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  • Open Access

    ARTICLE

    Pitcher Performance Prediction Major League Baseball (MLB) by Temporal Fusion Transformer

    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 >

  • Open Access

    ARTICLE

    An Adaptive Features Fusion Convolutional Neural Network for Multi-Class Agriculture Pest Detection

    Muhammad Qasim1,2, Syed M. Adnan Shah1, Qamas Gul Khan Safi1, Danish Mahmood2, Adeel Iqbal3,*, Ali Nauman3, Sung Won Kim3,*

    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 >

  • Open Access

    ARTICLE

    Research on Vehicle Safety Based on Multi-Sensor Feature Fusion for Autonomous Driving Task

    Yang Su1,*, Xianrang Shi1, Tinglun Song2

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5831-5848, 2025, DOI:10.32604/cmc.2025.064036 - 19 May 2025

    Abstract Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving. This study aims to enhance the learning efficiency of multi-sensor feature fusion in autonomous driving tasks, thereby improving the safety and responsiveness of the system. To achieve this goal, we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities: visual, radar, and lidar data. The model optimizes the feature fusion process through the introduction of two novel mechanisms: Sparse Channel Pooling (SCP) and Residual Triplet-Attention… More >

  • Open Access

    ARTICLE

    A Multi-Layers Information Fused Deep Architecture for Skin Cancer Classification in Smart Healthcare

    Veena Dillshad1, Muhammad Attique Khan2,*, Muhammad Nazir1, Jawad Ahmad2, Dina Abdulaziz AlHammadi3, Taha Houda2, Hee-Chan Cho4, Byoungchol Chang5,*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5299-5321, 2025, DOI:10.32604/cmc.2025.063851 - 19 May 2025

    Abstract Globally, skin cancer is a prevalent form of malignancy, and its early and accurate diagnosis is critical for patient survival. Clinical evaluation of skin lesions is essential, but several challenges, such as long waiting times and subjective interpretations, make this task difficult. The recent advancement of deep learning in healthcare has shown much success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics. Deep learning improves the speed and precision of skin cancer diagnosis, leading to earlier prediction and treatment. In this work, we proposed a novel deep architecture for skin cancer… More >

  • Open Access

    REVIEW

    Research Progress on Multi-Modal Fusion Object Detection Algorithms for Autonomous Driving: A Review

    Peicheng Shi1,*, Li Yang1, Xinlong Dong1, Heng Qi2, Aixi Yang3

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3877-3917, 2025, DOI:10.32604/cmc.2025.063205 - 19 May 2025

    Abstract As the number and complexity of sensors in autonomous vehicles continue to rise, multimodal fusion-based object detection algorithms are increasingly being used to detect 3D environmental information, significantly advancing the development of perception technology in autonomous driving. To further promote the development of fusion algorithms and improve detection performance, this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms. Starting from single-modal sensor detection, the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds. For image-based detection… More >

  • Open Access

    ARTICLE

    Rolling Bearing Fault Diagnosis Based on Cross-Attention Fusion WDCNN and BILSTM

    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 >

  • Open Access

    ARTICLE

    Multi-Modal Named Entity Recognition with Auxiliary Visual Knowledge and Word-Level Fusion

    Huansha Wang*, Ruiyang Huang*, Qinrang Liu, Xinghao Wang

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5747-5760, 2025, DOI:10.32604/cmc.2025.061902 - 19 May 2025

    Abstract Multi-modal Named Entity Recognition (MNER) aims to better identify meaningful textual entities by integrating information from images. Previous work has focused on extracting visual semantics at a fine-grained level, or obtaining entity related external knowledge from knowledge bases or Large Language Models (LLMs). However, these approaches ignore the poor semantic correlation between visual and textual modalities in MNER datasets and do not explore different multi-modal fusion approaches. In this paper, we present MMAVK, a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion, which aims to leverage the Multi-modal Large Language Model… More >

  • Open Access

    ARTICLE

    YOLO-AB: A Fusion Algorithm for the Elders’ Falling and Smoking Behavior Detection Based on Improved YOLOv8

    Xianghong Cao, Chenxu Li*, Haoting Zhai

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5487-5515, 2025, DOI:10.32604/cmc.2025.061823 - 19 May 2025

    Abstract The behavior safety testing of more and more elderly people living alone has become a hot research topic along with the arrival of an aging society. A YOLO-Abnormal Behaviour (YOLO-AB) algorithm for fusion detection of falling and smoking behaviors of elderly people living alone has been proposed in this paper, which can fully utilize the potential of the YOLOv8 algorithm on object detection and deeply explore the characteristics of different types of behaviors among the elderly, to solve the problems of single detection type, low fusion detection accuracy, and high missed detection rate. Firstly, datasets… More >

  • Open Access

    ARTICLE

    Multi-Stage Vision Transformer and Knowledge Graph Fusion for Enhanced Plant Disease Classification

    Wafaa H. Alwan1,*, Sabah M. Alturfi2

    Computer Systems Science and Engineering, Vol.49, pp. 419-434, 2025, DOI:10.32604/csse.2025.064195 - 30 April 2025

    Abstract Plant diseases pose a significant challenge to global agricultural productivity, necessitating efficient and precise diagnostic systems for early intervention and mitigation. In this study, we propose a novel hybrid framework that integrates EfficientNet-B8, Vision Transformer (ViT), and Knowledge Graph Fusion (KGF) to enhance plant disease classification across 38 distinct disease categories. The proposed framework leverages deep learning and semantic enrichment to improve classification accuracy and interpretability. EfficientNet-B8, a convolutional neural network (CNN) with optimized depth and width scaling, captures fine-grained spatial details in high-resolution plant images, aiding in the detection of subtle disease symptoms. In… More >

  • Open Access

    ARTICLE

    Optimal Evaluation of Photovoltaic Consumption Schemes in Distribution Networks Based on BASS Model for Photovoltaic Installed Capacity Prediction

    Chenyang Fu*, Xinghua Wang, Zilv Li, Xixian Liu, Xiongfei Zhang, Zhuoli Zhao

    Energy Engineering, Vol.122, No.5, pp. 1805-1821, 2025, DOI:10.32604/ee.2025.061172 - 25 April 2025

    Abstract With the large-scale promotion of distributed photovoltaics, new challenges have emerged in the photovoltaic consumption within distribution networks. Traditional photovoltaic consumption schemes have primarily focused on static analysis. However, as the scale of photovoltaic power generation devices grows and the methods of integration diversify, a single consumption scheme is no longer sufficient to meet the actual needs of current distribution networks. Therefore, this paper proposes an optimal evaluation method for photovoltaic consumption schemes based on BASS model predictions of installed capacity, aiming to provide an effective tool for generating and evaluating photovoltaic consumption schemes in… More > Graphic Abstract

    Optimal Evaluation of Photovoltaic Consumption Schemes in Distribution Networks Based on BASS Model for Photovoltaic Installed Capacity Prediction

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