Search Results - regression ((acs algorithm) OR (_ algorithm))

Detection of multiple outliners in linear regression using nonparametric methods by Adnan, Robiah

“…REFERENCES Agullo, J. (2000). New Algorithms for Computing the Least Trimmed Squares Regression Estimator. …”
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A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network by Ridwan M.A., Radzi N.A.M., Azmi K.H.M., Abdullah F., Ahmad W.S.H.M.W.

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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). …”
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Two steps hybrid calibration algorithm of support vector regression and K-nearest neighbors by Hamed, Y., Ibrahim Alzahrani, A., Shafie, A., Mustaffa, Z., Che Ismail, M., Kok Eng, K.

“…The hybrid algorithm was evaluated using a dataset of pipeline corrosion measurements collected by a Magnetic Flux Leakage (MFL) sensor (with an error margin of ±20 of the true values), and an Ultrasonic (UT) device (with an error margin of ±4). …”
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The influence of machine learning on the predictive performance of cross-project defect prediction: empirical analysis by Bala, Yahaya Zakariyau, Samat, Pathiah Abdul, Sharif, Khaironi Yatim, Manshor, Noridayu

“…Four ML algorithms have been carefully assessed in this study: random forest (RF), support vector machines (SVM), k-nearest neighbors (KNN), and logistic regression (LR). …”
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Predicting the onset of acute coronary syndrome events and in-hospital mortality using machine learning approaches / Song Cheen by Song , Cheen

“…The ML models for regression and classification were developed and optimized; the regression models aimed to predict ACS patients’ hospitalization and mortality rates, while the classification models were designed to predict the mortality risk of ACS patients under the influence of air pollution. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

“…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP approach. To assess the applicability and accuracy of the proposed method for long-term electrical energy consumption, its estimates are compared with those obtained from artificial neural network (ANN), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), rule-based data mining algorithm, GEP, linear, quadratic and exponential models optimized by particle swarm optimization (PSO), cuckoo search algorithm (CSA), artificial cooperative search (ACS) algorithm and backtracking search algorithm (BSA). …”
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Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions by Shamshad, H., Ullah, F., Ullah, A., Kebande, V.R., Ullah, S., Al-Dhaqm, A.

“…Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. …”
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Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization by Nanyonga Aziida, Sorayya Malek, Firdaus Aziz, Khairul Shafiq Ibrahim, Sazzli Kasim

“…Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SVM) and Logistic Regression (LR) were combined with RF, SVM, LR, and EN classifiers for 30-day mortality prediction. …”
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Time-series prediction of sea level change in the east coast of Peninsular Malaysia from the supervised learning approach by Lai V., Malek M.A., Abdullah S., Latif S.D., Ahmed A.N.

“…Decision making; Forecasting; Learning algorithms; Support vector machines; Support vector regression; Surface waters; Tide gages; Correlation coefficient; Marine management; Meteorological parameters; Regression support vector machines; Sea surface temperature (SST); Supervised learning approaches; Tide gauge data; Time series prediction; Sea level…”

A self-organizing approach: Time synchronization for the HeNodeBs in heterogeneous network by Hasan M.K., Ismail A.F., Abdalla A.H., Ramli H.A.M., Hashim W., Razzaque A., Khairolanuar M.H.

“…Femtocell; Heterogeneous networks; Internet; Internet protocols; Mobile telecommunication systems; Standards; Synchronization; HeNodeB; Heterogeneous Network (HetNet); IEEE 1588; Linear regression algorithms; LTE/LTE-Advanced; Message transmissions; Self-organizing approaches; Self-organizing method; Wireless telecommunication systems…”

AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking by Wisitponchai, Tanchanok, Shoombuatong, Watshara, Lee, Vannajan Sanghiran, Kitidee, Kuntida, Tayapiwatana, Chatchai

“…Subsequently, a re-scoring rank was generated by AnkPlex using a combination of a decision tree algorithm and logistic regression. AnkPlex achieved superior efficiency with ≥1 near-native complexes in the 10 top-rankings for nine X-ray complexes compared to ZDOCK, which only obtained six X-ray complexes. …”
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Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow by Khan N., Kamaruddin M.A., Ullah Sheikh U., Zawawi M.H., Yusup Y., Bakht M.P., Mohamed Noor N.

“…The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. …”

Performance analysis of three ANN models using improved fast evolutionary programming for power output prediction in grid-connected photovoltaic system / Puteri Nor Ashikin Megat Y... by Megat Yunus, Puteri Nor Ashikin, Sulaiman, Shahril Irwan, Omar, Ahmad Maliki

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Predict The Thyroid Abnormality Particular Disease Likelihood of The Symptoms’ Certainty Factor Value and Its Confidence Level: A Regression Model Analysis by Rosyid Ridlo, Al Hakim, Yanuar Zulardiansyah, Arief, Agung, Pangestu, Hexa Apriliana, Hidayah, Aditia Putra, Hamid, Aviasenna, Andriand, Nur Fauzi, Sulaiman, Machnun, Arif, Majmmoud Hussein, A. Alrahman

“…We used multiple linear regression (MLR) and multiple polynomial regression (MPR) to analyse the best regression model to solve the problem. …”
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Do CEO and chairman characteristics affect green innovation? evidence from a comparative analysis of machine learning models by Xue, Ruixiang, Ong, Tze San, Demir, Ezgi

“…Using the extreme gradient boosting (XGBoost) algorithm, which is at the forefront of machine learning algorithms, this study comprehensively examines the impact of CEO and chairman characteristics on corporate green innovation. …”
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Predictive analytic dashboard for desalter and crude distillation unit by Hassanudin, S.N., Aziz, I.A., Jaafar, J., Qaiyum, S., Zubir, W.M.A.M.

“…Artificial Neural Network (ANN) algorithm is used with R programming language for the forecasting. …”
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Short-Term forecasting of floating photovoltaic power generation using machine learning models by Mohd Herwan, Sulaiman, Mohd Shawal, Jadin, Zuriani, Mustaffa, Mohd Nurulakla, Mohd Azlan, Hamdan, Daniyal

“…Data were collected at 15-minute intervals from January 15 to January 21, 2024, encompassing nine input features such as ambient temperature, transient horizontal irradiation, daily horizontal irradiation, AC voltages, and AC currents for phases A, B, and C, with the total active power in kW as the target variable. …”
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Corrosion Inhibition Study Of Carboxymethyl Celluloseionic Liquid Via Electrochemical And Machine Learning Technique by Adi Hafizamri Ariffin, Wan Mohd Norsani Wan Nik, Samsuri Abdullah, Mohd Ikmar Nizam Mohamad Isa, Vincent Izionworu, Mohammad Fakhratul Ridwan Zulkifli

Developing an ensembled machine learning prediction model for marine fish and aquaculture production by Rahman L.F., Marufuzzaman M., Alam L., Bari M.A., Sumaila U.R., Sidek L.M.

“…Based on the feature importance scores, we select the group of climatic variables for three different ML models: linear, gradient boosting, and random forest regression. The past 20 years (2000�2019) of climatic variables and fish production data were used to train and test the ML models. …”