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  • Open Access

    ARTICLE

    Hybrid Techniques of Multi-CNN and Ensemble Learning to Analyze Handwritten Spiral and Wave Drawing for Diagnosing Parkinson’s Disease

    Mohammed Al-Jabbar1, Mohammed Alshahrani1,*, Ebrahim Mohammed Senan2,3, Ibrahim Abunadi4, Sultan Ahmed Almalki1, Eman A Alshari3,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2429-2457, 2025, DOI:10.32604/cmes.2025.063938 - 30 May 2025

    Abstract Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by tremors, rigidity, and decreased movement. PD poses risks to individuals’ lives and independence. Early detection of PD is essential because it allows timely intervention, which can slow disease progression and improve outcomes. Manual diagnosis of PD is problematic because it is difficult to capture the subtle patterns and changes that help diagnose PD. In addition, the subjectivity and lack of doctors compared to the number of patients constitute an obstacle to early diagnosis. Artificial intelligence (AI) techniques, especially deep and automated learning models, provide promising… More >

  • Open Access

    ARTICLE

    Multi Chunk Learning Based Auto Encoder for Video Anomaly Detection

    Xiaosha Qi1, Genlin Ji2,*, Jie Zhang2, Bo Sheng3

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1861-1875, 2022, DOI:10.32604/iasc.2022.027182 - 24 March 2022

    Abstract Video anomaly detection is essential to distinguish abnormal events in large volumes of surveillance video and can benefit many fields such as traffic management, public security and failure detection. However, traditional video anomaly detection methods are unable to accurately detect and locate abnormal events in real scenarios, while existing deep learning methods are likely to omit important information when extracting features. In order to avoid omitting important features and improve the accuracy of abnormal event detection and localization, this paper proposes a novel method called Multi Chunk Learning based Skip Connected Convolutional Auto Encoder (MCSCAE).… More >

  • Open Access

    ARTICLE

    Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

    Shahan Yamin Siddiqui1,2, Iftikhar Naseer3, Muhammad Adnan Khan4, Muhammad Faheem Mushtaq5, Rizwan Ali Naqvi6,*, Dildar Hussain7, Amir Haider8

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1033-1049, 2021, DOI:10.32604/cmc.2021.013952 - 12 January 2021

    Abstract Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally. According to clinical statistics, one woman out of eight is under the threat of breast cancer. Lifestyle and inheritance patterns may be a reason behind its spread among women. However, some preventive measures, such as tests and periodic clinical checks can mitigate its risk thereby, improving its survival chances substantially. Early diagnosis and initial stage treatment can help increase the survival rate. For that purpose, pathologists can gather support from nondestructive and efficient computer-aided diagnosis… More >

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