Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities by Renzhong Zhang, Haorui Li, Yunxiao Shen, Jiayi Yang, Wang Li, Dongsheng Zhao, Andong Hu

“…Looking ahead, research will focus on developing hybrid models that integrate physical modeling with deep learning, exploring adaptive learning algorithms and multi-modal data fusion techniques to enhance long-term predictive capabilities, particularly in addressing the impact of climate change on the ionosphere. …”
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Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach by Rahul Kumar Gupta, Asmaul Hassan, Samir Kumar Majhi, Nikhat Parveen, Abu Taha Zamani, Raju Anitha, Binayak Ojha, Abhinav Kumar Singh, Debendra Muduli

“…The particle swarm optimization (PSO) algorithm is employed to optimize ELM parameters, enhancing generalization and model convergence. …”
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No-Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation by Ke Gu, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang

“…Third, the saliency based parallax compensation, resulted from different stereoscopic image contents, is considerably valid to improve the prediction performance of image quality metrics. Experimental results confirm that our proposed stereoscopic image quality assessment paradigm has superior prediction accuracy as compared to state-of-the-art competitors.…”
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Multiobjective Optimization of Milling Parameters for Ultrahigh-Strength Steel AF1410 Based on the NSGA-II Method by Jin Xu, Fuwu Yan, Yan Li, Zhenchao Yang, Long Li

“…The influence of milling parameters (milling speed, each tooth feed, radial depth of cut, and axial depth of cut) on milling force and surface roughness is studied by ANOVA and established prediction model. Multiobjective optimization of milling parameters is accomplished based on nondominated sorting genetic algorithm II (NSGA-II) with milling force, surface roughness, and material removal rate as optimization objectives. …”
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COMPLEXITY EVALUATION IN THE PROJECTS INVOLVING MOBILE HETEROGENEOUS OBJECTS by A. I. Kuzmich, S. C. Mochulsky, A. N. Valvachev

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On constraint qualifications in mathematical programming by L. I. Minchenko, S. I. Sirotko

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THE LABORATORY-MODELLING COMPLEX FOR RESEARCH of QUALITY INDICATORS Of TELEVISION TYPE OpTiCAL loСation SYSTEM WORK by R. A. Hutsau, A. S. Solonar, S. V. Tsuprik

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TRUE CROSSING SELECTION OF BEARINGS BY REPLACEMENT METHOD IN TRIANGULATION TASK SOLUTION by U. A. Aparovich, A. I. Minau, T. U. Prakofyeva

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Search and detection of remote sensing objects by A. I. Mitsiukhin

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LOSSLESS COMPRESSION OF THE HALFTONE IMAGES BASED ON RUN-LENGTH ENCODING by H. K. Al-Bahadily, E. G. Makeichik, V. Yu. Tsviatkou, V. K. Kanapelka

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Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes by Liu Yang, Li Du, Yuanyuan Ge, Muhui Ou, Wanyan Huang, Xianmei Wang

“…Abstract Objective This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) using a machine learning (ML) algorithm. …”
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The design of copper flotation process based on multi-label classification and regression by Haipei Dong, Fuli Wang, Dakuo He, Yan Liu

“…Moreover, further improves the prediction effect through the following methods: (1) Referencing adaboost algorithm, the training set samples with large prediction error in the previous iteration are set with higher weight; (2) To enhance robustness, the label uncertainty coefficient is introduced; (3) To alleviate the over fitting of small sample machine learning, bootstrap aggregating is introduced for each sub label. …”
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Malware Detection in Forensic Memory Dumps: The Use of Deep Meta-Learning Models by Yalçın Özkan

“…First, it was subjected to the classification process with a deep learning algorithm, and a predictive model was acquired. The predictive model was found to have an accuracy metric of 98.25%. …”
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Field Test and Numerical Research of Blast-Induced Liquefaction in Calcareous Sand by Changchun Li, Yumin Chen, Yingkang Yao, Yonggang Gou, Qiongting Wang, Junwei Guo, Xiao Xie

“…The research results can provide a theoretical reference for the prediction of blast-induced liquefaction in saturated calcareous sand foundations.…”
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Capitalising on the Floristic Survey as a Non-Destructive Line of Evidence for Mineral Potential Modelling: A Case Study of Bauxite in South-Western Australia by Lewis Trotter, Grant Wardell-Johnson, Andrew Grigg, Sarah Luxton, Todd P. Robinson

“…Here, we combine plant species distributions with terrain metrics to produce predictive models showing the probability of bauxite presence. …”
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Geohazard impact and gas reservoir pressure dynamics in the Zagros Fold-Thrust Belt: An environmental perspective by Mahsa Asghari, Zahra Maleki, Ali Solgi, Mohammad Ali Ganjavian, Pooria Kianoush

“…A novel hybrid model is introduced that integrates geographic information system (GIS) mapping, decision support system (DSS) modeling, and machine learning algorithms. By analyzing a century's worth of seismic data alongside real-time environmental parameters, the model demonstrates a predictive accuracy of 92% using Random Forest algorithms, significantly outperforming traditional methods. …”
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Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients by Farnaz Khodami, Amanda S. Mahoney, James L. Coyle, Ervin Sejdic

“…By applying established algorithms to an unseen population, we demonstrated the utility of HRCA signals for swallowing assessment in individuals with NG tubes and showcased the generalizability of algorithms developed in our previous studies. …”
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Identifying opioid agonist treatment prescriber networks from health administrative data: A validation study. by Megan Kurz, Mark Tatangelo, Kristen A Morin, Michelle Zanette, Emanuel Krebs, David C Marsh, Bohdan Nosyk

“…Clinics were identified using modularity maximization, with sensitivity analyses applying Louvain, Walktrap, and Label Propagation algorithms. Concordance between network-identified facilities and the (gold standard) de-identified facility-level IDs was assessed using overall, positive and negative agreement, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).…”
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How large is the universe of RNA-like motifs? A clustering analysis of RNA graph motifs using topological descriptors. by Rui Wang, Tamar Schlick

“…Here we introduce a computational topology-based approach with unsupervised machine-learning algorithms to estimate the database size and content of RNA-like graph topologies. …”
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Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru by Lucia Enriquez, Kevin Ortega, Dennis Ccopi, Claudia Rios, Julio Urquizo, Solanch Patricio, Lidiana Alejandro, Manuel Oliva-Cruz, Elgar Barboza, Samuel Pizarro

“…Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). …”
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