Non-invasive prediction of cholesterol levels from photoplethysmogram (PPG)-based features using machine learning techniques: a proof-of-concept study by Erick Javier Argüello-Prada, Angie Vanessa Villota Ojeda, María Yoselin Villota Ojeda

“…Several variations of linear regressions (LR), regression trees (RT), support vector regressions (SVR), and Gaussian process regressions (GPR) were trained with the most relevant features. …”
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Mountain flood forecasting in small watershed based on loop multi-step machine learning regression model by Songsong Wang, Bo Peng, Ouguan Xu, Yuntao Zhang, Jun Wang

“….), use a short-term prediction window and loop multi-step input method to improve the Machine Learning (ML) regression models’ accuracy, which can reduce the ML model’s process error. The non-ensemble and ensemble ML regression models is constructed for forecasting by loop multi-step, the non-ensemble models including Linear Regression (LR), Support Vector Machine Regression (SVMR) and k-Nearest Neighbors Regression (k-NNR), and the ensemble ML models include Random Forest Regression (RFR) and Gradient Boosting Regression (GBR). …”
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A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest by Mengmeng Sun, Chunyang Wang, Shuangting Wang, Zongze Zhao, Xiao Li

“…The framework is composed of five parts: (1) random samples selection with (2) probabilistic output initial random forest classification processing based on the number of votes; (3) semisupervised classification, which is an improvement of the supervision classification of random forest based on the weighted entropy algorithm; (4) precision evaluation; and (5) a comparison with the traditional minimum distance classification and the support vector machine (SVM) classification. …”
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Evaluating the Effectiveness of Coxal Bone Measurements for Sex Estimation via Machine Learning by Diana Toneva, Silviya Nikolova, Gennady Agre, Nevena Fileva, Georgi Milenov, Dora Zlatareva

“…The pelvis is the most dimorphic part of the human skeleton, primarily because of its involvement in the birth process. Many sexually dimorphic traits are concentrated in the coxal bones, which form the larger part of the birth canal. …”
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Machine Learning Decision Support for Macronutrients in Neonatal Parenteral Nutrition by Setareh Derakhshandara, Valentina Franzoni, Daniele Mezzetti, Valentina Poggioni

“…After the data was presented, we compared different machine learning techniques (i.e., random forest, gradient boosting machine, support vector machine, multilayer perceptron) that could predict nutritional requirements. …”
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"Influence of flight angle of field synergy elongation element on performance of screw" by HUANG Shi-zheng1, PAN Wei1, ZHU Jia-wei1, YANG Wei-min2, ZENG Xian-kui1, JIAN Ran-ran1,2,3

“…The radial convergence channel of the field synergy elongation element changed the direction of the velocity field, causing the velocity vector to generate radial components, promoting the synergistic interaction between the velocity field, temperature gradient field, and velocity gradient field, and strengthening the heat and mass transfer and transportation process. …”
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Framework Heurístico para la Implementación de Sistemas Activos de Reconocimiento de Objetos by E. González, A. Adán, V. Feliú

“…De esta manera, se pueden emplear vectores de características que estimen la pose del objeto con mayor precisión que en los tradicionales sistemas estocásticos. …”
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Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes by Jiaxin Liu, Shuo Yang, Qichao Li, Leiming Ji, Xuefeng Hou, Liudong Hou, Jing Ma

“…The development of post-processing technology for spent nuclear fuel is essential to ensuring the sustainable growth of nuclear energy. …”
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Enhancing DDoS Attacks Mitigation Using Machine Learning and Blockchain-Based Mobile Edge Computing in IoT by Mahmoud Chaira, Abdelkader Belhenniche, Roman Chertovskih

“…Our solution leverages the decentralized and tamper-resistant nature of Blockchain technology to enable secure and efficient data collection and processing at the network edge. We evaluate multiple machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Transformer architectures, and LightGBM, using the CICDDoS2019 dataset. …”
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Dual-channel knowledge attention for traditional Chinese medicine syndrome differentiation by Boting Liu, Hengqiu Huang, Xiaoman Liu, Juntao Zhao, Jiean Mo

“…Abstract With the rapid advancement of Natural Language Processing (NLP) technologies, the application of NLP to enable intelligent syndrome differentiation in Traditional Chinese Medicine (TCM) has become a popular research focus. …”
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An Online-Traffic-Prediction Based Route Finding Mechanism for Smart City by Xiaoguang Niu, Ying Zhu, Qingqing Cao, Xining Zhang, Wei Xie, Kun Zheng

“…O-Sense firstly exploits a deep learning approach to process spatial and temporal taxi GPS traces shown in dynamic patterns. …”
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Physical activity in the legislative production of the Federal Executive Power (1990 to 2023) by Adson Pereira Silva, Temístocles Damasceno Silva, Saú da Silva Souza, Sérgio Donha Yarid

“…For the data collection, organization, and processing, the current study analyzed bills which mentioned the terms PA and body practices based on laws several presidents that ruled the Federative Republic of Brazil. …”
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Optimization of SA-Gel Hydrogel Printing Parameters for Extrusion-Based 3D Bioprinting by Weihong Chai, Yalong An, Xingli Wang, Zhe Yang, Qinghua Wei

“…A response mathematical model linking parameters to grey relational grade was established via support vector regression (SVR). Particle Swarm Optimization (PSO) then determined the optimal parameter combination: d = 0.6 mm, h = 0.3 mm, v₁ = 8 mm/s, and v₂ = 8 mm/s. …”
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A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment by Shahrukh Ashraf, Priyanka Aggarwal, Praveen Damacharla, Hong Wang, Ahmad Y Javaid, Vijay Devabhaktuni

“…Traditional optical flow measurement uses block matching for motion vector calculation that makes the measurement task computationally expensive and slow. …”
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A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes by Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada

“…We evaluated three optimization approaches—Grid Search (GS), Random Search (RS), and Bayesian Search (BS)—across three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). …”
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Effect of Hyperparameter Tuning on Performance on Classification model by Muhammad Sholeh, Uning Lestari, Dina Andayati

“…This research aims to analyze the effect of hyperparameter tuning on the performance of Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest, Random Forest Classifier, Naive Bayes algorithms.  …”
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<italic>OA-WinSeg:</italic> Occlusion-Aware Window Segmentation With Conditional Adversarial Training Guided by Structural Prior Information by Manuela F. Ceron-Viveros, Wolfgang Maass, Jiaojiao Tian

“…Window segmentation and vectorization remains a significant challenge, particularly in the absence of clean facade images. …”
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Development of a multi-epitope chimeric vaccine in silico against Babesia bovis, Theileria annulata, and Anaplasma marginale using computational biology tools and reverse vaccinolo... by Amam Zonaed Siddiki, Sabreena Alam, Farhan Fuad Bin Hossen, Md Abdul Alim

“…The best-designed chimeric vaccine construct was then reverse transcribed and adapted for the host E. coli K12 strain which was later inserted into the pET28a (+) vector for the cloning and expression of the vaccine. …”
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Machine learning-driven predictive modeling of mechanical properties in diverse steels by Movaffaq Kateb, Sahar Safarian

“…Moreover, other alternative approaches, such as support vector machines, extreme gradient boosting machines, and artificial neural networks, were also evaluated to ensure that the predictions made by the RF model are as accurate as possible. …”
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An integrative review of the combined use of mathematical and statistical models for estimating malaria transmission parameters by Alessandro Grosso, Niel Hens, Steven Abrams

“…Using the FOI dispenses the analyst from explicitly describing vector dynamics in compartmental modelling, simplifying the system of differential equations describing transmission dynamics. …”
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