Search Results - (( java implementation tree algorithm ) OR ( programming layer selection algorithm ))

Double-layered hybrid neural network approach for solving mixed integer quadratic bilevel problems by Shamshul Bahar, Yaakob, Prof. Madya, Watada, Junzo

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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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A hybrid intelligent algorithm for solving the bilevel programming models by Shamsul Bahar, Yaakob, Prof. Madya, Watada, Junzo

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An artificial bee colony-based double layered neural network approach for solving quadratic Bi-level programming problems by Watada, J., Roy, A., Wang, B., Tan, S.C., Xu, B.

“…In the current work, we devised a hybrid method involving a Double-Layer Neural Network (DLNN) for solving a quadratic Bi-Level Programming Problem (BLPP). …”
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Determining Suitable Program For SPM Holder Using Neural Network Approach by Noraisah, Sudin

“…The findings show that the most suitable predictive model comprises of 35 nodes in input-layer; three nodes in hidden layer and one node in output-layer. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

“…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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Classification model for hotspot occurrences using spatial decision tree algorithm by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

“…As the ID3 algorithm that uses information gain in the attribute selection, the proposed algorithm uses spatial information gain to choose the best splitting layer from a set of explanatory layers. …”
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Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

“…Also, this research involves in developing a machine-learning system and program via genetic algorithm that is capable of performing independent learning capability and optimization for scanning sequence using novel GA operators. …”
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A hybrid ANN for output power prediction and online monitoring in grid-connected photovoltaic system / Puteri Nor Ashikin Megat Yunus by Megat Yunus, Puteri Nor Ashikin

“…Improved Fast Evolutionary Programming (IFEP), Evolutionary Programming- Dolphin Echolocation Algorithm (EPDEA) and Evolutionary Programming-Firefly Algorithm (EPFA). …”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

“…Several algorithms, methods and spreadsheets are being proposed by various researchers in this field to improve materials selection. …”
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Firefly algorithm-based neural network for GCPV system output prediction / Nor Syakila Mohd Zainol Abidin by Mohd Zainol Abidin, Nor Syakila

“…Nonetheless,a MLFNN is not efficifient enough because it become to be a tedious process and time consuming since it required trial-and-error method for selecting MLFNN training parameters. Therefore, this thesis presents a hybrid Firefly Algorithm (FA)–based MLFNN for predicting the AC power output from a grid-connected photovoltaic (GCPV) system. …”
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Development of wwm system using mlnn-ga for ph prediction of water quality in catchment area by Muhammad Aznil, Ab Aziz

“…MLNN-GA contain three hidden layers and Genetic Algorithm (GA) will optimize the weight of neural network during the training process. …”
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Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization by Lee, Jesee Kar Ming

“…The ANN model topology is designed using selection from the best training algorithm, transfer function, number of training runs (1000-5000), number of hidden layers (1-3) and nodes (5-15). …”
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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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Application of artificial neural network in discriminating the agarwood oil quality using significant chemical compounds / Mohd Hezri Fazalul Rahiman … [et al.] by Rahiman, Mohd Hezri Fazalul, Ismail, Nurlaila, Taib, Mohd Nasir, Mohd Ali, Nor Azah, Tajuddin, Saiful Nizam

“…The ANN programming algorithm was developed and computed automatically via Matlab software version R2010a. …”
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A Data Mining-Based Model for Assessing Guangzhou's Higher Vocational Colleges 'New Energy Automobile Majors' Vocational Skills by He, Ling, Hamid, Hashima

“…The approach employs algorithms for data mining to enhance the accuracy and accessibility of results through the use of Random Forest (RF) and Generalized Additive Models (GAM) in a layering architecture. …”
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Numerical simulation and experimental verification on distortions induced by wire-arc additive manufacturing components and costing analysis / Keval Priapratama Prajadhiana by Prajadhiana, Keval Priapratama

“…Three numerical models which consists of TMM and two different lumping layers…”
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Rainfall time series modeling for a mountainous region in West Iran by Mekanik, Fatemeh

“…The models were trained based on the Levenberg-Marquardt algorithm with tansigmoid activation function for the hidden layer and purelin activation function for the output layer. …”
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Reservoir Inflow Forecasting Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques by Googhari, Shahram Karimi

“…The best architecture of the ANN model was a 9-13-1 model which means a model with 9 inputs, 1 hidden layer with 13 neurons and 1 output. The model was trained based on the Leven-berg Marquardt algorithm with sigmoid activation functions. …”
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