A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition

Convolutional neural networks are sensitive to unknown noisy condition in the test phase and so their performance degrades for the noisy data classification task including noisy speech recognition. In this research, a new convolutional neural network (CNN) model with data uncertainty handling; referred as NCNN (Neutrosophic Convolutional Neural Network); is proposed for classification task. Here, speech signals are used as input data and their noise is modeled as uncertainty. In this task, using speech spectrogram, a definition of uncertainty is proposed in neutrosophic (NS) domain. Uncertainty is computed for each Time-frequency point of speech spectrogram as like a pixel. Therefore, uncertainty matrix with the same size of spectrogram is created in NS domain. In the next step, a two parallel paths CNN classification model is proposed. Speech spectrogram is used as input of the first path and uncertainty matrix for the second path. The outputs of two paths are combined to compute the final output of the classifier. To show the effectiveness of the proposed method, it has been compared with conventional CNN on the isolated words of Aurora2 dataset. The proposed method achieves the average accuracy of 85.96 in noisy train data. It is more robust against Car, Airport and Subway noises with accuracies 90, 88 and 81 in test sets A, B and C, respectively. Results show that the proposed method outperforms conventional CNN with the improvement of 6, 5 and 2 percentage in test set A, test set B and test sets C, respectively. It means that the proposed method is more robust against noisy data and handle these data effectively.Comment: International conference on Pattern Recognition and Image Analysis (IPRIA 2019

Certainty of outlier and boundary points processing in data mining

Data certainty is one of the issues in the real-world applications which is caused by unwanted noise in data. Recently, more attentions have been paid to overcome this problem. We proposed a new method based on neutrosophic set (NS) theory to detect boundary and outlier points as challenging points in clustering methods. Generally, firstly, a certainty value is assigned to data points based on the proposed definition in NS. Then, certainty set is presented for the proposed cost function in NS domain by considering a set of main clusters and noise cluster. After that, the proposed cost function is minimized by gradient descent method. Data points are clustered based on their membership degrees. Outlier points are assigned to noise cluster and boundary points are assigned to main clusters with almost same membership degrees. To show the effectiveness of the proposed method, two types of datasets including 3 datasets in Scatter type and 4 datasets in UCI type are used. Results demonstrate that the proposed cost function handles boundary and outlier points with more accurate membership degrees and outperforms existing state of the art clustering methods.Comment: Conference Paper, 6 page

Some Results on Multigranulation Neutrosophic Rough Sets on a Single Domain

As a generalization of single value neutrosophic rough sets, the concept of multi-granulation neutrosophic rough sets was proposed by Bo et al., and some basic properties of the pessimistic (optimistic) multigranulation neutrosophic rough approximation operators were studied

Note on Square Neutrosophic Fuzzy Matrices

In this article, we shall define the addition and multiplication of two neutrosophic fuzzy matrices. Thereafter, some properties of addition and multiplication of these matrices are also put forward

A Novel Image Segmentation Algorithm Based on Neutrosophic Filtering and Level Set

Image segmentation is an important step in image processing and analysis, pattern recognition, and machine vision. A few of algorithms based on level set have been proposed for image segmentation in the last twenty years. However, these methods are time consuming, and sometime fail to extract the correct regions especially for noisy images. Recently, neutrosophic set (NS) theory has been applied to image processing for noisy images with indeterminant information. In this paper, a novel image segmentation approach is proposed based on the filter in NS and level set theory

PERBANDINGAN RUANG WARNA PADA PENGOLAHAN INFORMASI WARNA UNTUK SEGMENTASI CITRA MENGGUNAKAN NEUTROSOPHIC SET

Warna merupakan salah satu informasi yang dapat membedakan antar kelompok pada segmentasi citra. Informasi warna diekstraksi ke dalam ruang warna tertentu sebelum dilakukan proses segmentasi. Pemilihan ruang warna yang sesuai dengan karakteristik citra berwarna dapat meningkatkan hasil segmentasi citra. Ruang warna seragam (CIE L*a*b* dan L*u*v*) merupakan ruang warna yang sesuai dengan persepsi manusia, cocok digunakan untuk segmentasi citra. Neutrosophic set (NS), merupakan generalisasi dari fuzzy set, di mana setiap anggota himpunan mempunyai nilai kebenaran, kesalahan dan ketidakpastian. NS dapat digunakan untuk menyelesaikan ketidakpastian pada segmentasi citra. Pada penelitian ini, dibandingkan penggunaan tiga ruang warna (RGB, L*a*b* dan L*u*v*) pada segmentasi citra menggunakan NS. Ekstraksi warna pada suatu ruang warna akan ditransformasikan ke citra neutrosophic. Operasi -mean dan -enhancement dilakukan untuk mengurangi ketidakpastian pada citra neutrosophic berdasarkan nilai entropy citra. Proses segmentasi citra menggunakan -K-means clustering. Hasil uji coba perbandingan ruang warna menunjukkan bahwa pengolahan informasi warna pada ruang warna L*u*v* menghasilkan segmentasi citra lebih baik dibandingkan dengan ruang warna L*a*b* dan RGB. Hasil segmentasi ruang warna L*u*v* dengan -K-means clustering pada domain NS juga menghasilkan kinerja yang lebih baik dibanding Fuzzy C-means clustering pada domain NS maupun K-means clustering tanpa NS. Kata kunci : Neutrosophic set, ruang warna RGB, ruang warna L*a*b*, ruang warna L*u*v*, - K-Means clusterin

Multiple Criteria Evaluation Model Based on the Single Valued Neutrosophic Set

Gathering the attitudes of the examined respondents would be very significant in some evaluation models. Therefore, a multiple criteria approach based on the use of the neutrosophic set is considered in this paper. An example of the evaluation of restaurants is considered at the end of this paper with the aim to present in detail the proposed approach

On Neutrosophic Implications

In this paper, we firstly review the neutrosophic set, and then construct two new concepts called neutrosophic implication of type 1 and of type 2 for neutrosophic sets. Furthermore, some of their basic properties and some results associated with the two neutrosophic implications are proven