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  • Open Access


    OPT-BAG Model for Predicting Student Employability

    Minh-Thanh Vo1, Trang Nguyen2, Tuong Le3,4,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1555-1568, 2023, DOI:10.32604/cmc.2023.039334

    Abstract The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a job. Based on the results of this type of analysis, university managers can improve the employability of their students, which can help in attracting students in the future. In addition, learners can focus on the essential skills identified through this analysis during their studies, to increase their employability. An effective method called OPT-BAG (OPTimisation of BAGging classifiers) was therefore developed to model the problem of predicting the employability of students. This model can help predict the employability of students… More >

  • Open Access


    Classifying Hematoxylin and Eosin Images Using a Super-Resolution Segmentor and a Deep Ensemble Classifier

    P. Sabitha*, G. Meeragandhi

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1983-2000, 2023, DOI:10.32604/iasc.2023.034402

    Abstract Developing an automatic and credible diagnostic system to analyze the type, stage, and level of the liver cancer from Hematoxylin and Eosin (H&E) images is a very challenging and time-consuming endeavor, even for experienced pathologists, due to the non-uniform illumination and artifacts. Albeit several Machine Learning (ML) and Deep Learning (DL) approaches are employed to increase the performance of automatic liver cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations. In this work, we present a new Ensemble Classifier (hereafter called ECNet) to classify the H&E stained… More >

  • Open Access


    An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection

    Deris Stiawan1, Ali Bardadi1, Nurul Afifah1, Lisa Melinda1, Ahmad Heryanto1, Tri Wanda Septian1, Mohd Yazid Idris2, Imam Much Ibnu Subroto3, Lukman4, Rahmat Budiarto5,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1759-1774, 2023, DOI:10.32604/csse.2023.034047

    Abstract The Repository Mahasiswa (RAMA) is a national repository of research reports in the form of final assignments, student projects, theses, dissertations, and research reports of lecturers or researchers that have not yet been published in journals, conferences, or integrated books from the scientific repository of universities and research institutes in Indonesia. The increasing popularity of the RAMA Repository leads to security issues, including the two most widespread, vulnerable attacks i.e., Structured Query Language (SQL) injection and cross-site scripting (XSS) attacks. An attacker gaining access to data and performing unauthorized data modifications is extremely dangerous. This paper aims to provide an… More >

  • Open Access


    Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

    Hiam Alquran1,2, Yazan Al-Issa3, Mohammed Alslatie4, Isam Abu-Qasmieh1, Amin Alqudah3, Wan Azani Mustafa5,7,*, Yasmin Mohd Yacob6,7

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1653-1671, 2023, DOI:10.32604/csse.2023.033861

    Abstract Liver cancer is the second leading cause of cancer death worldwide. Early tumor detection may help identify suitable treatment and increase the survival rate. Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs. Magnetic Resonance Imaging (MRI), in particular, uses magnetic fields and radio waves to differentiate internal human organs tissue. However, the interpretation of medical images requires the subjective expertise of a radiologist and oncologist. Thus, building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses. This paper proposes a hybrid automated system to compare the performance of 3D features and 2D… More >

  • Open Access


    An Adaptive-Feature Centric XGBoost Ensemble Classifier Model for Improved Malware Detection and Classification

    J. Pavithra*, S. Selvakumarasamy

    Journal of Cyber Security, Vol.4, No.3, pp. 135-151, 2022, DOI:10.32604/jcs.2022.031889

    Abstract Machine learning (ML) is often used to solve the problem of malware detection and classification, and various machine learning approaches are adapted to the problem of malware classification; still acquiring poor performance by the way of feature selection, and classification. To address the problem, an efficient novel algorithm for adaptive feature-centered XG Boost Ensemble Learner Classifier “AFC-XG Boost” is presented in this paper. The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set. The model turns the XG Boost classifier in several stages to optimize performance. At preprocessing stage, the data… More >

  • Open Access


    Chi-Square and PCA Based Feature Selection for Diabetes Detection with Ensemble Classifier

    Vaibhav Rupapara1, Furqan Rustam2, Abid Ishaq2, Ernesto Lee3, Imran Ashraf4,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1931-1949, 2023, DOI:10.32604/iasc.2023.028257

    Abstract Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization. During the last few years, an alarming increase is observed worldwide with a 70% rise in the disease since 2000 and an 80% rise in male deaths. If untreated, it results in complications of many vital organs of the human body which may lead to fatality. Early detection of diabetes is a task of significant importance to start timely treatment. This study introduces a methodology for the classification of diabetic and normal people using an ensemble machine learning model… More >

  • Open Access


    Ensemble Classifier-Based Features Ranking on Employee Attrition

    Yok-Yen Nguwi*

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 189-199, 2022, DOI:10.32604/jai.2022.034064

    Abstract The departure of good employee incurs direct and indirect cost and impacts for an organization. The direct cost arises from hiring to training of the relevant employee. The replacement time and lost productivity affect the running of business processes. This work presents the use of ensemble classifier to identify important attributes that affects attrition significantly. The data consists of attributes related to job function, education level, satisfaction towards work and working relationship, compensation, and frequency of business travel. Both bagging and boosting classifiers were used for testing. The results show that the selected features (nine selected features) achieve the same… More >

  • Open Access


    Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

    P. M. Arunkumar1, Mehedi Masud2, Sultan Aljahdali2, Mohamed Abouhawwash3,4,*

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2265-2280, 2023, DOI:10.32604/csse.2023.032571

    Abstract Nowadays, the cloud environment faces numerous issues like synchronizing information before the switch over the data migration. The requirement for a centralized internet of things (IoT)-based system has been restricted to some extent. Due to low scalability on security considerations, the cloud seems uninteresting. Since healthcare networks demand computer operations on large amounts of data, the sensitivity of device latency evolved among health networks is a challenging issue. In comparison to cloud domains, the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing solutions. Previous fog computing… More >

  • Open Access


    Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

    Xuhui Zhu1,3, Pingfan Xia1,3, Qizhi He2,4,*, Zhiwei Ni1,3, Liping Ni1,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 653-671, 2023, DOI:10.32604/cmes.2022.022985

    Abstract Multiple classifier system exhibits strong classification capacity compared with single classifiers, but they require significant computational resources. Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers. However, current methods fail to identify precise solutions for constructing an ensemble classifier. In this study, we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm (ECDPB). Considering that extreme learning machines (ELMs) have rapid learning rates and good generalization ability, they can serve as the basic classifier for creating multiple candidates while using fewer computational resources. Meanwhile, we introduce a combined diversity measure… More >

  • Open Access


    An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

    Mahesh B. Shelke1, Jeong Gon Lee2,*, Sovan Samanta3, Sachin N. Deshmukh1, G. Bhalke Daulappa4, Rahul B. Mannade5, Arun Kumar Sivaraman6

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2457-2468, 2023, DOI:10.32604/csse.2023.027979

    Abstract In today’s digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative,… More >

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