scholarly journals Writer Identification with Hybrid Model using Hybrid HMM and ANN

2019 ◽  
Vol 8 (3) ◽  
pp. 1656-1661
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hybrid Model ◽  
Optimum Number ◽  
Multi Layer Perceptron ◽  
Writer Identification ◽  
Mlp Model

In this paper, writer identification is performed with three models, namely, HMMBW, HMMMLP and HMMCNN. The features are extracted from the HMM and are classified using Baum Welch algorithm (BW), Multi layer perceptron (MLP) model and Convolutional neural network (CNN) model. A dataset, namely, VTU-WRITER dataset is created for the experiential purpose and the performance of the models were tested. The test train ratio was varied to derive its relation to accuracy. Also the number of states was varied to determine the optimum number of states to be considered in the HMM model. Finally the performance of all the three models is compared

scholarly journals Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Michael D. Paskett ◽  
Mark R. Brinton ◽  
Taylor C. Hansen ◽  
Jacob A. George ◽  
Tyler S. Davis ◽  
...  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Activities Of Daily Living ◽  
Convolutional Neural Network ◽  
Daily Living ◽  
Complex Data ◽  
Multi Layer Perceptron ◽  
Experimental Conditions ◽  
Modified Kalman Filter ◽  
Prosthesis Control

Abstract Background Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm’s output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. Methods We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. Results Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. Conclusions These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

scholarly journals Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2399 ◽  
Author(s):  
Cunwei Sun ◽  
Yuxin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fangqing Wen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Hybrid Model ◽  
Data Augmentation ◽  
Small Sample ◽  
Restricted Boltzmann Machine ◽  
Small Samples ◽  
Boltzmann Machine ◽  
Training Time

The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.

AN OFF-LINE TEXT-INDEPENDENT PERSIAN WRITER IDENTIFICATION METHOD

2011 ◽  
Vol 20 (03) ◽  
pp. 489-509 ◽  
Author(s):  
BEHZAD HELLI ◽  
MOHSEN EBRAHIMI MOGHADDAM
Keyword(s):  
Neural Network ◽  
Gabor Filter ◽  
Research Topic ◽  
Experimental Results ◽  
Multi Layer Perceptron ◽  
Writer Identification ◽  
Handwriting Analysis ◽  
Handwritten Documents ◽  
Verification Methods ◽  
Evaluation Phase

The behavioral-biometrics methods of writer identification and verification have been considered as a research topic for many years. However, many writer identification and verification methods have been designed based on English handwriting properties, but because of many differences between English and Persian handwriting and the challenges facing Persian handwriting analysis, designing such methods has many interests in Persian yet. In this paper, we have presented a fully text-independent and texture based method for identifying writers of Persian handwritten documents. As a result of special properties of Persian handwriting, a modified version of Gabor filter that is called Extended Gabor (XGabor) filter has been used to extract the features. An MLP (Multi Layer Perceptron (Node)) neural network and a K-NN classifier have been employed to classify the extracted features. In the evaluation phase, an exhaustive database of Persian handwritten documents was prepared and the method applied on. The experimental results showed that the accuracy of proposed method is about 97% and it is competitive with others. We believe that the proposed method may be extended to identify writers in other languages by adjusting some parameters.

scholarly journals Evaluation of COVID-19 spread effect on the Commercial Instagram Posts (CIPs) using ANN: a case study on Holy Shrine, Mashhad, Iran

2021 ◽  
Author(s):  
Mohammad Javad Shooshtari ◽  
Hossein Etemadfard ◽  
Rouzbeh Shad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Social Media ◽  
Convolutional Neural Network ◽  
Business Owners ◽  
Shopping Centers ◽  
Multi Layer Perceptron ◽  
Commercial Activities ◽  
Enormous Amount

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the pandemic caused by the COVID-19 spread. This study presents a heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine – a sacred complex in Mashhad, Iran, surrounded by numerous shopping centers – were impacted by the pandemic. Two datasets of Instagram posts (one gathered data from March 14th to April 10th, 2020, when Holy Shrine and nearby shops were closed, and one extracted data from the same period in 2019), two word embedding models – aimed at vectorizing associated caption of each post, and two neural networks – multi-layer perceptron and convolutional neural network – were employed to classify CIPs in 2019. Among the scenarios defined for the 2019 CIPs classification, the results revealed that the combination of MLP and CBoW achieved the best performance, which was then used for the 2020 CIPs classification. It is found out that the fraction of CIPs to total Instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using Instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. Moreover, the portion of non-commercial Instagram posts (NCIPs) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the Holy Shrine was closed, Mashhad citizens and tourists could not visit it and take photos to post on their Instagram accounts.

scholarly journals HISTOGRAM OF ORIENTED GRADIENT UNTUK DETEKSI EKSPRESI WAJAH MANUSIA

2018 ◽  
Vol 10 (2) ◽  
pp. 81-86
Author(s):  
Marlinda Vasty Overbeek
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Facial Expressions ◽  
Moving Objects ◽  
Image Data ◽  
Gradient Algorithm ◽  
Multi Layer Perceptron ◽  
Histogram Of Oriented Gradient ◽  
Perceptron Algorithm ◽  
Feature Extracting

This research focuses on the detection of human facial expressions using the Histogram of Oriented Gradient algorithm. Whereas for the classification algorithm, Convolutional Neural Network is used. Image data used in the form of seven different expressions of humans with the extraction of 48x48 pixels. The use of Histogram of Oriented Gradient as a feature extracting algorithm, because Histogram of Oriented Gradient is good to be used in detecting moving objects. Whereas Convolutional Neural Network is used because it is an improvement of the Multi Layer Perceptron algorithm. Of the three epoches done, it produced the best accuracy of 77% re-introduction of human facial expressions. These results are quite convincing because it only uses three epochs.

scholarly journals Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2508 ◽  
Author(s):  
Guolong Zhang ◽  
Ping Wang ◽  
Haibing Chen ◽  
Lan Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gaussian Process ◽  
Hybrid Model ◽  
Nearest Neighbor ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Training Dataset ◽  
K Nearest Neighbor ◽  
Positive Effects

This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR.

Klasifikasi motif batik menggunakan Convolutional Neural Network

JNANALOKA ◽  
2020 ◽  
pp. 45-50
Author(s):  
Rizki Mawan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Multi Layer Perceptron

Batik adalah bentuk seni visual pada bahan tekstil yang diproduksi menggunakan teknik menggambar tradisional yang berasal dari Indonesia. Oleh karena itu dibutuhkan penelitian untuk meneliti batik yang bertujuan untuk mengetahui motif dan melestarikannya. Convolutional Neural Network(CNN) adalah salah satu metode machine learning dari pengembangan Multi Layer Perceptron (MLP) yang didesain untuk mengolah data dua dimensi. CNN termasuk dalam jenis Deep Neural Network karena dalamnya tingkat jaringan dan banyak diimplementasikan dalam data citra. Eksperimen menggunakan Dataset 120 potongan foto Batik (3 kelas) menunjukkan bahwa model yang menggunakan CNN mencapai rata-rata akurasi 65% sedangkan model CNN dikombinasi dengan Grayscale mencapai rata-rata akurasi 70%. Meskipun demikian dengan penambahan Grayscale akurasi bertambah 5%.

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