The genetic structure and diversity of smallholder dairy cattle in Rwanda by Oluyinka Opoola, Felicien Shumbusho, Innocent Rwamuhizi, Isidore Houaga, David Harvey, David Hambrook, Kellie Watson, Mizeck G. G. Chagunda, Raphael Mrode, Appolinaire Djikeng

“…The combined dataset was subject to quality control, data curation for use in population genetics and genomic analyses. …”
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Estimation of the air conditioning energy consumption of a classroom using machine learning in a tropical climate by Liliana Ortega-Diaz, Julian Jaramillo-Ibarra, German Osma-Pinto

“…In addition, two sensitivity analyses were performed by modifying the time interval of the data (1, 5, 10, 20, 30, and 60 min) and the data split (50:50, 60:40, 70:30, 80:20 and 90:10). …”
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Accelerometer-Measured Physical Activity and Neuroimaging-Driven Brain Age by Han Chen, Zhi Cao, Jing Zhang, Dun Li, Yaogang Wang, Chenjie Xu

“…Over 1,400 image-derived phenotypes (IDPs) were initially chosen to undergo data-driven feature selection for brain age prediction. …”
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Forecasting Human Core and Skin Temperatures: A Long-Term Series Approach by Xinge Han, Jiansong Wu, Zhuqiang Hu, Chuan Li, Boyang Sun

“…This study presents a deep learning model that combines a long-term series forecasting method with transfer learning techniques, capable of making precise, personalized predictions of T_cr and T_sk in high-temperature environments with only a small corpus of actual training data. To practically validate the model, field experiments were conducted in complex environments, and a thorough analysis of the effects of three diverse training strategies on the overall performance of the model was performed. …”
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Spatial sample weighted machine learning for multitemporal land cover change modeling with imbalanced datasets by Alysha van Duynhoven, Suzana Dragićević

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An Intelligent Self-Validated Sensor System Using Neural Network Technologies and Fuzzy Logic Under Operating Implementation Conditions by Serhii Vladov, Victoria Vysotska, Valerii Sokurenko, Oleksandr Muzychuk, Lyubomyr Chyrun

“…This article presents an intelligent self-validated sensor system developed for dynamic objects and based on the intelligent sensor concept, which ensures autonomous data collection and real-time analysis while adapting to changing conditions and compensating for errors. …”
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Corpus Development and NER Model for Identification of Legal Entities (Articles, Laws, and Sanctions) in Corruption Court Decisions in Indonesia by Edy Subowo, Imam Bukhori, warto

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Taxonomic review of Korean Siphonaria species (Mollusca, Gastropoda, Siphonariidae) by Yukyung Kim, Jina Park, Ui Wook Hwang, Joong-Ki Park

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Laor Initialization: A New Weight Initialization Method for the Backpropagation of Deep Learning by Laor Boongasame, Jirapond Muangprathub, Karanrat Thammarak

“…In contrast to traditional methods, Laor adopts a data-driven approach that enhances convergence’s stability and efficiency. …”
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Airborne gravimetry with quantum technology: observations from Iceland and Greenland by T. E. Jensen, B. Dale, A. Stokholm, R. Forsberg, A. Bresson, N. Zahzam, A. Bonnin, Y. Bidel

“…Although the two technologies lead to similar performance, further analysis indicates that the error characteristics are different and that the final estimates would benefit from a combination (data available at <a href="https://doi.org/10.57780/esa-58c58c5">https://doi.org/10.57780/esa-58c58c5</a>; see Jensen et al., 2024).…”
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Uncertainty quantification in tree structure and polynomial regression algorithms toward material indices prediction by Geng-Fu He, Pin Zhang, Zhen-Yu Yin

“…In the regions of sparse data, predicted uncertainty becomes larger as errors increase, demonstrating the validity of UQ. …”
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Combining review elements for modelling various multi-criteria collaborative recommendation models by Sumaia Mohammed AL-Ghuribi, Shahrul Azman Mohd Noah, Sabrina Tiun, Mawal A. Mohammed, Nur Izyan Yasmin Saat

“…A key innovation lies in a novel user profile modelling approach that dynamically combines these elements, enabling granular preference analysis. Comprehensive experiments on the large-scale Amazon dataset demonstrate that the Trust-based Multi-Criteria Similarity with Average Value (TMCSAV) model outperforms all proposed models and the state-of-the-art baselines, achieving the lowest prediction errors (MAE: 0.7473, RMSE: 0.9966) and superior relevance identification (F1-score: 0.65). …”
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Hyperdimensional computing: a framework for stochastic computation and symbolic AI by Mike Heddes, Igor Nunes, Tony Givargis, Alexandru Nicolau, Alex Veidenbaum

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Unsupervised clustering based coronary artery segmentation by Belén Serrano-Antón, Manuel Insúa Villa, Santiago Pendón-Minguillón, Santiago Paramés-Estévez, Alberto Otero-Cacho, Diego López-Otero, Brais Díaz-Fernández, María Bastos-Fernández, José R. González-Juanatey, Alberto P. Muñuzuri

“…This paper proposes an automatic segmentation methodology based on clustering algorithms and a graph structure, which integrates data from both the clustering process and the original images. …”
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A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022 by W. Li, W. Li, Z. Yao, Z. Yao, Y. Qu, Y. Qu, H. Yang, Y. Song, L. Song, L. Wu, Y. Cui, Y. Cui

“…A novel bias-corrected random forest (RF) algorithm was used for gap-filling and prolongation in the framework to produce seamless half-hourly and daily LE data. After analysis, the framework using the novel bias-corrected RF algorithm achieves excellent performance in both hourly gap-filling and daily prolongation, with mean root mean square error values of 33.86 and 16.58 <span class="inline-formula">W m⁻²</span>, respectively. …”
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Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations by Sumit Singh Dhanda, Deepak Panwar, Chia-Chen Lin, Tarun Kumar Sharma, Deependra Rastogi, Shantanu Bindewari, Anand Singh, Yung-Hui Li, Neha Agarwal, Saurabh Agarwal

“…Mental health prediction systems based on NLP and wearable data delivered up to 91% accuracy in stress and depression detection, while hospital resource forecasting models using deep learning minimized errors in predicting emergency admissions. …”
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ASM-SS: the first quasi-global high-spatial-resolution coastal storm surge dataset reconstructed from tide gauge records by L. Yang, T. Jin, T. Jin, W. Jiang, W. Jiang

“…In this paper, for the first time, an all-site modeling framework for a data-driven model was implemented on a quasi-global scale within areas severely affected by SSs caused by tropical cyclones. …”
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Estimation of long-term gridded cloud radiative kernel and radiative effects based on cloud fraction by X. Liu, T. He, Q. Wang, X. Xiao, Y. Ma, Y. Wang, S. Luo, L. Du, Z. Wu, Z. Wu

“…In Arctic-wide validation experiments, the root mean square error (RMSE) was decreased by approximately 2.5 W m<span class="inline-formula">⁻²</span>, and the bias was reduced by 1.23 W m<span class="inline-formula">⁻²</span>, which was an improvement of 8.7 % (reduction in RMSE) against the CERES EBAF (Clouds and the Earth's Radiant Energy System Energy Balanced and Filled). …”
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Revolutionizing classification: A novel gray level co-occurrence matrix and statistical feature-based segmentation approach by Abdelwahed Motwakel

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Shipborne comparison of infrared and passive microwave radiometers for sea surface temperature observations by G. Gacitúa, J. Lorentsen Høyer, S. Schmidl Søbjærg, H. Shi, S. Skarpalezos, I. Karagali, E. Alerskans, C. Donlon

“…The data analysis primarily focused on evaluating data variability, identifying discrepancies between IR and PMW SST, and assessing the overall uncertainty in the retrieval process. …”
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