The swelling use of computerized learning, accompanied by the rapid growth of information technology has become a surge of interest in the research community. Consequently, several technologies have been developed to maintain and promote computerized learning. In this study, we provided an in-depth analysis of two of the prominent computerized learning systems i.e., Intelligent Tutoring System (ITS) and Affective Tutoring System (ATS). An ITS is one of the training software systems, which use intelligent technologies to provide personalized learning content to students based on their learning needs with the aim of enhancing the individualized learning experience. Recently, researchers have demonstrated that the affect or emotional states of a student have an impact on the overall performance of his/her learning, which introduces a new trend of ITS development termed as ATS, which is the extended research of the previous one. Although there have been several studies on these tutoring systems, however, none of them has comprehensively analyzed both systems, particularly the transition from ITS to ATS. Therefore, this study examines these two tutoring systems more inclusively with regards to their architectures, models, and techniques and approaches used by taking into consideration the related researches conducted between 2014 to 2019. A crucial finding from the study is that ATS can be a promising tutoring system for the next generation learning environment by affiliating proper emotion recognition channels, along with computational intelligence approaches. Finally, this study concludes with research challenges and possible future directions and trends.

Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors.

Social media platforms like Twitter have become common tools for disseminating and consuming news because of the ease with which users can get access to and consume it. This paper focuses on the identification of false news and the use of cutting‐edge detection methods in the context of news, user, and social levels. Fake news detection taxonomy was proposed in this research. This study examines a variety of cutting‐edge methods for spotting false news and discusses their drawbacks. It also explored how to detect and recognize false news, such as credibility‐based, time‐based, social context‐based, and the substance of the news itself. Lastly, the paper examines various datasets used for detecting fake news and proposed an algorithm.

Students' motivation in computer graphic courses is a challenge for educators. Computer courses entail abstract notions and algorithmic features, and these difficulties affect the students' motivation. A problem-base learning serious game framework named Immersivio was designed. The researchers designed a quantitative study with a between subject design and implemented a treatment course using the problem-based serious game. The game was designed based on the principles of problem-based learning and serious games, i.e., higher order thinking, collaborative learning, and cognitive thinking and information visualasation theory. A non-random sampling procedure (purposive sampling) was used in this quasi-experimental study and the participants formed 2 groups, the experimental group (n= 24) and the control group (n= 26). MANOVA test results indicated that the experimental group participants who had experienced Immersivio were more motivated to learn computer graphics in terms of extrinsic motivation, intrinsic motivation, interest, attainment, cost, identification with academics, self-efficacy, and instrumentality. This study has implications for basic and advanced computer graphic lecturers and educators in this field.

The main purpose of k-Means clustering is partitioning patterns into various homogeneous clusters by minimizing cluster errors, but the modified solution of k-Means can be recovered with the guidance of Principal Component Analysis (PCA). In this paper, the linear Kernel PCA guides k-Means procedure using filter to modify images in situations where some parts are missing by k-Means classification. The proposed method consists of three steps: 1) transformation of the color space and using PCA to solve the eigenvalue problem pertaining to the covariance matrices of satellite image; 2) feature extraction from selected eigenvectors and are rearranged by applying the training map to extract the useful information as a set of new orthogonal variables called principal components; and 3) classification of the images based on the extracted features using k-Means clustering. The quantitative results obtained using the proposed method were compared with k-Means and k-Means PCA techniques in terms of accuracy in extraction. The contribution of this approach is the modification of PCA selection to achieve more accurate extraction of the water-body segmentation in satellite images.