Learning disabilities (LD) are problem that influence the brain capacity to receive, process, analyse and store information. These processing issues can interfere with learning fundamental abilities, for example, reading (Dyslexia), writing (Dysgraphia) and math (Dyscalculia). Fortunately, in today's world technology, we have numerous option techniques for educating and supporting fundamental skills in reading, writing and math such as mobile learning by using mobile app. However, the research in mobile app design model for Dyscalculia children is still scarce: very few studies have focus on how to design and develop appropriate mobile app for Dyscalculia children from the perspective of mobile app designers and developers. The purpose of this paper is to identify the appropriate components that should be included in the mobile app model. Interview sessions were conducted with Dyscalculia practitioners that involved a teacher, paediatric and an educational psychology. This has resulted in identifying 16 components that can be included in the mobile app design model.

Malicious attacks, malware, and ransomware families present essential risks to cybersecurity and may result in significant harm to computer systems, data clusters, networks, and mobile apps across a range of industries. Recently, there has been interest in ransomware classification using DNN and Bi-LSTM. DNN, a subset of machine learning techniques, has been found to improve ransomware detection and classification precision and efficacy. Ransomware has been affecting commercial, public, and governmental organizations' networks and computer systems for more than a decade, enabling new dynamic detection techniques to help DNNs detect ransomware. However, deep neural network-based architectures and DL classifiers (such as DNN, and Bi-LSTM classifiers) will be employed to detect ransomware. These networks may learn to correctly identify and categorize new ransomware incidents by integrating various datasets, including known and unknown ransomware samples. The classification of ransomware detection has been thoroughly investigated, and a model incorporating classic DL techniques with DNN and Bi-LSTM-based architecture will be proposed. A model execution experiment will be carried out to facilitate comparative testing of various approaches. This study focuses on the detection and classification of ransomware using DNN and Bi-LSTM. This study provides the groundwork for future investigations into the issues with ransomware detection. To protect against several ransomware attack types, deep neural networks have become an effective tool for ransomware detection. These networks combine machine learning and deep learning techniques.

Most of the human work has been replaced by computers in recent years. With the rise of mobile technology and Internet access, recent developments in machine learning (ML) have designed many algorithms to solve diverse human problems. However, due to a lack of exposure to image processing, identification technology is still not widely employed in Malaysia. This paper outlines the steps involved in creating a mobile application for coin identification using ML. In the literature review, the history of the coins is studied in more depth and the features of already existing coin identification mobile applications are compared by their advantages and disadvantages. In addition, using the neural network model, the classification accuracy of successfully identified coins is recorded and disclosed. This study includes the limitations of the prototype mobile application and future improvements that could be added.

Massive Open Online Courses (MOOCs) have emerged as a popular choice for learners as accessible and flexible education across the globe. Micro -are short skill-focused certifications offered within MOOCs to online learners. The interplay between multiple stakeholders, including universities, MOOCs providers, policy makers and industrial leaders, plays a decisive role in MOOC adoption. This study employed Educational Data Mining techniques to extract patterns in learner behavior, course design, institutional collaboration, etc., from the determinants of adoption and completion of the micro-credentials within MOOCs. The determinants were extracted from major online MOOCs databases, whereas additional parameters not captured in these databases were collected through an online survey from learners, industry professionals, and higher education institutions. A data mining-based framework is proposed to support stakeholders in planning effective course offerings, guiding learners in selecting suitable courses and helping MOOCs providers to align course credentials with market demands. Classification and predictive analysis revealed that course-related attributes, such as course certification type, course organization, course rating, course difficulty level, and whether the course was free or paid, play decisive roles in determining MOOC adoption. The decision tree classifier, based on the information gain and Gini index, ranked these attributes by order of preference with high accuracy, whereas regression analysis predicted multiple independent variables yielding good performance, as reflected in the confusion matrix.

Most of the human work has been replaced by computers in recent years. With the rise of mobile technology and Internet access, recent developments in machine learning (ML) have designed many algorithms to solve diverse human problems. However, due to a lack of exposure to image processing, identification technology is still not widely employed in Malaysia. This paper outlines the steps involved in creating a mobile application for coin identification using ML. In the literature review, the history of the coins is studied in more depth and the features of already existing coin identification mobile applications are compared by their advantages and disadvantages. In addition, using the neural network model, the classification accuracy of successfully identified coins is recorded and disclosed. This study includes the limitations of the prototype mobile application and future improvements that could be added.