Anandhavalli Muniasamy^1,*, Ashwag Alasmari²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 569-592, 2025, DOI:10.32604/cmes.2025.060484 - 11 April 2025

Abstract The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients. Today, the mass disease that needs attention in this context is cataracts. Although deep learning has significantly advanced the analysis of ocular disease images, there is a need for a probabilistic model to generate the distributions of potential outcomes and thus make decisions related to uncertainty quantification. Therefore, this study implements a Bayesian Convolutional Neural Networks (BCNN) model for predicting cataracts by assigning probability values to the predictions. It prepares convolutional neural network (CNN) and BCNN models. More > Graphic Abstract

Waleed Khalid Al-Ghanem¹, Emad Ul Haq Qazi^2,*, Tanveer Zia^2,3, Muhammad Hamza Faheem², Muhammad Imran⁴, Iftikhar Ahmad⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1009-1027, 2025, DOI:10.32604/cmes.2025.058352 - 11 April 2025

Abstract In the current digital era, new technologies are becoming an essential part of our lives. Consequently, the number of malicious software or malware attacks is rapidly growing. There is no doubt, the majority of malware attacks can be detected by most antivirus programs. However, such types of antivirus programs are one step behind malicious software. Due to these dilemmas, deep learning become popular in the detection and classification of malicious data. Therefore, researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and models. In More >

Penghui Liu^1,*, Tianyu Yang¹, Peng Zhang², Peiyuan Zou³

Energy Engineering, Vol.122, No.4, pp. 1387-1402, 2025, DOI:10.32604/ee.2025.062627 - 31 March 2025

Abstract The photovoltaic (PV) output process is inherently complex, often disrupted by a multitude of meteorological factors, while conventional detection methods at PV power stations prove inadequate, compromising prediction accuracy. To address this challenge, this paper introduces a power prediction method that leverages modal switching (MS), weight factor adjustment (WFA), and parallel long short-term memory (PALSTM). Initially, historical PV power station data is categorized into distinct modes based on global horizontal irradiance and converted solar angles. Correlation analysis is then employed to evaluate the impact of various meteorological factors on PV power, selecting those with strong… More >

Yi-Ze Wu, Yi-Cheng Huang^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 97-114, 2025, DOI:10.32604/cmc.2025.063441 - 26 March 2025

Abstract Precision steel balls are critical components in precision bearings. Surface defects on the steel balls will significantly reduce their useful life and cause linear or rotational transmission errors. Human visual inspection of precision steel balls demands significant labor work. Besides, human inspection cannot maintain consistent quality assurance. To address these limitations and reduce inspection time, a convolutional neural network (CNN) based optical inspection system has been developed that automatically detects steel ball defects using a novel designated vertical mechanism. During image detection processing, two key challenges were addressed and resolved. They are the reflection caused… More >

Fahad Ahmad^1,2,*, Saad Awadh Alanazi³, Kashaf Junaid⁴, Maryam Shabbir⁵, Asim Ali¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 381-405, 2025, DOI:10.32604/cmc.2025.062452 - 26 March 2025

Abstract Image processing plays a vital role in various fields such as autonomous systems, healthcare, and cataloging, especially when integrated with deep learning (DL). It is crucial in medical diagnostics, including the early detection of diseases like chronic obstructive pulmonary disease (COPD), which claimed 3.2 million lives in 2015. COPD, a life-threatening condition often caused by prolonged exposure to lung irritants and smoking, progresses through stages. Early diagnosis through image processing can significantly improve survival rates. COPD encompasses chronic bronchitis (CB) and emphysema; CB particularly increases in smokers and generally affects individuals between 50 and 70… More >

Nizar Zaghden¹, Emad Ibrahim², Mukaram Safaldin^2,*, Mahmoud Mejdoub³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1117-1147, 2025, DOI:10.32604/cmc.2025.061948 - 26 March 2025

Abstract The increasing elderly population has heightened the need for accurate and reliable fall detection systems, as falls can lead to severe health complications. Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques. This study introduces an advanced fall detection model integrating YOLOv8, Faster R-CNN, and Generative Adversarial Networks (GANs) to enhance accuracy and robustness. A modified YOLOv8 architecture serves as the core, utilizing spatial attention mechanisms to improve critical image regions’ detection. Faster R-CNN is employed for fine-grained human posture analysis, while GANs… More >

Sumaiya Noor¹, Salman A. AlQahtani², Salman Khan^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1435-1450, 2025, DOI:10.32604/cmc.2025.061700 - 26 March 2025

Abstract The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion, metabolism, detoxification, and immunity. Liver diseases result from factors such as viral infections, obesity, alcohol consumption, injuries, or genetic predispositions. Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates. Traditionally, diagnosing liver diseases relied heavily on clinical expertise, often leading to subjective, challenging, and time-intensive processes. However, early detection is essential for effective intervention, and advancements in machine learning (ML) have demonstrated remarkable success in predicting various conditions, including Chronic Obstructive… More >

Yi-Feng Luo¹, Guan-Jhu Chen^2,*, Chun-Liang Liu³, Yen-Tse Chung⁴

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 823-844, 2025, DOI:10.32604/cmc.2025.061498 - 26 March 2025

Abstract As lithium-ion batteries become increasingly prevalent in electric scooters, vehicles, mobile devices, and energy storage systems, accurate estimation of remaining battery capacity is crucial for optimizing system performance and reliability. Unlike traditional methods that rely on static alternating internal resistance (SAIR) measurements in an open-circuit state, this study presents a real-time state of charge (SOC) estimation method combining dynamic alternating internal resistance (DAIR) with artificial neural networks (ANN). The system simultaneously measures electrochemical impedance |Z| at various frequencies, discharge C-rate, and battery surface temperature during the discharge process, using these parameters for ANN training. The… More >

Chexiaole Zhang, Haiyan Fu^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1291-1306, 2025, DOI:10.32604/cmc.2025.060740 - 26 March 2025

Abstract As computer data grows exponentially, detecting anomalies within system logs has become increasingly important. Current research on log anomaly detection largely depends on log templates derived from log parsing. Word embedding is utilized to extract information from these templates. However, this method neglects a portion of the content within the logs and confronts the challenge of data imbalance among various log template types after parsing. Currently, specialized research on data imbalance across log template categories remains scarce. A dual-attention-based log anomaly detection model (LogDA), which leveraged data imbalance, was proposed to address these issues in More >

Borui Wang, Dongyue Gao^*, Haiyang Gu, Mengke Ding, Zhanjun Wu

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 455-473, 2025, DOI:10.32604/cmc.2025.060617 - 26 March 2025

Abstract Fatigue damage is a primary contributor to the failure of composite structures, underscoring the critical importance of monitoring its progression to ensure structural safety. This paper introduces an innovative approach to fatigue damage monitoring in composite structures, leveraging a hybrid methodology that integrates the Whale Optimization Algorithm (WOA)-Backpropagation (BP) neural network with an ultrasonic guided wave feature selection algorithm. Initially, a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves, thereby establishing a signal space that correlates with the structural condition. Subsequently, the Relief-F algorithm is applied for signal feature extraction,… More >