Atif Mahmood^1,*, Yasin Saleem¹, Usman Tariq², Yousef Ibrahim Daradkeh³, Adnan N. Qureshi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.073501 - 30 March 2026

Abstract Federated learning is a decentralized model training paradigm with significant potential. However, the quality of Federated Network’s client updates can vary due to non-IID data distributions, leading to suboptimal global models. To address this issue, we propose a novel client selection strategy called FedPA (Performance-Based Federated Averaging). This proposed model selectively aggregates client updates based on a predefined performance threshold. Only clients whose local models achieve an F1 score of 70% or higher after training are included in the aggregation process. Clients below this threshold receive the updated global model but do not contribute their… More >

Sara Tehsin^1,*, Muhammad John Abbas², Inzamam Mashood Nasir¹, Fadwa Alrowais³, Reham Abualhamayel⁴, Abdulsamad Ebrahim Yahya⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076515 - 12 March 2026

Abstract Globally, diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness. To treat these vision disorders effectively, proper diagnosis must occur in a timely manner, and with conventional methods such as fundus photography, optical coherence tomography (OCT), and slit-lamp imaging, much depends on an expert’s interpretation of the images, making the systems very labor-intensive to operate. Moreover, clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices. To solve these problems, we have developed the Efficient Channel-Spatial Attention Network (ECSA-Net), a new deep learning-based… More >

Tran Minh Bao¹, Kumar Shashvat², Nguyen Gia Nhu^3,*, Dac-Nhuong Le⁴

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075161 - 12 March 2026

Abstract The sudden growth of harmful web pages, including spam and phishing URLs, poses a greater threat to global cybersecurity than ever before. These URLs are commonly utilised to trick people into divulging confidential details or to stealthily deploy malware. To address this issue, we aimed to assess the efficiency of popular machine learning and neural network models in identifying such harmful links. To serve our research needs, we employed two different datasets: the PhiUSIIL dataset, which is specifically designed to address phishing URL detection, and another dataset developed to uncover spam links by examining the… More >

Mirna Yunita¹, Xiabi Liu^1,*, Zhaoyang Hai¹, Rachmat Muwardi²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075073 - 12 March 2026

Abstract Designing appropriate loss functions is critical to the success of supervised learning models. However, most conventional losses are fixed and manually designed, making them suboptimal for diverse and dynamic learning scenarios. In this work, we propose an Adaptive Meta-Loss Network (Adaptive-MLN) that learns to generate task-agnostic loss functions tailored to evolving classification problems. Unlike traditional methods that rely on static objectives, Adaptive-MLN treats the loss function itself as a trainable component, parameterized by a shallow neural network. To enable flexible, gradient-free optimization, we introduce a hybrid evolutionary approach that combines Genetic Algorithms (GA) for global More >

Basudev Nath¹, Deepak Sahoo¹, Sudhansu Shekhar Patra², Hassan Alkhiri³, Subrata Chowdhury⁴, Sheraz Aslam^5,6,*, Kainat Mustafa⁷

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.073486 - 12 March 2026

Abstract Accurate, up to date, and quick information related to any disaster supports disaster management team/authorities to perform quick, easy, and cost-effective response to enhance rescue operations to alleviate the possible loss of lives, financial risks, and properties. Due to damaged infrastructure in disaster-affected areas, social media is the only way to share/ exchange real time information. Therefore, ‘X’ (formerly Twitter) has become a major platform for disseminating real-time information during disaster events or emergencies, i.e., floods and earthquake. Rapid identification of actionable content is critical for effective humanitarian response; however, the brief and noisy nature… More >

Rafika Harrabi^1,2,*, Slim Ben Chaabane^1,2, Hassene Seddik²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.072054 - 12 March 2026

Abstract Securing restricted zones such as airports, research facilities, and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets. Face recognition has emerged as a key biometric approach for this purpose; however, existing systems are often sensitive to variations in illumination, occlusion, and pose, which degrade their performance in real-world conditions. To address these challenges, this paper proposes a novel hybrid face recognition method that integrates complementary feature descriptors such as Fuzzy-Gabor 2D Fisher Linear Discriminant (FG-2DFLD), Generalized 2D Linear Discriminant Analysis (G2DLDA), and Modular-Local Binary Patterns (Modular-LBP)… More >

Sumbul Azeem¹, Shazia Javed^1,*, Farheen Ibraheem², Uzma Bashir¹, Nazar Waheed³, Khursheed Aurangzeb⁴

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.071977 - 12 March 2026

Abstract Data serves as the foundation for training and testing machine learning and artificial intelligence models. The most fundamental part of data is its attributes or features. The feature set size changes from one dataset to another. Only the relevant features contribute meaningfully to classification accuracy. The presence of irrelevant features reduces the system’s effectiveness. Classification performance often deteriorates on high-dimensional datasets due to the large search space. Thus, one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the… More >

Syed Nisar Hussain Bukhari^*, Sehar Altaf

Journal on Artificial Intelligence, Vol.8, pp. 137-152, 2026, DOI:10.32604/jai.2026.077943 - 11 March 2026

Abstract Knee osteoarthritis is a progressive degenerative joint disorder that leads to pain, stiffness, and reduced mobility, significantly affecting quality of life. Early and reliable diagnosis is essential for effective disease management, yet conventional radiographic assessment remains time-consuming and subject to inter-observer variability. This study presents a comparative deep learning (DL) based approach for automated severity classification of knee osteoarthritis using plain radiographic images. Multiple pretrained convolutional neural network architectures, including EfficientNetB3, InceptionNet, VGG19, ResNet, and EfficientNetV2S, were evaluated within a transfer learning paradigm. All models were trained and assessed on a publicly available dataset to More >

Thanyapisit Buaprakhong¹, Varintorn Sithisint¹, Awirut Phusaensaart¹, Sinthon Wilke¹, Thatsamaphon Boonchuntuk¹, Thittaporn Ganokratanaa^1,*, Mahasak Ketcham²

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2026.069004 - 27 February 2026

Abstract The rapid digitalization of the energy sector has led to the deployment of large-scale smart metering systems that generate high-frequency time series data, creating new opportunities and challenges for energy anomaly detection. Accurate identification of anomalous patterns in building energy consumption is essential for optimizing operations, improving energy efficiency, and supporting grid reliability. This study investigates advanced feature engineering and machine learning modeling techniques for large-scale time series anomaly detection in building energy systems. Expanding upon previous benchmark frameworks, we introduce additional features such as oil price indices and solar cycle indicators, including sunset and… More >

Yu Zhou¹, Jiawei Tian², Kyungtae Kang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077373 - 26 February 2026

Abstract Arrhythmias are a frequently occurring phenomenon in clinical practice, but how to accurately distinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies. From a review of existing studies, two main factors appear to contribute to this problem: the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models. To overcome these limitations, this study proposes a dual-path multimodal framework, termed DM-EHC (Dual-Path Multimodal ECG Heartbeat Classifier), for ECG-based heartbeat classification. The proposed framework links 1D ECG temporal features with 2D time–frequency More >