Investigation into Bayesian inversion techniques for estimating mineral particle size distribution and abundance utilizing hyperspectral data by Dongxu Han, Chengbao Liu, Peng Zhang, Wanyue Liu, Ziwei Tian, Shijing He, Ziyi Zhang, Mingze Ma

“…In this study, we propose a new method based on the inversion of a two-layer Bayesian framework, which models the particle size parameters as continuous random variables and sets the particle size distribution parameters as hyperparameters, and takes into account the influence of the variability of the particle size features on the interpretation of mineral abundance. …”
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Research on Wind Turbine Main Shaft Bearing Fault Diagnosis Method Based on Unity 3D and Transfer Learning by Shuai Wang, Wenlei Sun, Han Liu, Shenghui Bao, Yunhao Wang, Xin Zhao

“…Additionally, the signal processing method (RB), combined with a random convolution layer and blind deconvolution, is employed to enhance the diversity of fault features. …”
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Using machine learning to identify frequent attendance at accident and emergency services in Lanarkshire by Fergus Reid, S. Josephine Pravinkumar, Roma Maguire, Ashleigh Main, Haruno McCartney, Lewis Winters, Feng Dong

“…Five classification models were tested: multinomial logistic regression (LR), random forests (RF), support vector machine (SVM) classifier, k-nearest neighbours (k-NN) and multi-layer perceptron (MLP) classifier. …”
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Plastic to apparel: an analysis of sustainable purchasing intention using a machine learning ensemble by Carmella Andrea L. Cabrera, Ardvin Kester S. Ong, John Francis T. Diaz, Maela Madel L. Cahigas, Ma. Janice J. Gumasing

“…To analyze the data, the study utilized machine learning methods, such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). …”
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Robust dispatch for electrical–thermal combined smart building considering impacts of uncertainties on thermal side by Weijie He, Fanrong Wei, Xiangning Lin, Samir M. Dawoud

“…At the same time, from the perspective of the building layer, uncertainties such as random solar radiation will affect not only the electrical side but also the thermal side. …”
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Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies by Pal Patel, Shreyansh Pampaniya, Ananya Ghosh, Ritu Raj, Deepa K, Saravanakumar Kandasamy

“…For hearing impairments, the analysis focuses on advanced models such as Support Vector Machines (SVM), Random Forests (RF), and Multi-Layer Perceptrons (MLP), examining their effectiveness in auditory assistive applications. …”
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Adjacent Inputs With Different Labels and Hardness in Supervised Learning by Sebastian A. Grillo, Julio Cesar Mello Roman, Jorge Daniel Mello-Roman, Jose Luis Vazquez Noguera, Miguel Garcia-Torres, Federico Divina, Pedro Esteban Gardel Sotomayor

“…We analyzed the relation that NIV has to random data and overfitting. We then demonstrated that a threshold of NIV may determine if a classification model can generalize to unseen data. …”
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A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language by Wenjuan Lu, Dongping Ming, Xi Mao, Jizhou Wang, Zhanjie Zhao, Yao Cheng

“…The model first performs semantic encoding on natural language spatiotemporal questions, extracts pre-trained features based on the DeBERTa model, inputs feature vector sequences into BiGRU (Bidirectional Gated Recurrent Unit) to learn text features, and finally obtains globally optimal label sequences through a CRF (Conditional Random Field) layer. Then, based on the encoding results, it performs classification and semantic parsing of spatiotemporal questions to achieve question intent recognition and conversion to Cypher query language. …”
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Cognitive performance classification of older patients using machine learning and electronic medical records by Monika Richter-Laskowska, Ewelina Sobotnicka, Adam Bednorz

“…These two models consistently outperform other ML techniques, such as K-Nearest Neighbors, Multi-Layer Perceptron, linear SVM, Naive Bayes, Quadratic Discriminant Analysis, Linear Discriminant Analysis, AdaBoost, and Gaussian Process Classifiers. …”
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Performance of the Direct Sequence Spread Spectrum Underwater Acoustic Communication System with Differential Detection in Strong Multipath Propagation Conditions by Jan H. SCHMIDT, Iwona KOCHAŃSKA, Aleksander M. SCHMIDT

“…The performed experiments allowed to draw important conclusions for the designing of a physical layer of the shallow-water UAC system. Both, m-sequences and Kasami codes allow to achieve a similar bit error rate, which at best was less than 10−3. …”
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Periodic Domain Inversion in Single Crystal Barium Titanate‐on‐Insulator Thin Film by Pragati Aashna, Hong‐Lin Lin, Yu Cao, Yuhui Yin, Yuan Gao, Sakthi Sanjeev Mohanraj, Di Zhu, Aaron Danner

“…First, the BTO thin film is grown on a dysprosium scandate substrate using pulsed laser deposition with a thin layer of strontium ruthenate later serving as the bottom electrode for poling. …”
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Three‐Dimensional Probabilistic Hydrofacies Modeling Using Machine Learning by Nafyad Serre Kawo, Jesse Korus, Yaser Kishawi, Erin Marie King Haacker, Aaron R. Mittelstet

“…The classification metrics show that the Stacking Classifier model performed better than other machine learning models in predicting hydrofacies. Multi‐Layer Perceptron and Stacking Classifier models show sharp vertical transitions between the low and high sand probability while other machine learning models show gradual transitions. …”
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Early diagnosis of Alzheimer’s disease and mild cognitive impairment using MRI analysis and machine learning algorithms by Helia Givian, Jean-Paul Calbimonte, and for the Alzheimer’s Disease Neuroimaging Initiative

“…Five machine learning classifiers; K-nearest neighborhood (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), and multi-layer perception (MLP) were used to distinguish between groups: cognitively normal (CN) vs AD, early MCI (EMCI) vs late MCI (LMCI), CN vs EMCI, CN vs LMCI, AD vs EMCI, and AD vs LMCI. …”
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Development and validation of machine learning models to predict unplanned hospitalizations of patients with diabetes within the next 12 months by A. E. Andreychenko, A. D. Ermak, D. V. Gavrilov, R. E. Novitskiy, A. V. Gusev

“…Logistic regression (LR), gradient boosting methods (LightGBM, XGBoost, CatBoost), decision tree-based methods (RandomForest and ExtraTrees), and a neural network-based algorithm (Multi-layer Perceptron) were compared. …”
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Modeling and Performance Analysis of MDM−WDM FSO Link Using DP-QPSK Modulation Under Real Weather Conditions by Tanmeet Kaur, Sanmukh Kaur, Muhammad Ijaz

“…Minimum received power and SNR values of −52 dBm and −33 dB have been obtained over the observed transmission range as a result of multiple impairments. Random forest (RF), k-nearest neighbors (KNN), multi-layer perceptron (MLP), gradient boosting (GB), and machine learning (ML) techniques have also been employed for estimating the SNR of the received signal. …”
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Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG by Adhe Rahmatullah Sugiharto Suwito P, Ayumi Ohnishi, Yudith Dian Prawitri, Riries Rulaningtyas, Tsutomu Terada, Masahiko Tsukamoto

“…The machine learning algorithms employed in this study were SVM, random forest (RF), and multi-layer perceptron (MLP). …”
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Geological Stratification Technology Based on Artificial Intelligence Algorithms and Its Application Effects by GAO Yuan, YAO Weihua, CAI Shaofeng, LI Liang, XUE Yuan, ZHAO Peipei, WANG Xiaoyang, WEI Wei

“…Based on the particularity of geological stratification tasks and geological structures, conditional random fields are added to constrain the layer order, and the mask autoencoder algorithm is further optimized. …”
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Hyperspectral Imaging Combined with a Dual-Channel Feature Fusion Model for Hierarchical Detection of Rice Blast by Yuan Qi, Tan Liu, Songlin Guo, Peiyan Wu, Jun Ma, Qingyun Yuan, Weixiang Yao, Tongyu Xu

“…The DCFM model extracted spectral features using successive projection algorithm (SPA), random frog (RFrog), and competitive adaptive reweighted sampling (CARS), and extracted spatial features from spectral images using MobileNetV2 combined with the convolutional block attention module (CBAM). …”
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A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases by Norma Latif Fitriyani, Muhammad Syafrudin, Nur Chamidah, Marisa Rifada, Hendri Susilo, Dursun Aydin, Syifa Latif Qolbiyani, Seung Won Lee

“…Through rigorous experimentation, the proposed model demonstrates superior performance compared to conventional machine learning approaches, such as Logistic Regression, Support Vector Classification, Gaussian Naïve Bayes, Multi-Layer Perceptron, k-nearest neighbors, Random Forest, AdaBoost, gradient boosting, and histogram gradient boosting. …”
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CMOS‐Compatible HfOx‐Based Radiation Hardening Component for Neuromorphic Computing Applications by Yao‐Feng Chang, Yifu Huang, Chin‐Han Chung, Ying‐Chen Chen

“…Abstract HfOx‐based resistive random‐access‐memory (ReRAM) devices (TiN/Ti/HfOx/RuOx/TiN) are fabricated by CMOS‐compatible materials (ruthenium (Ru)) and lithography‐lite process, potentially enabling a maskless, etching‐free process that can be implemented in the low earth orbit (LEO), the International Space Station (ISS), and commercial LEO destinations (CLDs). …”
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