Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer by Yuan Hong, Hanlin Wang, Qi Zhang, Peng Zhang, Kang Cheng, Guodong Cao, Renquan Zhang, Bo Chen

“…Diagnostic models were developed using deep learning (2D and 3D neural networks) and traditional machine learning (11 algorithms with PyRadiomics-derived features). Multimodal features underwent Principal Component Analysis (PCA) for dimension reduction and were fused for final analysis. …”
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Two-Scale Physics-Informed Neural Networks for Structural Dynamics Parameter Inversion: Numerical and Experimental Validation on T-Shaped Tower Health Monitoring by Xinpeng Liu, Xuemei Zhang, Yongli Zhong, Zhitao Yan, Yu Hong

“…We present a two-scale physics-informed neural network (TSPINN) algorithm to address structural parameter inversion problems involving small parameters. …”
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Modelling Gas Transport in Multiphasic Materials: Application to Semicrystalline Membranes by Lorenzo Merlonghi, Marco Giacinti Baschetti, Maria Grazia De Angelis

“…The results were analyzed considering existing macroscopic models and new analytical equations were proposed in order to account on such morphological effects for the prediction of the tortuosity in semicrystalline polymers.…”
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Analysis of the state of geometrization development and digital modeling in open-pit mining enterprises by M.S. Kunytska, D.S. Polishchyk, O.V. Shapochnikov

“…It was determined that their integrated application provides an increase in the accuracy of reserve assessment by 15–20 % and a reduction in operating costs by 10–15 %. Key development trends are identified: integration of digital technologies, improvement of methods of collecting and processing geospatial data, introduction of machine learning algorithms for risk prediction. …”
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The Application of Artificial Intelligence to Stock Forecasting: A Literature Review by Guan Jialin

“…The results of stock prediction algorithms rely on the selected indicators, including financial indicators and market sentiment indicators, and the algorithm model. …”
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6G virtualized beamforming: a novel framework for optimizing massive MIMO in 6G networks by Ahmed M. Alwakeel

“…Furthermore, the system incorporates predictive analytics for proactive beam steering and user allocation, enhancing network performance while minimizing power consumption. …”
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Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling by Yayang Feng, Guoshuai Wang, Jun Wang, Hexiang Zheng, Xiangyang Miao, Xiulu Sun, Peng Li, Yan Li, Yanhui Jia

“…The prediction of soil moisture conditions using multispectral data from unmanned aerial vehicles (UAVs) has advantages over ground measurements in terms of costs and monitoring range. …”
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Noise Elimination for Wide Field Electromagnetic Data via Improved Dung Beetle Optimized Gated Recurrent Unit by Zhongyuan Liu, Xian Zhang, Diquan Li, Shupeng Liu, Ke Cao

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Perbandingan Algoritme Machine Learning untuk Memprediksi Pengambil Matakuliah by Fitra A. Bachtiar, Indra K. Syahputra, Satrio A. Wicaksono

“…Thus, there is a need to build a system that can predict students taking courses. However, there are many algorithms that can be used for the prediction. …”
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From genetic data to kinship clarity: employing machine learning for detecting incestuous relations by Dejan Šorgić, Aleksandra Stefanović, Mladen Popović, Dušan Keckarević

“…Introduction:The aim of the study was to develop a predictive model based on STR profiles of mothers and children for the detection of incestuous conception.Methods:Based on allele frequency data from the USA and Saudi Arabia, STR profiles were generated and used to simulate offspring profiles corresponding to father-child and brother-sister incest scenarios. …”
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Capturing Preferences of New Users in Generative Tasks with Minimal Interactions Collaborative Filtering Using Siamese Networks and Soft Clustering by Subharag Sarkar, Manfred Huber

“…Prediction of user preferences is a challenge, in particular when the objective is to learn them without requiring the user to provide a profile or a significant number of interactions. …”
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ML modeling of ultimate and relative bond strength for corroded rebars based on concrete and steel properties by Alireza Hosseinzadeh Kashani, Mansour Ghalehnovi, Hossein Etemadfard

“…Abstract Corrosion-induced bond strength degradation between reinforcing bars and concrete significantly compromises the durability and structural performance of reinforced concrete (RC) systems. Accurate prediction of bond behavior under corrosion is critical for assessing residual capacity and informing rehabilitation strategies. …”
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Reinforcement Learning for Autonomous Process Control in Industry 4.0: Advantages and Challenges by Nuria Nievas, Adela Pagès-Bernaus, Francesc Bonada, Lluís Echeverria, Xavier Domingo

“…In recent years, the integration of intelligent industrial process monitoring, quality prediction, and predictive maintenance solutions has garnered significant attention, driven by rapid advancements in digitalization, data analytics, and machine learning. …”
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Soft computing approaches of direct torque control for DFIM Motor's by Zakariae Sakhri, El-Houssine Bekkour, Badre Bossoufi, Nicu Bizon, Mishari Metab Almalki, Thamer A.H. Alghamdi, Mohammed Alenezi

“…Our evaluation focuses on various aspects: torque and flux ripple reduction, speed tracking improvement, switching losses minimization, algorithmic complexity simplification, and sensitivity reduction to parameter variations. …”
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Semisupervised Community Preserving Network Embedding with Pairwise Constraints by Dong Liu, Yan Ru, Qinpeng Li, Shibin Wang, Jianwei Niu

“…These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. …”
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Bird Species Detection Net: Bird Species Detection Based on the Extraction of Local Details and Global Information Using a Dual-Feature Mixer by Chaoyang Li, Zhipeng He, Kai Lu, Chaoyang Fang

“…The prediction balance module balances the difference in feature space based on the pixel values of each category, thereby resolving category imbalances and improving the network’s detection accuracy. …”
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Optimising coronary imaging decisions with machine learning: an external validation study by Floor Groepenhoff, Leonard Hofstra, Sophie Heleen Bots, Saskia Haitjema, Imo Hoefer, L. Malin Overmars, Bram van Es, Mark C. H. De Groot, G. Aernout Somsen, I. Igor Tulevski, Hester M. den Ruijter, Wouter W. van Solinge

“…This study aimed to externally validate sex-stratified machine learning algorithms based on EHR data to predict the absence of coronary stenosis, evaluated in diverse clinical settings.Methods Sex-stratified XGBoost algorithms were trained on EHR data from patients who underwent coronary imaging at the University Medical Center Utrecht (n=14 674) and externally tested on EHR data of 13 Cardiology centres in the Netherlands (n=9252). …”
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Reaching machine learning leverage to advance performance of electrocatalytic CO2 conversion in non-aqueous deep eutectic electrolytes by Ahmed Halilu, Mohamed Kamel Hadj-Kali, Hanee Farzana Hizaddin, Mohd Ali Hashim, Emad M. Ali, Suresh Bhargava

“…Our findings demonstrate that ensemble and k-Nearest Neighbours algorithms learn the CO2RR dataset, achieving a prediction accuracy of over 99%. …”
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Design, Development and Implementation of iALERTS (Informatics Analytics for Long-term Evaluation and Repercussions Tracking of SARS-CoV-2 Infection): A Research Protocol by Krishna Mohan Surapaneni, Manmohan Singhal, Ashish Joshi

“…This platform employs machine learning algorithms to predict the likelihood of PASC development among Coronavirus Disease-19 (COVID-19) survivors. …”
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Hybrid Automata-Based Control Framework for Real-Time Optimization in Space-Based Solar Power Transmission by Patil Ankita, Ranjan Mritunjay, Deore Kalyani, Sonje Pranjal, Patil Kiran, Patil Rutuja

“…While usual machine learning algorithms may predict the production of energy, they cannot respond sufficiently in real time to alter according to dynamic environmental conditions. …”
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