Search Results - (( sequence classification learning algorithm ) OR ( parallel estimation method algorithm ))
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Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm
Published 2017“…The major problems in classifying protein sequences into existing families/superfamilies are the following: the selection of a suitable sequence encoding method, the extraction of an optimized subset of features that possesses significant discriminatory information, and the adaptation of an appropriate learning algorithm that classifies protein sequences with higher classification accuracy. …”
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Deep learning-based colorectal cancer classification using augmented and normalised gut microbiome data / Mwenge Mulenga
Published 2022“…At the same time, there has been growing interest from the research community in machine learning based detection of diseases using sequence based on gut microbiome data. …”
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A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms
Published 2024“…Many previous researchers have proposed this domain using classification algorithms or deep learning techniques. …”
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Genetic ensemble biased ARTMAP method of ECG-Based emotion classification
Published 2012“…The proposed system utilizes Biased ARTMAP for pattern learning and classification. The ARTMAP system is dependent on training sequence presentation to determine the effectiveness of the learning processes, as well as the strength of the biasing parameter, lambda λ. …”
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Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks
Published 2003“…These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. …”
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Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance
Published 2003“…These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. …”
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Parallel visualization approach of a 3D heart model
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Deep learning object detector using a combination of Convolutional Neural Network (CNN) architecture (MiniVGGNet) and classic object detection algorithm
Published 2020“…This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. …”
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Multitasking deep neural network models for Arabic dialect sentiment analysis
Published 2022“…The existing approaches are based on traditional machine learning algorithms, such as support vector machine (SVM). …”
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Digital quadrature compensators scheme for analog imperfections of quadrature modulator in wireless communication systems
Published 2016“…Offline on the other hand, is a mode where adaptive algorithms cannot estimate the imperfections in parallel with the transmission. …”
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Convolutional neural networks with feature fusion method for automatic modulation classification
Published 2023“…However, most existing modulation classification algorithms are neglecting the fact of mixing features between different representations, and the importance of features fusion method. …”
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Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques
Published 2024“…Clustering algorithms such as K-means, spectral clustering, and the Gaussian Mixer Model have been tested with manifold learning approaches for adaptive feature selection. …”
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Improved GART neural network model for pattern classification and rule extraction with application to power systems
Published 2023“…Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. …”
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A review on data stream classification
Published 2018“…As such the typical tasks of searching data have been linked to streams of data that are inclusive of clustering, classification, and repeated mining of pattern. This paper presents several data stream clustering approaches, which are based on density, besides attempting to comprehend the function of the related algorithms; both semi-supervised and active learning, along with reviews of a number of recent studies.…”
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Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…The issues addressed are the sequence of training data for supervised learning and optimum parameter tuning for parameters such as baseline vigilance. …”
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