Soil Organic Matter Content Prediction Using Multi-Input Convolutional Neural Network Based on Multi-Source Information Fusion by Li Guo, Qin Gao, Mengyi Zhang, Panting Cheng, Peng He, Lujun Li, Dong Ding, Changcheng Liu, Francis Collins Muga, Masroor Kamal, Jiangtao Qi

“…Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R² improvements ranging from 4% to 11%. …”
Get full text

Research Concept and Prospects of Key Technologies for Intelligent Perception, Early Warning, and Prevention of Bank Collapse Risks in the Middle and Lower Reaches of the Yangtze R... by Jinyou LU, Yinjun ZHOU, Caiyun DENG, Chao GUO, Lingyun LI

“…It develops a multi-scale universal model and a dynamic early warning technology system using coupled simulation, statistical analysis, and machine learning in prediction and early warning. …”
Get full text

A conceptual digital twin framework for supply chain recovery and resilience by Oluwagbenga Victor Ogunsoto, Jessica Olivares-Aguila, Waguih ElMaraghy

“…This aids supply chain (SC) managers in making informed decisions. In the first phase, machine learning algorithms, including logistic regression and Long Short-Term Memory (LSTM), were trained on Kerala India's precipitation data to predict floods. …”
Get full text

Self-lubricative performance of laser processed graphene structures for space applications via digital twin approach by Praveen Kumar Kanti, Prashantha Kumar H. G, V．Vicki Wanatasanappan, Hanen Karamti, Vijayalaxmi Mishra, Majed Alsubih, Prabhu Paramasivam, Abinet Gosaye Ayanie

“…Digital twin approach by machine learning, particularly Extreme Gradient Boosting (XGBoost), achieved an R² score of 0.92, confirming the model's accuracy in wear loss prediction. …”
Get full text

Real-Time Intelligent Recognition and Precise Drilling in Strongly Heterogeneous Formations Based on Multi-Parameter Logging While Drilling and Drilling Engineering by Aosai Zhao, Yang Yu, Bin Wang, Yewen Liu, Jingyue Liu, Xubiao Fu, Wenhao Zheng, Fei Tian

“…The optimized CatBoost machine learning model is subsequently utilized for lithology classification, enabling real-time and high-precision geological evaluation during directional drilling. …”
Get full text

Honeybee colony soundscapes: Decoding distance-based cues and environmental stressors by Nayan Di, Chunjing Zhu, Zongwen Hu, Muhammad Zahid Sharif, Baizhong Yu, Fanglin Liu

“…Using OpenL3 embeddings and machine learning models, the study achieved accurate classification of food source distances based on acoustic features, with the K-Nearest Neighbors (KNN) model demonstrating superior performance. …”
Get full text

Screening for breast cancer: a systematic review update to inform the Canadian Task Force on Preventive Health Care guideline by Alexandria Bennett, Nicole Shaver, Niyati Vyas, Faris Almoli, Robert Pap, Andrea Douglas, Taddele Kibret, Becky Skidmore, Martin Yaffe, Anna Wilkinson, Jean M. Seely, Julian Little, David Moher

“…To expedite the screening process, machine learning was used to prioritize relevant references. …”
Get full text

Risk assessment of water inrush from coal floor based on enhanced samples with class distribution by Shiwei Liu, Jiaxin Zhao, Hao Yu, Jiaqi Chen

“…This method was used to generate virtual samples and enhance the measured database. A prediction model of the water inrush risk for the coal seam floor was established using a coupled algorithm of extreme learning machines, self-adaptive differential evolution, and CDMTD (PCA-CDMTD-SaDE-ELM) and was used to evaluate the water inrush risk in the 19,105 working face of the Yunjialing Mine. …”
Get full text

Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma by Feiyu Qi, Guiming Zha, Yanfang Zhang, Sihua Liu, Yuhang Yang, Wanliang Sun, Dongdong Wang, Zhong Liu, Zheng Lu, Dengyong Zhang

“…Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. …”
Get full text

Neuromorphic imaging cytometry on human blood cells by Ziyao Zhang, Haoxiang Yang, Jiayin Li, Shin Wei Chong, Jason K Eshraghian, Ken-Tye Yong, Daniele Vigolo, Helen M McGuire, Omid Kavehei

“…Recently, this sensor has been adopted to address the limitations in IFC with prominent results in diverse modalities and machine learning approaches. Such a dataset serves as a baseline of healthy cell groups for both diagnostic and research purposes. …”
Get full text

Fast outlier detection for high-dimensional data of wireless sensor networks by Yan Qiao, Xinhong Cui, Peng Jin, Wu Zhang

“…Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. …”
Get full text

Research on the High Stability of an Adaptive Controller Based on a Neural Network for an Electrolysis-Free-Capacitor Motor Drive System by Danyang Bao, Haorui Shen, Wenxiang Ding, Hao Yuan, Yingying Guo, Zhendong Song, Tao Gong

“…Key innovations include: (1) BP neural network integration for dynamic parameter optimization, (2) impulse voltage suppression through adaptive control matching, and (3) enhanced transient response via machine learning-enhanced speed regulation. The test results demonstrate a 63% reduction in bus voltage fluctuations and 35% improvement in load transition responses compared to conventional PID-based systems, proving the strategy’s practical viability for industrial drive applications.…”
Get full text

Feasibility test of per-flight contrail avoidance in commercial aviation by Aaron Sonabend-W, Carl Elkin, Thomas Dean, John Dudley, Noman Ali, Jill Blickstein, Erica Brand, Brian Broshears, Sixing Chen, Zebediah Engberg, Mark Galyen, Scott Geraedts, Nita Goyal, Rebecca Grenham, Ulrike Hager, Deborah Hecker, Marco Jany, Kevin McCloskey, Joe Ng, Brian Norris, Frank Opel, Juliet Rothenberg, Tharun Sankar, Dinesh Sanekommu, Aaron Sarna, Ole Schütt, Marc Shapiro, Rachel Soh, Christopher Van Arsdale, John C. Platt

“…Predictions for regions prone to contrail formation came from a physics-based simulation model and a machine learning model. Participating pilots made altitude adjustments based on contrail formation predictions for flights assigned to the treatment group. …”
Get full text

Multi-decadal spatiotemporal dynamics of alpine plant functional types (PFTs) inferred from Landsat-derived fractional cover across the Yarlung Zangbo river basin, China by Qichi Yang, Lihui Wang, Xiaoqi Li, Xue Yan, Jinliang Huang, Yun Du, Feng Ling

“…In this study, we employed a regression-based unmixing model using synthetic data to develop a multi-temporal machine learning model aimed to estimate the fractions of alpine plant functional types (PFTs) from 1984 to 2024 in the Yarlung Zangbo River Basin (YZRB), China. …”
Get full text

Optimizing Outdoor Micro-Space Design for Prolonged Activity Duration: A Study Integrating Rough Set Theory and the PSO-SVR Algorithm by Jingwen Tian, Zimo Chen, Lingling Yuan, Hongtao Zhou

“…The innovative contribution of this study lies in the proposed data-driven optimization method that integrates machine learning and KE. This method not only offers a new theoretical perspective for OMS design but also establishes a scientific framework to accurately incorporate users’ emotional needs into the design process. …”
Get full text

Adaptive high frequency data streaming for Soft Real-Time Industrial AI: A scalable microservices based architecture with dynamic downsampling by Telmo Fernández De Barrena, Alcides Fernandes, Juan Luis Ferrando, Ander García, Hugo Landaluce, Ignacio Angulo

“…This paper presents a novel IoT architecture wherein an Edge server dynamically down-samples sensor data before transmission to a Fog (central) server equipped with upsampling, feature extraction, machine learning (ML) inference, and network latency analysis services. …”
Get full text

FiSC: A Novel Approach for Fitzpatrick Scale-Based Skin Analyzer’s Image Classification by Guillermo Crocker Garcia, Muhammad Numan Khan, Aftab Alam, Josue Obregon, Tamer Abuhmed, Eui-Nam Huh

“…Our method involves modeling image features as a nine-dimensional feature vector, followed by a dimensionality reduction process to identify the most influential features and dominant areas within the feature space, enabling deployment on low-power devices. …”
Get full text

Optical Versus Electronic Implementation of Probabilistic Graphical Inference and Experimental Device Demonstration Using Nonlinear Photonics by Masoud Babaeian, Patrick Keiffer, Mark A. Neifeld, Ratchaneekorn Thamvichai, Robert A. Norwood, Pierre-A. Blanche, John Wissinger, N. Peyghambarian

“…The probabilistic inference model has been widely used in various areas, such as error-control coding, machine learning, speech recognition, artificial intelligence, and statistics. …”
Get full text

A Study on the Spatial Perception and Inclusive Characteristics of Outdoor Activity Spaces in Residential Areas for Diverse Populations from the Perspective of All-Age Friendly Des... by Biao Yin, Lijun Wang, Yuan Xu, Kiang Chye Heng

“…Guided by the principles of all-age friendly and inclusive design, this study innovatively integrates eye-tracking and multi-modal physiological monitoring technologies to collect both subjective and objective perception data of different age groups regarding outdoor activity spaces in residential areas through human factor experiments and empirical interviews. Machine learning methods are utilized to analyze the data, uncovering the differentiated response mechanisms among diverse groups and clarifying the inclusive characteristics of these spaces. …”
Get full text

Projections of single-level indirect lumbar interbody fusion volume and associated costs for Medicare patients to 2050 by Kyle A. Mani, BS, Samuel N. Goldman, BS, Noel Akioyamen, MD, Emily Kleinbart, BS, Yaroslav Gelfand, MD, Saikiran Murthy, DO, Jonathan Krystal, MD, Ananth Eleswarapu, MD, Reza Yassari, MD, Mitchell S. Fourman, MD, MPhil

“…Methods: Data was acquired from the Centers for Medicare and Medicaid Services (CMS) from January 1, 2000 to December 31, 2022, using CPT codes to identify ALIF/OLIF/LLIF procedures. The Prophet machine learning algorithm, using Bayesian Inference, was applied to data from 2000 to 2019 to generate point forecasts for 2020 to 2050 with 95% forecast intervals (FIs). …”
Get full text