scholarly journals Uncertainty Handling using Improvised Intuitionistic Fuzzy ANN based Voice Disorder Detection

2019 ◽  
Vol 8 (2) ◽  
pp. 1225-1229
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Promising Result ◽  
Research Work ◽  
Voice Disorder ◽  
Hierarchical Processing ◽  
Intuitionistic Fuzzy ◽  
Health Related ◽  
Ann Models ◽  
Meyer Wavelet

The voice pathology detection is one of the essential process which has to be determined in the earlier stages because it is a sign for raising health related problems. The aim of this paper is to handle the uncertainty in voice dataset due to inconsistency in extracting potential features and vagueness in dealing voice signals. The raw voice signals are preprocessed by feature extraction using meyer wavelet and potential features involved in voice disorder detection are done using sequential forward feature selection methods as voice preprocessing. This research work introduced an improvised intuitionistic fuzzy artificial neural network which enhances the process of voice disorder detection is SVD database by using analytical hierarchical processing for assigning weights and thus the complete neural network performance was fine tuned instead of assigning the weights randomly. The simulation results proved the performance of the proposed model as best by producing more promising result while comparing with ANN, PANN and Fuzzy ANN models.

scholarly journals Real-Time Ethiopian Currency Recognition for Visually Disabled Peoples Using Convolutional Neural Network

2020 ◽  
Author(s):  
Mekonnen Legess Meharu ◽  
Hussien Seid Worku
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Promising Result ◽  
Low Vision ◽  
Research Work ◽  
Real Life ◽  
Non Governmental Organizations ◽  
Average Accuracy ◽  
Blind Persons

Abstract A survey report made by the Ethiopian Ministry of Health along with several non-governmental organizations in 2006 G.C, there were about 5.3% of the Ethiopian population lives with blindness and low vision problems. This research work aims to develop a Convolutional Neural Network-based model by using pre-trained models to enable vision-impaired peoples to recognize Ethiopian currency banknotes in real-time scenarios. The models attempt to accurately recognize Ethiopian currency banknotes even if the input images come up with partially or highly distorted and folded Birr notes. 8500 (1700 for each class) banknotes data are collected within real-life situations by using 9 blind persons. The models were evaluated with 500 real-time videos of different conditions. The whole training, classification, and detection tasks have been demonstrated by adopting Tensorflow Object Detection API and the pre-trained Faster R-CNN Inception, and SSD MobileNet models. All the codes are implemented using Python. The model tested using numerous Ethiopian currencies at different banknotes status and light conditions. In the case of Faster R-CNN Inception model an average accuracy, precision, recall, and F1-score of 91.8%, 91.8%, 92.8%, and 91.8% are obtained respectively and in the case of SSD MobileNet model an average accuracy, precision, recall, and F1-score of 79.4%, 79.4%, 93.6%, and 84.4% are obtained respectively within a real-time video. Therefore as the first research work, the model has shown good performance in both models but Faster R-CNN provides a promising result with an average accuracy of 91.8%.

Valid Signature Detection and Verification for Security of Individual Person

2016 ◽  
Vol 1 (1) ◽  
pp. 50-53 ◽  
Author(s):  
Varun Sharma ◽  
Narpat Singh
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Recent Work ◽  
Research Work ◽  
Early Research ◽  
Individual Person ◽  
Signature Detection ◽  
Online Signature ◽  
The Right ◽  
Handwritten Signature

In the recent research work, the handwritten signature is a suitable field to detection of valid signature from different environment such online signature and offline signature. In early research work, a lot of unauthorized person put the signature and theft the data in illegal manner from organization or industries. So we have to need identify, the right person on the basis of various parameters that can be detected. In this paper, we have proposed two methods namely LDA and Neural Network for the offline signature from the scan signature image. For efficient research, we have focused the comparative analysis in terms of FRR, SSIM, MSE, and PSNR. These parameters are compared with the early work and the recent work. Our proposed work is more effective and provides the suitable result through our method which leads to existing work. Our method will help to find legal signature of authorized use for security and avoid illegal work.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110195
Author(s):  
Sorin Grigorescu ◽  
Cosmin Ginerica ◽  
Mihai Zaha ◽  
Gigel Macesanu ◽  
Bogdan Trasnea
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Process Model ◽  
Network Performance ◽  
Vision System ◽  
Nonlinear Model Predictive Control ◽  
Scaled Model

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

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