A Study on Staging Cystic Echinococcosis Using Machine Learning Methods by Tuvshinsaikhan Tegshee, Temuulen Dorjsuren, Sungju Lee, Dolgorsuren Batjargal

“…This study explores the development of an advanced system that leverages artificial intelligence (AI) and machine learning (ML) techniques to classify CE cysts into stages using various imaging modalities, including computed tomography (CT), ultrasound (US), and magnetic resonance imaging (MRI). A total of ten ML algorithms were evaluated across these datasets, using performance metrics such as accuracy, precision, recall (sensitivity), specificity, and F1 score. …”
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Machine learning based shear strength prediction in reinforced concrete beams using Levy flight enhanced decision trees by Aybike Özyüksel Çiftçioğlu, Anıl Delikanlı, Torkan Shafighfard, Faramarz Bagherzadeh

“…A comprehensive dataset comprising 195 experimentally tested T-beams is used to train and evaluate six different regression models, including optimized Decision Tree, Random Forest, AdaBoost, K-Nearest Neighbors, Ridge Regression, and the proposed Levy-DT. …”
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Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations by Oluwole John Famoriji, Thokozani Shongwe

“…Because a machine learning algorithm is trained by utilizing data obtained from numerous 5G base stations, it exhibits the capability to estimate the strength of the electric field effectively at every point of arbitrary radiation, while the base station generates a network and serves various numbers of 5G terminals running in different modes of service. …”
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Emotion-Based Music Recommendation System Integrating Facial Expression Recognition and Lyrics Sentiment Analysis by V. S. G. S. Phaneendra Bottu, K. Ragavan

“…This study introduces a robust Convolutional Neural Network (CNN)-based model specifically designed for accurate FER, complemented by an innovative music recommendation system. The model was trained and evaluated using the publicly available FER-2013 dataset, which comprises 35,887 labelled facial images, along with a curated music dataset of 1,000 songs annotated with emotions. …”
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Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis by Cosmina-Mihaela Rosca, Adrian Stancu

“…The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. …”
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Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery by Sudha GC Upadhaya, Chongyuan Zhang, Sindhuja Sankaran, Timothy Paulitz, David Wheeler

“…Multiple machine learning algorithms, including random forest and artificial neural networks (ANNs), were trained and evaluated on previously unseen isolates. …”
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A NOVEL DEEP LEARNING APPROACHES FOR MULTI-CLASS HISTOPATHOLOGICAL SUB-IMAGE CLASSIFICATION USING PRIOR KNOWLEDGE by Riyam Ali Yassin, Morteza Valizadeh, Alaa Hussein Abdulaal

“…A new dataset comprising 3,600 sub-image histopathological images is presented, generated from the original Bach dataset. The study evaluates various pre-trained deep neural networks, including Inception V3, VGG19, GoogleNet, ResNet 101, and NASNet. …”
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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

“…Using a commercial simulator containing countermeasure algorithms, datasets representing large-scale scenarios have been created, and the collected data have been trained with the proposed Multilayer Perceptron model. …”
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Single-element ultrasound system for high-resolution jugular venous pulse contour detection by Navya Rose George, P. M. Nabeel, Kiran V. Raj, Rahul Manoj, Mohanasankar Sivaprakasam, Jayaraj Joseph

“…The ability to accurately evaluate the JVP contour characteristics can provide insights into right atrial hemodynamics, potentially facilitating the early detection and monitoring of vascular anomalies.…”
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Machine Learning-Enabled Prediction and Optimization of Sulfur Recovery Units: A Step towards Industry 4.0 Integration by Imran Khan, Husnain Saghir, Muhammad Ahsan

“…This study employs a machine learning algorithm to predict sulfur recovery efficiency under uncertain conditions. …”
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Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis by Ahmed Alfakeeh, Mhd Saeed Sharif, Abin Daniel Zorto, Thiago Pillonetto

“…The average F Score obtained by the nonensemble models trained in this paper is 0.83 compared to the ensemble techniques average of 0.94. …”
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High-speed threat detection in 5G SDN with particle swarm optimizer integrated GRU-driven generative adversarial network by R. Shameli, Sujatha Rajkumar

“…The attack detection in 5G SDN involves Machine learning (ML) and Deep learning (DL) algorithms to analyze large volumes of network data and identify patterns indicative of attacks. …”
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Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using Two-Dimensional Periapical Radiographs by Catalina Bennasar, Antonio Nadal-Martínez, Sebastiana Arroyo, Yolanda Gonzalez-Cid, Ángel Arturo López-González, Pedro Juan Tárraga

“…<b>Methods</b>: The DL model was trained and validated using leave-one-out cross-validation (LOOCV). …”
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Predicting 24-hour intraocular pressure peaks and averages with machine learning by Ranran Chen, Jinming Lei, Yujie Liao, Yiping Jin, Xue Wang, Xiaomei Li, Danping Wu, Hong Li, Yanlong Bi, Haohao Zhu

“…Predictive models based on five machine learning algorithms were trained and evaluated. Five time points (10:00 AM, 12:00 PM, 2:00 PM, 4:00 PM, and 6:00 PM) were tested to optimize prediction accuracy using their combinations. …”
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AI-assisted detection for chest X-rays (AID-CXR): a multi-reader multi-case study protocol by Fergus Gleeson, Alex Novak, Indrajeet Das, Ruchir Shah, Edwin van Beek, Sarim Ather, Howell Fu, Nabeeha Salik, Alan Campbell, Farhaan Khan, Abdala Trinidad Espinosa Morgado, Marusa Kotnik, Louise Wing, John Murchison, Jong Seok Ahn, Sang Hyup Lee, Ambika Seth

“…Performance testing will be carried out with readers from various clinical professional groups with and without the assistance of Lunit INSIGHT CXR to evaluate the utility of the algorithm in improving reader accuracy (sensitivity, specificity, AUROC), confidence and speed (paired sample t-test). …”
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Machine learning for prediction of 30-day mortality in patients with advanced cancer: comparing pan-cancer and single-cancer models by S. Bjerregaard-Michelsen, L.Ø. Poulsen, A. Bjerrum, M. Bøgsted, C. Vesteghem

“…Clinical data were used to train, validate, and test a pan-cancer model and 10 single-cancer models based on the eXtreme Gradient Boosting (XGBoost) algorithm. …”
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Diagnostic performance of actigraphy in Alzheimer’s disease using a machine learning classifier – a cross-sectional memory clinic study by Mathias Holsey Gramkow, Andreas Brink-Kjær, Frederikke Kragh Clemmensen, Nikolai Sulkjær Sjælland, Gunhild Waldemar, Poul Jennum, Steen Gregers Hasselbalch, Kristian Steen Frederiksen

“…We derived movement patterns (walking, running, resting, etc.) from raw accelerometry data using a proprietary algorithm. By evaluating the movement patterns during day and nighttime, we calculated 510 activity-related features, including robustness and fragmentation of the circadian rhythm. …”
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Exploring the potential of cell-free RNA and Pyramid Scene Parsing Network for early preeclampsia screening by Zhuo Zhao, Xiaoxu Liu, Yonghui Guan, Chunfang Li, Zheng Wang

“…Based on the differences in cfRNA expression profiles, the Calculated Ground Truth values of the NP and PE groups in the sequencing data were acquired (Calculated PRI). The differential algorithm was embedded in the PSPNet neural network and the network was then trained using the generated dataset. …”
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Sex-Specific Ensemble Models for Type 2 Diabetes Classification in the Mexican Population by Mendoza-Mendoza MM, Acosta-Jiménez S, Galván-Tejada CE, Maeda-Gutiérrez V, Celaya-Padilla JM, Galván-Tejada JI, Cruz M

“…Data are split by sex, and feature selection is performed using GALGO, a genetic algorithm-based tool. Classification models including Random Forest, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression are trained and evaluated. …”
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Retrieval of Atmospheric XCH₄ via XGBoost Method Based on TROPOMI Satellite Data by Wenhao Zhang, Yao Li, Bo Li, Tong Li, Zhengyong Wang, Xiufeng Yang, Yongtao Jin, Lili Zhang

“…The dataset constructed was used to train the XGBoost model and obtain the TRO_XGB_XCH₄ model. …”
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