Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications.

This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization.

Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

Machine learning in medical applications: A review of state-of-the-art methods.

Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas, such as medical, financial, environmental, marketing, security, and industrial applications. ML methods are characterized by their ability to examine many data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and wearable sensors. Finally, this survey provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions.

Analysing lecturers' perceptions on traditional vs. distance learning: A conceptual study of emergency transferring to distance learning during COVID-19 pandemic.

In recent years, and also due to the COVID-19 pandemic, education institutions worldwide have changed their education paradigm from a traditional to an online system. These institutions have rapidly accomplished their educational programs and activities by supporting various web applications, allowing the completion of the education process remotely. This motivated us to investigate the general perceptions of the faculty members who are teaching different courses for undergraduate students using the distance education system. The proposed technique is based on the use of advanced analysis methods to test the hypothesis of instructors' perceptions of online teaching effectiveness, compared with traditional teaching, will not be affected by the following eight factors, including gender, academic major, age, academic degree, country of teaching, teaching experience in traditional classes, teaching experience in virtual classes (VCs), and whether or not the department/faculty provided e-learning courses before the COVID-19 pandemic. A total of 187 lecturers from 71 departments in 24 educational institutions located in 11 countries participated in this research. Results indicate that the perceptions of instructors regarding the online teaching system generally do not change based on the studied factors. Furthermore, the quantitative analyses illustrate that no significant differences exist in all dimension scales in terms of gender, major specification, age, country of teaching, and virtual experience. Significant differences are found in the dimension scale of distance education effectiveness in terms of academic degree and the educator status in terms of supporting VCs and traditional experience dimension scales. Additionally, the study provides various recommendations, including that the departments should encourage instructors to positively adapt with online teaching by developing the required skills and knowledge. Moreover, departments and institutions should support the teaching staff with specialized education tools. In addition, instructors should strive to decrease the communication and interaction gap between distance education and traditional in-class teaching.