Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture by Safa E. El-Mahroug, Ayman A. Suleiman, Mutaz M. Zoubi, Saif Al-Omari, Qusay Y. Abu-Afifeh, Heba F. Al-Jawaldeh, Yazan A. Alta’any, Tariq M. F. Al-Nawaiseh, Nisreen Obeidat, Shahed H. Alsoud, Areen M. Alshoshan, Fayha M. Al-Shibli, Rakad Ta’any

“…Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. …”
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Comparison Of Reversible Image Watermarking Methods Based On Prediction-Errors by Burhan Baraklı, Emre Altınkaya

“…This study compares two reversible imagewatermarking algorithms applied to a digital image. The first algorithm is amethod based on adaptive watermarking of prediction-errors. …”
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Aggregating Image Segmentation Predictions with Probabilistic Risk Control Guarantees by Joaquin Alvarez, Edgar Roman-Rangel

“…In this work, we introduce a framework to combine arbitrary image segmentation algorithms from different agents under data privacy constraints to produce an aggregated prediction set satisfying finite-sample risk control guarantees. …”
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Nonlinear Compensation of Multi-CAP VLC System Employing Clustering Algorithm Based Perception Decision by Xingyu Lu, Kaihui Wang, Liang Qiao, Wen Zhou, Yiguang Wang, Nan Chi

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ProBoost: Reducing Uncertainty Using a Boosting Method for Probabilistic Models by Fabio Mendonca, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias, Antonio G. Ravelo-Garcia, Mario A. T. Figueiredo

“…Uncertainty analysis of classification or regression models is a key feature of probabilistic approaches to supervised learning, allowing the assessment of how trustworthy predictions are. Just as boosting algorithms aim at obtaining accurate ensembles of simple classifiers, using a process guided by the accuracy of each of these classifiers, the method proposed in this paper builds an ensemble guided by the uncertainty of each of its individual models. …”
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Portable X-ray fluorescence sensor for ecofriendly, low-cost, and fast assessment of eucalypt charcoal attributes by Renata Andrade, Lucas Benedet, Marcelo Mancini, Sérgio Henrique Godinho Silva, Camila da Silva Freitas, Marco Aurélio Carbone Carneiro, Nilton Curi

“…The objective of this study was to evaluate the use of pXRF data in machine-learning models trained to predict attributes of eucalypt charcoal. pXRF data (elemental contents) from 276 charcoal samples were used to train predictive models using six machine-learning algorithms. …”
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Machine learning modeling for predicting adherence to physical activity guideline by Ju-Pil Choe, Seungbak Lee, Minsoo Kang

“…Variables were categorized into demographic, anthropometric, and lifestyle categories. 18 prediction models were created by 6 ML algorithms and evaluated via accuracy, F1 score, and area under the curve (AUC). …”
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MissPred: A Robust Two-Stage Radar Echo Extrapolation Algorithm for Incomplete Sequences by Ziqi Zhao, Chunxu Duan, Lin Song, Qilin Zhang, Wenda Zhu, Yi Liu

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Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms by Ye Liu, Shixin Li, Liming Zhou, Haichen Liu, Zhiyu Li

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An Algorithm for the Best Way Connection among Cell-Phone Towers Using Feedforward Neural Network by Kais Ibraheem, Thamir Jarjis, Yahya Ibrahim

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QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions by Abolghasem Beheshti, Eslam Pourbasheer, Mehdi Nekoei, Saadat Vahdani

“…Results showed that the predictive ability of the model was satisfactory, and it can be used for designing similar group of antimalarial compounds.…”
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Assessment of methods for predicting physical and chemical properties of organic compounds by Tunga Salthammer

“…The algorithms underlying the respective tools are highly specialized and mathematically sophisticated. …”
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Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds by Muhamad Fadil, Muhamad Akrom, Wise Herowati

“…By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. …”
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A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases by Diego Mariano

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TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction by I Nyoman Kusuma Wardana, Suhaib A. Fahmy, Julian W. Gardner

“…Tiny machine learning (tinyML) involves the application of ML algorithms on resource-constrained devices such as microcontrollers. …”
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Improving earthquake prediction accuracy in Los Angeles with machine learning by Cemil Emre Yavas, Lei Chen, Christopher Kadlec, Yiming Ji

“…Abstract This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. …”
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A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks by Hongchun Qu, Libiao Lei, Xiaoming Tang, Ping Wang

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DEVELOPMENT AND JUSTIFICATION OF THE ESTIMATION ALGORITHM OF THE BONE SYSTEM METABOLISM IN CHILDREN WITH TYPE 1 DIABETES MELLITUS by L. G. Ivchenko, I. M. Bykov, A. A. Basov, F. N. Gilmiyarova, D. A. Domenyuk, G. M.-A. Budaychiev, S. O. Ivanyuta

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The RMaP challenge of predicting RNA modifications by nanopore sequencing by Jannes Spangenberg, Stefan Mündnich, Anne Busch, Stefan Pastore, Anna Wierczeiko, Winfried Goettsch, Vincent Dietrich, Leszek P. Pryszcz, Sonia Cruciani, Eva Maria Novoa, Kandarp Joshi, Ranjan Perera, Salvatore Di Giorgio, Paola Arrubarrena, Irem Tellioglu, Chi-Lam Poon, Yuk Kei Wan, Jonathan Göke, Andreas Hildebrandt, Christoph Dieterich, Mark Helm, Manja Marz, Susanne Gerber, Nicolo Alagna

“…Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. …”
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Application of Random Forest Algorithm to Analyze the Confidence Level of Forest Fire Hotspots in Riau Peatland by Mitra Unik, Imas Sukaesih Sitanggang, Lailan Syaufina, I Nengah Surati Jaya

“…This study employs the Random Forest (RF) algorithm to analyze the confidence levels of hotspots, aiming to predict potential fire occurrences and improve fire management strategies. …”
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