Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis by Subhrajit Roy, Diana Mincu, Lev Proleev, Chintan Ghate, Jennifer S. Graves, David F. Steiner, Fletcher Lee Hartsell, Katherine Heller

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Machine learning as a tool for diagnostic and prognostic research in coronary artery disease by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, V. Yu. Rublev

“…Machine learning (ML) are the central tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of large data, reveal hidden or non-obvious patterns and learn a new knowledge. …”
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Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics by Snehal Satish, Hari Gonaygunta, Akhila Reddy Yadulla, Deepak Kumar, Mohan Harish Maturi, Karthik Meduri, Elyson De La Cruz, Geeta Sandeep Nadella, Guna Sekhar Sajja

“…This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. …”
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Identification of key proteins and pathways in myocardial infarction using machine learning approaches by Chang Liu, Xing Zhang, Qian Xie, Binbin Fang, Fen Liu, Junyi Luo, Gulandanmu Aihemaiti, Wei Ji, Yining Yang, Xiaomei Li

“…This study combines proteomics, transcriptomics and machine learning (ML) to identify key proteins and pathways associated with AMI. …”
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Machine Learning Approaches for Speech-Based Alzheimer’s Detection: A Comprehensive Survey by Ahmed Sharafeldeen, Justin Keowen, Ahmed Shaffie

“…Recent advancements in machine learning (ML) and deep learning (DL) models have demonstrated significant potential for detecting AD using patient’s speech signals, as subtle changes in speech patterns, such as reduced fluency, pronunciation difficulties, and cognitive decline, can serve as early indicators of the disease, offering a non-invasive and cost-effective method for early diagnosis. …”
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Big data thinking of top executives and corporate innovation: based on machine learning by Xuesong Tang, Qiang Liao, Wen Li, Wang Liao

“…Corporate top executives’ adaptation to big data technological trends and timely transformation of traditional cognitive patterns play crucial roles in driving corporate innovation. …”
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A comprehensive survey of the machine learning pipeline for wildfire risk prediction and assessment by Naveed Ejaz, Salimur Choudhury

“…It highlights the integration of diverse data sources, including remote sensing, in-situ measurements, geospatial layers, and historical fire records and outlines pre-processing and feature engineering techniques to represent climatic, topographic, vegetation, anthropogenic, and temporal fire patterns. The paper categorizes a wide array of machine learning techniques applied in wildfire risk assessment, including traditional, deep learning, spatial, temporal, reinforcement learning, and hybrid approaches. …”
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Reliability analysis in curriculum development for social science education driven by machine learning by Rui Mao

“…This research aimed at applying machine learning models to improve reliability in the development of social science courses. …”
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Early Warning Systems for Plant Diseases in delta regions: Machine Learning Approaches by Biswas Debarghya, Sharma Priti

“…Some patterns and anomalies can indicate the onset of plant diseases, and the algorithms are trained to recognize them. …”
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A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems by Asli Kaya Karakutuk, Ozer Ozdemir

“…On the JAFFE dataset, the algorithm achieved an average classification accuracy of 94.55% when supported with local binary patterns and 94.27% with a histogram of oriented gradients. …”
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Unmasking Machine Learning With Tensor Decomposition: An Illustrative Example for Media and Communication Researchers by Yu Won Oh, Chong Hyun Park

“…Using a labeled spam review dataset as an illustrative example, this study demonstrates how the proposed approach uncovers patterns overlooked by traditional methods and enhances insights into language use. …”
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Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis by Liang Xu, Jing Li, Wei Gong

“…Extracellular vesicles (EVs), as a category of nanoparticles, carry a wealth of biological information and play a crucial role in tumor initiation and progression, thereby offering novel approaches for early tumor diagnosis. In recent years, machine learning (ML) technology in the medical field has gained momentum, which utilize various algorithms to analyze input data, identify potential patterns and trends, develop predictive models, and generate high-precision predictions of unknown data, demonstrating its clinical potential in disease diagnosis. …”
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AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…The study employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex spatial and temporal patterns, enabling more accurate and timely drought forecasting compared to traditional 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. However, the performance of machine learning algorithms can be affected by any factor, thus sometimes we found several algorithms that are generally known to be powerful, even showing unsatisfactory results. …”
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Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia by Cheng-Wen Lee, Mao-Wen Fu, Chin-Chuan Wang, Muh. Irfandy Azis

“…These findings emphasize the critical need for enhanced fraud detection frameworks, leveraging machine learning algorithms like Random Forest to identify fraud patterns effectively. …”
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A Survey on Android Malware Detection Techniques Using Supervised Machine Learning by Safa J. Altaha, Ahmed Aljughaiman, Sonia Gul

“…Android malware threatens users’ privacy, data security, and overall device performance. Machine learning (ML) plays a significant role in malware analysis and detection because it can process huge amounts of data, identify complex patterns, and adjust to changing threats. …”
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Machine Learning-Based Detection of Non-Technical Losses in Power Distribution Networks by Mahmut Türk, Heybet Kılıç, Cem Haydaroglu

“…In addition, from the classical methods, 67.5 accuracy rate was obtained with the k-Nearest Neighbor (k-NN) method and 62.25 accuracy rate was obtained with the Support Vector Machines (SVM) method. Comparisons with such traditional methods have revealed the superiority of CNN in determining complex leakage patterns. …”
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Leveraging machine learning for sustainable solid waste management: A global perspective by Ajaya Subedi, Sahil Shrestha, Anish Ghimire, Shukra Raj Paudel

“…In light of these challenges, this study primarily accentuates the transformative potential of Machine Learning (ML) for sustainable waste management. …”
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A quantum inspired machine learning approach for multimodal Parkinson’s disease screening by Diya Vatsavai, Anya Iyer, Ashwin A. Nair

“…For classification, we designed a simulatable quantum support vector machine (qSVM) that detects high-dimensional patterns, leveraging recent advancements in quantum machine learning. …”
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Near viewing behaviors predict educational system in a machine learning model by Ravid Doron, Einat Shneor, Lisa A. Ostrin, Ariela Gordon-Shaag, Ayelet Goldstein

“…Compared to standard school students, intensive school students had significantly more myopic refraction (P < 0.03), spent more time viewing very near distances (P < 0.004) and less time viewing intermediate distances (P < 0.008) and had shorter near-viewing distances (P < 0.0001). Machine learning identified far-viewing episodes > 5 min and viewing distance during near-viewing as predictors of educational background.These findings suggest that educational environments are associated with distinct visual behavior patterns that may be linked to refractive development. …”
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