S. M. Nihal Ahmed^*, Afrim Hossen Khan, Md. Mazbaur Rashid

Journal of Cyber Security, Vol.8, pp. 667-703, 2026, DOI:10.32604/jcs.2026.089357 - 14 September 2026

Abstract Phishing remains one of the most persistent attack vectors in cybersecurity, and the gradient-boosting models that now dominate its automated detection are frequently deployed as opaque classifiers, which limits analyst trust and slows incident response. This paper presents a cross-dataset, explainability-driven evaluation of three gradient boosting algorithms–XGBoost, LightGBM, and CatBoost—for phishing URL classification, paired with a SHAP (Shapley Additive Explanations)-based framework that attributes every prediction to specific, human-readable features rather than treating the classifier as a black box. To eliminate a data leakage risk present in an earlier evaluation protocol—in which hyperparameter tuning and final… More >

John Ojo Ajayi^*, Grace Egenti

Journal of Cyber Security, Vol.8, pp. 641-666, 2026, DOI:10.32604/jcs.2026.086419 - 14 September 2026

Abstract Artificial intelligence (AI)-based intrusion detection systems (IDS) have evolved to be fundamental in detecting cyberattacks in contemporary networks. Unfortunately, the significant class imbalance present in cybersecurity datasets may introduce unfair biases during model learning, producing unpredictable predictions and inflating false alarm alerts, which eventually hampers practical deployment. This research examines the influence of class imbalance mitigation on model performance, operational fairness, explainability, and the operational integrity of an AI-based IDS subject to extreme class imbalance conditions. Three learning strategies based on Random Forest (i.e., baseline, Synthetic Minority Over-sampling Technique (SMOTE), and cost-sensitive learning) are evaluated… More >

Ifeanyi C. Emeto^*, Adamu A. Galadima, Ikechukwu H. Ezeh, Emmanuel O. Atomatofa, Christiana A. Okoloegbo

Journal of Cyber Security, Vol.8, pp. 609-640, 2026, DOI:10.32604/jcs.2026.084424 - 14 September 2026

Abstract Software-Defined Networking (SDN) introduces centralised network control and programmability, but its centralised control plane creates significant security vulnerabilities that can be exploited by cyber attackers. This study proposes and evaluates a hybrid security framework that integrates machine learning (ML)-based anomaly detection with blockchain-based authentication to enhance the security of the SDN control plane. The study aimed to analyse vulnerabilities in SDN architectures, develop an ML-driven attack detection model, secure API communications using blockchain-based authentication, and evaluate the performance and scalability of the proposed framework. Vulnerability assessment was conducted using Nmap and STRIDE threat modelling, while… More >

Richard Adusei^1,2,*, Gaddafi Abdul-Salaam²

Journal on Artificial Intelligence, Vol.8, pp. 425-472, 2026, DOI:10.32604/jai.2026.085269 - 14 September 2026

Abstract Fine-grained visual categorization (FGVC) presents a class of recognition problems in which the discriminative signal is spatially concentrated, visually subtle, and easily destroyed by the preprocessing and augmentation strategies that serve coarse recognition well. Where standard image classification requires a model to distinguish birds from cars, FGVC requires it to distinguish one bird species from another, a task that demands localization, feature-space shaping, and representation learning to operate in close coordination. This survey synthesizes thirty recent works spanning 2021 to 2026, organizing them under five interlocking themes, namely discriminative region discovery, metric learning and loss… More >

Mohammadreza Nehzati^*

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 155-177, 2026, DOI:10.32604/jimh.2026.082983 - 14 September 2026

Abstract Background: Exponential growth of diverse clinical data presents challenges for real-time predictive analytics in healthcare. Federated learning offers a paradigm for multi-institutional model training without centralized data sharing, but large-scale deployment across diverse healthcare settings with real-world electronic health record (EHR) integration challenges remains limited. Methods: We implemented Phase 1 of a federated learning network deploying federated histogram-based XGBoost across 47 U.S. healthcare institutions from January to June 2023 as a quality improvement initiative. The system processes clinical data locally, transmitting only gradient and Hessian histograms with differential privacy (ε = 1.0, δ = 10⁻⁵). Primary… More >

Anurag Sinha^1,*, Pranto Halder², Aditya Pandey³, Avi Mohan Kumar Shukla⁴, Shravan Kumar⁵, Ashutosh Rastogi⁶, Asima Akter Chowdhury⁶, Suryansh Rai⁷, Sagar Singh⁸

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 125-153, 2026, DOI:10.32604/jimh.2026.075373 - 14 September 2026

Abstract Accurate classification of pancreatic endocrinogenesis-related abnormalities in capsule endoscopy images remains challenging because of class imbalance, high intra-class variability, and limited annotated data. This study proposes an optimal data distribution framework based on Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) perturbations to enhance automated classification performance. The workflow combines perturbation-guided data augmentation, feature-importance analysis, and deep-learning classifiers, including convolutional neural networks (CNNs), U-Net, and You Only Look Once (YOLO). The approach is evaluated on the Kvasir-CapsuleSeg dataset using accuracy, macro F1-score, balanced area under the receiver operating characteristic curve (AUC), and confusion-matrix More >

Oncology Research Editorial Office

Oncology Research, Vol.34, No.10, 2026, DOI:10.32604/or.2026.092122 - 14 September 2026

Abstract This article has no abstract. More >

Oncology Research Editorial Office

Oncology Research, Vol.34, No.10, 2026, DOI:10.32604/or.2026.092120 - 14 September 2026

Abstract This article has no abstract. More >

Oncology Research Editorial Office

Oncology Research, Vol.34, No.10, 2026, DOI:10.32604/or.2026.092118 - 14 September 2026

Abstract This article has no abstract. More >

Zhenyang Ye¹, Jinyang Luo¹, Ying Zhang¹, Longhua Lu¹, Min Lei¹, Shi Deng^2,*

Oncology Research, Vol.34, No.10, 2026, DOI:10.32604/or.2026.087565 - 14 September 2026

Abstract Programmed cell death regulates the tumor immune microenvironment. A comprehensive synthesis of how multiple programmed cell death pathways collectively orchestrate the remodeling of the urological immune landscape is currently lacking. This review summarizes and discusses how diverse programmed cell death modes, including ferroptosis, pyroptosis, autophagy, PANoptosis, necroptosis and cuproptosis, regulate immune evasion or activation in a context-dependent manner. Current preclinical evidence suggests that necroptosis, pyroptosis, and cuproptosis may enhance anti-tumor immunity by facilitating the release of damage-associated molecular patterns and increasing the infiltration of functional CD8⁺ T cells and dendritic cells, thereby potentially improving responses to… More >