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- Developmental Analysis of Speech using Neural Networks
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1 The NorthCap University, Gurugram, IN
1 The NorthCap University, Gurugram, IN
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Artificial Intelligent Systems and Machine Learning, Vol 10, No 6 (2018), Pagination: 137-141Abstract
Analysis of developmental patterns in speech of children plays a vital role in speech acquisition and developmental research. It is important to study the development of various speech features with increase in age. Many recent studies have found that the children’s speech exhibits different patterns in the acoustic speech features as compare to adults’ speech. The present study extracted various acoustic speech features such as pitch, intensity and formants from the speech sample of normally developing children and adults. This research differentiates or classifies speech of adult and children by training different classification models using these features set.Keywords
Acoustic Speech Features Feature Extraction, Pitch, Intensity and Formants.
References
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- Feature Extraction Techniques for Facial Expression Recognition
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Digital Image Processing, Vol 10, No 6 (2018), Pagination: 101-105Abstract
The area of automatic Facial Expression recognition has received an increasing attention from the researchers over last two decades due to its applications in various areas like human-computer interaction systems, Human Machine Intelligent Interaction (HMII), intelligent robotics, human centered transportation system for driver’s fatigue measurement, tools for people with lower vision and health care robots etc. An efficient feature extraction technique is an essential component of an automatic FER system. The feature extraction techniques widely used are categorized into: Appearance based methods and Geometric methods. Several works presented by the researchers use a hybrid approach combining both methods to create the feature vector for classification. In this paper, a survey is presented on different types of feature extraction techniques.
Keywords
Facial Expression Recognition, Feature Extraction, Geometric Features, Appearance Features, Hybrid Features.- Stock Market Analysis using a combination of Textual Data and Numeric Time-Series
Abstract Views :347 |
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Authors
Affiliations
1 Department of Computer Science Engineering, The NorthCap University, Gurugram, IN
2 The Northcap University, Gurugram, IN
1 Department of Computer Science Engineering, The NorthCap University, Gurugram, IN
2 The Northcap University, Gurugram, IN
Source
Data Mining and Knowledge Engineering, Vol 11, No 1 (2019), Pagination: 7-11Abstract
This study identifies the feasibility of predicting stock market using the combination of Sentiment Analysis and Linear Regression. The factors on which the stock market depends were identified and then a consolidation of these factors was constructed to build up a prediction model. This prediction model incorporated both textual data from news articles published by authentic sources as well as numerical data of various economic identifiers. To analyze the textual data, Naïve Bayes Classifier and Lexicon based Sentiment Analysis was carried out and for numerical values, data analysis was performed using linear regression techniques to obtain optimal results. This model computed MMRE of 0.1561.
Lastly, a combination of identifiers that worked the best for prediction of stock values was computed and therefore, prioritized.
Keywords
Analysis, Stock Prediction, Regression, Economic Identifiers, Close Price.References
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