Progress and perspectives on genomic selection models for crop breeding by Dongfeng Zhang, Feng Yang, Jinlong Li, Zhongqiang Liu, Yanyun Han, Qiusi Zhang, Shouhui Pan, Xiangyu Zhao, Kaiyi Wang

“…Genomic selection, a molecular breeding technique, is playing an increasingly important role in improving the efficiency of artificial selection and genetic gain in modern crop breeding programs. A series of algorithms have been proposed to improve the prediction accuracy of genomic selection. …”
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Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China by Mingxue Xiang, Gang Fu, Jianghao Cheng, Tao Ma, Yunqiao Ma, Kai Zheng, Zhaoqi Wang

“…Therefore, the random forest algorithm based on climate data and/or the NDVImax demonstrated superior predictive performance in modeling these biogeochemical parameters.…”
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Facial emotion based smartphone addiction detection and prevention using deep learning and video based learning by C. Joseph, P. Uma Maheswari

“…Experimental results demonstrate significant improvements in students’ behavior and reductions in smartphone usage post-intervention. The TMVM system achieves high accuracy in emotion detection and behavioral outcome prediction while fostering engagement in school and social activities. . …”
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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

“…Our findings showed that XGB and RF could predict alfalfa crop height with an R² of 0.79 and a mean absolute error (MAE) of around 4 cm Our findings indicated that SVR exhibited the lowest accuracy among the three algorithms tested, with R² of 0.69 and an MAE of 4.63 cm. …”
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Efficient Resource Allocation for Blockchain-Enabled Mobile Edge Computing: A Joint Optimization Approach by Moein Valitabar, Mohammad Fathi, Keivan Navaie

“…Performance evaluation results demonstrate the effectiveness of these algorithms, achieving significant reductions in total energy consumption while maximizing the efficiency of communication and computational resources. …”
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Vectorized building rooftop prints of the Qinghai-Tibetan Plateau and its neighboring regions by Tao Ye, Hongyu Shan, Jidong Wu, Qiang Zhou, Mingfu Ma, Wenzhi Zhao, Ru Ya, Yuan Gao, Lizheng Wu

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Simulation and experimental verification of the precision finishing method for optical free-form surface segmentation. by Chenhua Jiang, Enzhong Zhang, Wei Zhang, Jiaqi Hu, Jiechen Guo, Xiaodong Li

“…Finally, experiments revealed a 12.28% reduction in machining path length and a 12.56% reduction in machining time using the segmentation machining method. …”
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Assessing habitat suitability for aoudad (Ammotragus lervia) reintroduction in southeastern morocco to promote ecotourism by Lahbib Naimi, El Mahi Bouziane, Lamya Benaddi, Abdeslam Jakimi, Mohamed Manaouch

“…Subsequently, a machine learning algorithm called Bagging was employed to develop a predictive model. …”
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AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study by Mihaela Hnatiuc, Domnica Alpetri, Sorin-Robertino Sintea, Bogdan Hnatiuc, Gabriel Margarit Raicu, Mirel Paun, Ionica Dina

“…Machine learning (ML) algorithms, running on a server with an NVIDIA R3900 card, process this data to predict potential infections caused by pathogens such as Plasmopara viticola, Uncinula necator, and Botrytis. …”
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Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

“…Data-driven machine learning (ML) algorithms were utilized to identify complex patterns and predict relationships between input variables and output responses. …”
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miRTARGET: An integrated web tool for the identification of microRNA targets with potential therapeutic or prognostic value in cancer by Matjaz Rokavec, Heiko Hermeking

“…It integrates experimental miRNA-related datasets and computational algorithms to generate prediction scores for targets of 1744 human miRNAs. …”
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A Hybrid Forecasting Approach by Emilian Dobrescu

“…The objective of the paper is to establish the appropriateness of integrating in predictive simulation an econometric estimation of a given variable into a standard moving average process (a linear algorithm with constant positive weights of distributed lags). …”
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Applications of Machine Learning and Remote Sensing in Soil and Water Conservation by Ye Inn Kim, Woo Hyeon Park, Yongchul Shin, Jin-Woo Park, Bernie Engel, Young-Jo Yun, Won Seok Jang

“…As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. …”
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Design and analysis of quantum machine learning: a survey by Linshu Chen, Tao Li, Yuxiang Chen, Xiaoyan Chen, Marcin Wozniak, Neal Xiong, Wei Liang

“…Thirdly, we conduct discussions on the applications of quantum machine learning in image recognition, drug efficacy prediction and cybersecurity. Finally, we summarise the challenges of quantum machine learning consisting of algorithm design, hardware limitations, data encoding, quantum landscapes, noise and decoherence.…”
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Characterization of Cuproptosis-Related LncRNAs Prognostic Signature and Identification of LINC02285 as a Novel Biomarker for Ovarian Cancer by Lei H, Zhou Z, Liu C, Chen W, Li Y, Shu G, Wang M, Guo K, Pan Q, Yin G

“…ROC curve analysis further validated the predictive capacity of the signature. Additionally, the low-risk group had a favorable prognosis associated with a protective immune microenvironment and a better response to targeted drugs. …”
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Artificial Intelligence–Enabled ECG Screening for LVSD in LBBB by Hak Seung Lee, MD, Sooyeon Lee, MD, Sora Kang, MS, Ga In Han, MS, Ah-Hyun Yoo, MS, Jong-Hwan Jang, PhD, Yong-Yeon Jo, PhD, Jeong Min Son, MD, Min Sung Lee, MD, MS, Joon-myoung Kwon, MD, MS, Kyung-Hee Kim, MD, PhD

“…Conclusions: Our findings indicate that a broad AI-ECG model reliably detects LVSD in LBBB patients, and transfer learning offers modest improvements without requiring curated LBBB data sets. Evaluating algorithms in representative clinical populations is essential.…”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…In contrast, nuclear magnetic resonance (NMR) measurements, which provide information on pore body size distribution, are faster and can be leveraged to estimate capillary pressure using machine learning algorithms. Recently, artificial intelligence methods have also been applied to capillary pressure prediction (Qi et al., 2024).Currently, no readily applicable predictive model exists for estimating an entire capillary pressure curve directly from standard petrophysical logs and core data. …”
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Early Detection of Soil Salinization by Means of Spaceborne Hyperspectral Imagery by Giacomo Lazzeri, Robert Milewski, Saskia Foerster, Sandro Moretti, Sabine Chabrillat

“…Both datasets were pre-processed with multiple data transformation algorithms and 2D correlograms, PLSR and the Random Forest regressor were tested to identify the best model for salinity detection. …”
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Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion by Rocío Aznar-Gimeno, Jose Luis Perez-Lasierra, Pablo Pérez-Lázaro, Irene Bosque-López, Marina Azpíroz-Puente, Pilar Salvo-Ibáñez, Martin Morita-Hernandez, Ana Caren Hernández-Ruiz, Antonio Gómez-Bernal, María de la Vega Rodrigalvarez-Chamarro, José-Víctor Alfaro-Santafé, Rafael del Hoyo-Alonso, Javier Alfaro-Santafé

“…Finally, machine learning models were developed using these variables to predict sarcopenia and CD. <b>Results</b>: Models based on sensor data, CV data, and a combination of both technologies achieved high predictive accuracy, particularly for CD. …”
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Pharmacological potential of 3,5-dimethyl-4-(3-(5-nitrofuran-2-yl)allylidenamino)-1-alkyl-1,2,4-triazolium bromides by A. S. Hotsulia, O. I. Panasenko, T. S. Brytanova

“…A molecular docking method that uses a variety of computational algorithms to predict and analyze interactions, including determining the presence of possible binding sites, estimating binding energies, and the spatial arrangement of molecules. …”
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