Search Results - (( data classification using algorithm ) OR ( program solution using algorithm ))
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Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif
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Research Reports -
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Hand gesture recognition for autism diagnosis using Support Vector Machine (SVM) Algorithm / Muhammad Asyraf Mohamad Zain
Published 2020“…From the accuracy test, SVM are proven to be one of the best classifier to classify the image data. For the future work, this system need to be improved by using dataset that are related to the ASD and by using other classification algorithm.…”
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Classification models for higher learning scholarship award decisions
Published 2018“…In this study, a data mining approach was used to propose a classification model of scholarship award result determination. …”
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Angle Based Protein Tertiary Structure Prediction Using Bees Optimization Algorithm
Published 2010“…WEKA program application was used for main chain angles (Phi and Psi) data classification. …”
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Kernel methods and support vector machines for handwriting recognition
Published 2023“…Finding the solution hyperplane involves using quadratic programming which is computationally intensive. …”
Conference paper -
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Towards a better feature subset selection approach
Published 2010“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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Problem restructuring in interger programming for reduct searching
Published 2003“…In effect, they are very useful in generating rules when solving the classification problem that is inherent in data mining. …”
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An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection
Published 2023“…The emergence of Deep Learning (DL) models allow more training possibilities and improvement in performance. DL models often use gradient descent optimization, i.e., the Back-Propagation (BP) algorithm; therefore, their training and optimization procedures suffer from local sub-optimal solutions. …”
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Framework for pedestrian walking behaviour recognition to minimize road accident
Published 2021“…The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. …”
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Framework for pedestrian walking behaviour recognition to minimize road accident
Published 2021“…The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. …”
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Logistics center location-inventory-routing problem optimization: a systematic review using PRISMA method
Published 2022“…When dealing with the LIRP, heuristic and metaheuristic algorithms are the most widely used solution methodologies in the literature. …”
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Development of cost reduction mathematical model for natural gas transmission network system
Published 2012“…Thus, the total cost of the network was decreased. Therefore, the data clearly exhibit that the proposed method provides a solution that was nearer to an optimized network.…”
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…In cluster generating process, the developed BBSI algorithm was used to select the best band combination for generating cluster by using Iterative self- Organizing Data Analysis (ISODATA) technique. …”
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Case Slicing Technique for Feature Selection
Published 2004“…Since the 1960s, many algorithms for data classification have been proposed. …”
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Dengue classification system using clonal selection algorithm / Karimah Mohd
Published 2012“…Some of the dengue data are used to test the dengue classification system to produce the classification accuracy. …”
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Enhancing Classification Algorithms with Metaheuristic Technique
Published 2024“…Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. …”
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Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…Classification is a data mining technique used to classify varied data types according to a specific criterion. …”
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Attribute related methods for improvement of ID3 Algorithm in classification of data: A review
Published 2020“…Nevertheless, there are some limitations in ID3 algorithm that can affect the performance in the classification of data. …”
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