Keypoint-based modeling reveals fine-grained body pose tuning in superior temporal sulcus neurons by Rajani Raman, Anna Bognár, Ghazaleh Ghamkhari Nejad, Albert Mukovskiy, Lucas Martini, Martin Giese, Rufin Vogels

“…Some anterior STS units exhibited better predictive performances with a view-dependent 3D model. …”
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Simulation of Land Use and Land Cover Using the MOLUSCE Plugin Integrated with QGIS for the Western Himalayan Region of India by S. Kumari, S. Kumari, A. Roy

“…MOLUSCE tool is used to simulate LULC change by incorporating predictive algorithms such as Artificial Neural Networks (ANN) and Weights of Evidence (WoE). …”
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Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy by Min Liang, Min Liang, Fuyuan Luo

“…Prognostic features were selected through univariate logistic regression and Lasso analyses. Predictive modeling was performed using advanced machine learning algorithms, including XGBoost, Multilayer Perceptron, K-Nearest Neighbor, and Random Forest. …”
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Study on the relationship between vaginal dose and radiation-induced vaginal injury following cervical cancer radiotherapy, and model development by Fei Kong, Ziqin Yan, Liying Gao, Jianping Long, Xu Wang, Zhangcai Zheng

“…Univariate and multivariate analyses were conducted to identify key factors influencing radiation-induced vaginal injury, and five distinct algorithms were applied to develop predictive models. …”
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Precise application of water and fertilizer to crops: challenges and opportunities by Yingying Xing, Xiukang Wang

“…It examines the integration of advanced sensors, remote sensing, and machine learning algorithms in precision agriculture, assessing their roles in optimizing irrigation and nutrient management. …”
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NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil by Gaia Meoni, Leonardo Tenori, Francesca Di Cesare, Stefano Brizzolara, Pietro Tonutti, Chiara Cherubini, Laura Mazzanti, Claudio Luchinat

“…By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. …”
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Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system. by Robert W Mathes, Ramona Lall, Alison Levin-Rector, Jessica Sell, Marc Paladini, Kevin J Konty, Don Olson, Don Weiss

“…We evaluated six temporal and four spatial/spatio-temporal detection methods using syndromic surveillance data spiked with simulated injections. The algorithms were compared on several metrics, including sensitivity, specificity, positive predictive value, coherence, and timeliness. …”
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Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design by Paul Adunola, Luis Felipe V. Ferrão, Juliana Benevenuto, Camila F. Azevedo, Patricio R. Munoz

“…Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. …”
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Machine learning analysis of molecular dynamics properties influencing drug solubility by Zeinab Sodaei, Saeid Ekrami, Seyed Majid Hashemianzadeh

“…The Gradient Boosting algorithm achieved the best performance with a predictive R2 of 0.87 and an RMSE of 0.537 in test set. …”
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Future Outdoor Safety Monitoring: Integrating Human Activity Recognition with the Internet of Physical–Virtual Things by Yu Chen, Jia Li, Erik Blasch, Qian Qu

“…Advanced HAR–IoPVT algorithms and predictive analytics would identify potential hazards, enabling timely interventions and reducing accidents. …”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…This research leverages various machine learning algorithms to develop predictive models trained on a comprehensive dataset of sensor-based soil moisture, matric suction, and soil temperature obtained from prototype ET covers, with known resistivity values. …”
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Diagnostic value of leukocytosis in patients presenting to the emergency department with abdominal pain: A retrospective observational study by Erkan Boğa

“…Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of leukocytosis in predicting surgical needs were calculated. …”
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An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations by Junbao Xia, Yanping Liu, Haozhong Yang, Guodong Zhu

“…By processing remote sensing data to mitigate noise and integrating it with conventional urban datasets, an innovative index system and predictive model were developed. Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. …”
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Index or illusion: The case of frailty indices in the Health and Retirement Study. by Yi-Sheng Chao, Hsing-Chien Wu, Chao-Jung Wu, Wei-Chih Chen

“…The input variables better predicted mortality than the FIs. The continuous FIs predicted mortality better than the frailty statuses. …”
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Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy by Feng Zhang, Chunrui Li, Lulu Zhang

“…Immune cell infiltration patterns were quantified via single-sample gene set enrichment analysis (ssGSEA). A predictive model for HCM was developed through systematic evaluation of 113 combinations of 12 machine-learning algorithms, employing 10-fold cross-validation on training datasets and external validation using an independent cohort (GSE180313).ResultsA total of 271 DEGs were identified, primarily enriched in multiple biological pathways. …”
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Design and Application of an Energy Management System Based on Artificial Intelligence Technology by Hongye Lin, Xuanying Bai, Chun Li, Shenghan Xu, Haibin Xu, Zne-Jung Lee, Yun Lin, Qunshan Zhou, Jingxun Cai

“…The system adopts sensor technology and data acquisition equipment to monitor various types of energy consumption in buildings in real time, efficiently process and predict these data through machine learning algorithms, and finally visualize the results. …”
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Soil Condition Classification Based on Natural Water Content Using Computer Vision Technique by Mark Miller, Yong Fang, Yubo Wang, Sergey Kharitonov, Vladimir Akulich

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WPD-ResNeSt: Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer Learning by Ting Yang, Yucheng Hou, Yachuang Liu, Feng Zhai, Rongze Niu

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Bayesian approach for localizing cardiac sources in Magnetocardiography using Vectorcardiography based total variational priors by Vikas R. Bhat, Karunakar Kotegar, H. Anitha

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What factors enhance students' achievement? A machine learning and interpretable methods approach. by Hui Mao, Ribesh Khanal, ChengZhang Qu, HuaFeng Kong, TingYao Jiang

“…Our framework enables educators to transform predictive analytics into actionable pedagogical improvements.…”
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