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This systematic literature review investigates autonomous UAV route planning for coverage maximization in environmental monitoring, analyzing 562 records and reporting preliminary findings on algorithmic families, metrics, and validation practices.
This systematic review evaluates methodological reliability in machine learning models for early Chronic Kidney Disease prediction, revealing that data leakage inflates reported accuracy by over 15% and that more than 80% of predictors lack stability across studies.
A systematic review of non-social media free-text datasets for mental health disorder detection, identifying biases and gaps in current resources.
This paper evaluates the use of small language models (SLMs) to assist title and abstract screening in systematic reviews of social-physical human-robot interaction (spHRI). While SLMs did not match human performance, they operated locally at high speed and identified additional relevant papers, demonstrating their potential to augment human reviewers for large-scale literature reviews.
This paper presents a systematic review and benchmark of 24 black-box uncertainty estimation methods for large language models across 4 models and 4 dataset settings, finding that no single method dominates but hybrid methods that combine multiple uncertainty signals perform well.
A systematic review of machine learning and deep learning techniques for cattle identification, covering methods like CNNs and YOLO, feature extraction techniques, and challenges such as limited datasets and real-time processing.
This systematic review of 204 EduNLP papers reveals that teachers are underrepresented as beneficiaries despite being most affected, real-world deployment is rare, and ethical engagement tends toward acknowledgement rather than action, highlighting a tension between private-sector incentives and foundational educational needs.
This systematic scoping review examines three categories of large AI models in dental healthcare: language-generative models, discriminative vision foundation models, and dental-specific foundation models, analyzing 97 studies to show that general-purpose and domain-specific models play complementary roles, with integrated pipelines outperforming single-model approaches.
This paper presents a systematic literature review of hybrid approaches for interval wind speed forecasting, combining deep learning, modal decomposition, and statistical methods to enhance prediction accuracy and reliability.
This systematic review of 139 studies proposes a unified framework and meta-analysis for document classification via multimodal and multiview information fusion, finding that fusion improves accuracy (mean gain of +5.28 percentage points) but highlights reproducibility challenges.