Identifying key risk factors of polycyclic aromatic hydrocarbons and benzene exposure in Korean adult males using machine learning approaches by Haewon Byeon

“…Methods: Isolation Forest, a machine learning algorithm specialized in anomaly detection, was employed to identify key variables influencing urinary biomarkers such as 1-Hydroxypyrene, 2-Hydroxynaphthalene and trans-Muconic acid. …”
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Snow Distribution Patterns Revisited: A Physics‐Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub‐Arctic by R. L. Crumley, C. L. Bachand, K. E. Bennett

“…We validated hybrid model outputs using in‐situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. …”
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Pioneering machine learning techniques to estimate thermal conductivity of carbon-based phase change materials: A comprehensive modeling framework by Raouf Hassan, Alireza Baghban

“…A dataset of 482 samples, incorporating various nanoparticle types, concentrations, PCM types, and operating temperatures, was curated and refined using the Monte Carlo Outlier Detection algorithm. Extensive machine learning algorithms were explored; however, CatBoost, XGBoost, ANN, Random Forest, and Gradient Boosting emerged as the most accurate models. …”
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Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy for Lithium Metal Battery Degradation via Machine Learning by Qianli Si, Shoichi Matsuda, Yasunobu Ando, Toshiyuki Momma, Yoshitaka Tateyama

“…In this study, a machine learning (ML) framework is proposed that combines electrochemical impedance spectroscopy (EIS) with the XGBoost algorithm to develop two predictive models: one for estimating capacity degradation and another for detecting the knee point (KP)—a critical inflection point in the degradation trajectory. …”
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A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images by Xudong Huang, Biao Zhang, William Perrie

“…Existing methods for detecting oil spills using synthetic aperture radar (SAR) often face challenges related to algorithm complexity, limited data availability, and practical implementation. …”
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HyQ2: A Hybrid Quantum Neural Network for NextG Vulnerability Detection by Yifeng Peng, Xinyi Li, Zhiding Liang, Ying Wang

“…We address the limitations of classical machine learning models in processing the intrinsic linkage relationships of high-dimensional data. …”
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Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF) by Daniel Lafontaine, Stephanie Chahwan, Gustavo Barraza, Burcin Agridag Ucpinar, Gunjan Kayal, Nicolás Gómez-Banoy, Paul Cohen, John L. Humm, Heiko Schöder

“…The introduction of machine learning algorithms, such as the PET/CT algorithm implemented in the TriDFusion (3DF) Image Viewer, represents a significant advancement in BAT detection. …”
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Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree by Nasir Tayarani, Mehrdad Jalali

“…The ability and power of the SentiStrength algorithm to detect sentiment has also led to a thorough analysis of sentiment in tweets. …”
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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, E. N. Dzhakhnaeva

“…The abstract describes the construction of a binary classification model for predicting the type of job advertisement in cloud-based ATS (Applicant Tracking Systems) as either legitimate or fraudulent. Various machine learning algorithms can be employed to address this issue. …”
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Application of Open-Source, Low-Code Machine-Learning Library in Python to Diagnose Parkinson's Disease Using Voice Signal Features by Daniel Hilário da Silva, Caio Tonus Ribeiro, Leandro Rodrigues da Silva Souza, Adriano Alves Pereira

“…We leveraged the PyCaret Python library's ML algorithms and functions, which were introduced in this article, to demonstrate their simplicity and effectiveness in dealing with real-world data. …”
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Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System by Nasrettin Koksal, AbdulRahman Ghannoum, William Melek, Patricia Nieva

“…In this paper, we propose an Intelligent Bluetooth Virtual Door (IBVD) OM system for the indoor/outdoor tracking of individuals using the interaction between a BLE device worn by the occupant and two BLE beacons located at the entrance/exit points of a doorway. ML algorithms are used to perform intelligent OM through pattern detection from the BLE RSSI signal(s). …”
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Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward Relay by Gangsan Jeong, Xianglan Jin

“…Specifically, we present supervised learning (SL) and unsupervised learning (USL) methodologies, with the latter employing a novel loss function designed to avoid the need for extensive training data collection. …”
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Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. by Sadia Nazim, Muhammad Mansoor Alam, Syed Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Suud

“…There has been relatively little study on explainability, especially when dealing with malware imagery data, irrespective of the fact that DL/ML algorithms have revolutionized malware detection. …”
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Objective dairy cow mobility analysis and scoring system using computer vision–based keypoint detection technique from top-view 2-dimensional videos by Shogo Higaki, Guilherme L. Menezes, Rafael E.P. Ferreira, Ariana Negreiro, Victor E. Cabrera, João R.R. Dórea

“…These findings indicate that the computer vision-based keypoint detection technique is effective for extracting mobility variables relevant to mobility scores from top-view 2D videos, and the machine learning classification model based on the newly extracted variables has the potential for objective mobility scoring in dairy cows.…”
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Finding New Hot Subdwarf Stars in SDSS Images by Jiangchuan Zhang, Yude Bu, Huili Wu, Yuhang Zhang, Shanshan Li, Zhenxin Lei, Zhenping Yi, Xiaoming Kong, Meng Liu

“…With the accumulated image data in the digital sky surveys, we proposed the Hot Subdwarf Detector (HsdDet), a novel multiscale object detection algorithm designed to directly locate hot subdwarf stars’ coordinates in Sloan Digital Sky Survey (SDSS) images. …”
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AI-Powered Innovations in Food Safety from Farm to Fork by Binfeng Yin, Gang Tan, Rashid Muhammad, Jun Liu, Junjie Bi

“…Secondly, during food processing, sensors combined with machine learning algorithms are utilized to ensure regulatory compliance and monitor production parameters. …”
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Quality monitoring of hybrid welding processes: A comprehensive review by Solomon Habtamu Tessema, Dariusz Bismor

“…Furthermore, the review highlights the integration of sensor data with AI/ML algorithms and digital twin technologies, enhancing the capabilities of quality monitoring systems. …”
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Attention-based hybrid deep learning model with CSFOA optimization and G-TverskyUNet3+ for Arabic sign language recognition by Ahmed A. Mohamed, Abdullah Al-Saleh, Sunil Kumar Sharma, Ghanshyam Tejani

“…In addition, employing a novel metaheuristic algorithm, the Crisscross Seed Forest Optimization Algorithm, which combines the Crisscross Optimization and Forest Optimization algorithms to determine the best features from the extracted texture, color, and deep learning features. …”
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Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques by Miguel Diaz, Luis Lopez, Michel Amancio, Italo Inocente, Jhianpiere Salinas, Sergio Isuhuaylas, Erika Flores, Edisson Moscoso

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Adaptation of k-means to automated forecasting of poorly structured time series of economic dynamics by Dunskaia Lada, Popova Elena

“…Data mining methods used in machine learning or deep learning allow to take into account complex patterns and nonlinear dependencies in the data. …”
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