scholarly journals Analysis of Children’s Prosodic Features Using Emotion Based Utterances in Urdu Language

2018 ◽  
Vol 8 (3) ◽  
pp. 2954-2957
Author(s):  
S. Khan ◽  
S. A. Ali ◽  
J. Sallar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Significant Role ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Prosodic Features ◽  
Basic Emotions ◽  
Learning Classifiers ◽  
Artificial Neural

Emotion plays a significant role in identifying the states of a speaker using spoken utterances. Prosodic features add sense in spoken utterances providing speaker emotions. The objective of this research is to analyze the behavior of prosodic features (individual and in combination with others’ prosodic features) with different learning classifiers on emotion based utterances of children in the Urdu language. In this paper, three different prosodic features (intensity, pitch, formant and their combinations) with five different learning classifiers(ANN, J-48, K-star, Naïve Bayes, decision stump) and four basic emotions (happy, sad, angry, and neutral) were used to develop the experimental framework. Demonstrative experiments expressed that, in terms of classification accuracy, artificial neural networks show significant results with both individual and combination of prosodic features in comparison with other learning classifiers.

scholarly journals Comparison of Data Mining Algorithms Using Artificial Neural Networks (ANN) and Naive Bayes for Preterm Birth Prediction

2020 ◽  
Vol 1641 ◽  
pp. 012068
Author(s):  
Diah Puspitasari ◽  
Kresna Ramanda ◽  
Adi Supriyatna ◽  
Mochamad Wahyudi ◽  
Erma Delima Sikumbang ◽  
...  
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Preterm Birth ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithms ◽  
Artificial Neural ◽  
Mining Algorithms

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

scholarly journals Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals

Sensors ◽  
2011 ◽  
Vol 11 (2) ◽  
pp. 1721-1743 ◽  
Author(s):  
Birsel Ayrulu-Erdem ◽  
Billur Barshan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Discrete Wavelet ◽  
Daubechies Wavelet ◽  
Motion Classification ◽  
Artificial Neural ◽  
Leg Motion ◽  
Function Selection ◽  
Selection Of

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWTdecomposition and reconstruction.

scholarly journals Application of Artificial Intelligence (Neural Networks) in Education

2021 ◽  
Vol 19 (1) ◽  
pp. 78-87
Author(s):  
Arman ZAKARYAN
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Significant Role ◽  
Educational Environment ◽  
Educational Systems ◽  
Training Systems ◽  
Teachers And Students ◽  
Artificial Neural

This article focuses on the use of artificial intelligence in the educational environment. We consider the latest technology, which already plays a huge role for both teachers and students. Currently, there are many systems for the development of training systems, among which artificial neural networks occupy a substantial place. The article presents an example of the use of artificial neural networks, which can play a significant role in developing educational systems.

scholarly journals A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

2020 ◽  
Author(s):  
Mohamed El Boujnouni
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The World ◽  
Artificial Neural

Abstract Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin.

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