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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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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Development of data-driven machine learning models and their potential role in predicting dengue outbreak by Bushra Mazhar, Nazish Mazhar Ali, Farkhanda Manzoor, Muhammad Kamran Khan, Muhammad Nasir, Muhammad Ramzan

“…The current article endeavors to present an overview of predicting dengue outbreaks through data-based machine-learning models. …”
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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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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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Systematic review of prognostic models for predicting recurrence and survival in patients with treated oropharyngeal cancer by Janine Dretzke, Hisham Mehanna, David J Moore, Malcolm J Price, Ahmad K Abou-Foul, Katie Scandrett, Esther Albon, Bethany Hillier, Paul Nankivell

“…The Prediction model Risk Of Bias ASsessment Tool was used to assess risk of bias (RoB). …”
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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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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…These observations emphasize the need to match model selection with data structure, performance objectives, and practical resource constraints when predicting and improving innovation outcomes at the firm level.…”
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NDVI Prediction with RGB UAV Imagery Utilizing Advanced Machine Learning Regression Models by I. Aydin, U. G. Sefercik

“…In the literature, RGB camera-based NDVI prediction studies involving machine learning and deep learning algorithms have focused on the correlation of the results with the reference data (R²) or the model accuracy of the algorithms used. …”
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Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete by Waleed Bin Inqiad, Muhammad Faisal Javed, Deema Mohammed Alsekait, Naseer Muhammad Khan, Majid Khan, Fahid Aslam, Diaa Salama Abd Elminaam

“…To get increased insights into the model prediction process, shapely and individual conditional expectation analyses were carried out on the XGB algorithm which highlighted that water, cement, and time after mixing are the most influential parameters to estimate both fresh properties of concrete. …”
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Feature importance analysis of solar flares and prediction research with ensemble machine learning models by Yun Yang, Yun Yang

“…In this study, these models were used to classify and predict flares with a magnitude ≥ C- and M-class, respectively. …”
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Assessment of models for the prediction of the Travelling Ionospheric Disturbance activity index in mid-latitude Europe by Ferreira Arthur Amaral, Borries Claudia, Borges Renato Alves

“…The feasibility of predicting LSTIDs in this region has been demonstrated using a linear regression model. …”
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Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction by Yunfei Chen, Zuyu Liu, Ting Long, Xiuhua Liu, Yaowei Gao, Sibo Wang

“…However, the nonlinear and non-stationary characteristics of ET_o time series pose challenges for conventional prediction models. Given this, in this study we evaluate eight decomposition-hybrid models that integrate various decomposition techniques with a long short-term memory (LSTM) network to enhance short-term (5-day, 7-day, and 10-day) ET_o forecasting. …”
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Benchmarking ML in ADMET predictions: the practical impact of feature representations in ligand-based models by Gintautas Kamuntavičius, Tanya Paquet, Orestis Bastas, Dainius Šalkauskas, Alvaro Prat, Hisham Abdel Aty, Aurimas Pabrinkis, Povilas Norvaišas, Roy Tal

“…Abstract This study, focusing on predicting Absorption, Distribution, Metabolism, Excretion, and Toxicology (ADMET) properties, addresses the key challenges of ML models trained using ligand-based representations. …”
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