Search Results - (( layer perceptions based algorithm ) OR ( java application mining algorithm ))
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Direct approach for mining association rules from structured XML data
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Thesis -
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Article -
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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Conference or Workshop Item -
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Final Year Project / Dissertation / Thesis -
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Mining Sequential Patterns using I-PrefixSpan
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Citation Index Journal -
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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Thesis -
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IoT Soil Moisture Monitoring and Irrigation System Development
Published 2020“…At the same time, with a self-sufficient and self-organized irrigation system based on the water-control algorithm. The developed system covered the three layers in IoT architecture: perception layer, network layer and application layer. …”
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Proceeding -
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ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal
Published 2014“…Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. …”
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Interactive learning package for artificial neural network (Demonstration Module) / Camellia Mohd Kamal
Published 2004“…For Perception there will be the Description Neuron Model, Perceptron Basic Architecture and perceptron Algorithm with one example of solved problem. …”
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Thesis -
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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.]
Published 2014“…Data Processing - ANN Application ( Data pre-processing using Z-score, ANN design structure/architecture - parameter optimisation, training and testing the algorithm) Result & Discussion: ANN parameter optimisation - final error for learning rate, momentum rate and hidden layer size ANN final design parameter - Nodes in input layer: 7, Nodes in hidden layer size: 2, Output layer size: 1, learning rate: 0.9, Momentum rate: 0.7, Error goal: 0.01, Epochs: 100 ANN prediction: high accuracy for training and testing prediction (refer to the figure in poster) Patent & List of contributions: 1. …”
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Book Section -
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Artificial neural network modeling of grinding of ductile cast iron using water based sio2 nanocoolant
Published 2014“…An artificial neural network model is developed for predicting the surface roughness and MRR. Multi-layer perception and a batch back propagation algorithm are used. …”
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Optimization of abrasive machining of ductile cast iron using water based SiO2 nanocoolant : a radial basis function
Published 2012“…Artificial neural network (ANN) model is developed for predicting the results of the surface roughness and MRR. Multi-Layer Perception (MLP) along with batch back propagation algorithm are used. …”
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Undergraduates Project Papers -
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Cyber-Crime Detection: Experimental Techniques Comparison Analysis
Published 2023“…The objective of this research paper is to conduct experimental techniques comparison analysis for cyber-crime detection by reviewing all possible machine learning algorithms for automatic detection. The key focus of the study is on the use of eight classifiers models which are Logistic Regression (LR), Decision Tree (DT), K-nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and Multiple layer perception (MLP). …”
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