Search Results - (( using function _ algorithm ) OR ( data classification modified algorithm ))
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1
A hybrid-based modified adaptive fuzzy inference engine for pattern classification
Published 2011“…A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data set. …”
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2
Feature fusion using a modified genetic algorithm for face and signature recognition system
Published 2015“…A modified fitness function in Wrapper GA was introduced by adding a function to maintain the balanced of the selected features. …”
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3
A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The components of the AIRS2 algorithm that pose problems will be modified. This thesis proposes three new hybrid algorithms: The FRA-AIRS2 algorithm uses fuzzy logic to improve data reduction capability of AIRS2 and to solve the linearity problem associated with resource allocation of AIRS. …”
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4
Logistic regression methods for classification of imbalanced data sets
Published 2012“…The imbalanced problem of both proposed general classification algorithms which is the limitation of accuracy performance specifically in classifying on the minority class has motivated this research to improve their classification performance on imbalanced data sets. …”
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5
Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…Nevertheless, AC is not required for LCM if the original multi-spectral image is used. The last phase involves developing a new fusion algorithm using SVM and Fuzzy K-Means Clustering (FKM) algorithms for Sentinel 2 data to enhance LCM accuracy. …”
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6
A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition
Published 2002“…Classification rules were generated from training feature vectors set, and a modified form of the standard voter classification algorithm, that use the rough sets generated rules, was applied. …”
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7
A framework of modified adaptive neuro-fuzzy inference engine
Published 2012“…The Takagi-Sugeno-Kang (TSK) type fuzzy inference system was chosen and constructed by an automatic generation of clusters as well as membership functions and minimal rules through the use of hybrid fuzzy clustering and the modified apriori algorithms respectively. …”
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8
RMIL/AG: A new class of nonlinear conjugate gradient for training back propagation algorithm
Published 2018“…This paper introduced a new class of efficient second order conjugate gradient (CG) for training BP called Rivaie, Mustafa, Ismail and Leong (RMIL)/AG. The RMIL uses the value of adaptive gain parameter in the activation function to modify the gradient based search direction. …”
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9
Modern fuzzy min max neural networks for pattern classification
Published 2019“…Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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10
Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network
Published 2017“…In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. …”
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11
Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…In this regard, this thesis proposes five FS methods for efficient EMG signals classification. The first method is the Binary Tree Growth Algorithm (BTGA), which implements a hyperbolic tangent function to convert the Tree Growth Algorithm into the binary version. …”
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12
The effect of pre-processing techniques and optimal parameters on BPNN for data classification
Published 2015“…In this research, a performance analysis based on different activation functions; gradient descent and gradient descent with momentum, for training the BP algorithm with pre-processing techniques was executed. …”
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13
Development of a new robust hybrid automata algorithm based on surface electromyography (SEMG) signal for instrumented wheelchair control
Published 2020“…Total of ten control methods determined from population and individual data were tested against another 10 healthy persons to evaluate the algorithm performance. …”
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14
An efficient anomaly intrusion detection method with feature selection and evolutionary neural network
Published 2020“…This research designed an anomaly-based detection, by adopting the modified Cuckoo Search Algorithm (CSA), called Mutation Cuckoo Fuzzy (MCF) for feature selection and Evolutionary Neural Network (ENN) for classification. …”
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15
A fuzzy approach for early human action detection / Ekta Vats
Published 2016“…In order to perform early human action detection, the conventional classification problem is modified into frame-by-frame level classification. …”
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16
Modified word representation vector based scalar weight for contextual text classification
Published 2024“…In addition, a contextual text classification experiment is conducted using benchmarked datasets to assess the performance of the modified word vectors in the targeted classification task. …”
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17
Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan
Published 2023“…Based on these challenges, the goals of this project are to design a web system that can visualize online reviews of Malaysian fast food restaurants using Twitter sentiment analysis. This project uses an algorithm called Naïve Bayes and the visualization is aided by the Plotly library in Python. …”
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18
A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network
Published 2014“…Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. …”
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19
A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…Nowadays, the use of SVM is very perspective for the big data classification. SVM provides a global solution for data classification but SVM is highly sensitive to noise data and may not be effective when the level of noise data is high. …”
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20
A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…Nowadays, the use of SVM is very perspective for the big data classification. SVM provides a global solution for data classification but SVM is highly sensitive to noise data and may not be effective when the level of noise data is high. …”
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