Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles. by Arne F Meyer, Jan-Philipp Diepenbrock, Max F K Happel, Frank W Ohl, Jörn Anemüller

“…The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. …”
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Experimental and numerical study on FRP-rehabilitated RC beam-column joints at high temperature with artificial neural network by R. Surya Prakash, N. Parthasarathi

“…However, generalizing predictions beyond the studied range may introduce over fitting risks, and the model remains sensitive to data quality. In summary, CFRP demonstrated optimal performance, particularly at 400 °C before rehabilitation and 500 °C afterward, making it the most effective choice for high-temperature FRP-based RC joint rehabilitation. …”
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Development of an Automated Management System for Agricultural Technologies in Horticulture by D. O. Khort, A. I. Kutyrev, I. G. Smirnov, I. V. Voronkov

“…They showed that the system automatically optimized machine technologies for the cultivation of horticultural crops according to biological (realization of the potential biological productivity of crops) and economic (increasing the efficiency of using production resources) criteria.…”
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Leveraging Sustainable Household Energy and Environment Resources Management with Time-Series by José Cecílio, Tiago Rodrigues, Márcia Barros, Alan Oliveira de Sá

“…Following established literature, we developed and implemented machine learning models that comprehensively validate the data. …”
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Research on Tool Wear Monitoring Based on ET-GD and K-nearest Neighbor Algorithm by QIN Yiyuan, LIU Xianli, YUE Caixu, GUO Bin, DING Mingna

“…The fitting degree and evaluation measure of the three K-nearest neighbor models before and after the two optimization are compared and analyzed. …”
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Evaluation of ground based spectral imaging for real time maize biomass monitoring by Andrea Szabó, Andrea Szabó, Nxumalo Gift Siphiwe, Nxumalo Gift Siphiwe, Erika Buday-Bódi, Erika Buday-Bódi, Blessing Ademola, János Tamás, János Tamás, Attila Nagy, Attila Nagy

“…These findings underscore the potential of combining proximal sensing and biomass data to enhance the prediction of plant properties, providing valuable insights for optimizing precision agriculture through machine learning.…”
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Estimating the Risk of Lower Extremity Complications in Adults Newly Diagnosed With Diabetic Polyneuropathy: Retrospective Cohort Study by Alyce S Adams, Catherine Lee, Gabriel Escobar, Elizabeth A Bayliss, Brian Callaghan, Michael Horberg, Julie A Schmittdiel, Connie Trinacty, Lisa K Gilliam, Eileen Kim, Nima S Hejazi, Lin Ma, Romain Neugebauer

“…ObjectiveThis study aimed to develop an electronic medical record–based machine learning algorithm that would predict lower extremity complications. …”
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Method and Experimental Research of Transmission Line Tower Grounding Body Condition Assessment Based on Multi-Parameter Time-Domain Pulsed Eddy Current Characteristic Signal Extra... by Yun Zuo, Jie Wang, Xiaoju Huang, Yuan Liu, Zhiwu Zeng, Ruiqing Xu, Yawen Chen, Kui Liu, Hongkang You, Jingang Wang

“…Four defects with different corrosion levels were classified using the optimized SVM model, with an accuracy rate of 95%. …”
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Identification of Salt Marsh Vegetation in the Yellow River Delta Using UAV Multispectral Imagery and Deep Learning by Xiaohui Bai, Changzhi Yang, Lei Fang, Jinyue Chen, Xinfeng Wang, Ning Gao, Peiming Zheng, Guoqiang Wang, Qiao Wang, Shilong Ren

“…This study investigated the effectiveness of spectral features and machine learning models in separating typical salt marsh vegetation types in the Yellow River Delta using uncrewed aerial vehicle (UAV)-derived multispectral imagery. …”
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Magnitude and precision of absolute blood volume estimated during hemodialysis by Rammah Abohtyra, Tyrone Vincent, Daniel Schneditz

“…Background: Management of body fluid volumes and adequate prescription of ultrafiltration (UF) remain key issues in the treatment of chronic kidney disease patients.Objective: This study aims to estimate the magnitude as well as the precision of absolute blood volume ([Formula: see text]) modeled during regular hemodialysis (HD) using standard data available with modern dialysis machines.Methods: The estimation utilizes a two-compartment fluid model and a mathematical optimization technique to predict UF-induced changes in hematocrit measured by available on-line techniques. …”
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Geochemical inversion study of potassium and phosphorus in soil based on neural network and ZY1-02D hyperspectral data by Ziyang Li, Junxu Chen, Zhifang Zhao, Xiaotong Su, Shuanglan Yang, Xinle Zhang, Gaoqiang Xiao, Tao Fu, Lei Niu

“…Phosphorus showed a higher correlation in the bare area than in the vegetated area. (2) The optimal prediction models for potassium and phosphorus in both the vegetated and bare areas were based on the ELM model. …”
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An Artificial Neural Network for Short Time Air Temperature Prediction by Olivia S. Gomes, Manuel O. Binelo, Marcia de F. B. Binelo, Joao Paulo C. Oliveira, Emerson Galvani, Rogerio Rozolen Alves

“…Air temperature is an extremely important factor in agriculture, from planting to post-harvest processes, and having the ability to predict air temperature can be a valuable tool for avoiding damage, maximizing production quality, and optimizing resources. In this work, we propose a simple air temperature prediction model based on a small neural network with a relatively small volume of training data. …”
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Advancing personalized diagnosis and treatment using deep learning architecture by Rahat Ullah, Nadeem Sarwar, Mohammed Naif Alatawi, Abeer Abdullah Alsadhan, Hathal Salamah Alwageed, Maqbool Khan, Aitizaz Ali

“…Comparative evaluations demonstrate that ImmunoNet outperforms traditional machine learning models, achieving a 98% accuracy rate in predicting autoimmune disorders. …”
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Diagnostic, Therapeutic, and Prognostic Applications of Artificial Intelligence (AI) in the Clinical Management of Brain Metastases (BMs) by Kyriacos Evangelou, Panagiotis Zemperligkos, Anastasios Politis, Evgenia Lani, Enrique Gutierrez-Valencia, Ioannis Kotsantis, Georgios Velonakis, Efstathios Boviatsis, Lampis C. Stavrinou, Aristotelis Kalyvas

“…Future endeavors should thus prioritize the development of generalized AI models, the combination of large and diverse datasets, and the integration of clinical and molecular data into imaging, in an effort to maximally enhance the clinical application of AI in BM care and optimize patient outcomes.…”
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Wavelet CNN‐LSTM time series forecasting of electricity power generation considering biomass thermal systems by William Gouvêa Buratto, Rafael Ninno Muniz, Ademir Nied, Carlos Frederico de Oliveira Barros, Rodolfo Cardoso, Gabriel Villarrubia Gonzalez

“…Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real‐world applications. …”
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Characterization of 2D precision and accuracy for combined visual-haptic localization by Madeline Fischer, Umberto Saetti, Martine Godfroy-Cooper, Douglas Fischer

“…Overall, the lack of improvement in precision for bimodal cueing relative to the best unimodal cueing modality, vision, is in favor of sensory combination rather than optimal integration predicted by the Maximum Likelihood Estimation (MLE) model. …”
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Subtropical region tea tree LAI estimation integrating vegetation indices and texture features derived from UAV multispectral images by Zhong-Han Zhuang, Hui Ping Tsai, Chung-I Chen, Ming-Der Yang

“…Additionally, minimum redundancy-maximum relevance analysis was utilized to assess the mutual information of features for regression analysis using Polynomial Regression (PR), Ridge Regression, Decision Tree Regression, and Random Forest Regression (RFR) models. The results showed that: (1) AFM had higher in situ LAI values (mean = 4.323, SD = 1.594) compared to CFM (mean = 3.901, SD = 1.816), with less seasonal variation, mainly attributed to agronomic practices like harvesting and winter pruning. (2) Optimal image features for LAI estimation were identified by extracting pixel-based features from tea tree regions to enhance the correlation between LAI and imagery. (3) Combining VIs and TFs improved LAI estimation accuracy. …”
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Applying Canny edge detection and Hough transform algorithms to identify irrigation channel boundaries in irrigation districts by LU Hongfei, MAO Hanyu, ZHOU Hao, ZHEN Bo, ZHONG Yao, YANG Bo

“…For example, when fitting the straight boundary of the Jurong River, the R2 value was 0.959 2 for the quadratic model and 0.949 2 for the linear model. The quadratic fitting was also more robust in handling boundary noise caused by obstacles. …”
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Individualized Analysis of Nipple‐Sparing Mastectomy Versus Modified Radical Mastectomy Using Deep Learning by Enzhao Zhu, Linmei Zhang, Pu Ai, Jiayi Wang, Chunyu Hu, Huiqing Pan, Weizhong Shi, Ziqin Xu, Yidan Fang, Zisheng Ai

“…Methods To develop treatment recommendations for breast cancer patients, five machine learning models were trained. To mitigate bias in treatment allocation, advanced statistical methods, including propensity score matching (PSM) and inverse probability treatment weighting (IPTW), were applied. …”
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Analyzing Random Forest’s Predictive Capability for Type 1 Diabetes Progression by Niels F. Cleymans, Mark Van De Casteele, Julie Vandewalle, Aster K. Desouter, Frans K. Gorus, Kurt Barbe

“…Research in first-degree relatives of known T1D patients has shown that disease progression can be predicted by genetic and immune biomarkers, but these predictions are limited by using the traditional statistical approaches such as Cox regression models. This explorative study aims to uncover the potential of random forest machine learning algorithms as survival models within the biomedical context of T1D. …”
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