Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

The implementation of usability in mobile augmented reality (MAR) learning applications has been utilized in a myriad of standards, methodologies, and techniques. The usage and combination of techniques within research approaches are important in determining the quality of usability data collection. The purpose of this study is to identify, study, and analyze existing usability metrics, methods, techniques, and areas in MAR learning. This study adapts systematic literature review techniques by utilizing research questions and Boolean search strings to identify prospective studies from six established databases that are related to the research context area. Seventy-two articles, consisting of 45 journals, 25 conference proceedings, and two book chapters, were selected through a systematic process. All articles underwent a rigorous selection protocol to ensure content quality according to formulated research questions. Post-synthesis and analysis, the output of this article discusses significant factors in usability-based MAR learning applications. This paper presents five identified gaps in the domain of study, modes of contributions, issues within usability metrics, technique approaches, and hybrid technique combinations. This paper concludes five recommendations based on identified gaps concealing potential of usability-based MAR learning research domains, varieties of unexplored research types, validation of emerging usability metrics, potential of performance metrics, and untapped correlational areas to be discovered.

Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of-the-art denoising methods, including traditional algorithms and deep learning-based techniques, are conducted, focusing on denoising effectiveness, computational efficiency, and preservation of image details. The proposed approach demonstrates superior performance in all aspects, highlighting its potential as a promising solution for image-denoising tasks. Implemented in Python, the hybrid model showcases the benefits of combining Bi-LSTM, optimized CNN, and SI-OPA for advanced image-denoising applications.