efficacy has been substantially constrained by the complex tumor microenvironment (TME) and immune evasion mechanisms. With the rapid advancement of artificial intelligence (AI) technologies, AI has demonstrated great potential in the early detection of pancreatic cancer, prediction of immunotherapeutic responses, and design of personalized treatment strategies. This review systematically summarizes the latest advances in the More >

Leonidas Theodorakopoulos, Alexandra Theodoropoulou^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082270 - 15 June 2026

Abstract Auditable autonomy is becoming a practical requirement for deploying large language model (LLM) agents in operational workflows where recommendations can trigger consequential actions. Many autonomy claims remain hard to evaluate because studies emphasize task completion or fluent explanations while underreporting tool privileges, verification conditions, rollback feasibility, and trace completeness. This review develops a decision-making–centered framework that treats autonomy as an auditable engineering property. It introduces a three-plane big data foundation: an evidence plane with provenance and freshness constraints; a decision-trace plane that records retrieval identifiers, tool invocations, intermediate checks, and policy evaluations; and an outcomes More >

Naeem Ullah^1,*, Javed Ali Khan², Michelina Ruocco³, Antonio Della Cioppa⁴, Ivanoe De Falco⁵, Giovanna Sannino⁵

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081477 - 15 June 2026

Abstract Fruit diseases significantly impact agricultural productivity, yet automated detection systems often fail to provide interpretable predictions and are sensitive to background variations in images, particularly in orange fruit disease datasets. Current deep learning approaches are prone to background bias, which reduces explainability and generalization. To address this, we propose a deep learning framework that explicitly reduces background noise and bias in orange fruit disease image classification while providing interpretable, pixel-level predictions. The framework integrates existing architectural components, including grouped convolutions with channel shuffling, Leaky ReLU and clipped ReLU activations, and attention-based feature extraction, within a… More >

AKM Bahalul Haque^1,*, Al Amin Islam Ridoy², Mohammad Rayhan³, Ivan Porres¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079525 - 15 June 2026

Abstract Agentic AI is gaining new insights and advancements in the field of Artificial Intelligence, fostering significant potential to enable rapid transformation across various domains. This rapid advancement and the potential to revolutionize various domains advocate the need for a deeper understanding and firm grasp of the technology. Moreover, an investigation into state-of-the-art research directions in agentic AI needs to be conducted to comprehensively assess the potential scope for improvement and application. Therefore, to address these objectives, a comprehensive review can provide researchers and practitioners with valuable insights into the current state and future research scopes… More >

Nikita Sakovich¹, Dmitry Aksenov¹, Ekaterina Pleshakova^1,*, Sergey Gataullin^1,2

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079522 - 15 June 2026

Abstract We introduce the DARE-Q (Distribution-Aware Residual Entropy Quantization) method—a post-training quantization method for neural network weights designed to reduce bit-width with minimal degradation of model quality. Unlike traditional approaches that solely optimize the mean squared error of weight approximation, DARE-Q additionally considers the entropy of the quantization residual, allowing for control over the statistical properties of the resulting error. The method is based on channel-wise symmetric uniform quantization with scaling based on a combined loss function that includes L2 distortion and entropy regularization. The DARE-Q method is implemented as a compact DAREQuantLinear module which can… More >

Manjodh Kaur¹, Princy Randhawa^2,*, Jitendra Jaiswal², Deepak Dubal³, Ravindra N. Bulakhe^4,5, Deepanraj Balakrishnan⁶, Nithesh Naik^7,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079503 - 15 June 2026

Abstract This review emphasizes the growing role of artificial intelligence (AI) in transforming the materials discovery process into a data-driven and autonomous approach. It systematically traces the evolution of scientific paradigms in materials science and examines how machine learning, generative models, and AI agents are revolutionizing the design, screening, and optimization of materials. A key contribution is a detailed, step-by-step machine learning framework that guides researchers through data collection, preprocessing, feature engineering, model development, and validation, utilizing publicly available materials databases and computational tools. Additionally, the review discusses the latest advances in generative AI and autonomous More >

Haitian Du^*

Journal of Cyber Security, Vol.8, pp. 241-279, 2026, DOI:10.32604/jcs.2026.075976 - 25 May 2026

Abstract Modern cyberattacks evolve rapidly, overwhelming static and rule-based defenses. This paper proposes GenAI-Powered Autonomous Cyber Offense-Defense, a closed-loop framework in which large language models (LLMs) control both a red-team attacker and a blue-team defender. The agents operate in a simulated enterprise network, generate natural-language rationales for every action, and update defensive policies through a self-adaptive learning loop. We instantiate the framework with LLM-based agents that plan multi-stage attacks, detect anomalies, and autonomously execute containment and hardening actions. In experiments on a three-host virtualized testbed and a scalable multi-node emulation, the adaptive blue agent reduces the More >

Nader Karmous¹, Leila Bousbia¹, Mohamed Ould-Elhassen Aoueileyine¹, Imen Filali^2,*, Ridha Bouallegue¹

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077454 - 08 May 2026

Abstract This study proposes an intelligent Intrusion Detection and Prevention System (IDPS) integrated into a centralized Ryu Software-Defined Networking (SDN) controller to mitigate replay attacks within Internet of Things (IoT) environments. To address the scarcity of specialized datasets, a comprehensive dataset was generated using a real-time SDN-IoT testbed encompassing Mininet, multiple OpenFlow 1.3 switches, and a single Ryu controller. The experimental setup featured the exchange of legitimate and malicious Message Queuing Telemetry Transport (MQTT) traffic between hosts and IoT devices to simulate realistic network behaviors and attack vectors. Our methodology introduces a novel feature engineering framework… More >

Wei-Chen Tang, Xin Chen, Fei Dong^*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.4, 2026, DOI:10.32604/fdmp.2026.079861 - 07 May 2026

Abstract Boiling heat transfer remains a cornerstone of efficient thermal management, with far-reaching implications for energy systems and industrial processes. Advances in this field not only deepen fundamental scientific understanding but also enable transformative improvements in energy efficiency, equipment performance, and operational safety. Contemporary research in this area focuses on accurate parameter prediction, intelligent image analysis, and quantitative characterization of bubble dynamics, collectively advancing both mechanistic insight and engineering optimization. In this context, artificial intelligence (AI), encompassing machine learning and deep learning techniques, has emerged as a powerful paradigm, offering significant advantages in predictive accuracy, data-driven… More >