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 enhanced use of artificial intelligence (AI) in organizations has changed and revolutionized the approaches to solving problems, processing information, and decision making. While the algorithms turned out to be highly effective, AI systems faced adversarial attacks, which can be described as slight alterations of inputs that would fool an AI algorithm. These attacks remain major challenges to the dependability and protection of AI systems and thus the need to develop stable and flexible protection strategies and procedures. The aim of this article is to discuss the existing trends in adversarial attack techniques and protection mechanisms. To this end, papers, exact match, and systematically applied operative inclusion/exclusion criteria pertinent to protection strategies against adversarial attacks in multiple databases were incorporated and used. Specifically, 1988 papers were retrieved from Web of Science, IEEE Explore, and Science Direct, which were published between January 1, 2021, and July 1, 2024, where we used 51 of the identified journal articles for the quantitative synthesis in the final stage. Thus, the protection taxonomy, which resulted from our analysis, discusses the motivation, and best practices in relation to the threats in question. The taxonomy also describes challenges and suggests other ideas on how to improve the robustness of adversarial attack systems. Not only this study is a response to gaps in the literature but it also presents the reader with a map for further studies. It is necessary to draw attention to the fact that an objective criterion must be introduced to measure the degree of defense, collaboration with researchers of other fields, and the necessity to consider the ethical implications of the created defense mechanisms. Our results shall assist industry practitioners, researchers, and policymakers in designing an optimal AI security that can protect AI systems against dynamic adversary strategies. This review provides a reference of entry to the topic of AI security and the challenges that may be encountered together with the measures that can be taken to forward the studies.

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.

Background: With the unprecedented growth of mobile technology, a plethora of dialysis diet apps have been developed to promote patient dietary self-management. Nevertheless, the utility of such apps remains questionable. Objective: This study aimed to evaluate the content, features, and quality of commercial dialysis diet apps for adult dialysis patients. Methods: This study consisted of a quantitative content analysis of commercial dialysis diet apps downloaded from Google Play and the Apple App Store available in the Asian marketplace, searched for using the following keywords in English: dialysis diet and diet for kidney disease. Free and paid apps available in English that provide nutrition information for adult dialysis patients were included. Apps that were not relevant to the dialysis diet, not meant for patient self-management, or redundant were excluded. Apps were evaluated for language medium (subscore=1), credibility (subscore=1), food database (subscore=1), valuable features (subscore=12), health-behavior theory constructs (subscore=60), and technical quality (subscore=25). The relationships among the variables of interest were determined by Pearson correlation. Stepwise multiple linear regression analysis was performed to identify the features that contribute to greater technical quality of dialysis diet apps. Statistical significance was defined as P<.05. Results: A total of 22 out of 253 apps (8.7%) were eligible for evaluation. Based on a 100-point scale, the mean overall score of the apps was 31.30 (SD 14.28). Only 5% (1/22) of the apps offered relevant language options, and 46% (10/22) contained food databases. In addition, 54% (12/22) of the apps were not credible. The mean score for valuable features was 3.45 (SD 1.63) out of 12, in which general education (16/22, 73%), free download (15/22, 68%), and usability (13/22, 59%) were the three most popular features. However, the apps scored a mean of 13.41 (SD 11.56) out of 60 for health-behavior theory constructs. The overall app technical quality was considered poor, with a mean score of 2.70 (SD 0.41) out of 5. The scores of valuable features (r=.65, P=.001) and health-behavior theory constructs (r=.55, P=.009) were positively correlated with the overall technical quality of the commercial dialysis diet apps. Features such as free download (β=.43, P=.03) and usability (β=.41, P=.03) could significantly determine the functional quality of the apps. Health-behavior theory constructs such as self-monitoring could significantly predict both the subjective quality (β=.55, P=.008) and the engagement quality (β=.66, P=.001) of the apps, whereas the information quality domain could be determined by plan or orders (β=.48, P=.007) and knowledge (β=.45, P=.01). Conclusions: Although most of the available commercial dialysis diet apps are free and easy to use, they are subject to theory deficiency, limited language options, and a lack of food databases, credibility, tailored education, and overall technical quality.

Augmented reality (AR) technology shows some potential in providing new approach of interaction with computer. It shares similar potential in virtual reality (VR) but at lower cost. In this paper, an AR application is developed to explore the capability of the interaction approach called occlusion based interaction using low cost device. The implementation of the application is utilizing the ARToolKit library as the main library to handle the AR part while OpenGL and GLUT to handle the graphics manipulation and windows management respectively.

The development of real-time triage applications for Autism Spectrum Disorder (ASD) is a critical challenge due to the rising prevalence of ASD and the urgent need for efficient resource allocation in healthcare systems. Previous studies have applied machine learning (ML) to ASD triage; however, most approaches overlook robustness against adversarial attacks, provide limited benchmarking across multiple evaluation criteria, and lack explainability to support clinical adoption. Building on our earlier work, which introduced a fuzzy evaluation and benchmarking framework using the 2-Tuple Linguistic Fermatean Fuzzy Decision by Opinion Score Method (2TLFFDOSM), this study proposes a comprehensive five-stage validation and evaluation framework. The framework systematically validates fuzzy-based rankings against raw performance metrics, conducts dual-perspective analysis under normal and adversarial conditions, performs sensitivity analysis across ten weighting scenarios, and integrates explainable AI (LIME, PFI, Integrated Gradients, and PDP) to interpret feature contributions before and after adversarial perturbations. Finally, a checklist benchmarking approach is used to position the framework against five recent studies.