Regression Model Employing Spiking Neural Network for Bio-Signal Analysis With Hardware Integration by Choongseop Lee, Geunbo Yang, Jaewoo Baek, Yuntae Park, Mingyu Cheon, Jongkil Park, Cheolsoo Park

“…However, their potential in regression tasks remains relatively unexplored. This study focuses on leveraging the spiking neural architecture in conjunction with Fourier analysis and support vector regression to estimate heart rates from electrocardiogram signal. …”
Get full text

Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement by Sri Rossa Aisyah Puteri Baharie, Sugiyarto Surono, Aris Thobirin

“…This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. …”
Get full text

Hydrogen Enhancement in Syngas Through Biomass Steam Gasification: Assessment with Machine Learning Models by Yunye Shi, Diego Mauricio Yepes Maya, Electo Silva Lora, Albert Ratner

“…This study assesses the effectiveness of various machine learning algorithms in engineering, focusing on a comparative analysis of artificial neural networks (ANNs), support vector machines (SVMs), tree-based models, and regularized regression models. …”
Get full text

Quinary Classification of Human Gait Phases Using Machine Learning: Investigating the Potential of Different Training Methods and Scaling Techniques by Amal Mekni, Jyotindra Narayan, Hassène Gritli

“…Preprocessing methods such as Min–Max Scaling (MMS), Standard Scaling (SS), and Principal Component Analysis (PCA) were applied to the dataset to ensure optimal performance of the machine learning models. Several algorithms were implemented, including <i>k</i>-Nearest Neighbors (<i>k</i>-NNs), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (Gaussian, Bernoulli, and Multinomial) (NB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). …”
Get full text

Dynamic Workload Management System in the Public Sector: A Comparative Analysis by Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Gerasimos Vonitsanos, Dimitris Papadopoulos, Ioanna Giannoukou, Spyros Sioutas

“…Using a dataset encompassing public/private sector experience, educational history, and age, we evaluate the effectiveness of seven machine learning algorithms: Linear Regression, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Bagged Decision Trees, and XGBoost in predicting employee capability and optimizing task allocation. …”
Get full text

A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms by Mohammad Ghattas, Antonio M. Mora, Suhail Odeh

“…Employing various classification algorithms, including Support Vector Machines (SVMs), Logistic Regression, and Random Forest, we compare their effectiveness on both original and feature-selected datasets. …”
Get full text

Predictive Model to Analyse Real and Synthetic Data for Learners' Performance Prediction Using Regression Techniques by SHABNAM ARA S.J, Tanuja R, Manjula S.H

“…This study presents an empirical comparison of real, synthetic, and mixed (real + synthetic) data sets in forecasting learner performance, deploying an array of regression-based ML algorithms, including Random Forest, Gradient Boosting, XG Boost, K-nearest Neighbor, and Support Vector Regression. …”
Get full text

Nondestructive Detection of Rice Milling Quality Using Hyperspectral Imaging with Machine and Deep Learning Regression by Zhongjie Tang, Shanlin Ma, Hengnian Qi, Xincheng Zhang, Chu Zhang

“…Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Convolutional Neural Networks (CNNs), and Backpropagation Neural Networks (BPNNs) were used to establish both single-task and multi-task models for the prediction of milling quality attributes. …”
Get full text

Simulating the Deterioration Behavior of Tunnel Elements Using Amalgamation of Regression Trees and State-of-the-Art Metaheuristics by Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf, Moaaz Elkabalawy, Abdelhady Omar, Ghasan Alfalah

“…Comparative analyses against conventional regression trees, artificial neural networks, and support vector machines demonstrated that the hybrid model consistently outperformed baseline techniques regarding predictive accuracy and generalizability. …”
Get full text

Optimal Multi-Physics Synthesis of a Dual-Frequency Power Inductor Using Deep Neural Networks and Gaussian Process Regression by Paolo Di Barba, Arash Ghafoorinejad, Maria Evelina Mognaschi, Fabrizio Dughiero, Michele Forzan, Elisabetta Sieni

“…., to compute the temperature profile on the disk, given the amplitudes and frequencies of the supply currents, three methods have been used (Support Vector Regression (SVR), fully connected Neural Network (NN) and Gaussian Process Regression (GPR)). …”
Get full text

A hybrid machine learning algorithm approach to predictive maintenance tasks: A comparison with machine learning algorithms by Jorge Paredes, Danilo Chávez, Ramiro Isa-Jara, Diego Vargas

“…The results indicate that the proposed hybrid approach increases accuracy by 15% compared to models that use a single supervised learning algorithm, such as support vector regression (SVR), multi-layer perceptron (MLP), convolutional neural networks (CNN), and long short-term memory (LSTM), and an increase in accuracy of 4% over other hybrid algorithms, such as convolutional neural networks and long short-term memory. …”
Get full text

LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers by Durmus Ozkan Sahin, Sedat Akleylek, Erdal Kilic

“…As a result, remarkable performances are obtained with classification algorithms based on linear regression models without the need for very complex classification algorithms.…”
Get full text

Cultural intelligence and inclusive leadership in a diverse IT sector: a framework for effective collaboration by Камрун Нахар, Омар Фарук, Захир Райхан

Get full text

Scenario Modelling for Reproducing Investment Potential of Institutional Sectors in the Regions of the Siberian Federal District by I. V. Naumov, A. V. Trynov, A. O. Safonov

“…The authors developed an algorithm of scenario modelling for reproducing investment potential of institutional sectors in regional systems. …”
Get full text

Predicting Patients&#x2019; Revisit Intention Based on Satisfaction Scores: Combination of Penalized Regression and Neural Networks by Farshid Abdi, Shaghayegh Abolmakarem, Amir Karbassi Yazdi, Paul Leger, Yong Tan, Giuliani Coluccio

“…In addition to feature selection models such as Random Forest, Genetic Algorithm, and Lasso Regression, the study employs various methods, including Neural Networks, Support Vector Machines, Decision Trees, k-Nearest Neighbors, Rule-based systems, and Naive Bayes algorithms. …”
Get full text

Comparative Study of Machine Learning Techniques for Predicting UCS Values Using Basic Soil Index Parameters in Pavement Construction by Mudhaffer Alqudah, Haitham Saleh, Hakan Yasarer, Ahmed Al-Ostaz, Yacoub Najjar

“…The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). …”
Get full text

The Algorithm to Automatically Extract Body Sizes and Shapes by Mong Hien Thi Nguyen, Tuong Quan Vo, Mai Huong Bui, Van Anh Pham

“… This study presents an algorithm to automatically extract the size and body shape of a 3D scanned model. …”
Get full text

Integration of Genetic Algorithm with Machine Learning for Properties Prediction by Rathachai Chawuthai, Siripan Murathathunyaluk, Nalin Amornratthamrong, Run Arunchaipong, Amata Anantpinijwatna

“…Consequently, ML’s predictive capabilities have been extended to encompass a broader range of properties, including Partition Coefficient, Boiling Point, and Solubility, among others, for oxygenated hydrocarbon derivatives. Algorithms such as Linear Regression, Support Vector Machine, Random Forest, and Gaussian Process are selected through trial-and-error to identify the most suitable approach. …”
Get full text

Identification of Rice Varieties Using Machine Learning Algorithms by Murat Koklu, İlkay Çınar

“…For classification, models were created with algorithms using machine learning techniques of k-nearest neighbor, decision tree, logistic regression, multilayer perceptron, random forest and support vector machines. …”
Get full text

COMPARATIVE MACHINE LEARNING ALGORITHM FOR CARDIOVASCULAR DISEASE PREDICTION by Ashish Mishra, Jyoti Mishra, Victor Hugo, Aloísio Vieira Lira Neto

“…KNN 86%, Decision Trees 79%, Logistic Regression 85%, Naive Bayes 86%, and Support Vector Machines 87% can predict heart disease 89% accurately. …”
Get full text