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  • Open Access


    Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

    Ierin Babu1,*, R. MathuSoothana2, S. Kumar2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3621-3636, 2023, DOI:10.32604/iasc.2023.033791

    Abstract Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students’ grades and retention. Prediction of students’ performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately… More >

  • Open Access


    An Efficient Allocation for Lung Transplantation Using Ant Colony Optimization

    Lina M. K. Al-Ebbini*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1971-1985, 2023, DOI:10.32604/iasc.2023.030100

    Abstract A relationship between lung transplant success and many features of recipients’/donors has long been studied. However, modeling a robust model of a potential impact on organ transplant success has proved challenging. In this study, a hybrid feature selection model was developed based on ant colony optimization (ACO) and k-nearest neighbor (kNN) classifier to investigate the relationship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing (UNOS). The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’ functional status (i.e., quality of life) after… More >

  • Open Access


    Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

    Jaber S. Alzahrani1, Reem M. Alshehri2, Mohammad Alamgeer3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4, Ishfaq Yaseen4

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4267-4281, 2022, DOI:10.32604/cmc.2022.027369

    Abstract Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby… More >

  • Open Access


    Dynamic Feature Subset Selection for Occluded Face Recognition

    Najlaa Hindi Alsaedi*, Emad Sami Jaha

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 407-427, 2022, DOI:10.32604/iasc.2022.019538

    Abstract Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded… More >

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