Predicting Social Unrest Events with Hidden Markov Models Using GDELT by Fengcai Qiao, Pei Li, Xin Zhang, Zhaoyun Ding, Jiajun Cheng, Hui Wang

“…In this paper, we use autocoded events dataset GDELT (Global Data on Events, Location, and Tone) to build a Hidden Markov Models (HMMs) based framework to predict indicators associated with country instability. …”
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Assessment of Machine Learning Models for Predicting Aboveground Biomass in the Indian Subcontinent by S. Mamgain, B. Ghale, H. C. Karnatak, A. Roy

“…This study evaluates three machine learning models—Random Forest (RF), Gradient Tree Boosting (GTB), & Classification and Regression Trees (CART)—for predicting AGB across the subcontinent. …”
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Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects by Tariq Shahzad, Sunawar Khan, Tehseen Mazhar, Wasim Ahmad, Khmaies Ouahada, Habib Hamam

“…In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. …”
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Comparative Analysis of Advanced Models for Predicting Housing Prices: A Review by Inmaculada Moreno-Foronda, María-Teresa Sánchez-Martínez, Montserrat Pareja-Eastaway

“…ML models (neural networks, decision trees, random forests, among others) provide high predictive capacity and greater explanatory power due to the better fit of their statistical measures. …”
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Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels by Chengce Yuan, Yimin Shi, Zhichen Ba, Daxin Liang, Jing Wang, Xiaorui Liu, Yabei Xu, Junreng Liu, Hongbo Xu

“…This study presents a machine-learning-based model for predicting the performance of radiative cooling aerogels (RCAs). …”
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A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature by Trilok Nath Pandey, Vishvajeet Ravalekar, Sidharth D. Nair, Sunil Kumar Pradhan

“…As the amount and complexity of time-series data in numerous fields continues to expand, the investigation of advanced computational models becomes critical for efficient analysis and prediction. …”
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A simple model for predicting transpiration of greenhouse cucumber by H. Kage, M. Krämer, O. Körner, A. Fricke

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Machine Learning Model for Predicting Net Environmental Effects by Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam, Zhang Yujiao

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Modeling and predicting of the spatial variations Precipitation cores in Iran by hossein naserzadeh, fariba sayadi, meysam toulabi nejad

“…The first type of data is the monthly precipitation of 86 synoptic stations with the statistical period of 1986-1989 and the second type of predicted data from the output of the CCSM4 model under the three scenarios (RCP2.6, RCP4.5, and RCP6) from 2016 to 2036. …”
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A model for predicting dropout of higher education students by Anaíle Mendes Rabelo, Luis Enrique Zárate

“…Based on the characterization of the dropout problem and the application of a knowledge discovery process, an ensemble model is proposed to improve dropout prediction. …”
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Model for predicting the risk of severe bronchial asthma in children by O. E. Semernik, A. A. Lebedenko, E. B. Tyurina, M. V. Dudareva

“…Objective: to develop a model for predicting the severe course of bronchial asthma (BA) in children.   …”
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Development of a model for predicting money laundering rate by E. S. Anisimov, J. M. Beketnova

“…The article suggests model for predicting the level of money laundering on the basis of data from the Ministry of Internal Affairs of the Russian Federation on the state of economic crime in Russia since the beginning of 2011. …”
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Predicting the Evolution of the Supercontinuum Generation With CNN-LSTM Model by Yi Feng, Ruiyuan Liu, Xinyue Chang, Xiangzhen Huang, Yuan He, Ning Li, Tiantian Zhou, Chujun Zhao

“…The hybrid model can use the CNN model to extract and map the local features of the sequence, followed by the LSTM to predict the overall trend of the SC generation. …”
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Simplified kinetic modeling for predicting the stability of complex biotherapeutics by Mitja Zidar, Stefano Cucuzza, Matjaž Bončina, Drago Kuzman

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Predicting waste generation using Bayesian model averaging by M.G. Hoang, T. Fujiwara, S.T. Pham Phu, K.T. Nguyen Thi

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Modeling and Predicting Time Series with Non-stationarity and Volatility by FENG Qiang, ZHAO Jianguang, YANG Rong, NIU Baoning

“…When dealing with volatility, LSTM models with a single-head attention mechanism are usually used, which have weak ability to capture global dependencies and affect prediction accuracy. …”
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Economic-mathematical model for predicting financial market dynamics by Artur R. Musin

“…The main study purpose is developing a predictive economic-mathematical model that allows combining both approaches. …”
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Modeling and Model Predictive Power and Rate Control of Wireless Communication Networks by Cunwu Han, Dehui Sun, Song Bi, Lei Liu, Zhengxi Li

“…A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI) techniques. …”
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Language models outperform cloze predictability in a cognitive model of reading. by Adrielli Tina Lopes Rego, Joshua Snell, Martijn Meeter

“…Computational models of reading traditionally use cloze norming as a proxy of word predictability, but what cloze norms precisely capture remains unclear. …”
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Improving Hepatitis B outcome prediction with ensemble machine learning: A study on predictive models and interpretability by Abid Bin Ahosan, Forhadul Islam, Khandaker Mohammad Mohi Uddin, Nahid Hasan, Md Ashraf Uddin

“…This study investigates the effectiveness of various machine learning (ML) techniques in predicting patient outcomes in HBV infection. Methods The Chi-squared test was used for feature selection to find the most important factors, which were later applied to train and evaluate various ML models. …”
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