Search Results - (( knowledge utilization using algorithm ) OR ( based classification based algorithm ))

Knowledge base processing method based on text classification algorithm by Baisheng Zhong, Mohd Shamrie Sainin, Tan Soo Fun

“…The text classification algorithm's knowledge base processing method utilizes existing data from the knowledge base to guide the construction and training of the classification model. …”
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An improved method using fuzzy system based on hybrid boahs for phishing attack detection by Noor Syahirah, Nordin

“…The experiment was executed by using k-fold cross validation techniques for predicting the classification algorithm performance. …”
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Region-growing based segmentation and bag of features classification for breast ultrasound images by Lee, Lay Khoon

“…For the preprocessing stage, median filtering has been used to reduce the noise in ultrasound. In the next stage, which is the segmentation stage, region growing algorithm is used to automatically detect tumors in ultrasound images. …”
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An extended ID3 decision tree algorithm for spatial data by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

“…The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. …”
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Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm by Acharya, U.R., Faust, O., Molinari, F., Sree, S.V., Junnarkar, S.P., Sudarshan, V.

“…We then review the state-of-the-art US-based CAD techniques that utilize a range of image texture based features like entropy, Local Binary Pattern (LBP), Haralick textures and run length matrix in several automated decision making algorithms. …”
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Image classification of Aedes mosquitoes using transfer learning / Zetty Ilham Abdullah by Abdullah, Zetty Ilham

“…In recent years, the importance, and uses of computer vision, notably image classification, to tackle real-world issues have grown. …”
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On equivalence of FIS and ELM for interpretable rule-based knowledge representation by Wong S.Y., Yap K.S., Yap H.J., Tan S.C., Chang S.W.

“…Classification (of information); Computer aided diagnosis; Fault detection; Fuzzy systems; Knowledge acquisition; Knowledge representation; Learning systems; Matrix algebra; Membership functions; Pattern recognition; Extreme learning machine; Fault detection and diagnosis; Fuzzy if-then rules; Fuzzy inference systems; Fuzzy membership function; Initialization technique; Interpretable rules; Rule based; Fuzzy inference; algorithm; artificial intelligence; artificial neural network; benchmarking; classification; electric power plant; factual database; feedback system; fuzzy logic; machine learning; nerve cell; reproducibility; statistical model; Algorithms; Artificial Intelligence; Benchmarking; Classification; Databases, Factual; Feedback; Fuzzy Logic; Machine Learning; Models, Statistical; Neural Networks (Computer); Neurons; Power Plants; Reproducibility of Results…”

Data mining techniques for disease risk prediction model: A systematic literature review by Ahmad W.M.T.W., Ghani N.L.A., Drus S.M.

Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line by Olufemi, Osaji Emmanuel

“…Hence, eliminate the utility grid relay operation setting compromise. The lack of healthy lines detailed protection useful information knowledge has limited the existing APS performance, as only faulty lines' measured parameters (voltage, current, and phase angle) are mostly used in the relaying protection scheme design. …”
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Feature selection for Malaysian medicinal plant leaf shape identification and classification by Sainin, Mohd Shamrie, Alfred, Rayner

“…Malaysian medicinal plants may be abundant natural resources but there has not been much research done on preserving the knowledge of these medicinal plants which enables general public to know the leaf using computing capability.Therefore, in this preliminary study, a novel framework in order to identify and classify tropical medicinal plants in Malaysia based on the extracted patterns from the leaf is presented.The extracted patterns from medicinal plant leaf are obtained based on several angle features.However, the extracted features create quite large number of attributes (features), thus degrade the performance most of the classifiers.Thus, a feature selection is applied to leaf data and to investigate whether the performance of a classifier can be improved.Wrapper based genetic algorithm (GA) feature selection is used to select the features and the ensemble classifier called Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) is used as a classifier.The performance of the feature selection is compared with two feature selections from Weka.In the experiment, five species of Malaysian medicinal plants are identified and classified in which will be represented by using 65 images.This study is important in order to assist local community to utilize the knowledge and application of Malaysian medicinal plants for future generation.…”
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A framework for malware identification based on behavior by Mohamad Fadli, Zolkipli

“…The IF-THEN Prediction Rules which is generated using the data mining technique, ID3 Algorithm is used. …”
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Electroencephalography Simulation Hardware for Realistic Seizure, Preseizure and Normal Mode Signal Generation by Mohamed, Shakir, Qidwai, Uvais, Malik, Aamir Saeed, Kamel , Nidal

“…With controlled data types, healthy/normal, seizure and pre-seizure classes, tuning of algorithms for detection and classification applications can be attained. …”
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Stress Classification using Deep Learning with 1D Convolutional Neural Networks by Abdulrazak Yahya, Saleh, Khai Xian, Lau

“…The 1D CNNs are trained and tested using 10-fold cross-validation using the PS dataset. The algorithm performance is evaluated based on accuracy and loss matrices. …”
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EEG Simulation Hardware for Realistic Seizure, Preseizure and Normal Mode Signal Generation by Shakir, Mohamed, Qidwai, Uvais, Malik, Aamir Saeed, Kamel, Nidal

“…With controlled data types, healthy/normal, seizure and pre-seizure classes, tuning of algorithms for detection and classification applications can be attained. …”
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Classification with degree of importance of attributes for stock market data mining by Khokhar, Rashid Hafeez, Md. Sap, Mohd. Noor

“…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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Integrated artificial intelligence-based classification approach for prediction of acute coronary syndrome by Salari, Nader

“…The following results were obtained when classification of the ACS types used the conventional “single AI-based” methods. …”
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Implementation of Hybrid Indexing, Clustering and Classification Methods to Enhance Rural Development Programme in South Sulawesi by Muhammad, Faisal

“…Using the cosine similarity algorithm for knowledge recommendation is village identified, utilizing community feedback as the foundation. …”
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Predicting STEM academic performance in secondary schools: data mining approach by Termedi @Termiji, Mohammad Izzuan, Ab. Jalil, Habibah

“…Three different data mining classification algorithms which are Decision Tree (DT), Artificial Neural Networks (ANN), and Naive Bayes (NB) will be used on the dataset. …”
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Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines by Aker, Elhadi Emhemed Alhaaj Ammar

“…The hybrid discrete wavelet multiresolution analyses and machine learning (DWMRA-ML) algorithm is deployed to discover the hidden useful knowledge extraction from the 1-cycle short circuit transient fault signals (voltage and current) from healthy and fault lines section. …”
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Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset by Abd Rahman, M. Hafiz Fazren, Wan Salim, Wan Wardatul Amani, Abd-Wahab, Firdaus

“…This research aims to develop a robust prediction model for classification of type 2 diabetes mellitus (T2DM), with the interest of a Malaysian population, using several well-known machine learning algorithm such as Decision Tree, Support Vector Machine and Naïve Bayers. …”
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