This investigation delves into Green AI and Sustainable AI literature through a dual-analytical approach, combining thematic analysis with BERTopic modeling to reveal both broad thematic clusters and nuanced emerging topics. It identifies three major thematic clusters: (1) Responsible AI for Sustainable Development, focusing on integrating sustainability and ethics within AI technologies; (2) Advancements in Green AI for Energy Optimization, centering on energy efficiency; and (3) Big Data-Driven Computational Advances, emphasizing AI’s influence on socio-economic and environmental aspects. Concurrently, BERTopic modeling uncovers five emerging topics: Ethical Eco-Intelligence, Sustainable Neural Computing, Ethical Healthcare Intelligence, AI Learning Quest, and Cognitive AI Innovation, indicating a trend toward embedding ethical and sustainability considerations into AI research. The study reveals novel intersections between Sustainable and Ethical AI and Green Computing, indicating significant research trends and identifying Ethical Healthcare Intelligence and AI Learning Quest as evolving areas within AI’s socio-economic and societal impacts. The study advocates for a unified approach to innovation in AI, promoting environmental sustainability and ethical integrity to foster responsible AI development. This aligns with the Sustainable Development Goals, emphasizing the need for ecological balance, societal welfare, and responsible innovation. This refined focus underscores the critical need for integrating ethical and environmental considerations into the AI development lifecycle, offering insights for future research directions and policy interventions.

This bibliometric study analyzes 1433 former reviews on Sustainable Development Goals (SDGs) and Sustainability, providing a comprehensive overview of the evolving research landscape in this domain. Notably, we observe a substantial annual growth rate of 74% in publications and a remarkable 171% increase in total citations from 2016 to 2022, reflecting a growing interest in this area. We identify the leading countries and institutions contributing to quantitative reviews on SDGs and Sustainability. SDG 12 (Sustainable Consumption and Production) emerges as the most extensively studied and is highly represented in influential journals like Sustainability and the Journal of Cleaner Production. Across various research fields, SDGs 12 and 11 (Sustainable Cities and Communities) stand out, with SDGs 4 (Quality Education), 5 (Gender Equality), and 15 (Life on Land) showing significance in specific domains. Thematic analysis reveals key topics like environmental protection, circular economy, life cycle assessment, and supply chain management, with strong connections to SDG 12. Further clusters highlight environmental management, renewable energy, and energy policy linked to SDG 7 (Affordable and Clean Energy), along with a smaller cluster focusing on urbanization driven by SDG 11. Network analysis emphasizes the critical roles of SDGs 12 and 9 (Industry Innovation and Infrastructure) in achieving a sustainable future. However, alternative social network indicators highlight the potential influence of SDGs 8 (Decent Work and Economic Growth), 16 (Peace, Justice and Strong Institutions), and 17 (Partnerships for the Goals) on other goals. Intriguingly, mainstream SDG research predominantly focuses on SDGs 3 and 7, presenting challenges due to the volume and complexity of related publications. While SDG 7 could find suitable outlets in leading journals, addressing SDG 3’s (Good Health and Well Being) complexity remains a formidable task. Nevertheless, conducting bibliometric studies on SDGs 3, 7, and 13 (Climate Action) offers promising opportunities in future if the associated challenges are addressed effectively.

Despite significant business-related research publications, extant studies are yet to show how well business research directly maps to 17 Sustainable Development Goals (SDGs). Our study uses a qual-quant approach combining the PRISMA framework, performance analysis, and content analysis to find answers to this research gap. Using the Australia Business Deans Council list of 2500+ journals as a proxy for business research during 2016–2022, our study shows that only about 25% of business research is directly mapping to SDGs with SDG 3 (Good Health and Well Being) and SDG 7 (Affordable and Clean Energy) being the most mapped. The most cited journals are the Journal of Cleaner Production, Applied Energy, and the European Journal of Public, which largely focus on SDG 7, SDG 13 (Climate Action), and SDG 3. Top authors focus their research mainly on SDG 7, SDG 13, and SDG 12 (Responsible Consumption and Production), with few publications dedicated to SDG 1 (No Poverty), SDG 8 (Decent Work and Economic Growth), and SDG 5 (Gender Equality). Applied Economics has emerged as the dominant field for SDGs. Further, our study is the first to incorporate social network analysis to understand the interconnected nature of SDGs in the context of business research. This research provides substantial policy insight, particularly insight relevant to the formulation of regulations that promote the alignment of business researchers with SDGs.

Software risk management plays a vital role in successful software project management. In fact, all the phases of the software development life cycle (SDLC) are potential sources of software risks since it involves hardware, software, technology, people, cost, and schedule. There are a number of software risk factors that affect the whole software development process. However, finding the correlation between risk factors and project outcome is the main focus of present research on software risk analysis. In this paper, a probabilistic software risk estimation model is proposed using Bayesian Belief Network (BBN) that focuses on the top software risk indicators for risk assessment in software development projects. In order to assess the constructed model, an empirical experiment has been performed, based on the data collected from software development projects used by an organization

The development of deep convolutional neural networks has been largely responsible for the significant strides forward made in steganography over the past decade. In the field of image steganography, generative adversarial networks (GAN) are becoming increasingly popular. This study describes current development in image steganographic systems based on deep learning. The authors' goal is to lay out the various works that have been done in image steganography using deep learning techniques and provide some notes on the various methods. This study proposed a result that could open up some new avenues for future research in deep learning based on image steganographic methods. These new avenues could be explored in the future. Moreover, the pros and cons of current methods are laid out with several promising directions to define problems that researchers can work on in future research avenues.