Search Results - (( pattern classification problems algorithm ) OR ( using classification system algorithm ))

Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

“…In pattern recognition system, achieving high accuracy in pattern classification is crucial. …”
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A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification by Quteishat, A., Lim, C.P., Tan, K.S.

“…In this paper, a two-stage pattern classification and rule extraction system is proposed. …”
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Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm by Mohamed, M. E, Samir, B. B., Azween, Abdullah

“…In this work, we integrate artificial immune algorithm with non-linear classification of pattern recognition and machine learning methods to solve the problem of intrusion detection in network systems. …”
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Evaluation of fall detection classification approaches by Kerdegari, Hamideh, Samsudin, Khairulmizam, Ramli, Abdul Rahman, Ghotoorlar, Saeid Mokaram

“…This paper presents the comparison of different machine learning classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) platform for classifying falling patterns from ADL patterns. …”
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Classification of credit card holder behavior using K Nearest Neighbor algorithm / Ahmad Faris Rahimi by Rahimi, Ahmad Faris

“…This indicates that the performance of KNN is acceptable and promising in this classification problem. Since KNN is the simplest form of artificial intelligence, future work could combine this algorithm with other classification algorithm. …”
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Biceps brachii surface EMG classification using neural networks by Chong, Yee Lin

“…With these satisfactory results, the effectiveness of the proposed classifiers in EMG pattern classification problem is proven.…”
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A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani by Mahmoud Reza, Saybani

“…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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Multilevel learning in Kohonen SOM network for classification problems by Mohd. Yusof, Norfadzila

“…Self-organizing map (SOM) is a feed-forward neural network approach that uses an unsupervised learning algorithm has shown a particular ability for solving the problem of classification in pattern recognition. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…The aim of data mining is to search and find undetermined patterns in huge databases. A well known task is classification that predicts the class of new instances using known features or attributes automatically. …”
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Improved GART neural network model for pattern classification and rule extraction with application to power systems by Yap K.S., Lim C.P., Au M.T.

“…To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering. � 2006 IEEE.…”

Evaluation and optimization of frequent association rule based classification by Izwan Nizal Mohd Shaharanee, Jastini Jamil

“…Deriving useful and interesting rules from a data mining system is an essential and important task. …”
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Non-invasive pathological voice classifications using linear and non-linear classifiers by Hariharan, Muthusamy

“…Two types of experiments are conducted using the proposed feature extraction and classification algorithms. In the first experiment, classification of normal and pathological voice has been investigated. …”
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Rough Set Discretize Classification of Intrusion Detection System by Noor Suhana, Sulaiman, Rohani, Abu Bakar

“…Many pattern classification tasks confront with the problem that may have a very high dimensional feature space like in Intrusion Detection System (IDS) data. …”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

“…The algorithm enhances the recognition ability of the system compared to manual extraction and labeling of pattern classes. …”
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Character classification for license plate recognition system based on image processing using MATLAB by Hashim, Mustafa Adil

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A hybrid algorithm of interval type-2 fuzzy logic system and generalized adaptive resonance theory for medical data classification by Leow S.Y., Wong S.Y., Yap K.S., Yap H.J.

Plant recognition based on identification of leaf image using image processing / Nor Silawati Sha’ari by Sha’ari, Nor Silawati

“…NN such as Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) is trained in developing a classification system for agriculture purpose. ANN and KNN is applied to solve the problems in image analysis, pattern recognition and classification. …”
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Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah by Abu Samah, Abdul Hafiz

“…Based on investigation different architecture and parameter, the suitable deep learning model has been presented to get optimize best result and testing time. To solving pattern classification problem, the optimization deep learning architecture and parameter by using four convolution layers is set up to classify the three pathological signs; HEM, MA and exudate. …”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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An improved multiple classifier combination scheme for pattern classification by Abdullah,

“…The ant system (AS) algorithm is used to partition feature set in developing feature subsets which represent the number of classifiers. …”
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