Search Results - (( java implementation mining algorithm ) OR ( rate activation function algorithm ))
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Direct approach for mining association rules from structured XML data
Published 2012“…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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Thesis -
2
Study and Implementation of Data Mining in Urban Gardening
Published 2019“…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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3
Scalable approach for mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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4
Mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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5
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 -
6
Image clustering comparison of two color segmentation techniques
Published 2010“…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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7
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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8
Particle swarm optimization for neural network learning enhancement
Published 2006“…In Backpropagation Neural Network (BPNN), there are many elements to be considered such as the number of input, hidden and output nodes, learning rate, momentum rate, bias, minimum error and activation/transfer functions. …”
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9
The effect of adaptive parameters on the performance of back propagation
Published 2012“…The activation functions are adjusted by the adaptation of gain parameters together with adaptive momentum and learning rate value during the learning process. …”
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10
Differential evolution for neural networks learning enhancement
Published 2008“…In ANN, there are many elements need to be considered, and these include the number of input nodes, hidden nodes, output nodes, learning rate, momentum rate, bias parameter, minimum error and activation/transfer functions. …”
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11
Green network planning and operational power consumption optimization in LTE-A using artificial intelligence
Published 2015“…Moreover, the optimum rate of active relay is a function of the traffic pattern, average relay station load factor, their derivatives, and the relative RS to BS capacity factor. …”
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12
Development of a scalable video compression algorithm
Published 2012“…To improve the perceptual quality of coded video in a computation-constrained scenario, through controlling per-frame complexity, a rate-distortion optimised (RDO) rate control algorithm for encoding low bit rate video helped to achieve the target bitrates and PSNR using Lagrangian multiplier function. …”
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Proceeding Paper -
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Wireless network power optimization using relay stations blossoming and withering technique
Published 2017“…In this paper, a new relay switching perspective is introduced for relay blossoming and withering algorithm. First, relay switching is considered as a function of time representing the rate of active relays. …”
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15
Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant
Published 2014“…The error function is minimized directly by approximate Frechet tuning algorithm without explicit estimation of the model. …”
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16
HF-AQM: An efficient active queue management scheme for wireless local area network environment
Published 2017“…An Active Queue Management (AQM) is a proactive scheme that controls the network congestion by avoiding the congestion before it happened.When implementing AQM in wireless networks several contemporary issues must be considered, such as interference, collisions, multipath-fading, propagation distance, shadowing effects and route failure, and whether the wireless networks is WLAN or other type.Therefore, the needs for AQM algorithm that can perform in WLAN network as good and efficient as in wired network become so crucial.This paper proposes a new AQM algorithm called Hybrid Fair AQM (HF-AQM) that can achieve better fairness and higher utilization in WLAN environment by hybridizing queue delay and input rate to measure network congestion. …”
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Conference or Workshop Item -
17
An efficient anomaly intrusion detection method with evolutionary neural network
Published 2020“…Although activation functions are important for MLP to learn but for nonlinear complex functional mappings it has complicated calculation which reduces the accuracy of classification. …”
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18
Smart fall detection by enhanced SVM with fuzzy logic membership function
Published 2023“…Because combining these two algorithms is not an easy task, we leverage SVM with a kernel comprised of a fuzzy membership function and thus build a new model known as FSVM. …”
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Forecasting of photovoltaic output using hybrid particle swarm optimization-artificial neural network model / Muhamad Faizol Adli Abdullah
Published 2010“…In Backpropagation Neural Network (BPNN), there are many elements to be considered such as the number of input, hidden and output nodes, learning rate, momentum rate, bias, minimum error and activation/transfer functions. …”
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20
PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM
Published 2011“…Particularly, GA is utilized to determine the optimal number of hidden layers, number of neurons in each hidden layer, type of training algorithm, type of activation function of hidden and output neurons, initial weight, learning rate, momentum term, and epoch size of a multilayer feed-forward ANN. …”
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