Delving into the Porosity Domain Continuum in Hardwood Growth Rings: What Can We Learn from Computer Vision Wood Identification Models? by Alex Wiedenhoeft, Prabu Ravindran, Adriana Costa, Rubin Shmulsky, Frank Owens

Get full text

Simple Yet Powerful: Machine Learning-Based IoT Intrusion System With Smart Preprocessing and Feature Generation Rivals Deep Learning by Kazim Kivanc Eren, Kerem Kucuk, Fatih Ozyurt, Omar H. Alhazmi

“…Our workflow emphasizes the importance of well-structured preprocessing pipelines missing data handling, categorical feature encoding, and multicollinearity reduction, paired with classical machine learning models. …”
Get full text

Tracking Poultry Drinking Behavior and Floor Eggs in Cage-Free Houses with Innovative Depth Anything Model by Xiao Yang, Guoyu Lu, Jinchang Zhang, Bidur Paneru, Anjan Dhungana, Samin Dahal, Ramesh Bahadur Bist, Lilong Chai

Get full text

Predictive Control for Steel Rib Bending Based on Deep Learning by Yijiang Xia, Jinhui Luo, Zhuolin Ou, Xin Han, Junlin Deng, Ning Wu

“…This study proposes control methods for cold bending machines based on deep learning models to address this challenge, including CNN and Transformer-CNN (T-CNN), to predict the elastic spring-back rate of cold-processed metal profiles and generate precise control pulses for achieving target bending angles. …”
Get full text

A Bayesian-Optimized Surrogate Model Integrating Deep Learning Algorithms for Correcting PurpleAir Sensor Measurements by Masrur Ahmed, Jing Kong, Ningbo Jiang, Hiep Nguyen Duc, Praveen Puppala, Merched Azzi, Matthew Riley, Xavier Barthelemy

Get full text

AGE AND GENDER CLASSIFICATION FROM IRIS IMAGES OF THE EYE USING MACHINE LEARNING TECHNIQUES by Martins E. Irhebhude, Adeola O. Kolawole, Halima Abemi

“…The 3D histogram with PCA recorded an excellent classification performance accuracy of 99.27% as against the EfficientNet deep learning model which recorded 52.29%. The recommended feature technique can help to adequately classify gender and age from iris images leading to a more robust recognition model. …”
Get full text

Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…The study explores resource allocation in Federated Machine Learning (FedML) for the Industrial Internet of Things (IIoT), focusing on efficient and privacy-conscious data processing. …”
Get full text

Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals by Agostino Marengo, Vito Santamato, Massimo Iacoviello

“…The framework integrates several predictive models—including Random Forest, Support Vector Machines, and Logistic Regression—developed in Python using the scikit-learn library. …”
Get full text

Machine Learning to Improve Resident Scheduling: Harnessing Artificial Intelligence to Enhance Resident Wellness by Aazad Abbas, Jay Toor, Denis Ariel Margalik, Jin Tong Du, Anne Versteeg, Nicholas J Yee, Joel A Finkelstein, Jihad Abouali, Markku T Nousiainen, Hans J Kreder, Jeremy Hall, Cari Whyne, Jeremie Larouche

“…While other industries have adopted machine learning models (MLMs) to optimize scheduling and employee well-being, medicine has lagged. …”
Get full text

Application of machine learning with gradient descent method for load forecasting: a performance analysis by Saroj Kumar Panda, Manoj Kumar Panda

“…One method of forecasting, short-term load forecasting (STLF) is used in this research, and machine learning like deep neural network (DNN) is the method used here for the analysis of STLF. …”
Get full text

Prediction of reduced sound wave intensity in floor systems using machine learning methods by Hamid Mohammadnezhad, Fardin Jafari, Nahad Sedighi

“…The required data for machine learning methods were obtained by simulation of different floor systems with varying material and thickness in the INSUL software. …”
Get full text

City-level total and sub-category energy intensity estimation using machine learning by Fei Shen, Xiwen Lin, Hao Chen, Jinji Ma, Kaifang Shi, Weidong Cao

“…This study proposes a city-level total and subcategories (coal, oil, gas) energy intensity estimation method based on multi-source remote sensing data and machine learning models. The performance of the machine learning models is validated using the four-fold cross-validation approach. …”
Get full text

Machine learning with label-free Raman microscopy to investigate ferroptosis in comparison with apoptosis and necroptosis by Joost Verduijn, Eva Degroote, André G. Skirtach

“…Data analysis was performed by machine learning (ML), here SVMs, where the model utilizing the spectra directly into a support vector machine (SVM) outperforms other SVM strategies correctly predicting 73% of all spectra. …”
Get full text

POD-Based Machine Learning Approach for Coupled EM-Thermal Analysis in Microwave Heating by Jeong-Wan Lee, Gyu-Sik Choi, Sung-Jun Yang

“…In this paper, we propose a machine learning-based approach for reduced-order modeling that efficiently predicts the specific absorption rate (SAR) distributions in coupled EM and thermal analyses. …”
Get full text

Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook by Pengcheng Xue, Rui Qiu, Chuchuan Peng, Zehang Peng, Kui Ding, Rui Long, Liang Ma, Qifeng Zheng

“…Abstract The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. …”
Get full text

Machine Learning Reveals Microbial Taxa Associated with a Swim across the Pacific Ocean by Garry Lewis, Sebastian Reczek, Osayenmwen Omozusi, Taylor Hogue, Marc D. Cook, Jarrad Hampton-Marcell

“…Multivariate analysis was used to analyze the microbial community structure, and machine learning (random forest) was used to model the microbial dynamics over time using R statistical programming. …”
Get full text

Scalable and robust machine learning framework for HIV classification using clinical and laboratory data by Qian Sui, Gaoxu Li, Yaqi Peng, Jiasheng Zhang, Yibo Zhang, Riyang Zhao

“…We evaluate five machine learning models, identifying the Random Forest Classifier (RFC) and Decision Tree Classifier (DTC) as the most effective, as they demonstrate higher classification performance compared to the other models. …”
Get full text

Predicting the infecting dengue serotype from antibody titre data using machine learning. by Bethan Cracknell Daniels, Darunee Buddhari, Taweewun Hunsawong, Sopon Iamsirithaworn, Aaron R Farmer, Derek A T Cummings, Kathryn B Anderson, Ilaria Dorigatti

“…Despite these challenges, the best performing machine learning algorithm achieved 76.3% (95% CI 57.9-89.5%) accuracy on the out-of-sample test set in predicting the infecting serotype from PRNT data. …”
Get full text

Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam. by Raja Das, Pham Van Tien, Karl W Wegmann, Madhumita Chakraborty

“…The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. …”
Get full text

Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…These findings highlight the considerable potential of machine learning algorithms in predicting mixing ellipses and parameterizing eddy mixing processes within climate models.…”
Get full text