Search Results - Multilayer perceptron and its stacking integration algorithm

Developing an optimal individual tree diameter growth model for uneven-aged Pinus yunnanensis forests using machine learning algorithms by Deng, Longfeng, Wang, JianMing, Yin, JiTing, Guan, YaDong

“…: Random Forest, XGBoost, Multilayer Perceptron, and Stacked Multilayer Perceptron (Stacked…”
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Urban Flood Risk Assessment through the Integration of Natural and Human Resilience Based on Machine Learning Models by Zhang, Wenting, Hu, Bin, Liu, Yongzhi, Zhang, Xingnan, Li, Zhixuan

“… traditional machine learning models, including support vector machine, random forest, multilayer perceptron, and gradient boosting decision tree…”
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Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas by Tavakkoli Piralilou, Sepideh, Shahabi, Hejar, Jarihani, Ben, Ghorbanzadeh, Omid, Blaschke, Thomas, Gholamnia, Khalil, Meena, Sansar, Aryal, Jagannath

“…) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection…”
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Automatic lithological mapping from potential field data using machine learning: a case study from Mundiyawas-Khera Cu deposit, Rajasthan, India by Singh, Bhawesh Kumar, Gangumalla, Srinivasa Rao, Arasada, Rama Chandrudu, Kumar, Thinesh

“… Initially, five different ML algorithms, random forest (RF), K -nearest neighbor, support vector machine, multi-layer perceptron (MLP…”
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Enhancing Hydrogen Energy Consumption Prediction Based on Stacked Machine Learning Model with Shapley Additive Explanations by Elshewey, Ahmed M., Hassan, Samah A. Z., Youssef, Rasha Y., El-Bakry, Hazem M., Osman, Ahmed M.

“… This paper compares random forest regressor, multi-layer perceptron, support vector regressor, and gradient boosting regressor to forecast hydrogen consumption, using production and consumption…”
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A robot process automation based mobile application for early prediction of chronic kidney disease using machine learning by Bijoy, Md. Hasan Imam, Mia, Md. Jueal, Rahman, Md. Mahbubur, Arefin, Mohammad Shamsul, Dhar, Pranab Kumar, Shimamura, Tetsuya

“… (logistic regression, K-nearest neighbors, AdaBoost, decision tree classifier, random forest, and multilayer perceptron…”
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Moisture Prediction in Bird’s Nest Drying with Machine Learning Models by Jin, Hai Tao, Chen, Zhiyuan, Law, Chung Lim

“… moisture levels in bird’s nest products. The proposed model comprises two base models and a multi-layer perceptron (MLP…”
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Advancing Human-Computer Interaction: A Stacking Classifier Approach to Textual Sentiment Analysis using Ensemble Machine Learning by Ali, Afrin, Thakare, Anuradha

“… This research study proposed a Stacking Classifier by integrating machine learning algorithms…”
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Advanced Cybersecurity Framework for Detecting Fake Data Using Optimized Feature Selection and Stacked Ensemble Learning by Alajlan, Abrar M.

“… The dimensionality issues are mitigated by eliminating redundant features via the proposed Elite Tuning Strategy-Enhanced Polar Bear Optimization algorithm…”
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An Improved Ensemble Method With Data Resampling for Credit Risk Prediction by Aruleba, Idowu, Sun, Yanxia

“…) as base learners, with the multilayer perceptron (MLP) serving as a meta-learner. To address the data imbalance, the Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors (SMOTE-ENN…”
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A Stacking Multi-Learning Ensemble Model for Predicting Near Real Time Energy Consumption Demand of Residential Buildings by Ves, Andreea Valeria, Ghitescu, Nicoleta, Pop, Claudia, Antal, Marcel, Cioara, Tudor, Anghel, Ionut, Salomie, Ioan

“… for a seamless integration with the session-based Energy Markets developed within Smart Energy Grids that integrate renewable energy sources…”
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High‐throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi‐principal element alloys by Jin, Chengchen, Xiong, Kai, Luo, Congtao, Fang, Hui, Pu, Chaoguang, Dai, Hua, Zhang, Aimin, Zhang, Shunmeng, Wang, Yingwu

“… A novel stacking ensemble…”
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Optimizing Diabetes Care Through Fuzzy Rule-Based Models and Machine Learning Algorithms by Mishra, Debaswapna, Behera, Bichitrananda, Patnaik, Sanghamitra, Gourisaria, Mahendra Kumar, Pattnayak, Parthasarathi

“… This study uses a simulated fuzzy diabetes dataset validated by medical professionals to evaluate ten supervised machine learning approaches-Multilayer Perceptron (MLP…”
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