Artificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial for automatic diagnosis. Diagnostic radiology is evolving from a subjective perceptual talent to a more objective science thanks to AI. Automatic object detection in medical images is an essential AI technology in medicine. The problem of detecting brain tumors at an early stage is well advanced with convolutional neural network (CNN) and deep learning algorithms (DLA). The problem is that those algorithms require a training phase with a big database of more than 500 images and time-consuming with a complex computational and expensive infrastructure. This study proposes a classical automatic segmentation method for detecting brain tumors in the early stage using MRI images. It is based on a multilevel thresholding technique on a harmony search algorithm (HSO); the algorithm was developed to suit MRI brain segmentation, and parameters selection was optimized for the purpose. Multiple thresholds, based on the variance and entropy functions, break the histogram into multiple portions, and different colors are associated with each portion. To eliminate the tiny arias supposed as noise and detect brain tumors, morphological operations followed by a connected component analysis are utilized after segmentation. The brain tumor detection performance is judged using performance parameters such as Accuracy, Dice Coefficient, and Jaccard index. The results are compared to those acquired manually by experts in the field. The results were further compared with different CNN and DLA approaches using Brain Images dataset called the “BraTS 2017 challenge”. The average Dice Index was used as a performance measure for the comparison. The results of the proposed approach were found to be competitive in accuracy to those obtained by CNN and DLA methods and much better in terms of execution time, computational complexity, and data management.

This study introduces a novel deep-learning methodology that is customized to automatically diagnose Alzheimer’s disease (AD) through the analysis of MRI datasets. The process of diagnosing AD via the visual examination of magnetic resonance imaging (MRI) presents considerable challenges. The visual diagnosis of mild to very mild stages of AD is challenging due to the MRI similarities observed between a brain that is aging normally and one that has AD. The detection of AD with extreme precision is critical during its early stages. Deep-learning techniques have recently been shown to be significantly more effective than human detection in identifying various stages of AD, enabling early-stage diagnosis. The aim of this research is to develop a deep-learning approach that utilizes pre-trained convolutional neural networks (CNNs) to accurately detect the severity levels of AD, particularly in situations where the quantity and quality of available datasets are limited. In this approach, the AD dataset is preprocessed via a refined image processing module prior to the training phase. The proposed method was compared to two well-known deep-learning algorithms (VGG16 and ResNet50) using four Kaggle AD datasets: one for the normal stage of the disease and three for the mild, very mild, and moderate stages, respectively. This allowed us to evaluate the effectiveness of the classification results. The three models were compared using six performance metrics. The results achieved with our approach indicate an overall detection accuracy of 99.3%, which is superior to the other existing models.

The integration of Student Response Systems (SRSs) into classroom teaching is a pioneering progression in social sciences research that has shown potential in boosting student engagement and elevating academic success. However, no extensive study has examined the impact of its use on academic achievement within a sizable number of students of diverse cofactors, such as scientific discipline and study levels. The current study aims to conduct a comprehensive score analysis investigating the effect of SRS use on academic performance. It involved a total of 6047 male and female undergraduate students from four scientific disciplines, seven colleges, four campuses, and 13 courses covering all study levels within King Saud University. The student’s scores along with their attributes were anonymously collected from the university system. A voluntary anonymous survey was distributed to collect students’ perceptions of SRS along with their personal attributes, such as learning style, and class interaction preferences. Upon data collection, the Python programming language was exclusively implemented for comprehensive data analysis including grouping, validation, random sampling, visualization, and statistical analysis. The overall score analysis study showed a non-significant effect of SRS use on student scores compared to the control (non-SRS) group, while the survey findings proved a significant enhancement of students’ scores (in courses that utilized SRS) compared to their overall GPA. In addition, the differential score and survey analysis within various study subcategories showed significant positive effects in certain subcategories, particularly science and community colleges, and four of their representative courses. SRS showed higher levels of overall student satisfaction (average—4.4/5.0), yet it was also significantly influenced by scientific discipline, preferred interaction methods, and study levels. Overall, SRS provides a highly engaging tool with excellent student acceptance and potential academic performance enhancement.

Enhancing faculty performance and student interaction during the lecture is essential to achieve sustainable learning development. The current study aims to evaluate the effect of using “Student response systems (SRS)” on faculty performance and student interaction in the classroom. The faculty members at King Saud University were encouraged to join a university-scale educational project that involve utilizing SRSs within their classes. From Fall 2016 to Fall 2019, a total of 371 faculty members and 19,746 students were enrolled in the current study. By the end of each semester, faculty and student satisfaction surveys were distributed to evaluate their perceptions of using SRS in the class. The faculty members’ and students’ response rates were 75.7% and 38.1%, respectively, and represented 18 different colleges from different disciplines within the university. Furthermore, the study covered a wide range of study levels for bachelor’s degrees ranging from levels 1–10. The study demographics showed that 60% of the total participating faculty members and 64% of students were females. Interestingly, the majority of participating faculty members (40%) and students (44%) belong to health colleges. Among the most beneficial effects of using SRSs, is that it increased the interaction, focus, and participation of students in the lecture and stimulated their desire to attend and prepare for the lecture. It also helped the faculty members to improve their teaching strategies and enabled them to know the weaknesses or strengths of students, which in turn led to the improvement of the entire educational process. The majority of faculty members as well as the students recommend applying it in other courses and future semesters. These findings were generally consistent over the whole studied seven semesters. SRSs offer a potential tool to improve faculty teaching practices, enhance student engagement, and achieve sustainable learning development among different disciplines.

Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.