In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%.

Mobile banking is a service provided by a bank that allows full remote control of customers’ financial data and transactions with a variety of options to serve their needs. With m-banking, the banks can cut down on operational costs whilst maintaining client satisfaction. This research examined the most crucial factors that could predict the Jordanian customer’s continued intention toward the use of m-banking. Following the proposed model, the research was conducted by using a self-conducted questionnaire and the responses were collected electronically from a convenience sample of 403 Jordanian customers of m-banking through social networks. The suggested model was adapted from the theory of planned behavior (TPB), the unified theory of acceptance and use of technology (UTAUT), and the technology acceptance model (TAM). The research model was further expanded by considering the factors of service quality and moderating factors (age, gender, educational level, and Internet experience). The collected data of customers were analyzed, validated, and verified by using a structural equation modeling (SME) approach including a confirmatory factor analysis (CFA), in addition to machine learning (ML) methods, artificial neural network (ANN), support vector machine (SMO), bagging reduced error pruning tree (RepTree), and random forest. Results showed that effort expectancy, performance expectancy, perceived risk, perceived trust, social influence, and service quality impacted behavioral intention, whereas facilitating conditions did not. Furthermore, behavioral intention impacted upon word of mouth and facilitating conditions (the latter regarding the continued intention to use m-banking), and had the highest coefficient value. Results also confirmed that all moderating factors affect the behavioral intention to continue using m-banking applications.

Using mobile applications in e-government for the purpose of health protection is a new idea during COVID-19 epidemic. Hence, the goal of this study is to examine the various factors that influence the use of SANAD App As a health protection tool. The factors were adopted from well-established models like UTAUT, TAM, and extended PBT. Using survey data from 442 SANAD App from Jordan, the model was empirically validated using AMOS 20 confirmatory factor analysis, structural equation modeling (SEM) and machine learning (ML) methods were performed to assess the study hypotheses. The ML methods used are ANN, SMO, the bagging reduced error pruning tree (RepTree), and random forest. The results suggested several key findings: the respondents’ performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, trust, and perceived service quality of this digital technology were significant antecedents for their attitude to using it. The strength of these relationships is affected by the moderating variables, including age, gender, educational level, and internet experience on behavioral intention. Yet, perceived risk did not have a significant effect on attitude towards SANAD App The study adds to literature by empirically testing and theorizing the effects of SANAD App on public health protection.

The speech entailed in human voice comprises essentially paralinguistic information used in many voice-recognition applications. Gender voice is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e., male or female) from a voice signal. The contributions are three-fold including (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. The experimental results show the importance of subfeatures over others, which are vital for enhancing the efficiency of classification models’ performance. Experimentation reveals that the best recall value is equal to 99.97%; the best recall value is 99.7% for two models of deep learning (DL) and support vector machine (SVM), and with feature selection, the best recall value is 100% for SVM techniques.

In recent decades, predicting the performance of students in the academic field has revealed the attention by researchers for enhancing the weaknesses and provides support for future students. In order to facilitate the task, educational data mining (EDM) techniques are utilized for constructing prediction models built from student academic historical records. These models present the embedded knowledge that is more readable and interpretable by humans. Hence, in this paper, the contributions are presented in three folds that include the following: (i) providing a thorough analysis about the selected features and their effects on the performance value using statistical analysis techniques, (ii) building and studying the performance of several classifiers from different families of machine learning (ML) techniques, (iii) proposing an ensemble meta-based tree model (EMT) classifier technique for predicting the student performance. The experimental results show that the EMT as the ensemble technique gained a high accuracy performance reaching 98.5% (or 0.985). In addition, the proposed EMT technique obtains a high performance, which is a superior result compared to the other techniques.