Artificial Neural Network Modeling of NixMnxOx based Thermistor for Predicative Synthesis and Characterization by T.D. Dongale, K.G. Kharade, N.B. Mullani, G.M. Naik, R.K. Kamat

“…The accomplished ANN modeling evidences a lower number of hidden neuron architecture exhibiting optimum performance as regards to prediction accuracy. The lower number of hidden neurons signifies a lesser amount of memory required for prediction of different chemical composition. …”
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The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection by Dong Dai, Zhenyu Wang, Hao Huang, Xu Mao, Yehong Liu, Hao Li, Du Chen

“…For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R² = 0.9846, MSE = 0.2768, MAE = 0.3986). …”
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Disrupted fetal carbohydrate metabolism in children with autism spectrum disorder by Serena B. Gumusoglu, Brandon M. Schickling, Donna A. Santillan, Lynn M. Teesch, Mark K. Santillan

“…Untargeted principle components analysis of all metabolites did not reveal group differences, while targeted biomarker assessment (using only Fructose 6-phosphate, D-Mannose, and D-Fructose) by a Random Forest algorithm generated an area under the curve (AUC) = 0.766 (95% CI: 0.612–0.896) for ASD prediction. …”
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Environmental adaptations in metagenomes revealed by deep learning by Johanna C. Winder, Simon Poulton, Taoyang Wu, Thomas Mock, Cock van Oosterhout

“…To enhance biological interpretability of these predictions, we compared this model with a genetic algorithm (GA), which, although it had lower predictive ability, provided transparent classification rules and predictors. …”
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Interpretable Machine Learning for Thermospheric Mass Density Modeling Using GRACE/GRACE‐FO Satellite Data by Qian Pan, Chao Xiong, ShunZu Gao, Zhou Chen, Artem Smirnov, Chunyu Xu, Yuyang Huang

“…A critical aspect of our work is minimizing the number of input parameters while maintaining high prediction accuracy. And the interpretability analysis of the input parameters using the Shapley additive explanation algorithm has been applied, revealing that the altitude, solar activity index P10.7 and solar zenith angle are the three most influential parameters affecting TMD variations at GRACE satellite. …”
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Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning by ShengHui Miao, YiJing Liu, Min Li, Jing Yan

“…Incorporating early ICU baseline variables into an XGBoost algorithm enables effective prediction of high-mortality risk subgroups (AUC > 0.8). …”
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Assessment and application of melting-layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model by R. Mangla, R. Mangla, M. Borderies, P. Chambon, A. Geer, J. Hocking

“…<p>Because of their high sensitivity to hydrometeors and high vertical resolutions, spaceborne radar observations are emerging as an undeniable asset for numerical weather prediction (NWP) applications. The EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) NWP SAF (Satellite Application Facility for Numerical Weather Prediction) released an active sensor module within version 13 of the RTTOV (Radiative Transfer for TIROS Operational Vertical Sounder) software with the goal of simulating both active and passive microwave instruments within a single framework using the same radiative transfer assumptions. …”
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A Predictor-Corrector Method for Solving Equilibrium Problems by Zong-Ke Bao, Ming Huang, Xi-Qiang Xia

“…One step serves to predict the next point; the other helps to correct the new prediction. …”
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Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence–Enabled ECGs by Scott Anjewierden, Donnchadh O'Sullivan, Kathryn E. Mangold, Grace Greason, Itzhak Zachi Attia, Francisco Lopez‐Jimenez, Paul A. Friedman, Samuel J. Asirvatham, Jason Anderson, Benjamin W. Eidem, Jonathan N. Johnson, Shisheer Havangi Prakash, Talha Niaz, Malini Madhavan

“…The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. …”
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A Model for Fat Content Detection in Walnuts Based on Near-Infrared Spectroscopy by Langqin Luo, Honghua Zhang, Yu Wang, Jianliang Zhang, Rui Zhang, Shan Gao, Yuanyong Dian, Zijin Bai, Chunhui Feng, Ze Zhang

“…After first optimizing the initial spectrum data using five preprocessing methods, we established separate prediction models for walnut kernel fat content based on either a back propagation neural network or a support vector regression (SVR) algorithm. …”
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Utilizing Principal Singular Vectors for 2D DOA Estimation in Single Snapshot Case with Uniform Rectangular Array by Yuntao Wu, Xiaobing Pei, Hing Cheung So

“…Computer simulations are included to demonstrate the effectiveness of the proposed algorithm.…”
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Adaptive WSN Scheduling for Lifetime Extension in Environmental Monitoring Applications by Jong Chern Lim, Chris Bleakley

“…For two-tier networks, the proposed algorithm outperforms a highly cited previously published algorithm by up to 512% in terms of lifetime and by up to 30% in terms of prediction accuracy. …”
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Evaluating the impact of metabolic indicators and scores on cardiovascular events using machine learning by Guanmou Li, Cheng Luo, Teng Ge, Kunyang He, Miao Zhang, Jinlin Hu, Baoshi Zheng, Rongjun Zou, Xiaoping Fan

“…Through cross-validation to validate model performance, the XGBoost algorithm demonstrated the most accurate performance in predicting cardiovascular outcomes, particularly for diseases like angina and heart failure. …”
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Parameter estimation for networked SIR models with stochastic perturbations using JEKF: a study using COVID-19 daily data from Indian states by Prince Achankunju, Saroj Kumar Dash

“…This sophisticated estimation algorithm iteratively predicts and refines the state based on current estimates, combining model predictions with observed data to enhance accuracy, making it effective for estimating critical parameters in dynamic systems. …”
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Interpretable Machine Learning for the German residential rental market – shedding light into model mechanics by Severin Bachmann

“…We compare the drivers in Machine learning models and give insights into their strengths and weaknesses predicting rental prices. The study employs SHAP values to measure feature importance. …”
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Litter accumulation and fire risks show direct and indirect climate-dependence at continental scale by Mark A. Adams, Mathias Neumann

“…Results provide guidance for future decomposition studies. Algorithms reported here can significantly improve accuracy and reliability of predictions of carbon and nutrient dynamics and fire risk.…”
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How Re-Infections and Newborns Can Impact Visible and Hidden Epidemic Dynamics? by Igor Nesteruk

“…With the use of effective algorithms for parameter identification, the proposed approach can ensure effective predictions of visible and hidden numbers of cases and infectious and removed patients.…”
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Exploring the Role of Artificial Intelligence in Dental Implantology: A Scholarly Review by Abdulrahman Ahmed Aseri

“…AI-based technologies, such as machine learning algorithms, neural networks, and computer vision, are being increasingly utilized to interpret diagnostic imaging, predict implant outcomes, and customize treatment protocols tailored to individual patient needs. …”
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Employee loyalty evaluation using machine learning in technology-based small and medium-sized enterprises by Yong Shi, Yuan Wang, Hongkun zuo

“…Through several machine learning models and algorithms to predict employee loyalty, the feasibility of machine learning to predict employee loyalty is proved, and the evaluation of talent in TSMEs is supported by decision analysis. …”
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Potential geographical distribution of Carob tree (Ceratonia siliqua L., Leguminosae) in Morocco under climate change by Jalal Kassout, Soufian Chakkour, Abdeltif El Ouahrani, Younes Hmimsa, Salama El Fatehi, Yanzheng Yan, Rachid Hadria, Mohammed Ater

“…Our hypothesis suggests a reduction in suitable habitats for the carob tree under climate change scenarios. …”
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