Prediction of Temperature and Precipitation Changes for Serbia Using Time Series Models with Machine Learning by Miroslav Tomić, Marija Đukić, Slavica Kordić, Vladimir Dimitrieski, Ivan Luković

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Viral particle prediction in wastewater treatment plants using nonlinear lifelong learning models by Jianxu Chen, Ibrahima N’Doye, Yevhen Myshkevych, Fahad Aljehani, Mohammad Khalil Monjed, Taous-Meriem Laleg-Kirati, Pei-Ying Hong

“…Tests on total viral prediction across four municipal WWTPs in Saudi Arabia showed the lifelong learning model’s value for adaptive viral particle prediction and performance enhancement.…”
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Forecasting CO2 emissions in BRICS countries using the grey breakpoint prediction models by Huiping Wang, Xinge Guo

Subjects: “…Grey breakpoint prediction model…”
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Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices by G. M. Casolino, M. de Santis, L. Di Stasio, C. Noce, P. Varilone, P. Verde

“…Based on this means of identification, the technique offers two distinct models for predicting each kind of sag. The final goal is to implement the procedure in a measurement system that can automatically pre-analyze the recorded sags and choose the best technique for prediction depending on the type of sag. …”
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Cellular automata models for simulation and prediction of urban land use change: Development and prospects by Baoling Gui, Anshuman Bhardwaj, Lydia Sam

“…Among them, Cellular Automata (CA) models have become key tools for predicting urban expansion, optimizing land-use planning, and supporting data-driven decision-making. …”
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Machine Learning in Schizophrenia: A Systematic Review and Meta-Analysis of Diagnostic and Predictive Models by Oluwatobi Idowu, Nicholas Aderinto, Gbolahan Olatunji, Emmanuel Kokori

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Predictions of species distributions based only on models estimating future climate change are not reliable by Spyros Tsiftsis, Zuzana Štípková, Marcel Rejmánek, Pavel Kindlmann

“…To reduce the effect of these factors, we need reliable predictions of future species distributions. This is usually done by utilizing species distribution models (SDMs) based on expected climate. …”
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Fluid Flow Behavior Prediction in Naturally Fractured Reservoirs Using Machine Learning Models by Mustafa Mudhafar Shawkat, Abdul Rahim Bin Risal, Noor J. Mahdi, Ziauddin Safari, Maryam H. Naser, Ahmed W. Al Zand

“…The datasets used in this study were collected from previous studies “i.e., Texas oil and gas fields” to build an intelligence-based predictive model for fluid flow characteristics. The prediction process was conducted based on interporosity flow coefficient, storativity ratio, wellbore radius, matrix permeability, and fracture permeability as input data. …”
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Development of prediction models for carbapenem-resistant Klebsiella pneumoniae acquisition and prognosis in adult patients by Huijuan Yao, Yu Yang, Huimin Yao, Shuhong Bu, Lixia Li, Fang Wang, Jian Zhang, Jihui Chen

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Country-level indices in predictive models of helminth infections: Perspectives from Southeast Asia. by Nathkapach Kaewpitoon Rattanapitoon, Chutharat Thanchonnang, Schawanya Kaewpitoon Rattanapitoon

“…Predictive models integrating country-level indices with individual variables offer valuable insights into soil-transmitted helminth (STH) infection risk among migrant populations. …”
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Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease by Edgar A. Bernal, Shu Yang, Konnor Herbst, Charles S. Venuto

“…Abstract Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic models to deep learning methods remains understudied. …”
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Establishing predictive machine learning models for drug responses in patient derived cell culture by Abbi Abdel-Rehim, Oghenejokpeme Orhobor, Gareth Griffiths, Larisa Soldatova, Ross D. King

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Enhancing diabetes risk prediction through focal active learning and machine learning models. by Wangyouchen Zhang, Zhenhua Xia, Guoqing Cai, Junhao Wang, Xutao Dong

“…To improve the effectiveness of diabetes risk prediction, this study proposes a novel method based on focal active learning strategies combined with machine learning models. …”
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Predictive values of brain age models to rTMS effects in neurocognitive disorder with depression by Hanna LU

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Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus by J. M. Manel K. Herath, Hemalika T. K. Abeyasundara, W. A. Priyanka P. De Silva, Thilini C. Weeraratne, S. H. P. Parakrama Karunaratne

“…Another prediction model was developed using OVI and RH with one month lag period (R2 (sq) = 70.21%; F = 57.23; model: OVI predicted = 15.1 + 0.528∗ Lag 1 month RH; RMSE = 2.01). …”
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Dissociable Neural Mechanisms for Human Inference Processing Predicted by Static and Contextual Language Models by Takahisa Uchida, Nicolas Lair, Hiroshi Ishiguro, Peter Ford Dominey

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Short-term prediction of regional energy consumption by metaheuristic optimized deep learning models by Ngoc-Quang Nguyen, Phuong-Thao-Nguyen Nguyen, Quynh-Chau Truong

“…This study proposed a hybrid deep learning model, called I-CNN-JS, by incorporating a jellyfish search (JS) algorithm into an ImageNet-winning convolutional neural network (I-CNN) to predict week-ahead energy consumption. …”
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A literature review: AI models for road safety for prediction of crash frequency and severity by Muneeb Shehzad Butt, Muhammad Awais Shafique

“…That's because what has transpired with the confluence of AI, ML, and road safety initiatives is a major step forward to reduce traffic incidents and improve roadway safety, from early statistical models to sophisticated systems capable of predicting crashes and identifying opportunities for intervention Fig. 2.…”
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Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19. by Aldo Córdova-Palomera, Csaba Siffel, Chris DeBoever, Emily Wong, Dorothée Diogo, Sandor Szalma

“…Overall, the results indicate that simple models based on health-related epidemiological factors measured years before COVID-19 onset can achieve high predictive power. …”
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Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate by Felipe Kenji Nakano, Anna Åkesson, Jasper de Boer, Klest Dedja, Robbe D’hondt, Fateme Nateghi Haredasht, Jonas Björk, Marie Courbebaisse, Lionel Couzi, Natalie Ebert, Björn O. Eriksen, R. Neil Dalton, Laurence Derain-Dubourg, Francois Gaillard, Cyril Garrouste, Anders Grubb, Lola Jacquemont, Magnus Hansson, Nassim Kamar, Christophe Legendre, Karin Littmann, Christophe Mariat, Toralf Melsom, Lionel Rostaing, Andrew D. Rule, Elke Schaeffner, Per-Ola Sundin, Arend Bökenkamp, Ulla Berg, Kajsa Åsling-Monemi, Luciano Selistre, Anders Larsson, Ulf Nyman, Antoine Lanot, Hans Pottel, Pierre Delanaye, Celine Vens

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