Hyperspectral data of understory elements in boreal forests: In situ and laboratory measurementsMendeley DataMendeley Data by Audrey Mercier, Susanna Karlqvist, Aarne Hovi, Miina Rautiainen

“…These data support the analysis of vegetation characteristics, training of classification algorithms and improvement of forest radiative transfer models, and could be used to evaluate the potential of hyperspectral data to discriminate the understory elements of boreal forest.…”
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Advances in Aircraft Skin Defect Detection Using Computer Vision: A Survey and Comparison of YOLOv9 and RT-DETR Performance by Nutchanon Suvittawat, Christian Kurniawan, Jetanat Datephanyawat, Jordan Tay, Zhihao Liu, De Wen Soh, Nuno Antunes Ribeiro

“…Beyond a detailed review, we experimentally evaluate the accuracy and feasibility of existing low-cost, easily deployable hardware (drone) and software solutions (computer vision algorithms). Specifically, real-world data were collected from an abandoned aircraft with visible defects using a drone to capture video footage, which was then processed with state-of-the-art computer vision models—YOLOv9 and RT-DETR. …”
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Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review. by Muhammad Ibtsaam Qadir, Jackson A Baril, Michele T Yip-Schneider, Duane Schonlau, Thi Thanh Thoa Tran, C Max Schmidt, Fiona R Kolbinger

“…Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. Thematically, most studies reported models augmenting differential diagnosis (n = 9,36%) or risk stratification (n = 10,40%) rather than IPMN detection (n = 5,20%) or IPMN segmentation (n = 2,8%). …”
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Integration of geospatial techniques and machine learning in land parcel prediction by Nekkanti Haripavan, Subhashish Dey, Chimakurthi Harika Mani Chandana

“…This flexibility guarantees that models can concentrate on the spatial features that have the biggest influence on the desired outcomes, improving the forecasts' overall performance and interpretability.…”
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Navigating the landscape: Unveiling the reflection of AI in the sharing economy through bibliometric-based approach by Veljko Uskoković, Milica Maričić, Aleksandra Dacić-Pilčević, Veljko Jeremić

“…Furthermore, AI optimizes resource allocation and improves matching algorithms on online labour platforms, thereby boosting platform efficiency. …”
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Recent advances in machine learning applications for MXene materials: Design, synthesis, characterization, and commercialization for energy and environmental applications by Sodiq Abiodun Kareem, Makinde Akindeji Ibrahim, Justus Uchenna Anaele, Olajesu Favor Olanrewaju, Emmanuel Omosegunfunmi Aikulola, Michael Oluwatosin Bodunrin

“…Recent studies confirm that ML models have been instrumental in improving MXene synthesis processes, enabling higher yields and optimization of properties, better purity, and scalability through real-time process control and reinforcement learning. …”
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Unveiling Immune Response Mechanisms in Mpox Infection Through Machine Learning Analysis of Time Series Gene Expression Data by Qinglan Ma, Xianchao Zhou, Lei Chen, Kaiyan Feng, Yusheng Bao, Wei Guo, Tao Huang, Yu-Dong Cai

“…Then, the incremental feature selection method was applied to each list to extract key genes and build efficient prediction models and classification rules for each list. This procedure employed twelve classification algorithms and the Synthetic Minority Oversampling Technique. …”
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Transformative applications in biology education: A case study on the efficacy of adaptive learning with numerical insights by Shin I. Aleksandrovich, Tyan Ramazan, Raissa Utegaliyeva, Balzat Sarimbayeva, Gaukhar Keubassova, Rakhima Bissalyyeva, Kuanysh Syman, Gaukhar Abdikarimova

“…Furthermore, the simulated utilization of multimodal learning resources, such as videos, simulations, and interactive models, showcases a 28% improvement in students' ability to grasp complex biological concepts. …”
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DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States by Parnian Ghaneei, Hamid Moradkhani

“…Abstract High quality baseflow data is important for advancing water resources modeling and management, as it captures the critical role of groundwater and delayed sources in contributing to streamflow. …”
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Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning. by Hailin Li, Quhuan Li, Zuyan Fan, Yue Shen, Jiao Li, Fengxia Zhang

“…Multiple machine-learning algorithms were used to screen and construct diagnostic models to identify hub differentially expressed podocyte marker genes (DE-podos), revealing ARHGEF26 as a significantly downregulated marker in DKD. …”
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Exploring the Impact of Predictive Analytics and AI in STEM Education by Tai Ki Kim

“…Through a systematic literature review of studies published between 2020 and 2023, this paper highlights the effectiveness of predictive analytics in improving STEM education outcomes while emphasizing the importance of inclusive practices. …”
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A systematic review of machine learning approaches in cochlear implant outcomes by Anu Prasad Sreenivasan Nair, Srikanta K. Mishra, Pedro Andres Alba Diaz

“…Additionally, ML-based speech enhancement algorithms showed promise in improving speech recognition in noisy environments, a major challenge for CI users. …”
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Research progress in predicting the conversion from mild cognitive impairment to Alzheimer’s disease via multimodal MRI and artificial intelligence by Min Ai, Yu Liu, Dan Liu, Chengxi Yan, Xia Wang, Xun Chen

“…Artificial intelligence (AI) methods based on deep learning and machine learning, with their powerful data processing and pattern recognition capabilities, have shown great potential in mining the features of multimodal MRI data and constructing prediction models for MCI conversion. Therefore, this paper systematically reviews the research progress of multimodal MRI techniques in capturing brain changes related to MCI conversion, as well as the practical experience of AI algorithms in constructing efficient prediction models, analyses the current technical challenges faced by the research, and discusses future directions, with the goal of providing a scientific reference for the early and accurate prediction of MCI conversion and the formulation of intervention strategies.…”
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An FPGA-accelerated multi-level AI-integrated simulation framework for multi-time domain power systems with high penetration of power converters by Chen Liu, Peng Su, Hao Bai, Xizheng Guo, Alber Filbà Martínez, Jose Luis Dominguez Garcia

“…The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports, ensuring compatibility with artificial intelligence algorithms. …”
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Developing advanced datadriven framework to predict the bearing capacity of piles on rock by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Fabián Danilo Reyes Silva, José Luis Allauca Palta, José Luis Llamuca Llamuca, Siva Avudaiappan

“…Abstract Developing accurate predictive models for pile bearing capacity on rock is crucial for optimizing foundation design and ensuring structural stability. …”
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Hole Cleaning during Drilling Oil and Gas Wells: A Review for Hole-Cleaning Chemistry and Engineering Parameters by Mohammed Al-Rubaii, Mohammed Al-Shargabi, Dhafer Al-Shehri

“…The development of hole-cleaning models is important for use as hole-cleaning indicators to ensure optimized drilling efficiency. …”
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Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optim... by Opy Das, Muhammad Hamza Zafar, Filippo Sanfilippo, Souman Rudra, Mohan Lal Kolhe

“…DT concepts facilitate the creation of virtual models mirroring physical assets, streamlining real-time data management and analysis. …”
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Research Progress and Applications of Artificial Intelligence in Agricultural Equipment by Yong Zhu, Shida Zhang, Shengnan Tang, Qiang Gao

“…The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. …”
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An End-to-End Solution for Large-Scale Multi-UAV Mission Path Planning by Jiazhan Gao, Liruizhi Jia, Minchi Kuang, Heng Shi, Jihong Zhu

“…The MHA module effectively models global spatial-task dependencies among nodes, enhancing solution quality. …”
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AI-Driven Robotics: Innovations in Design, Perception, and Decision-Making by Lei Li, Li Li, Mantian Li, Ke Liang

“…Artificial intelligence (AI), driven by large datasets and advanced algorithms, plays a pivotal role in addressing these challenges and advancing robotics. …”
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