Innovations in animal health: artificial intelligence-enhanced hematocrit analysis for rapid anemia detection in small ruminants by Aftab Siddique, Sudhanshu S. Panda, Sophia Khan, Seymone T. Dargan, Savana Lewis, India Carter, Jan A. Van Wyk, Ajit K. Mahapatra, Eric R. Morgan, Thomas H. Terrill

“…Subsequently, these images were examined in correlation with established PCV values obtained from conventional PCV analysis. Four separate machine learning models (ML) supported models, namely support vector machine (SVM), K-nearest neighbors (KNN), backpropagation neural network (BPNN), and image classification-based Keras model, were created and assessed using the image dataset. …”
Get full text

Dilute Species Transport During Generalized Newtonian Fluid Flow in Porous Medium Systems by Christopher A. Bowers, Cass T. Miller

Get full text

Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospectiv... by Shingo Fukuma, Yukio Yuzawa, Daijo Inaguma, Hiroki Hayashi, Ryosuke Yanagiya, Akira Koseki, Toshiya Iwamori, Michiharu Kudo

“…We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach.Design Retrospective single-centre cohort study.Settings Tertiary referral university hospital in Toyoake city, Japan.Participants A total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m2 and eGFR decline of ≥30% within 2 years.Primary outcome Our main outcome was extremely rapid eGFR decline. …”
Get full text

An FSM-Assisted High-Accuracy Autonomous Magnetic Compensation Optimization Method for Dual-Channel SERF Magnetometers Used in Weak Biomagnetic Signal Measurement by Xinran Tian, Bo Bao, Ridong Wang, Dachao Li

“…To further improve the compensation stability and accuracy, a novel finite state machine (FSM)-assisted iterative optimization magnetic field compensation algorithm is proposed. …”
Get full text

Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics by Leonidas Theodorakopoulos, Aristeidis Karras, George A. Krimpas

“…In this study, we analyze the performance of the machine learning operators in Apache Spark MLlib for K-Means, Random Forest Regression, and Word2Vec. …”
Get full text

Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm by Youle Song, Yuting Duan, Tong Rao

“…Meanwhile, the improvement of support vector machine parameter selection has strengthened the recognition and analysis of fault characteristics, providing an effective solution for power equipment fault diagnosis. …”
Get full text

Transformer Fault Diagnosis Based on Multi-Strategy Enhanced Dung Beetle Algorithm and Optimized SVM by Shuming Zhang, Hong Zhou

“…To address the challenge of low accuracy in transformer fault diagnosis using support vector machines (SVMs), an enhanced fault diagnosis model is proposed, which utilizes an improved dung beetle optimization algorithm (IDBO) to optimize an SVM. …”
Get full text

Self-Sensing of Piezoelectric Micropumps: Gas Bubble Detection by Artificial Intelligence Methods on Limited Embedded Systems by Kristjan Axelsson, Mohammadhossien Sheikhsarraf, Christoph Kutter, Martin Richter

Get full text

Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles by Ziyi Fan, Yuqing Ye, Jiale Chen, Ying Ma, Jesse Zhu

“…Four optimal machine learning models were selected for subsequent inhalation parameter prediction, given their superior generalization ability. …”
Get full text

Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis by Cosmina-Mihaela Rosca, Adrian Stancu

“…A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. …”
Get full text

Production Dynamic of Coal-bed Methane After Well Pressure Based on Multi-layer Perceptron Model Inversion Study by LI Jingsong, WANG Tao, WANG Jinwei, WEI Zhipeng, XIAO Cong, TANG Jizhou

“…The results show that: (1) Using a small number of training samples (only 100 simulated samples are required for this case study), the machine learning model can accurately simulate the relationship between fracture/reservoir parameters and daily and cumulative gas production of shale gas wells; (2) The intelligent inversion algorithm based on machine learning agent assistance has high convergence efficiency and can quickly obtain a reasonable reservoir fracture parameter combination model with high inversion accuracy. …”
Get full text

EnsembleXAI-Motor: A Lightweight Framework for Fault Classification in Electric Vehicle Drive Motors Using Feature Selection, Ensemble Learning, and Explainable AI by Md. Ehsanul Haque, Mahe Zabin, Jia Uddin

“…A lightweight framework for fault diagnosis in EV drive motors is presented with the aid of Recursive Feature Elimination with Cross-Validation (RFE-CV), parameter optimization, and in-depth preprocessing. …”
Get full text

Hollow Direct Air-Cooled Rotor Windings: Conjugate Heat Transfer Analysis by Avo Reinap, Samuel Estenlund, Conny Högmark

“…CHT-based thermal calculations provide not only reliable results compared to experimental work and lumped parameter thermal circuits with adjusted aggregate parameters, but also insight related to pressure and cooling flow distribution, thermal loads, and cooling integration issues that are necessary for the development of high power density and reliable electrical machines. …”
Get full text

Modeling, Simulation, and Control of a Rotary Inverted Pendulum: A Reinforcement Learning-Based Control Approach by Ruben Hernandez, Ramon Garcia-Hernandez, Francisco Jurado

Get full text

Capturing spatiotemporal variation in salt marsh belowground biomass, a key resilience metric, through geoinformatics by Kyle D. Runion, Deepak R. Mishra, Merryl Alber, Mark A. Lever, Jessica L. O'Connell

“…This suggests that BGB varies more spatially than temporally. We used the BERM machine learning algorithms to evaluate how variables relating to biological, climatic, hydrologic, and physical attributes covaried with these BGB observations. …”
Get full text

Deep Learning-Augmented Evolutionary Strategies for Intelligent Global Optimization by Absalom El-Shamir Ezugwu, Olaide Nathaniel Oyelade, Jeffrey Ovre Agushaka, Apu Kumar Saha

Get full text

Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

“…Solid ink density is an important control parameter in the manufacturing process of offset prints—the size of which has a significant impact on the color performance of the prints—in which the determination of the optimal solid ink density is critical for the pre-press phase of industrial production. …”
Get full text

A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare by Siddhesh Fuladi, D. Ruby, N. Manikandan, Animesh Verma, M. K. Nallakaruppan, Shitharth Selvarajan, Shitharth Selvarajan, Shitharth Selvarajan, Preeti Meena, V. P. Meena, Ibrahim A. Hameed

“…The ever-evolving domain of machine learning has witnessed significant advancements with the advent of federated learning, a paradigm revered for its capacity to facilitate model training on decentralized data sources while upholding data confidentiality. …”
Get full text

The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection by Dong Dai, Zhenyu Wang, Hao Huang, Xu Mao, Yehong Liu, Hao Li, Du Chen

Get full text

Classification Evaluation Method for Complex Reservoirsin Xinglong Structural Belt by LI Xiaofeng, CAI Jun, XIAO Chengwen, CAI Wenyuan, YU Weigao, FU Shaoqing, GUO Yuqing

“…Using reservoir quality factor index and macroscopic effective pore permeability index, a reservoir classification method based on machine learning algorithm and multi parameter fusion as the core has been established, effectively solving the problem of low accuracy in reservoir classification evaluation due to the lack of nuclear magnetic logging. …”
Get full text