The application of artificial intelligence (AI) in monitoring and managing ocean waste reveals considerable promise for improving sustainable strategies to combat marine pollution. This study performs a bibliometric analysis to examine research trends, knowledge frameworks, and future directions in AI-driven sustainable ocean waste management. This study delineates key research themes, prominent journals, influential authors, and leading nations contributing to the field by analysing scientific publications from major databases. Research from citation networks, keyword analysis, and co-authorship patterns highlights significant topics such as AI algorithms for waste detection, machine learning models for predictive mapping of pollution hotspots, and the application of autonomous drones and underwater robots in real-time waste management. The findings indicate a growing global focus on utilising AI to enhance environmental monitoring, optimise waste reduction methods, and support policy development for sustainable marine ecosystems. This bibliometric study provides a comprehensive analysis of the current knowledge landscape, identifies research gaps, and underscores the importance of AI as a crucial enabler for sustainable ocean waste management, offering vital insights for researchers, industry leaders, and environmental policymakers dedicated to preserving ocean health.

Artificial intelligence (AI) can significantly improve the efficiency of the process, as well as optimize resources and the sustainability of the biogas production process. As part of the United Nations’ Sustainable Development Goals (SDGs), this comprehensive review investigates the role of artificial intelligence-driven technologies in enhancing biogas yield, process monitoring, and waste management. This study aims to analyse the literature on AI’s functions in assessing feedstock availability and quality for achieving Sustainable Development Goals, forecasting biogas yield, and overseeing biogas production processes. This systematic review leverages the Preferred Reporting Items for Systematic Literature Review and Meta-Analysis, PRISMA, to painstakingly review research papers published between 2016 and 2023 using Pubmed, Scopus and Web of Science to understand how AI can assist farmers and biogas plant operators in making informed decisions, identifying anomalies, and forecast biogas yield, ultimately contributing to sustainable development. We analysed the papers to identify research gaps and different artificial intelligence like Artificial Neural Networks (ANN), Genetic Algorithms (GA), Tree-based Pipeline Optimization Tools (TPOT), Random Tree (RT), Random Forest (RF), Adaptive-Network-based Fuzzy Inference System (ANFIS) etc., which have been used in previous research relevant to this study. The result from our study emphasizes recent progress, obstacles, and prospective research avenues for integrating AI into biogas production. It can be concluded that AI-augmented biogas systems, such as machine learning, predictive analytics, and process automation, enhance anaerobic digestion, mitigate greenhouse gas emissions, and facilitate the adoption of renewable energy (SDG 7), facilitate waste-to-energy projects, promoting sustainable consumption and production (SDG 12) while alleviating environmental pollution (SDG 13). Hence, AI can substantially aid in sustainable energy transitions and circular bioeconomy frameworks, bolstering global initiatives to attain various SDGs.

Heart failure (HF) prediction models using machine learning (ML) must achieve a balance between performance, fairness, and real-world clinical utility. This paper assesses the potential of ML and DL models in the context of heterogeneous databases (UCI, MIMIC) and aims to derive applicable schemes for equitable deployment in healthcare. Although the Transformer models depicted notable AUC-ROC in the UCI data (0.986), they suffered a considerable performance degradation in the MIMIC data, suggesting their generalizability issue. Interestingly, we found contrasting gender preferences: SVM better detected females (AUC-ROC = 0.869 vs 0.796 for males), while XGBoost favored males. Decision curve analysis (DCA) demonstrated that a greater AUC-ROC was not necessarily preferred. For the 0.5 threshold, the net benefit of SVM (0.050) was higher than that of other models. SHAP analysis confirmed sex, ejection fraction, and NT-proBNP as the main predictors, but demonstrated inconsistent feature relations between datasets. We argue that (1) gender-specific threshold optimization, (2)ensemble methods to counteract bias, and (3)robust external validation are necessary and provide a possible path for clinically deployable, fairness-aware HF prediction tools.

Urban buildings significantly contribute to global carbon emissions, with urbanization increasing energy demand and reliance on fossil fuels, leading to environmental damage. This study investigates the role of biogas in reducing urban carbon footprints through a thematic literature review of 526 publications from 2004 to 2024, refined to 33 relevant studies focusing on biogas, carbon emissions, and urban infrastructure. The research concludes that biogas systems present a clean, renewable energy alternative that enhances waste management and energy efficiency within urban settings. Despite facing economic, logistical, and social challenges, integrating biogas could provide substantial environmental benefits and is vital for meeting climate targets and transforming urban energy systems.

The growing amounts of fish waste from South Africa’s fishing and aquaculture sectors pose environmental challenges and present opportunities for renewable energy production. South Africa’s current energy mix heavily relies on coal, with renewable sources representing only a fraction of the total supply. The country’s waste management is also constrained by the large volumes of organic waste sent to landfills, increasing greenhouse gas emissions and pollution. The review synthesizes findings from various studies on the anaerobic digestion of fish waste, assessing its feasibility, efficiency, and ecological advantages as an energy source. Fish waste, high in proteins and lipids, shows promise as an effective feedstock for biogas production, particularly when codigested with other organic materials to improve microbial activity and methane output. However, the variability in results across studies highlights the need for standardized methodologies and consistent reporting, as differences in study designs and digestion parameters affect outcomes. Key limitations identified include the heterogeneity in research approaches, inconsistent biogas yield reporting, and a lack of thorough sustainability evaluations, which hinder the generalizability of findings. The review recommends further empirical studies to optimize the anaerobic digestion of fish waste tailored to South Africa’s specific context. The methodological quality of the reviewed studies supports a JBI Grade B recommendation, indicating moderate promise for fish waste as a renewable energy resource. In conclusion, fish waste represents a valuable yet underutilized resource for renewable energy that aligns with South Africa’s energy and waste reduction objectives. Expanding biogas production from fish waste could help decrease reliance on fossil fuels, reduce greenhouse gas emissions, and divert organic waste from landfills. Policymakers and energy practitioners are urged to explore fish waste digestion within the renewable energy framework. At the same time, future research should focus on optimizing co-digestion processes and addressing logistical and regulatory challenges. This review lays the groundwork for future research and policy efforts to harness fish waste for sustainable biogas production.