Search Results - (( developing activation function algorithm ) OR ( data classification problems algorithm ))
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1
Jogging activity recognition using k-NN algorithm
Published 2022“…Jogging activity recognition using the k-NN algorithm is a system that can help users collect information data of user speed movement using speed sensor and give the classification of jogging activity to the user. …”
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Enhancement of new smooth support vector machines for classification problems
Published 2011“…Research on Smooth Support Vector Machine (SSVM) for classification problem is an active field in data mining. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…This learning algorithm represents an automatic generation of membership functions and rules from the data. …”
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Development of a new robust hybrid automata algorithm based on surface electromyography (SEMG) signal for instrumented wheelchair control
Published 2020“…This research aims are to develop an open-source Arduino based sEMG data acquisition device by formulating hybrid automata algorithm to differentiate MUAP activity during wheelchair propulsion. …”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…A novel feature extraction algorithm was developed to extract the feature vectors. …”
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Electricity load profile determination by using fuzzy C-means and probability neural network / Norhasnelly Anuar
Published 2015“…This method will give the best result when clustering the overlapped data in load profile. PNN is a fast training process to do the classification activities. …”
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Tracking and recognizing the activity of multi resident in smart home environments
Published 2017“…Existing works mainly manipulate data association and algorithm modification on extra auxiliary of graphical nodes to model human tracking information in an environment to incorporate with the problems. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms
Published 2020“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer�s Disease
Published 2022“…It outperformed the fractal and Pearson-based connectivity approaches by 16.4 and 17.2, respectively. The classification algorithm developed based on the nonfractal connectivity feature and support vector machine classifier has shown an excellent performance, with an accuracy of 90.3 and 83.3 for the XHSLF dataset and ADNI dataset, respectively. …”
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Modular deep neural network in reducing overfitting to enhance generalization / Mohd Razif Shamsuddin
Published 2024“…Most of those research results varies as it uses different data, different network design, different parameters and optimizing algorithm. …”
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Dengue classification system using clonal selection algorithm / Karimah Mohd
Published 2012“…This project focused on three main objectives: to investigate dengue data and Clonal Selection Algorithm for classification of Dengue, to design and develops Clonal Selection Classification System (CSCS) and to evaluate Clonal Selection Classification System symptoms. …”
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Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification
Published 2020“…Compared to state-of-the-art algorithms and other common methods, our method outperformed them in terms of sensitivity, specificity, and accuracy. …”
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Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…The study verified that the FID3-DBD algorithm could classify the continuous data, and the BFID3-DBD algorithm overcame the overfitting issue, reduced high variance, and increased test data classification accuracy.…”
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An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…This paper addresses the classification problem in machine learning focusing on predicting class labels for datasets with continuous features. …”
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An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. …”
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A derivative-free optimization method for solving classification problem
Published 2010“…Problem statement: The aim of data classification is to establish rules for the classification of some observations assuming that we have a database, which includes of at least two classes. …”
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Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…Next the problem of data classification is studied as a problem of global, non-smooth and non-convex optimization; this approach consists of describing clusters for the given training sets. …”
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Attribute related methods for improvement of ID3 Algorithm in classification of data: A review
Published 2020“…Decision tree is an important method in data mining to solve the classification problems. …”
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Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…The result has shown that the proposed integration system could be applied to increase the performance of the classification. However, further study is needed in the feature extraction and clustering algorithms part as the performance of the pattern classification is still depending on the data input.…”
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Enhancement of bearing defect diagnosis via genetic algorithm optimized feature selection
Published 2015“…In addition, GA has the limitation on generalization which causes the problem of overfitting to the training data. Therefore a correlation-based filtering algorithm is embedded into GA feature selection to solve the over-fitting problem and increase the adaptability of the diagnostic scheme to unpredictable input data. …”
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