ML‐UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper by Warren G. Hill, Bryce MacIver, Gary A. Churchill, Mariana G. DeOliveira, Mark L. Zeidel, Marcelo Cicconet

“…We have developed a machine learning algorithm based on Region‐based Convolutional Neural Networks (Mask‐RCNN) that was trained in object recognition to detect and quantitate urine spots across a broad range of sizes—ML‐UrineQuant. …”
Get full text

Machine Learning Insights into the Last 400 Years of Etna Lateral Eruptions from Historical Volcanological Data by Arianna Beatrice Malaguti, Claudia Corradino, Alessandro La Spina, Stefano Branca, Ciro Del Negro

“…Here, we applied a machine learning technique to automate the analysis of these datasets, handling intricate patterns that are not easily captured by explicit commands. …”
Get full text

Improving ICESat-2 photon classification and tree height estimation using Moran's I and machine learning by Mei-Kuei Lu, Sorin Popescu, Lonesome Malambo

“…However, its ATL08 data product, designed for canopy height and terrain classification, exhibits classification inaccuracies due to algorithm limitations and noise contamination. This study aimed to address these challenges by leveraging local spatial autocorrelation, Moran's I, as a feature input in machine learning methods to enhance photon classification accuracy. …”
Get full text

Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

Get full text

Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems by Fukui Wu, Hanzhong Tan, Linfeng Zhang, Shuangbing Wen, Tao Hu

“…Subsequently, five machine learning algorithms and three deep learning algorithms are employed to predict traffic flow. …”
Get full text

Identification of potential diagnostic markers and molecular mechanisms of asthma and ulcerative colitis based on bioinformatics and machine learning by Chenxuyu Zhang, Chenxuyu Zhang, Zheng Luo, Liang Ji

“…Gene Set Enrichment Analysis (GSEA) explored pathway alterations, while immune infiltration patterns were analyzed using the CIBERSORT algorithm. …”
Get full text

Two Decades of Coastal Development across the Arabian Gulf: Insights from Remote Sensing and Machine Learning by B. Dahy, R. Sousa, M. Sultan, J. A. Burt

Get full text

Computational analysis of reproducibility and image quality of 2011 and 2017 models of Heidelberg Spectralis SD-OCT machines by Kyoung A Viola Lee, Corey Tesdahl, Keith Zimmerman, Kimberly Jun, Sabrina Khalil, Alexander Shahin, Abdullah Abou-Samra, Ramesh Ayyala, Radouil Tzekov

Get full text

Synergistic use of satellite, legacy, and in situ data to predict spatio-temporal patterns of the invasive Lantana camara in a savannah ecosystem by Lilly Theresa Schell, Emma Evers, Sarah Schönbrodt-Stitt, Konstantin Müller, Maximilian Merzdorf, Drew Arthur Bantlin, Insa Otte

“…In this study, we modeled the suitable habitat and potential distribution of the notorious invader Lantana camara in the Akagera National Park (1,122 km²), a savannah ecosystem in Rwanda. Spatiotemporal patterns of Lantana camara from 2015 to 2023 were predicted at a 30-m spatial resolution using a presence-only species distribution model, implementing a Random Forest classification algorithm and set up in the Google Earth Engine. …”
Get full text

Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm by Sandeep Samantaray, Abinash Sahoo, Falguni Baliarsingh

“…In order to simulate GWL, five data-driven (DD) models, including the hybridization of support vector regression (SVR) with two optimisation algorithms i.e., firefly algorithm and particle swarm optimisation (FFAPSO), SVR-FFA, SVR-PSO, SVR and Multilayer perception (MLP), have been examined in the present study. …”
Get full text

Effects of individuality, education, and image on visual attention: Analyzing eye-tracking data using machine learning by Sangwon Lee, Yongha Hwang, Yan Jin, Sihyeong Ahn, Jaewan Park

“…Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. …”
Get full text

Monitoring the dynamics of coastal wetlands ecosystems in Brittany (France) using LANDSAT time series and machine learning by Adrien Le Guillou, Simona Niculescu

“…The study exploits the potential of satellite image time series (SITS), machine learning (ML), and Random Forest (RF) algorithms.These algorithms enable the software to learn autonomously from multiple datasets, including Landsat 4/5 and 8 SITS archive images. …”
Get full text

Safeguarding against Cyber Threats: Machine Learning-Based Approaches for Real-Time Fraud Detection and Prevention by Vikas R. Shetty, Pooja R., Rashmi Laxmikant Malghan

“…These findings provide valuable guidance for companies on choosing effective anti-fraud strategies and shed light on the adaptability of algorithms to real financial contexts, contributing to machine learning-based fraud detection.…”
Get full text

Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model by Jintak Choi, Zuobin Xiong, Kyungtae Kang

“…When rolling pins are machining with CNC equipment, a sensor system is installed to collect acoustic data, analyze failure patterns, and apply RUL prediction algorithms. …”
Get full text

Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data by Shehu Mohammed, Neha Malhotra

“…The importance of this work is in the opportunity to shift diagnostic paradigms by employing deep learning algorithms, including CNNs, LSTM networks, and GNNs to analyze spatial, temporal, and relational patterns across multi-modal data. …”
Get full text

Leveraging Artificial Intelligence for Smart Healthcare Management: Predicting and Reducing Patient Waiting Times with Machine Learning by Kristijan CINCAR, Todor IVAŞCU

“…The proposed system is built on a multitude of machine-learning algorithms such as Random Forest Regression, XGBoost, Support Vector Regression (SVR), and Artificial Neural Networks (ANNs) to render accurate estimations of patient waiting times. …”
Get full text

Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…Furthermore, these workflows lay the foundation for implementing long-term learning algorithms, a pivotal increment for monitoring initiatives. …”
Get full text

Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…Furthermore, these workflows lay the foundation for implementing long-term learning algorithms, a pivotal increment for monitoring initiatives. …”
Get full text

A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…Abstract In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. …”
Get full text

Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski, Izabela Rojek

“…Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. …”
Get full text