Search Results - (( pattern classification system algorithm ) OR ( quality classification based algorithm ))

Performance of Levenberg-Marquardt neural network algorithm in power quality disturbances classification / Adibah I’zzah Mohamad Kasim by Mohamad Kasim, Adibah I’zzah

“…The classification process incorporated Root Mean Square (RMS) analysis for feature extraction and multilayer perceptron (MLP) neural networks for pattern recognition. …”
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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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Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah by Abu Samah, Abdul Hafiz

“…To improve the performance from current systems, this work has investigation on different of image pre-processing enhancement technique to support accuracy on deep learning for DR classification. …”
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Hybrid genetic random forest algorithm for the identification of ISI-indexed articles / Mohammadreza Moohebat by Mohammadreza, Moohebat

“…After ensuring that the classification technique was able to accomplish this work, Hybrid Genetic Random Forests (HGRF) was introduced as a new ensemble classifier based on a Random Forest algorithm, but altered slightly with some innovations. …”
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A framework of modified adaptive neuro-fuzzy inference engine by Hossen, Md. Jakir

“…Neuro-fuzzy inference engine and/or system is knowledge based data processing system and can manage the human reasoning course and create decisions based on uncertainty and imprecise situations. …”
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An interpretable fuzzy-ensemble method for classification and data analysis / Adel Lahsasna by Adel, Lahsasna

“…In addition, we proposed a feature selection-based method that aims to improve the quality of the non-dominated fuzzy rule-based systems especially those generated from high dimensional data sets by allowing the genetic algorithm (GA) to start from a good initial population. …”
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Underwater Image Recognition using Machine Learning by Divya, N.K., Manjula, Sanjay Koti, Priyadarshini, S

“…It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase the performance of the algorithms. …”
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Application of image quality assessment module to motion-blurred wood images for wood species identification system by Rajagopal, Heshalini, Khairuddin, Anis Salwa Mohd, Mokhtar, Norrima, Ahmad, Azlin, Yusof, Rubiyah

“…The proposed system includes image acquisition, image quality assessment module (IQA), image deblurring, feature extraction and classification. …”
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A comparative study between rough and decision tree classifiers by Mohamad Mohsin, Mohamad Farhan

“…Theoretically, a good set of knowledge should provide good accuracy when dealing with new cases.Besides accuracy, a good rule set must also has a minimum number of rules and each rule should be short as possible.It is often that a rule set contains smaller quantity of rules but they usually have more conditions.An ideal model should be able to produces fewer, shorter rule and classify new data with good accuracy.Consequently, the quality and compact knowledge will contribute manager with a good decision model.Because of that, the search for appropriate data mining approach which can provide quality knowledge is important.Rough classifier (RC) and decision tree classifier (DTC) are categorized as RBC.The purpose of this study is to investigate the capability of RC and DTC in generating quality knowledge which leads to the good accuracy.To achieve that, both classifiers are compared based on four measurements that are accuracy of the classification, the number of rule, the length of rule, and the coverage of rule.Five dataset from UCI Machine Learning namely United States Congressional Voting Records, Credit Approval, Wisconsin Diagnostic Breast Cancer, Pima Indians Diabetes Database, and Vehicle Silhouettes are chosen as data experiment.All datasets were mined using RC toolkit namely ROSETTA while C4.5 algorithm in WEKA application was chosen as DTC rule generator.The experimental results indicated that both classifiers produced good classification result and had generated quality rule in different types of model – higher accuracy, fewer rule, shorter rule, and higher coverage.In term of accuracy, RC obtained higher accuracy in average while DTC significantly generated lower number of rule than RC.In term of rule length, RC produced compact and shorter rule than DTC and the length is not significantly different.Meanwhile, RC has better coverage than DTC.Final conclusion can be decided as follows “If the user interested at a variety of rule pattern with a good accuracy and the number of rule is not important, RC is the best solution whereas if the user looks for fewer nr, DTC might be the best choice”…”
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Configuration and analysis of piezoelectric-based in socket sensory system for transfemoral prosthetic Gait detection / Farahiyah Jasni by Farahiyah , Jasni

“…Hence, the quality of the sensory system for the MPC prosthetic leg is very important. …”
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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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Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier by Siow, Shien Loong

“…Due to the current methods in feature extraction are still improving, this project proposed a new feature extraction method to increase the performance of iris classification. In this project, a classification system is proposed with the one-dimensional local binary pattern algorithm (1D-LBP) with the K-Nearest Neighbour (K-NN) classifier and the system is developed by using a Raspberry Pi 3.There are eight different subjects used to classify in this classification system and each subject consists of seven samples of normalized iris image as input to the system. …”
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Genetic algorithm fuzzy logic for medical knowledge-based pattern classification by Tan, Chin Hooi, Tan, Mei Sze, Chang, Siow Wee, Yap, Keem Siah, Yap, Hwa Jen, Wong, Shen Yuong

“…The proposed algorithm was applied and tested in four critical illness datasets in medical knowledge pattern classification. …”
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A hybrid-based modified adaptive fuzzy inference engine for pattern classification by Sayeed, Md. Shohel, Ramli, Abdul Rahman, Hossen, Md. Jakir, Samsudin, Khairulmizam, Rokhani, Fakhrul Zaman

“…The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a hybrid Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. …”
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The Potential of a Classification-based Algorithm to Calculate Calories in Real-Time Via Pattern Recognition by M. A., Ameedeen, Marhaini, M. S.

“…While the algorithm helped to classify different types of wavelengths produced from the sensor, a classification-based algorithm via Pattern Recognition Method will be used to classify and match the food components. …”
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The application of neural network data mining algorithm into mixed pixel classification in geographic information system environment by Nanna Suryana, Herman

“…This , paper discusses the development of data mining and pattern recognition algorithm to handle the complexity of hyperspectral remote sensing images in Geographical Information Systems environment. …”
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Condition monitoring of deep drilling process for cooling channel making in hot press die by Muhamad Aslam, Abdul Raub

“…To classify this data, machine learning method such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) was employed. SVM performs classification process based on the data input vector that comprise as fault in the machine. …”
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Songket pattern classification using backpropagation neural network / Nik Aidil Syawalni Nik Mazlan by Nik Mazlan, Nik Aidil Syawalni

“…The study's outcomes underscore the capability of the BPNN-based algorithm to attain remarkable accuracy in Songket pattern classification, thus showcasing its viability for real-world applications.…”
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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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