One crucial and intricate problem in the education sector that must be dealt with is children who initially enrolled in schools but later dropped out before finishing mandatory primary education. These children are generally referred to as out-of-school children. To contribute to the discuss, this paper presents the development of a robust Multilayer Perceptron (MLP) based Neural Network Model (NN) for optimal prognostic learning of out-of-school children trends in Africa. First, the Bayesian optimization algorithm has been engaged to determine the best MLP hyperparameters and their specific training values. Secondly, MLP-tuned hyperparameters were employed for optimal prognostic learning of different out-of-school children data trends in Africa. Thirdly, to assess the proposed MLP-NN model's prognostic performance, two error metrics were utilized, which are the Correlation coefficient (R) and Normalized root means square error (NRMSE). Among other things, a higher R and lower NRMSE values indicate a better MLP-NN precision performance. The all-inclusive results of the developed MLP-NN model indicate a satisfactory prediction capacity, attaining low NRMSE values between 0.017 - 0.310 during training and 0.034 - 0.233 during testing, respectively. In terms of correlation fits, the out-of-school children's data and the ones obtained with the developed MLP-NN model recorded high correlation precision training/testing performance values of 0.9968/0.9974, 0.9801/0.9373, 0.9977/0.9948 and 0.9957/0.9970, respectively. Thus, the MLP-NN model has made it possible to reliably predict the different patterns and trends rate of out-of-school children in Africa. One of the implications for counselling, among others, is that if every African government is seriously committed to funding education at the foundation level, there would be a reduction in the number of out-of-school children as observed in the out-of-school children data.

This paper presents the Test of Creativity and Divergent Thinking-Digital (TCD-D) mobile application, a digital version of the Williams Test for assessing creativity through graphic production. The Test of Creativity and Divergent Thinking-Digital is a new, simple and intuitive mobile application developed by a team of psychologists and computer scientists to remain faithful to the paper version of the test but to provide a faster assessment of divergent thinking. In fact, creativity assessment tests are currently still administered in paper form, which requires a lot of time and human resources for scoring, especially when administered to large samples, as is the case in educational studies. Several digital prototypes of creativity assessment instruments have been developed over the past decade, some of which are derived from paper instruments and some of which are completely new. Of all these attempts, no one has yet worked on a digital version of the Williams Test of Creativity and Divergent Thinking, although this instrument is widely used in Europe and Asia. Moreover, of all the prototypes of digital tools in the literature, none has been developed as a mobile app for tablets, a tool very close to the younger generation. The app was developed to provide a quicker and more contemporary assessment that accommodates the technological interests of digital natives through the use of touch in drawing and adds some additional indices to those of the paper tool for assessing fluency, flexibility, originality and creative elaboration. The application for Android tablets speeds up the assessment of divergent thinking and supports the monitoring of creative potential in educational and learning contexts. The paper discusses how the application works, the preferences and opinions of the students who tested it, and the future developments planned for the implementation of the application.

Private tutoring was a non-formal education, it was used as an alternative by parents to help support and maximize the learning process that students get at school. Sometimes parents have difficulty in adjusting the desired and needed criteria with available alternatives or teachers. To overcome these obstacles, this research used the MADM approach in providing alternative recommendations, based on the criteria used as the basis for decision making. MADM consists of SAW, WP, TOPSIS, and AHP. The advantages of the SAW, WP, and TOPSIS methods in managing cost and benefit data were used in the ranking process. While the weaknesses of the three methods in the weighting process can be overcome by the AHP method, which was able to provide more objective weighting results. Therefore, this research aimed to analyze the comparison of the combination of AHP-SAW, AHP-WP, and AHP-TOPSIS methods in the selection of private tutors. The combination of these methods was compared based on accuracy, ranking, and preference to get the best combination of MADM methods in determining the selection of private tutors. The criteria used in this research were education, experience, cost, duration, rating, and distance. The comparison of the three combinations of methods showed the AHP-SAW method has an accuracy rate of 88.14%, AHP-WP of 68.64%, and AHP-TOPSIS of 66.95%. The average ranking showed the AHP-SAW method gave results of 91%, AHP-WP of 88%, and AHP-TOPSIS of 89%. In addition, the average preference showed the AHP-SAW method gave a value of 0.771, AHP-WP of 0.073, and AHP-TOPSIS of 0.564. Thus, it showed the AHP-SAW gave better results in the case of private tutor selection than the AHP-WP and AHP-TOPSIS.

The foundational tenet of any nation's prosperity, character, and progress is education. Thus, a lot of emphasis is laid on quality of education and education delivery system in India with current financial year (2022-23) education budget outlay of Rs. 1,04,277.72 crores. This research contributes in analyzing how students perform in academics depending upon the time spent on their extracurricular activities with the help of three Machine Learning prediction algorithms namely Decision Tree, Random Forest and KNN. Additionally, in order to comprehend the underlying causes of the shortcomings in each machine learning technique, comparisons of the prediction outcomes obtained by these various techniques are made. On our dataset, the Decision Tree outscored all other algorithms, achieving F1 84 and an accuracy of 85%. The research, which is at an introductory level, is meant to open the door for more complexes, specialised, and in-depth studies in the area of predicting the performance in academics.

Many people are using Twitter for thought expression and information sharing in real-time. Twitter is one of the trendiest social media applications that cybercriminals also widely use to harass the victim in the form of cyberstalking. Cyberstalkers target the victim through sexism, racism, offensive language, hate language, trolling, and fake accounts on Twitter. This paper proposed a framework for automatic cyberstalking detection on Twitter in real-time using the hybrid approach. Initially, experimental works were performed on recent unlabeled tweets collected through Twitter API using three different methods: lexicon-based, machine learning, and hybrid approach. The TF-IDF feature extraction method was used with all the applied methods to obtain the feature vectors from the tweets. The lexicon-based process produced maximum accuracy of 91.1%, and the machine learning approach achieved maximum accuracy of 92.4%. In comparison, the hybrid approach achieved the highest accuracy of 95.8% for classifying unlabeled tweets fetched through Twitter API. The machine learning approach performed better than the lexicon-based, while the performance of the proposed hybrid approach was outstanding. The hybrid method with a different approach was again applied to classify and label the live tweets collected by Twitter Streaming in real-time. Once again, the hybrid approach provided the outstanding result as expected, with an accuracy of 94.2%, recall of 94.1%, the precision of 94.6%, f-score of 94.1%, and the best AUC of 98%. The performance of machine learning classifiers was measured in each dataset labeled by all three methods. Experimental results in this study show that the proposed hybrid approach performed better than other implemented approaches in both recent and live tweets classification. The performance of SVM was better than other machine learning algorithms with all applied approaches.

Today, large volumes of complex data are collected in many application domains such as health, finance and business. However, using traditional data visualization techniques, it is challenging to visualize abstract information to gain valuable insights into complex multidimensional datasets. One major challenge is the higher cognitive load in interpreting information. In this context, 3D metaphor-based information visualization has become a key research area in helping to gain useful insight into abstract data. Therefore, it has become critical to investigate the evolution of 3D metaphors with HCI techniques to minimize the cognitive load on the human brain. However, there are only a few recent reviews can be found for 3D metaphor-based data visualization. Therefore, this paper provides a comprehensive review of multidimensional data visualization by investigating the evolution of 3D metaphoric data visualization and interaction techniques to minimize the cognitive load on the human brain. Complying with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines this paper performs a systematic review of 3D metaphor-based data visualizations. This paper contributes to advancing the present state of knowledge in 3D metaphoric data visualization by critically analyzing the evolution of interactive 3D metaphors for information visualization. Further, this review identifies six main 3D metaphor categories and ten cognitive load minimizing techniques used in modern data visualization. In addition, this paper contributes three taxonomies by synthesizing the literature with a critical review of the strengths and weaknesses of metaphors. Finally, the paper discusses potential exploration paths for future research improvements.