Search Results - (( pattern learning algorithm ) OR ( patterns ((scoping algorithm) OR (warping algorithms)) ))*
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1
Forex trading prediction using linear regression line, artificial neural network and dynamic time warping algorithms
Published 2013“…Forex prediction has become a challenging task in the Forex market since the late 1970s due to uncertainty movement of exchange rates.In this paper, we utilised linear regression equation to analyse the historical data and discover the trends patterns in Forex.These trends patterns are modeled and learned by Artificial Neural Network algorithm, and Dynamic Time Warping algorithm is used to predict the near future trends.Our experiment result shows a satisfactory result using the proposed approach.…”
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2
Financial time series predicting using machine learning algorithms
Published 2013“…Subsequently, Dynamic Time Warping (DTW) algorithm is utilised through brute force to predict the trend movement. …”
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3
Prediction of forex trend movement using linear regression line, two-stage of multi-layer perceptron and dynamic time warping algorithms
Published 2016“…Thus, this motivates us to investigate possibility of repeated trend patterns from historical Forex data. This paper aims to investigate the repeated trend patterns as features from historical Forex data, which proposes new combination techniques - Linear Regression Line, two-stage of Multi-Layer Perceptron and Dynamic Time Warping algorithms in order to improve the performance of prediction significantly, thus achieving greater accuracy.…”
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4
Prediction of Forex trend movement using linear regression line, two-stage of multi-layer perceptron and dynamic time warping algorithms
Published 2016“…Thus, this motivates us to investigate possibility of repeated trend patterns from historical Forex data. This paper aims to investigate the repeated trend patterns as features from historical Forex data, which proposes new combination techniques - Linear Regression Line, two-stage of Multi-Layer Perceptron and Dynamic Time Warping algorithms in order to improve the performance of prediction significantly, thus achieving greater accuracy…”
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5
Auto raise hand in Microsoft teams (API/Extension)
Published 2023“…To be more specific, it is regarding facial expression recognition based on deep learning. Artificial Intelligence focuses on developing intelligences of machines, by developing algorithms, machines are able to learn from data and patterns, even perform tasks that require human intelligence, such as visual perception, speech recognition, and decision-making. …”
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6
Improving named entity recognition accuracy of gene and protein in biomedical text
Published 2011“…Typically there are four approaches for Named Entity Recognition, namely: Dictionary-Based, Rule-Based, Statistical and Machine Learning, and Hybrid approaches. In this study, to handle the above issues in recognizing gene and protein names, a statistical similarity measurement as a pattern matching function is proposed. …”
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7
One dimensional image processing for eye tracking using derivative dynamic time warping
Published 2010“…Derivative Dynamic Time Warping (DDTW) is chosen as the classifier for this experiment since it can match patterns from one dimension data sequences with varying length. …”
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8
NN with DTW-FF Coefficients and Pitch Feature for Speaker Recognition
Published 2006“…This paper proposes a new method to extract speech features in a warping path using dynamic programming (DP). The new method presented in this paper described how the LPC feature is extracted and those coefficients are normalized against the template pattern according to the selected average number of frames over the samples collected. …”
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9
Local DTW coefficients and pitch feature for back-propagation NN digits recognition
Published 2006“…This paper presents a method to extract existing speech features in dynamic time warping path which originally was derived from LPC. …”
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10
Local DTW Coefficients and Pitch Feature for Back-Propagation NN Digits Recognition
Published 2006“…This paper presents a method to extract existing speech features in dynamic time warping path which originally was derived from LPC. …”
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11
Robust speech recognition using fusion techniques and adaptive filtering
Published 2009“…The study proposes an algorithm for noise cancellation by using recursive least square (RLS) and pattern recognition by using fusion method of Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). …”
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12
Animal voice recognition for identification (ID) detection system
Published 2011“…In this paper, an animal identification (ID) detection system based on animal voice pattern recognition algorithm has been developed. …”
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13
Dog voice identification (ID) for detection system
Published 2012“…In this paper, an animal identification (ID) detection system based on animal voice pattern recognition algorithm has been developed. …”
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14
Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis
Published 2021“…For learning and predicting the event patterns, dynamic Bayesian network (DBN) with Hidden Markov Model (HMM) and heuristic search learning algorithms have been a popular technique used in which structure learning is trained to classify complex events pattern. …”
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15
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…There are two general paradigms for pattern recognition classification which are supervised and unsupervised learning. …”
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16
Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation
Published 2023Subjects:Conference paper -
17
Image Stitching Of Aerial Footage
Published 2021“…The algorithm performance is evaluated using the Orchard datasets, consisting of L-shape flight pattern and lawnmower flight pattern. …”
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18
Clustering ensemble learning method based on incremental genetic algorithms
Published 2012“…In the first and second phases, a threshold fuzzy c-means clustering algorithm as a clusterer and a pattern ensemble learning method based on the incremental genetic-based algorithms are proposed respectively. …”
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19
An algorithm for Elliott Waves pattern detection
Published 2018“…The Random Decision Forest and the Support Vector Machine are the machine learning algorithms employed for this task. The accuracy of trend prediction above 70 proves the relevancy of EW patterns on stock market data as well as the validity of the algorithm as a tool for detection of such patterns. …”
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20
An algorithm for Elliott Waves pattern detection
Published 2018“…The Random Decision Forest and the Support Vector Machine are the machine learning algorithms employed for this task. The accuracy of trend prediction above 70 proves the relevancy of EW patterns on stock market data as well as the validity of the algorithm as a tool for detection of such patterns. …”
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