Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L. by Mahboobe Hojati, Ruhollah Naderi, Mohsen Edalat, Hamid Reza Pourghasemi

“…Results showed that The RF (ROC: 0.99), BRT (ROC: 0.98), and SVM (ROC: 0.96) models were highly accurate in predicting the habitat suitability of S. marianum. The results of the RF algorithm also revealed that factors such as distance from roads, elevation, and mean annual rainfall had the most significant influence on the habitat suitability of S. marianum. …”
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Unlocking the structural, vibrational, electronic, optical and thermoelectric properties of K2X (X=S, Se, Te) monolayers via DFT and ML by G. Sneha, R.D. Eithiraj

“…AFLOW-PLMF model was deployed for the electronic band gap value predictions. Machine learning algorithms namely Decision Tree Regressor and Gradient Boosting Regressor out-performs the other ML models with least root mean square error (RMSE) and accurate R2 values. …”
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Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention. by Dovilė Kurmanavičiūtė, Hanna Kataja, Lauri Parkkonen

“…We then trained support vector machine classifiers to predict the target of attention from unaveraged trials of MEG/EEG. …”
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The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection by Pak Hung Chan, Boda Li, Gabriele Baris, Qasim Sadiq, Valentina Donzella

“…The results clearly show that network performance is affected by ground truth variations, and removing clear errors is beneficial for predicting real-world objects only for some networks. …”
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Attention-Based Color Difference Perception for Photographic Images by Hua Qiang, Xuande Zhang, Jinliang Hou

“…Finally, a dual-branch network for high-frequency and low-frequency predictions is used for score estimation, and the final score is obtained through a weighted sum. …”
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Artificial intelligence in environmental monitoring: in-depth analysis by Emran Alotaibi, Nadia Nassif

“…Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. …”
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Damage Scene Change Detection Based on Infrared Polarization Imaging and Fast-PCANet by Min Yang, Jie Yang, Hongxia Mao, Chong Zheng

“…Finally, the false-detection points predicted by the Fast-PCANet are further corrected by region filling and filtering to obtain the final binary change map of the damage scene. …”
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Countermeasuring Anti-Ship Missiles for Surface Naval Platforms: A Machine Learning Approach With Explainable Artificial Intelligence by Murat Ertop, Ali Oter, Ali Kara

“…This model has been used to predict target parameters. The simulator includes parameters for the ship, missile, and chaff/flare. …”
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AI-driven precision diagnosis and treatment in Parkinson’s disease: a comprehensive review and experimental analysis by Bhekisipho Twala

“…The integrated approach showed particular strength in identifying subtle motor fluctuations and predicting treatment response patterns.ConclusionAI-driven approaches demonstrate substantial potential for revolutionizing PD diagnosis and treatment personalization. …”
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Machine Learning Detection of Melting Layers From Radar Observations by Yan Xie, Fraser King, Claire Pettersen, Mark Flanner

“…Furthermore, the U‐Net model provides additional information of detection uncertainty based on ensemble predictions. The U‐Net model and the data extraction tool can be applied to similar profiling radar instruments in different regions of the world, contributing to an enhanced understanding of the distribution and variations of melting layers.…”
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Casualty Analysis of the Drivers in Traffic Accidents in Turkey: A CHAID Decision Tree Model by Zeliha Cagla Kuyumcu, Hakan Aslan, Nilufer Yurtay

“…This study aims to establish a model to predict the driver’s status (survived–injured–dead) as a result of the fatal-injury type of accident. …”
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Ship Magnetic Signature Classification Using GRU-Based Recurrent Neural Networks by Kajetan Zielonacki, Jaroslaw Tarnawski, Miroslaw Woloszyn

“…Features, advantages and limitations of developed algorithms are derived strictly from the nature of neural networks.…”
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Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcinoma by Yun Zhao, Yun Zhao, Jing Yang, Yamei Jiang, Jingbiao Wu

“…The MSCC model, developed using the StepCox [both]+plsRcox algorithm, demonstrated superior prognostic performance compared to existing models. …”
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ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses by Yangyan Li, Tingting Chen

“…ISCCO integrates deep learning with advanced optimization algorithms. It focuses on minimizing transportation costs by accurately predicting customer behavior and dynamically allocating goods. …”
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REDalign: accurate RNA structural alignment using residual encoder-decoder network by Chun-Chi Chen, Yi-Ming Chan, Hyundoo Jeong

“…Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of $$O(L^6)$$ O ( L 6 ) for each RNA pair. …”
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A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network by Constantinos M. Mylonakis, Zaharias D. Zaharis

“…The proposed model&#x0027;s performance is evaluated based on its capacity to predict angles of arrival from any direction in a three-dimensional space, encompassing azimuth angles within the interval <inline-formula><tex-math notation="LaTeX">$[0^\circ, 360^\circ)$</tex-math></inline-formula> and polar angles within <inline-formula><tex-math notation="LaTeX">$[0^\circ, 60^\circ ]$</tex-math></inline-formula>. …”
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Elucidating Parasitic Currents in Proton‐Exchange‐Membrane Electrolytic Cells via Physics‐Based and Data‐Driven Modeling by Violeta Karyofylli, K. Ashoke Raman, Linus Hammacher, Yannik Danner, Hans Kungl, André Karl, Eva Jodat, Rüdiger‐A. Eichel

“…ABSTRACT Proton‐exchange membrane (PEM) water electrolysis is pivotal for green hydrogen production, necessitating accurate predictive models to manage their non‐linearities and expedite commercial deployment. …”
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A New Method for Identifying Key and Common Themes Based on Text Mining: An Example in the Field of Urban Expansion by Yanwei Zhang, Xinhai Lu, Chaoran Lin, Feng Wu, Jinqiu Li

“…First, the latent Dirichlet allocation algorithm is used to capture the thematic clustering of scientific literature. …”
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DEVELOPMENT METHODS FOR CALCULATING POWER LOSSES AND THE VOLTAGE LEVELS IN COMPLEX DISTRIBUTION NETWORKS by I. M. Valeev, Ha. D. Nguyen

“…The paper presents an analysis and evaluation of the effectiveness different methods for reduction the power loss and voltage in the distribution networks by changing and building a new network topology using the software PSS/ADEAPT. …”
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Guidelines for releasing a variant effect predictor by Benjamin J. Livesey, Mihaly Badonyi, Mafalda Dias, Jonathan Frazer, Sushant Kumar, Kresten Lindorff-Larsen, David M. McCandlish, Rose Orenbuch, Courtney A. Shearer, Lara Muffley, Julia Foreman, Andrew M. Glazer, Ben Lehner, Debora S. Marks, Frederick P. Roth, Alan F. Rubin, Lea M. Starita, Joseph A. Marsh

“…Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. …”
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