Wonbyung Lee, Jang Hyun Kim^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5393-5412, 2025, DOI:10.32604/cmc.2025.065413 - 19 May 2025

Abstract Predicting player performance in sports is a critical challenge with significant implications for team success, fan engagement, and financial outcomes. Although, in Major League Baseball (MLB), statistical methodologies such as sabermetrics have been widely used, the dynamic nature of sports makes accurate performance prediction a difficult task. Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions. This study addresses this challenge by employing the temporal fusion transformer (TFT), an advanced and cutting-edge deep learning model for complex data, to predict pitchers’ earned run average (ERA), a key More >

Muhammad Qasim^1,2, Syed M. Adnan Shah¹, Qamas Gul Khan Safi¹, Danish Mahmood², Adeel Iqbal^3,*, Ali Nauman³, Sung Won Kim^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4429-4445, 2025, DOI:10.32604/cmc.2025.065060 - 19 May 2025

Abstract Grains are the most important food consumed globally, yet their yield can be severely impacted by pest infestations. Addressing this issue, scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods. Traditional approaches often rely on preprocessed datasets, but there is a growing need for solutions that utilize real-time images of pests in their natural habitat. Our study introduces a novel two-step approach to tackle this challenge. Initially, raw images with complex backgrounds are captured. In the subsequent step, feature extraction is performed using both hand-crafted algorithms (Haralick, LBP, and Color… More >

Takanobu Hirosawa^*, Taro Shimizu

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3919-3944, 2025, DOI:10.32604/cmc.2025.063803 - 19 May 2025

Abstract Artificial Intelligence (AI) is fundamentally transforming medical diagnostics, driving advancements that enhance accuracy, efficiency, and personalized patient care. This narrative review explores AI integration across various diagnostic domains, emphasizing its role in improving clinical decision-making. The evolution of medical diagnostics from traditional observational methods to sophisticated imaging, laboratory tests, and molecular diagnostics lays the foundation for understanding AI’s impact. Modern diagnostics are inherently complex, influenced by multifactorial disease presentations, patient variability, cognitive biases, and systemic factors like data overload and interdisciplinary collaboration. AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches, employing machine… More >

Taher Alzahrani^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4575-4606, 2025, DOI:10.32604/cmc.2025.063448 - 19 May 2025

Abstract The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, and governments, highlighting the urgent need for robust malware detection mechanisms. Conventional machine learning-based approaches rely on static and dynamic malware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures. Although machine learning algorithms (MLAs) offer promising detection capabilities, their reliance on extensive feature engineering limits real-time applicability. Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,… More >

Milinda Priyankara Bandara Gamawelagedara¹, Mian Usman Sattar¹, Raza Hasan^2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4061-4084, 2025, DOI:10.32604/cmc.2025.062826 - 19 May 2025

Abstract Fuel station drive-offs, wherein the drivers simply drive off without paying, are a major issue in the UK (United Kingdom) due to rising fuel costs and financial hardships. The phenomenon has increased greatly over the last few years, with reports indicating a substantial increase in such events in the major cities. Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases. Such systems typically involve costly camera installation and maintenance and are consequently out of the budget of small fuel stations. These conventional approaches also fall short regarding real-time… More >

Yingyong Zou^*, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4699-4723, 2025, DOI:10.32604/cmc.2025.062625 - 19 May 2025

Abstract High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is… More >

Hui Xu, Jiahui Chen^*, Zhonghao Hu

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4495-4513, 2025, DOI:10.32604/cmc.2025.062189 - 19 May 2025

Abstract Nowadays, abnormal traffic detection for Software-Defined Networking (SDN) faces the challenges of large data volume and high dimensionality. Since traditional machine learning-based detection methods have the problem of data redundancy, the Metaheuristic Algorithm (MA) is introduced to select features before machine learning to reduce the dimensionality of data. Since a Tyrannosaurus Optimization Algorithm (TROA) has the advantages of few parameters, simple implementation, and fast convergence, and it shows better results in feature selection, TROA can be applied to abnormal traffic detection for SDN. However, TROA suffers from insufficient global search capability, is easily trapped in… More >

Jiling Wan, Lifeng Cao^*, Jinlong Bai, Jinhui Li, Xuehui Du

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4775-4794, 2025, DOI:10.32604/cmc.2025.061964 - 19 May 2025

Abstract Blockchain platforms with the unique characteristics of anonymity, decentralization, and transparency of their transactions, which are faced with abnormal activities such as money laundering, phishing scams, and fraudulent behavior, posing a serious threat to account asset security. For these potential security risks, this paper proposes a hybrid neural network detection method (HNND) that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain. In HNND, the Temporal Transaction Graph Attention Network (T2GAT) is first designed to learn biased aggregation representation of multi-attribute transactions among More >

Huiru Cao¹, Shaoxin Li², Xiaomin Li^3,*, Yongxin Liu⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5575-5593, 2025, DOI:10.32604/cmc.2025.061954 - 19 May 2025

Abstract Efficient flight path design for unmanned aerial vehicles (UAVs) in urban environmental event monitoring remains a critical challenge, particularly in prioritizing high-risk zones within complex urban landscapes. Current UAV path planning methodologies often inadequately account for environmental risk factors and exhibit limitations in balancing global and local optimization efficiency. To address these gaps, this study proposes a hybrid path planning framework integrating an improved Ant Colony Optimization (ACO) algorithm with an Orthogonal Jump Point Search (OJPS) algorithm. Firstly, a two-dimensional grid model is constructed to simulate urban environments, with key monitoring nodes selected based on… More >

Huiling Yu¹, Xibei Jia², Yongfeng Niu¹, Yizhuo Zhang^1,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4329-4352, 2025, DOI:10.32604/cmc.2025.061882 - 19 May 2025

Abstract The counterflow burner is a combustion device used for research on combustion. By utilizing deep convolutional models to identify the combustion state of a counterflow burner through visible flame images, it facilitates the optimization of the combustion process and enhances combustion efficiency. Among existing deep convolutional models, InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt. It has garnered significant attention for its computational efficiency, remarkable model accuracy, and exceptional feature extraction capabilities. However, since this model still has limitations in the combustion state recognition task, we propose… More >