Flow cytometry is a powerful analytical technique that generates high-dimensional single-cell data widely used in biological research and clinical diagnostics. This survey traces the evolution of flow cytometry analysis from traditional manual gating to modern algorithmic approaches and explores emerging deep-learning (DL) paradigms. We delineate three distinct analysis paradigms: traditional (manual gating), modern (algorithm-assisted population identification), and future (direct cell-to-sample DL models). While the modern paradigm employs sophisticated computational methods to identify cell populations and derive features, the emerging future paradigm bypasses discrete population identification entirely, utilizing DL to directly connect cellular information to sample-level insights. We discuss key technical challenges including batch effects, panel variations, and data availability that currently limit widespread adoption of more advanced analytical approaches. The survey also examines promising solutions such as transfer learning, data augmentation, and self-supervised learning techniques. As flow cytometry technology continues to advance, particularly with spectral cytometry enabling higher-dimensional analysis, these computational methods will become increasingly essential for extracting maximum biological insight from complex datasets. The development of standardized data repositories, robust batch correction tools, and comprehensive benchmarking frameworks will be crucial for realizing the full potential of these advanced analytical approaches.
Traditional query optimization struggles with modern, heterogeneous workloads. This PRISMA-guided survey examines 27 recent studies (2018–2025) on machine learning for query optimization, addressing two research questions: (RQ1) how learning-based approaches intervene across the classical optimization pipeline and what trade-offs characterize each stage, and (RQ2) what limitations persist in generalization, benchmark standardization, and deployment readiness. We synthesize the literature across five intervention points: component-level estimation, learned enumeration, hint steering, end-to-end optimization, and emerging trends. Results show a paradigm shift from isolated predictors to integrated optimizers, while gaps in generalization and standardized evaluation persist. We conclude with a roadmap for robust and reproducible data-driven query optimization.
Electroencephalography (EEG) is a valuable, low-cost tool for capturing brain signals, typically through non-invasive scalp recordings. However, analyzing EEG signals poses challenges due to noise interference and the need for specialized professionals. Recent advancements in deep learning have addressed many of these issues, leading to significant progress. Despite numerous systematic reviews, there remains a gap in deep learning research focused on assessing consciousness, brain injury, and comatose states. To bridge this gap, we conducted a systematic literature review following the PRISMA protocol, targeting human studies published between 2018 and 2023 across various databases. We identified 475 studies, of which 37 met our eligibility criteria. Their analyses revealed key trends: i) most focus on classification tasks, particularly for consciousness assessment and seizure detection; ii) most datasets involve fewer than 100 participants, with few being publicly available; iii) Convolutional Neural Networks dominate the learning architecture, although emerging approaches like Graph Attention Networks and Transformers show promising performance. However, challenges remain in distributed data acquisition, data augmentation, automated and multimodal machine learning, hyperparameter optimization, and model explainability, which are underexplored. In conclusion, while deep learning has advanced EEG signal analysis, further research is essential to build larger datasets and improve multimodal models for broader applications in Brain Function and Injury assessment.
Global connectivity has increased societal diversity worldwide, fostering growth and dispelling negative stereotypes. However, ensuring equality remains challenging, with some groups favoured and others facing unequal opportunities. The evolution of Artificial Intelligence (AI) promises automation, innovation, and industry transformation. Yet, as AI becomes pervasive, establishing trust in algorithmic decision-making is crucial to eliminate bias and ensure fair outcomes. Adversarial attacks, particularly backdoor attacks, pose significant threats to AI systems. Research must focus on mitigating discrimination and bias in AI models, emphasising ethical considerations and model interpretability. Accordingly, this paper surveys the critical aspects of fairness, bias, and backdoor attacks in AI systems, integrating significant and state-of-the-art research. This paper begins by defining key concepts, including fairness, bias, and backdoor attacks, and exploring related elements. A thorough survey highlights key studies on fairness and bias analysis, metrics for measurement, and various fairness attacks. This paper further examines methodologies for performing backdoor attacks and their implications for AI systems. Additionally, this paper explores the latest defence mechanisms addressing fairness and bias issues and mitigating backdoor attacks, showcasing advancements in safeguarding AI integrity. The survey concludes with discussions on popular datasets, future research directions, and potential limitations in ongoing developments. This is the first study combining surveys of backdoor attacks and fairness in AI models to examine security vulnerabilities that can lead to bias susceptibility and unfair outcomes. By consolidating crucial research insights, this survey provides a comprehensive understanding of the challenges and progress in ensuring fairness and security in AI systems, offering valuable perspectives for researchers and practitioners.
Renewable energy generation and storage have become increasingly important in recent years, in line with the transition to cleaner and more sustainable energy. Efficient management of renewable energy systems is vital for their reliable operation, and this entails gathering and analysing data from sensors to detect and address potential system faults. The combination of the Internet of Things (IoT) and machine learning (ML) has emerged as a promising solution to improve the management of renewable energy generation and storage systems. IoT facilitates the integration of numerous sensors and devices, while ML provides advanced data analytics for predictive maintenance and fault detection. In this comprehensive article, we review the current state of renewable energy generation and storage management systems, including many important studies not present in existing surveys. This review emphasises the importance of reliable management in securing a greener future.
