Applying machine learning to classify table olives using bacterial metataxonomic data by Elio López-García, Antonio Benítez-Cabello, Francisco Noé Arroyo-López

“…Moreover, advances in bioinformatics and machine learning (ML) have expanded resources for analyzing these metataxonomic data. …”
Get full text

Innovative data techniques for centrifugal pump optimization with machine learning and AI model. by Gaurav Sandeep Dave, Amar Pradeep Pandhare, Atul Prabhakar Kulkarni, Dhananjay Vasant Khankal

“…The data recorded from DAQ system undergoes thorough in-depth analysis, processing & transformation before being incorporated into machine learning (ML) or artificial intelligence models. …”
Get full text

Big data thinking of top executives and corporate innovation: based on machine learning by Xuesong Tang, Qiang Liao, Wen Li, Wang Liao

“…Further analysis demonstrates that executives’ big data thinking effectively improves innovation quality. …”
Get full text

The Impact of data-driven learning on the improvement of grammatical proficiency in the ESL classroom environment by Sabina Sultana, Manvender Kaur Sarjit Singh, Rabiul Islam, Hafizah Hajimia

“…Corpus-based data-driven learning (DDL) is an innovative approach that utilises electronic text collections for linguistic analysis, thereby enhancing teaching practices and learning skills for ESL/EFL students. …”
Get full text

Predicting and analyzing ferry transit delays using open data and machine learning by Malek Sarhani, Abtin Nourmohammadzadeh, Stefan Voß, Mohammed EL Amrani

“…Our approach leverages General Transit Feed Specification (GTFS) data, ridership and vessel information, and hourly weather data, combined with SHAP explainable artificial intelligence analysis to assess key delay determinants. …”
Get full text

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

“…We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. …”
Get full text

Evaluation of unsupervised learning algorithms for the classification of behavior from pose estimation data by Jakub Mlost, Rame Dawli, Xuan Liu, Ana Rita Costa, Iskra Pollak Dorocic

“…However, these tools do not automate behavioral classification. Unsupervised learning algorithms address this gap by identifying clusters of recurring behavioral motifs from pose-tracking data without requiring pre-labeled datasets, reducing observer bias and uncovering novel patterns. …”
Get full text

Environmental Data Analytics for Smart Cities: A Machine Learning and Statistical Approach by Ali Suliman AlSalehy, Mike Bailey

Get full text

Using machine learning to identify Parkinson’s disease severity subtypes with multimodal data by Hwayoung Park, Changhong Youm, Sang-Myung Cheon, Bohyun Kim, Hyejin Choi, Juseon Hwang, Minsoo Kim

“…This study aims to address the clinical applicability and heterogeneity of PD using PD severity subtypes classification and digital biomarker development by combining objective multimodal data with machine learning (ML) approaches. Methods We analyzed datasets that combine clinical characteristics, physical function and lifestyle data, gait parameters in motion analysis systems, and wearable sensors collected from persons with PD (n = 102) to perform clustering for subtype classification. …”
Get full text

Application of Machine Learning Methods for Employee Turnover Prediction Based on Open Data by A. N. Kazinets

“…The application of machine learning methods for predicting staff turnover in organizations using open data is studied. …”
Get full text

Machine Learning and Data Science in Social Sciences: Methods, Applications, and Future Directions by Elias Dritsas, Maria Trigka

“…Artificial intelligence (AI) is transforming social science research by enabling scalable data analysis, predictive modeling, and causal inference, thereby reshaping the methodological foundations of fields such as political science, economics, and psychology. …”
Get full text

Ensemble learning for multi-class COVID-19 detection from big data. by Sarah Kaleem, Adnan Sohail, Muhammad Usman Tariq, Muhammad Babar, Basit Qureshi

“…In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. …”
Get full text

Learning tissue representation by identification of persistent local patterns in spatial omics data by Jovan Tanevski, Loan Vulliard, Miguel A. Ibarra-Arellano, Denis Schapiro, Felix J. Hartmann, Julio Saez-Rodriguez

Get full text

Evaluation of the Performance of Unsupervised Learning Algorithms for Intrusion Detection in Unbalanced Data Environments by Gutierrez-Portela Fernando, Almenares Mendoza Florina, Calderon-Benavides Liliana

“…This study evaluated the performance of unsupervised machine learning algorithms for intrusion detection in unbalanced data environments using the BoT-IoT dataset. …”
Get full text

Dataset of knowledge retention and learning satisfaction in patient–nurses safetyMendeley Data by Rose A. Nain, Deena Clare Thomas, Johari Daud Makajil, Rista Fauziningtyas

“…The data was collected through structured questionnaires administered to participants before and after implementing a safety-focused learning module. …”
Get full text

Global Prediction of Whitecap Coverage Using Transfer Learning and Satellite-Derived Data by Jinpeng Qi, Yongzeng Yang, Jie Zhang

“…To effectively utilize these satellite-derived data, we propose a transfer learning approach for predicting global whitecap coverage. …”
Get full text

Data-driven network intrusion detection using optimized machine learning algorithms by Dauda Adeite Adenusi, Oladosu Oyebisi Oladimeji, Theopilus Adekunle Oyekola, Korede Solomon Olagunju

“…Experimental results demonstrate exceptional performance of tree-based methods, with DT and RF achieving accuracy rates of 0.9997 and 0.9996 respectively, alongside precision rates exceeding 0.99. Comparative analysis with existing approaches, including deep learning methods, shows that our optimized tree-based models achieve comparable or superior performance while maintaining computational efficiency. …”
Get full text

Machine learning approaches for improving atomic force microscopy instrumentation and data analytics by Nabila Masud, Jaydeep Rade, Md. Hasibul Hasan Hasib, Adarsh Krishnamurthy, Adarsh Krishnamurthy, Anwesha Sarkar

“…Significant progress has been made recently in artificial intelligence (AI) and deep learning (DL), extending into microscopy. In this review, we summarize how researchers have implemented machine learning approaches so far to improve the performance of atomic force microscopy (AFM), make AFM data analytics faster, and make data measurement procedures high-throughput. …”
Get full text

Benchmarking Deep Learning for Wetland Mapping in Denmark Using Remote Sensing Data by Muhammad Rizwan Asif

“…We also assess the impact of incorporating near-infrared and DEM data in addition to traditional optical imagery. …”
Get full text

Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance by M. Elhoseny, Deepak Dasaratha Rao, Bala Dhandayuthapani Veerasamy, Noha Alduaiji, J. Shreyas, Piyush Kumar Shukla

“…This study evaluates the use of deep learning for improved sensor data fusion in fault identification and tolerance using the KITTI dataset. …”
Get full text