Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation by Hua Xie, Minghua Zhang, Jiaming Ge, Xinfang Dong, Haiyan Chen

“…Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship between factors and complexity. …”
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Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca by Mohammad Farid Naufal

“…KNN dan SVM merupakan algoritma klasifikasi dari Machine Learning sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. …”
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“Play by play”: A dataset of handball and basketball game situations in a standardized spaceZenodo by Bruno Cabado, Bertha Guijarro-Berdiñas, Emilio J. Padrón

“…We refer to this standardized coordinate space as the 'unified space'.The dataset holds significant potential for reuse in various domains related to sports analytics and machine learning research. It can serve as a valuable resource for researchers, coaches, and sports enthusiasts, contributing to improvements in player performance, game strategies, match retransmissions, and sports-related technologies.…”
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Quantifying the Spatiotemporal Evolution of Radiation Belt Electrons Scattered by Lower Band Chorus Waves: An Integrated Model by Weixin Chen, Song Fu, Xin Ma, Binbin Ni, Deyu Guo, Qiongyue Zhang, Xiangyuan Tong, Yibo Zhao, Xing Cao, Zheng Xiang, Yuan Lei

“…Here we present a new model, by integrating the machine learning technique and kinetic simulations, to analyze the spatiotemporal evolution of radiation belt electrons scattered by lower band chorus (LBC). …”
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Integrating Renewable Energy with Internet of Things (IoT): Pathways to a Smart Green Planet by Shojib Mia, Firoz Ahmed, Ibrahim Khan, Md. Emamul Kabir, Mehedi H. Roni, Khadijatul Cobra, Anjuman Ara Khatun, Shahriar Mahmud

“…By leveraging data analytics and machine learning, these systems can predict energy consumption, optimize resources, and maintain renewable energy assets proactively. …”
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Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation by Chouaib Labiod, Redha Meneceur, Ali Bebboukha, Abdelmoumene Hechifa, Kamel Srairi, Adel Ghanem, Ievgen Zaitsev, Mohit Bajaj

“…The HHO algorithm is combined with an advanced machine learning model, XGBoost, to accurately detect faults within the PV system. …”
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Cacao floral traits are shaped by the interaction of flower position with genotype by Seunghyun Lim, Insuck Baek, Seok Min Hong, Yoonjung Lee, Silvas Kirubakaran, Moon S. Kim, Lyndel W. Meinhardt, Sunchung Park, Ezekiel Ahn

“…These findings emphasize the phenotypic diversity of cacao flowers and demonstrate the potential of machine learning in genotype identification, offering valuable insights for breeding and cultivation strategies to enhance cacao productivity.…”
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Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los Angeles by Yuwei Kong, Karina Jimenez, Christine M. Lee, Sophia Winter, Jasmine Summers-Evans, Albert Cao, Massimiliano Menczer, Rachel Han, Cade Mills, Savannah McCarthy, Kierstin Blatzheim, Jennifer A. Jay

“…Despite limitations from cloud cover and spatial resolution, the findings suggest that integrating satellite data with machine learning can enhance large-scale, efficient turbidity monitoring in coastal waters.…”
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Analysis of Protein-Ligand Interactions of SARS-CoV-2 Against Selective Drug Using Deep Neural Networks by Natarajan Yuvaraj, Kannan Srihari, Selvaraj Chandragandhi, Rajan Arshath Raja, Gaurav Dhiman, Amandeep Kaur

“…In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. …”
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Detection of early relapse in multiple myeloma patients by Tereza Růžičková, Monika Vlachová, Lukáš Pečinka, Monika Brychtová, Marek Večeřa, Lenka Radová, Simona Ševčíková, Marie Jarošová, Josef Havel, Luděk Pour, Sabina Ševčíková

“…All results were analyzed by machine learning. Conclusion Mass spectrometry coupled with machine learning shows potential as a reliable, rapid, and cost-effective preliminary screening technique to supplement current diagnostics.…”
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Photovoltaic Farm Production Forecasting: Modified Metaheuristic Optimized Long Short-Term Memory-Based Networks Approach by Aleksandar Stojkovic, Bosko Nikolic, Miodrag Zivkovic, Nebojsa Bacanin

“…Finally, the applicability of the top-performance models was validated with tiny machine learning (TinyML).…”
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Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower by Guy Atsmon, Anna Brook, Tom Avikasis Cohen, Fadi Kizel, Hanan Eizenberg, Ran Nisim Lati

“…These VIs, reflecting changes in canopy reflectance over time, were then analyzed using various machine learning models, including a pattern recognition neural network (PRNN). …”
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2D physically unclonable functions of the arbiter type by V. N. Yarmolik, A. A. Ivaniuk

“…It seems promising to further develop the ideas of constructing two-dimensional physically unclonable functions of the arbiter type, as well as experimental study of their characteristics, as well as resistance to various types of attacks, including using machine learning.…”
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Association between triglyceride-glucose index and carotid atherosclerosis in Chinese steelworkers: a cross-sectional study by Haoyue Cao, Qinglin Li, Juxiang Yuan

“…In LASSO regression, TyG index and other covariables are screened as important feature variables to be incorporated into the development of machine learning models. The TyG index is associated with an increased risk of CAS among steelworkers, underscoring its potential as a reliable and practical predictive tool for assessing CAS risk in this population. …”
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A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols by Nicolas Francos, Daniela Heller-Pearlshtien, José A. M. Demattê, Bas Van Wesemael, Robert Milewski, Sabine Chabrillat, Nikolaos Tziolas, Adrian Sanz Diaz, María Julia Yagüe Ballester, Asa Gholizadeh, Eyal Ben-Dor

“…Soil spectral libraries (SSLs) are important big-data archives (spectra associated with soil properties) that are analyzed via machine-learning algorithms to estimate soil attributes. …”
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Psychological and Behavioral Insights From Social Media Users: Natural Language Processing–Based Quantitative Study on Mental Well-Being by Xingwei Yang, Guang Li

“…We empirically evaluated the effectiveness of our framework by applying machine learning models to detect depression, reporting accuracy, recall, precision, and F1-score using social media status updates from 1047 users along with their associated depression diagnosis questionnaire scores. …”
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Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction by Zhibo Wang, Xin Chen, Xi Tan, Lingfeng Yang, Kartik Kannapur, Justin L. Vincent, Garin N. Kessler, Boshu Ru, Mei Yang

“…For comparison, we also tested multiple traditional machine learning models including logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost). …”
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Mood Detection from Physical and Neurophysical Data Using Deep Learning Models by Zeynep Hilal Kilimci, Aykut Güven, Mitat Uysal, Selim Akyokus

“…Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. …”
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Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT by Minmini Selvam, Abjasree Sadanandan, Anupama Chandrasekharan, Sidharth Ramesh, Arunan Murali, Ganapathy Krishnamurthi

“…Lung nodules were classified using 13 machine-learning algorithms, including Linear Discriminant Analysis, Random Forest, AdaBoost, and eXtreme Gradient Boosting. …”
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Novel insights into immunopathogenesis and crucial biomarkers between primary open‐angle glaucoma and systemic lupus erythematosus by Yixian Liu, Mengling You, Zhou Zeng, Jing Wang, Rong Rong, Xiaobo Xia

“…The 10 key genes identified through DEA and WGCNA were predominantly involved in immune, inflammatory, and autophagy pathways. Additionally, machine learning identified five biomarkers, and we established associated transcription factors and miRNA regulatory networks. …”
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