Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary, Muhammad Salman Saeed

“…In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. …”
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Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model by Xing Tu, Zixing Zou, Jiahui Li, Simiao Zeng, Zhengchao Luo, Gen Li, Yuanxu Gao, Kang Zhang, Jing Ni

“…We employed a series of AI methods, including large language and graph neural network models, to identify the target compounds of RIPK3. …”
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Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors by Shiqi Yuan, Qing Liu, Xiaxuan Huang, Shanyuan Tan, Zihong Bai, Juan Yu, Fazhen Lei, Huan Le, Qingqing Ye, Xiaoxue Peng, Juying Yang, Yitong Ling, Jun Lyu

“…Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the most advanced technology for survival analysis to date. …”
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Development and Validation of a Photoplethysmography System for Noninvasive Monitoring of Hemoglobin Concentration by Hongyun Liu, Fulai Peng, Minlu Hu, Jinlong Shi, Guojing Wang, Haiming Ai, Weidong Wang

“…To facilitate real-time total hemoglobin (tHb) monitoring, a portable prototype of a noninvasive Hb detection system was developed, and the accuracy of Hb predicted based on partial least squares (PLS) as well as backpropagation artificial neural network (BP-ANN) models was validated. Results. …”
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Klasifikasi Pola Pergerakan Bola Mata Menggunakan Metode Multilayer Backpropagation by Karina Amadea, Fitra A. Bachtiar, Gusti Pangestu

“…The presence of a neural network in the iris, helps humans to be able to find out the response to all changes in the body including changes in the spirit of life, character or even a person's nature. …”
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Drug-induced autoimmune-like hepatitis: A disproportionality analysis based on the FAERS database. by Wangyu Ye, Yuan Ding, Meng Li, Zhihua Tian, Shaoli Wang, Zhen Liu

“…Positive signal drugs were identified using Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM). …”
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Construction and Comparison of Machine Learning-Based Risk Prediction Models for Major Adverse Cardiovascular Events in Perimenopausal Women by Chen A, Chang X, Bian X, Zhang F, Ma S, Chen X

“…In the training set, Random Forest (RF) algorithm, backpropagation neural network (BPNN) and Logistic Regression (LR) were used to construct a MACE risk prediction model for perimenopausal women, and the test set was used to verify the model. …”
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Estimating ocean heat content from the ocean thermal expansion parameters using satellite data by V. P. Kondeti, S. Palanisamy

“…To achieve this objective, artificial neural network (ANN) models were developed to derive thermosteric sea level (TSL) from a given dataset of sea surface temperature, sea surface salinity, geographical coordinates, and climatological TSL. …”
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DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm by Abdulqader M. Almars

“…This suggested framework leverages an attention-based convolutional neural network (CNN) and a genetic algorithm (GA) to enhance detection accuracy while optimizing the hyperparameters of the proposed model. …”
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Kombinasi Feature Selection Fisher Score dan Principal Component Analysis (PCA) untuk Klasifikasi Cervix Dysplasia by Krisan Aprian Widagdo, Kusworo Adi, Rahmat Gernowo

“…And then PCA transforms candidate features into a new uncorrelated dataset. Artificial Neural Network Backpropagation used to evaluate performance combination FScore PCA. …”
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Characteristics and Associated Factors of Insomnia Among the General Population in the Post-Pandemic Era of COVID-19 in Zhejiang, China: A Cross-Sectional Study by Da M, Mou S, Hou G, Shen Z

“…Six machine learning models were employed to develop a predictive model for insomnia, namely logistic regression, random forest, neural network, support vector machine, CatBoost, and gradient boosting decision tree.Results: The study obtained 2769 and 1161 valid responses in T1 and T2, respectively. …”
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Predicting 28-day all-cause mortality in patients admitted to intensive care units with pre-existing chronic heart failure using the stress hyperglycemia ratio: a machine learning-... by Xiao-han Li, Xing-long Yang, Bin-bin Dong, Qi Liu

“…The predictive performance was verified through four machine learning algorithms, with the neural network algorithm being the best (AUC 0.801). For patients with both acute critical illness and pre-existing CHF, SHR was an independent predictor of 28-day all-cause mortality. …”
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Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics by Theyazn H. H. Aldhyani, Manish R. Joshi, Shahab A. AlMaaytah, Ahmed Abdullah Alqarni, Nizar Alsharif

“…This paper presents three adapting intelligence models: support vector machine regression (SVMR), artificial neural network using particle swarm optimisation (ANNPSO), and our intelligent time series (INTS) to predict influenza epidemics. …”
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Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning by Chun-Hao Wang, Yue-Tian-Si Ji, Li Ruan, Joshua Luhwago, Yin-Xuan Saw, Sokhey Kim, Tao Ruan, Li-Min Xiao, Rui-Jue Zhou

“…Our multisource accident datasets-driven deep learning model is composed of the following three submodels: (1) the structured data accident model using our accident feature-driven bidirectional long short-term memory (Bi-LSTM) and accident feature-driven bidirectional conditional random field (Bi-CRF) model to extract labels, (2) the unstructured traffic accident data model using our accident feature-driven piecewise convolutional neural network (PCNN) model to identify the extracted labels, and (3) the semistructured traffic accident data processing model. …”
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Marigold: a machine learning-based web app for zebrafish pose tracking by Gregory Teicher, R. Madison Riffe, Wayne Barnaby, Gabrielle Martin, Benjamin E. Clayton, Josef G. Trapani, Gerald B. Downes

“…By leveraging a highly efficient, custom-designed neural network architecture, Marigold achieves reasonable training and inference speeds even on modestly powered computers lacking a discrete graphics processing unit. …”
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Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy by Yingchun Ren, Xiaoying Shan, Gengchao Ding, Ling Ai, Weiying Zhu, Ying Ding, Fuzhou Yu, Yun Chen, Beijiao Wu

“…Thirteen machine learning techniques, including Random Forest, Support Vector Machine, and Artificial Neural Network, were employed. Based on their various classification performances on the training set, the top five models were selected for internal validation. …”
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Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study by Emma Holler, Christina Ludema, Zina Ben Miled, Molly Rosenberg, Corey Kalbaugh, Malaz Boustani, Sanjay Mohanty

“…We trained logistic regression, random forest, extreme gradient boosting (XGB), and neural network models to predict POD using 143 features derived from routine EHR data available at the time of hospital admission. …”
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Automatic Recognition of Authors Identity in Persian based on Systemic Functional Grammar by Fatemeh Soltanzadeh, Azadeh Mirzaei, Mohammad Bahrani, Shahram Modarres Khiabani

“…Multilayer perceptron classifier, a type of neural network, was used for learning phase which resulted in a desirable accuracy in evaluation phase. …”
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Evaluation of linear, nonlinear and ensemble machine learning models for landslide susceptibility assessment in southwest China by Bingwei Wang, Qigen Lin, Tong Jiang, Huaxiang Yin, Jian Zhou, Jinhao Sun, Dongfang Wang, Ran Dai

“…Linear models represented by logistic regression (LR), nonlinear models represented by support vector machine (SVM), artificial neural network (ANN) and classification 5.0 decision tree (C5.0 DT), and ensemble models represented by random forest (RF) and categorical boosting (Catboost) were selected. …”
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A multidimensional assessment of adverse events associated with paliperidone palmitate: a real-world pharmacovigilance study using the FAERS and JADER databases by Siyu Lou, Zhiwei Cui, Yingyong Ou, Junyou Chen, Linmei Zhou, Ruizhen Zhao, Chengyu Zhu, Li Wang, Zhu Wu, Fan Zou

“…Utilizing disproportionality analyses such as the reporting odds ratios (ROR), proportional reporting ratios (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item Poisson shrinkage (MGPS), significant associations between ADEs and paliperidone palmitate were evaluated. …”
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