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  • Open Access


    Towards Lessening Learners’ Aversive Emotions and Promoting Their Mental Health: Developing and Validating a Measurement of English Speaking Demotivation in the Chinese EFL Context

    Chili Li1, Xinxin Zhao2, Ziwen Pan3, Ting Yi4, Long Qian5,6,*

    International Journal of Mental Health Promotion, Vol.26, No.2, pp. 161-175, 2024, DOI:10.32604/ijmhp.2023.029896

    Abstract While a plethora of studies has been conducted to explore demotivation and its impact on mental health in second language (L2) education, scanty research focuses on demotivation in L2 speaking learning. Particularly, little research explores the measures to quantify L2 speaking demotivation. The present two-phase study attempts to develop and validate an English Speaking Demotivation Scale (ESDS). To this end, an independent sample of 207 Chinese tertiary learners of English as a Foreign Language (EFL) participated in the development phase, and another group of 188 Chinese EFL learners was recruited for the validation of the scale. Exploratory Factor Analysis (EFA)… More >

  • Open Access


    Towards Innovative Research Approaches to Investigating the Role of Emotional Variables in Promoting Language Teachers’ and Learners’ Mental Health

    Ali Derakhshan1, Yongliang Wang2,*, Yongxiang Wang2,*, José Luis Ortega-Martín3

    International Journal of Mental Health Promotion, Vol.25, No.7, pp. 823-832, 2023, DOI:10.32604/ijmhp.2023.029877

    Abstract The adequacy of language education largely depends on the favorable and unfavorable emotions that teachers and students experience throughout the education process. Simply said, emotional factors play a key role in improving the quality of language teaching and learning. Furthermore, these emotional factors also promote the well-being of language teachers and learners and place them in a suitable mental condition. In view of the favorable impact of emotional factors on the mental health of language teachers and learners, many educational scholars around the world have studied these factors, their background, and their pedagogical consequences. Nonetheless, the majority of previous studies… More >

  • Open Access


    Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer

    Sait Can Yucebas*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 49-71, 2023, DOI:10.32604/iasc.2023.036871

    Abstract The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyper-parameter optimization to get the best performance from different models, which is overlooked in… More >

  • Open Access


    Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

    I. Mettildha Mary1,*, K. Karuppasamy2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2667-2685, 2023, DOI:10.32604/csse.2023.031114

    Abstract CC’s (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations between them. This problem can… 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


    An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

    Mingyong Li1, Lirong Tang1, Longfei Ma1, Honggang Zhao1, Jinyu Hu1, Yan Wei1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2349-2371, 2023, DOI:10.32604/cmes.2023.022604

    Abstract The learning status of learners directly affects the quality of learning. Compared with offline teachers, it is difficult for online teachers to capture the learning status of students in the whole class, and it is even more difficult to continue to pay attention to students while teaching. Therefore, this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion. Specifically, a facial expression recognition model and an eye state recognition model are constructed to detect students’ emotions and fatigue, respectively. By integrating the detected data with the homework test score data after… More >

  • Open Access


    Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

    Swarna Sudha Muppudathi1, Valarmathi Krishnasamy2,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1027-1042, 2023, DOI:10.32604/iasc.2023.031165

    Abstract Social Networking Sites (SNSs) are nowadays utilized by the whole world to share ideas, images, and valuable contents by means of a post to reach a group of users. The use of SNS often inflicts the physical and the mental health of the people. Nowadays, researchers often focus on identifying the illegal behaviors in the SNS to reduce its negative influence. The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide annotated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy. To overcome these issues, the proposed… More >

  • Open Access


    Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

    P. N. Ramesh1,*, S. Kannimuthu2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3845-3860, 2023, DOI:10.32604/iasc.2023.031329

    Abstract The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’ current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system based on learners’ skill level… More >

  • Open Access


    Artificial Intelligence Techniques Based Learner Authentication in Cybersecurity Higher Education Institutions

    Abdullah Saad AL-Malaise AL-Ghamdi1, Mahmoud Ragab2,3,4,*

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3131-3144, 2022, DOI:10.32604/cmc.2022.026457

    Abstract Education 4.0 is being authorized more and more by the design of artificial intelligence (AI) techniques. Higher education institutions (HEI) have started to utilize Internet technologies to improve the quality of the service and boost knowledge. Due to the unavailability of information technology (IT) infrastructures, HEI is vulnerable to cyberattacks. Biometric authentication can be used to authenticate a person based on biological features such as face, fingerprint, iris, and so on. This study designs a novel search and rescue optimization with deep learning based learning authentication technique for cybersecurity in higher education institutions, named SRODL-LAC technique. The proposed SRODL-LAC technique… More >

  • Open Access


    Machine Learning Enabled e-Learner Non-Verbal Behavior Detection in IoT Environment

    Abdelzahir Abdelmaboud1, Fahd N. Al-Wesabi1,2,3, Mesfer Al Duhayyim4, Taiseer Abdalla Elfadil Eisa5, Manar Ahmed Hamza6,*, Mohammed Rizwanullah6, Abu Serwar Zamani6, Radwa Marzouk7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 679-693, 2022, DOI:10.32604/cmc.2022.024240

    Abstract Internet of Things (IoT) with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning applications. At the same time, machine learning (ML) and data mining approaches are presented for accomplishing prediction and classification processes. With this motivation, this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection (IML-ELNVBD) in IoT environment. The proposed IML-ELNVBD technique allows the IoT devices such as audio sensors, cameras, etc. which are then connected to the cloud server for further processing. In addition, the modelling and extraction of behaviour… More >

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