Revolutionizing total hip arthroplasty: The role of artificial intelligence and machine learning by Umile Giuseppe Longo, Sergio De Salvatore, Alice Piccolomini, Nathan Samuel Ullman, Giuseppe Salvatore, Margaux D'Hooghe, Maristella Saccomanno, Kristian Samuelsson, Rocco Papalia, Ayoosh Pareek

“…Abstract Purpose There has been substantial growth in the literature describing the effectiveness of artificial intelligence (AI) and machine learning (ML) applications in total hip arthroplasty (THA); these models have shown the potential to predict post‐operative outcomes using algorithmic analysis of acquired data and can ultimately optimize clinical decision‐making while reducing time, cost and complexity. …”
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Machine learning classification of consumption habits of creatine supplements in gym goers by Patrícia C. Magalhães, Samuel Encarnação, Andre C. Schneider, Pedro Forte, José Teixeira, Antonio Miguel Monteiro, Tiago M. Barbosa, Ana M. Pereira

“…The study was applied to gym goers in Bragança, where a QR code for a survey was released. 158 people participated, 65 non-consumers of creatine supplementation (37.34% men; 22.78% women) and 95 consumers (15.19% men; 24.68% women). Five machine learning algorithms were implemented to classify creatine consumption in gym goers: Logistic Regression, Gradient Boosting Classifier, Ada Boost Classifier, Xgboost Classifier. …”
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A Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization by Zeinab Hassani, vahid Hajihashemi, Keivan Borna, Iman Sahraei Dehmajnoonie

“…Spam leads to a growing problem in a personal email, so it would be essential to detect it. Machine learning is very useful to solve this problem as it shows good results in order to learn all the requisite patterns for classification due to its adaptive existence. …”
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Adaptive algorithm for reducing pulse noise level in images from CCTV cameras by Andrey Sadchenko, Oleg Kushnirenko, Alexander Troyanskiy, Yurii Savchuk

“…The proposed algorithm can be used for pre-preprocessing images intended for recognition by machine vision systems, scanning text, and improving subjective image characteristics, such as sharpness and contrast.…”
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PredβTM: A Novel β-Transmembrane Region Prediction Algorithm. by Amrita Roy Choudhury, Marjana Novič

“…Here, we describe PredβTM, a transmembrane region prediction algorithm for β-barrel proteins. Using amino acid pair frequency information in known β-transmembrane protein sequences, we have trained a support vector machine classifier to predict β-transmembrane segments. …”
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SECONDGRAM: Self-conditioned diffusion with gradient manipulation for longitudinal MRI imputation by Brandon Theodorou, Anant Dadu, Mike Nalls, Faraz Faghri, Jimeng Sun

“…We evaluate SECONDGRAM on the UK Biobank dataset and show that it not only models MRI patterns better than existing baselines but also enhances training datasets to achieve better downstream results over naive approaches. …”
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Study Comparison Deep Learning and Support Vector Machine for Face Mask Detection by Rani Kurnia Putri, Muhammad Athoillah*

“…Both algorithms have proven to be powerful tools for any classification problem specially to classify or identify image patterns. …”
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Integration of Deep Learning and Machine Learning Techniques for Advancing the Detection of Plant Diseases by Abdul Karim, Touqeer Ahmed Jumani, Muhammad Amir Raza, Shadi Khan Baloch, Nouman Qadeer Soomro, Muhammad Masud, Muhammad Salman Saeed, Mouaaz Nahas, Aamir Ali Patoli

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Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning by Chengping Chai, Omar Marcillo, Monica Maceira, Junghyun Park, Stephen Arrowsmith, James O. Thomas, Joshua Cunningham

“…Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. …”
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Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study by Aurélie Jourdan, Aurélie Jourdan, Caroline Dania, Caroline Dania, Maxime Cambournac, Maxime Cambournac

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Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects by A. Presno Vélez, M. Z. Fernández Muñiz, J. L. Fernández Martínez

“…Structural health monitoring (SHM) systems used sensors to detect damage indicators such as vibrations and cracks, which were crucial for predicting service life and planning maintenance. Machine learning (ML) enhanced SHM by analyzing sensor data to identify damage patterns often missed by human analysts. …”
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A Multi-Area Software-Defined Vehicular Network Control Plane Deployment Mechanism Oriented to Traffic Prediction by Hao Li, Hongming Li, Yuqing Ji, Ziwei Wang

“…A comprehensive comparison of various machine learning and deep learning algorithms is then conducted to evaluate their efficacy in forecasting SDVN traffic patterns, which is crucial for system efficiency. …”
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Forecasting Delivery Time of Goods in Supply Chains Using Machine Learning Methods by V. K. Rezvanov, O. M. Romakina, E. V. Zaytseva

“…The presented study aims to fill these gaps and demonstrate the efficiency of using open, accessible data and known algorithms. The research objective is to describe a pattern of appropriate selection of the least resource-intensive delivery forecasting model based on the analysis of machine learning algorithms.Materials and Methods. …”
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Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques by Mohamed Ali Kazi

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Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners by Gulnur Arkin, Tangnur Abdukelim, Hankiz Yilahun, Askar Hamdulla

“…A speech corpus containing samples from advanced, intermediate, and elementary learners (N = 50) and standard speakers (N = 10) was constructed, with a total of 5880 samples. Support Vector Machine (SVM) and ID3 decision tree algorithms were employed to classify vowel formant parameters (F1-F2) patterns. …”
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The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data by Sudarmaji Saroji*, Ekrar Winata, Putra Pratama Wahyu Hidayat, Suryo Prakoso, Firman Herdiansyah

“…The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. …”
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Time Evolution of Bacterial Resistance Observed with Principal Component Analysis by Claudia P. Barrera Patiño, Mitchell Bonner, Andrew Ramos Borsatto, Jennifer M. Soares, Kate C. Blanco, Vanderlei S. Bagnato

“…<b>Results</b>: The processing and data analysis with machine learning algorithms performed on this FTIR spectral database allowed for the identification of patterns across minimum inhibitory concentration (MIC) values associated with different exposure times and both clusters from hierarchical classification and PCA. …”
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Machine learning approach for Studying the multifunctionality of soil against global climate changes by Xiangng Hu, Yingying Xie, Qixing Zhou, Li Mu

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Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control by Jibran Saleem, Umar Raza, Mohammad Hammoudeh, William Holderbaum

“…This research presents the SmartIoT Hybrid Machine Learning (ML) Model, a novel integration of Attribute-Based Authentication and a lightweight machine learning algorithm designed to enhance security while minimising computational overhead. …”
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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…The field of dermatology has seen a change in recent years due to the convergence of artificial intelligence and medicine, which has produced creative methods for computer-aided diagnostics. Machine learning has become a potent tool in the search for more precise and effective diagnostic techniques because of its capacity to analyze enormous volumes of data and identify intricate patterns. …”
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