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Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorder that is prevalent in children and adolescents. ADHD affects student’s learning due to its symptoms which are inability to stay focused, hyperactivity, and challenges in self-control. In this paper, we explore the use of mobile games to engage children diagnosed with ADHD. Mobile games are now widely used in learning, which is part of mobile learning. Previous studies have shown a positive relationship between digital games and learning for children with ADHD. Therefore, we designed and developed a mobile game based on existing literature on techniques used to retain the attention and engage children with ADHD. The study was evaluated based on the duration of time the children spent playing the game and their activities and interactions while playing the game. Our findings showed that mobile-based games can be used to engage children with ADHD.

PurposeSequel movies are very popular; however, there are limited studies on sequel movie revenue prediction. The purpose of this paper is to propose a sentiment analysis based model for sequel movie revenue prediction and to propose a missing value imputation method for the sequel revenue prediction dataset.Design/methodology/approachA sequel of a successful movie will most likely also be successful. Therefore, we propose a supervised learning approach in which data are created from sequel movies to predict the box-office revenue of an upcoming sequel. The algorithms used in the prediction are multiple linear regression, support vector machine and multilayer perceptron neural network.FindingsThe results show that using four sequel movies in a franchise to predict the box-office revenue of a fifth sequel achieved better prediction than using three sequels, which was also better than using two sequel movies.Research limitations/implicationsThe model produced will be beneficial to movie producers and other stakeholders in the movie industry in deciding the viability of producing a movie sequel.Originality/valuePrevious studies do not give priority to sequel movies in movie revenue prediction. Additionally, a new missing value imputation method was introduced. Finally, sequel movie revenue prediction dataset was prepared.

Corporate bankruptcy prediction is an important task in the determination of corporate solvency, that is, whether a company can meet up to its financial obligations or not. It is widely studied as it has a significant effect on employees, customers, management, stockholders, bank lending assessments, and profitability. In recent years, machine learning techniques, particularly Artificial Neural Network (ANN), have widely been studied for bankruptcy prediction since they have proven to be a good predictor, especially in financial applications. A critical process in learning a network is weight training. Although the ANN is mathematically efficient, it has a complex weight training process, especially in computation time when involving a large training data. Many studies improved ANN’s weight training using metaheuristic algorithms such as Evolutionary Algorithms (EA), and Swarm Intelligence (SI) approaches for bankruptcy prediction. In this study, two metaheuristics algorithms, Magnetic Optimization Algorithm (MOA) and Particle Swarm Optimization (PSO), have been enhanced through hybridization to propose a new method MOA-PSO. Hybrid algorithms have been proven to be capable of solving optimization problems faster, with better accuracy. The MOA-PSO was used in training ANN to improve the performance of the ANN in bankruptcy prediction. The performance of the hybrid MOA-PSO was compared with that of four existing algorithms. The proposed hybrid MOA-PSO algorithm exhibits promising results with a faster and more accurate prediction, with 99.7% accuracy.

This paper examines the impact of Sifteo Cube Technology in engaging children with attention deficit hyperactivity disorder (ADHD). ADHD is an early childhood neurodevelopmental disorder which starts as early as five years of age. The core symptoms are inattentiveness, impulsiveness, and hyperactivity. By identifying the history of games development and ADHD, the researchers explored the viability of using the kinesthetic technology of Sifteo Cubes to engage these children. Engaging children with ADHD can lead to better management of the children, administering of learning materials, or aid other therapeutic methods. To carry out this research, an adventure game playable on the Sifteo Cubes using open-source games development kit called Wildflower was developed and evaluated with children with ADHD for qualitative analysis. Results obtained from video capture and ethnography proved favourable. Sifteo Cubes can effectively engage the children for an average of 20 minutes. The results show that Sifteo Cubes is effective is in engaging children with ADHD.

For many languages, the lack of sufficient parallel data to train translation models have resulted in using the monolingual data, source and target, through self-learning and back-translation respectively. Most works that implemented the self-learning approach utilized a quality estimation system to ensure that the resulting additional training data is of sufficient quality to improve the model. However, the quality estimation system may not be available for many low resource languages, restricting the implementation of such approach to a very few. This work proposes the utilization of the data selection technique as an alternative to quality estimation. The approach will ensure that the models will learn only from the data that is closer to the domain of the test set, improving the performance of the translation models. While this approach is applicable to many, if not all, languages, we obtained similar and, in some implementations, even better results (\(+\)0.53 BLEU) than the self-training approach that was implemented using the quality estimation system on low resource IWSLT’14 English-German dataset. We also showed that the proposed approach can be used to improve the performance of the back-translation approach, gaining \(+\)1.79 and \(+\)0.23 over standard back-translation and self-learning with quality estimation enhanced back-translation respectively.

This work investigates the use of distance learning in saving students' academic year amid COVID-19 lockdown. It assesses the adoption of distance learning using various online application tools that have gained widespread attention during the coronavirus infectious disease 2019 (COVID-19) pandemic. Distance learning thrives as a legitimate alternative to classroom instructions, as major cities around the globe are locked down amid the COVID-19 pandemic. To save the academic year, educational institutions have reacted to the situation impulsively and adopted distance learning platforms using online resources. This study surveyed random undergraduate students to identify the impact of trust in formal and informal information sources, awareness and the readiness to adopt distance learning. In this study, we have hypothesized that adopting distance learning is an outcome of situational awareness and readiness, which is achieved by the trust in the information sources related to distance learning. The findings indicate that trust in information sources such as institute and media information or interpersonal communication related to distance learning programs is correlated with awareness (β = 0.423, t = 12.296, p = 0.000) and contribute to readiness (β = 0.593, t = 28.762, p = 0.001). The structural model path coefficient indicates that readiness strongly influences the adoption of distance learning (β = 0.660, t = 12.798, p = 0.000) amid the COVID-19 pandemic. Our proposed model recorded a predictive relevance (Q2) of 0.377 for awareness, 0.559 for readiness, and 0.309 for the adoption of distance learning, which explains how well the model and its parameter estimates reconstruct the values. This study concludes with implications for further research in this area.

Extracting people’s opinions from social media has attracted a large number of studies over the years. This is as a result of the growing popularity of social media. People share their sentiments and opinions via these social media platforms. Therefore, extracting and analyzing these sentiments is beneficial in many ways, for example, business intelligence. However, despite a large number of studies on extracting and analyzing social media data, only a fraction of these studies focuses on its practical application. In this study, we focus on the use of product reviews for identifying whether the reviews signify the intention of purchase or not. Therefore, we propose a novel lexicon-based approach for the classification of product reviews into those that signify the intention of purchase and those that do not signify the intention of purchase. We evaluated our proposed approach using a benchmark dataset based on accuracy, precision, and recall. The experimental results obtained prove the efficiency of our proposed approach to purchase intention identification.

Students with ADHD find it difficult to pay attention to one thing, stay focused, are hyperactive and face difficulty in controlling behavior. They might also not perform well in school, get along with friends or finish their assignments on time. The advent of smart mobile devices brought about new methods of learning like mobile learning. The objective of this research is to determine whether mobile based games can be used to engage children with ADHD. The design of the mobile game was based on existing literature about how to retain the attention and engage children with ADHD. The game developed was evaluated based on the duration of time the children played the game for and their actions while playing the game. It was concluded that that mobile – based games can help in retaining the attention of children with ADHD. This opens room for a complete new approach that can be used for teaching students with ADHD and other students with attention problems.