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  • Open Access


    ABMRF: An Ensemble Model for Author Profiling Based on Stylistic Features Using Roman Urdu

    Aiman1, Muhammad Arshad1, Bilal Khan1, Khalil Khan2, Ali Mustafa Qamar3,*, Rehan Ullah Khan4

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 301-317, 2024, DOI:10.32604/iasc.2024.045402

    Abstract This study explores the area of Author Profiling (AP) and its importance in several industries, including forensics, security, marketing, and education. A key component of AP is the extraction of useful information from text, with an emphasis on the writers’ ages and genders. To improve the accuracy of AP tasks, the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1 (ABM1) and Random Forest (RF). The work uses an extensive technique that involves text message dataset pretreatment, model training, and assessment. To evaluate the effectiveness of several machine learning (ML) algorithms in classifying age… More >

  • Open Access


    BMRMIA: A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

    Yankun Cao1, Lina Xu3, Zhi Liu2, Xiaoyan Xiao4, Mingyu Wang5, Qin Li6, Hongji Xu2, Geng Yang6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 851-863, 2022, DOI:10.32604/cmes.2022.018424

    Abstract Contribution: This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology. The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system, and strengthen the teaching practice of deep learning related courses in hospitals, so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis. Background: In recent years, deep learning has been widely used in academia, industry, and medicine. An increasing number of companies are starting to recruit a large number… More >

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