scholarly journals Handwriting Character Recognition using Vector Quantization Technique

2019 ◽  
Vol 2 (2) ◽  
pp. 82
Author(s):  
Haviluddin Haviluddin ◽  
Rayner Alfred ◽  
Ni’mah Moham ◽  
Herman Santoso Pakpahan ◽  
Islamiyah Islamiyah ◽  
...  
Keyword(s):  
Vector Quantization ◽  
Learning Process ◽  
Network Architecture ◽  
Character Recognition ◽  
Mean Square ◽  
Processing Stage ◽  
Optimal Variant ◽  
Hidden Layer ◽  
Time Required ◽  
Accuracy Degree

This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results. 

scholarly journals Pengenalan Karakter Tulisan Menggunakan Metode Learning Vector Quantization

2019 ◽  
Vol 1 (2) ◽  
pp. 23
Author(s):  
Dinda Izmya Nurpadillah ◽  
Haviluddin Haviluddin ◽  
Herman Santoso Pakpahan ◽  
Islamiyah Islamiyah ◽  
Hario Jati Setyadi
Keyword(s):  
Mean Square Error ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Mean Square ◽  
Total Data ◽  
Hidden Layer

Artikel ini mengimplementasikan metode Learning Vector Quantization (LVQ) dalam mengenali pola aksara Sunda. Berdasarkan hasil eksperimen dengan berbagai parameter seperti learning rate dan jumlah hidden layer maka metode LVQ cukup akurat dalam mengenali pola aksara Sunda dengan nilai akurasi sebesar 6.66% dari data yang berhasil dikenali sebanyak 28 data dengan total data uji sebanyak 42 data dengan variasi learning rate sebesar 0.01 dan jumlah hidden layer sebanyak 90 layer. Hasil akurasi tersebut didapatkan dengan waktu pembelajaran yaitu selama 17 menit 22 detik. Adapun mean square error (MSE) yang dihasilkan sebesar 0.0408. Dari hasil akurasi, MSE dan waktu pembelajaran yang didapatkan maka dapat dikatakan metode LVQ belum optimal dalam memecahkan masalah pengenalan pola terutama aksara Sunda. Teknik optimalisasi kepada proses pembelajaran LVQ dengan algoritma-algoritma optimasi merupakan rencana penelitian selanjutnya.

scholarly journals Image reconstruction through a multimode fiber with a simple neural network architecture

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Changyan Zhu ◽  
Eng Aik Chan ◽  
You Wang ◽  
Weina Peng ◽  
Ruixiang Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Training Time ◽  
Modal Dispersion ◽  
Multimode Fibers ◽  
Speckle Patterns ◽  
Hidden Layer ◽  
Simple Neural Network

AbstractMultimode fibers (MMFs) have the potential to carry complex images for endoscopy and related applications, but decoding the complex speckle patterns produced by mode-mixing and modal dispersion in MMFs is a serious challenge. Several groups have recently shown that convolutional neural networks (CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in terms of training time and computing resources required. The trained networks can accurately reconstruct MMF images collected over a week after the cessation of the training set, with the dense network performing as well as the CNN over the entire period.

Designing an efficient predictor model using PSNN and crow search based optimization technique for gold price prediction

2021 ◽  
pp. 1-9
Author(s):  
Rajashree Dash ◽  
Anuradha Routray ◽  
Rasmita Dash ◽  
Rasmita Rautray
Keyword(s):  
Mean Square Error ◽  
Optimization Technique ◽  
Absolute Error ◽  
Time Frame ◽  
Gold Price ◽  
Future Price ◽  
Mean Square ◽  
Learning Speed ◽  
Predictor Model ◽  
Hidden Layer

Predicting future price of Gold has always been an intriguing field of investigation for researchers as well as investors who desire to invest in present and gain profit in the future. Since ancient time, Gold is being arbitrated as a leading asset in monetary business. As the worth of gold changes within confined boundaries, reducing the effect of inflation, so it is a beneficial property favoured by many stakeholders. Hence, there is always an urge of a more authenticate model for forecasting the gold price based upon the changes in it in a previous time frame. This study focuses on designing an efficient predictor model using a Pi-Sigma Neural Network (PSNN) for forecasting future gold. The underlying motivation of using PSNN is its quick learning and easy implementation compared to other neural networks. The fixed unit weights used in between hidden and output layer of PSNN helps it in achieving faster learning speed compared to other similar types of networks. But estimating the unknown weights used in between the input and hidden layer is still a major challenge in its design phase. As final outcome of the network is highly influenced by its weight, so a novel Crow Search based nature inspired optimization algorithm (CSA) is proposed to estimate these adjustable weights of the network. The proposed model is also compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of PSNN. The model is validated over two historical datasets such as Gold/INR and Gold/AED by considering three statistical errors such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Empirical observations clearly show that, the developed CSA-PSNN predictor model is providing better prediction results compared to PSO-PSNN and DE-PSNN model.

scholarly journals BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition

2021 ◽  
Vol 11 (15) ◽  
pp. 6845
Author(s):  
Abu Sayeed ◽  
Jungpil Shin ◽  
Md. Al Mehedi Hasan ◽  
Azmain Yakin Srizon ◽  
Md. Mehedi Hasan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Character Recognition ◽  
High Performance ◽  
Low Cost ◽  
Traditional Learning ◽  
Neural Network Architecture ◽  
Handwritten Character Recognition ◽  
Handwritten Character

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.

Fourier Neural Networks and Generalized Single Hidden Layer Networks in Aircraft Engine Fault Diagnostics

2005 ◽  
Vol 128 (4) ◽  
pp. 773-782 ◽  
Author(s):  
H. S. Tan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Fourier Transforms ◽  
Aircraft Engine ◽  
Fault Classification ◽  
Noise Robustness ◽  
Fault Diagnostics ◽  
Multiple Faults ◽  
Hidden Layer

The conventional approach to neural network-based aircraft engine fault diagnostics has been mainly via multilayer feed-forward systems with sigmoidal hidden neurons trained by back propagation as well as radial basis function networks. In this paper, we explore two novel approaches to the fault-classification problem using (i) Fourier neural networks, which synthesizes the approximation capability of multidimensional Fourier transforms and gradient-descent learning, and (ii) a class of generalized single hidden layer networks (GSLN), which self-structures via Gram-Schmidt orthonormalization. Using a simulation program for the F404 engine, we generate steady-state engine parameters corresponding to a set of combined two-module deficiencies and require various neural networks to classify the multiple faults. We show that, compared to the conventional network architecture, the Fourier neural network exhibits stronger noise robustness and the GSLNs converge at a much superior speed.

scholarly journals Diagnosa Penyakit Demam Berdarah Dengue (DBD) menggunakan Metode Learning Vector Quantization (LVQ)

2020 ◽  
Vol 4 (3) ◽  
pp. 56
Author(s):  
Firman Tawakal ◽  
Ahmedika Azkiya
Keyword(s):  
Neural Network ◽  
Typhoid Fever ◽  
Vector Quantization ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Learning Vector Quantization ◽  
Mean Square ◽  
Northern Australia ◽  
Neural Network Learning ◽  
Network Learning

Dengue Hemorrhagic Fever is a disease that is carried and transmitted through the mosquito Aedes aegypti and Aedes albopictus which is commonly found in tropical and subtropical regions such as in Indonesia to Northern Australia. in 2013 there are 2.35 million reported cases, which is 37,687 case is heavy cases of DHF. DHF’s symthoms have a similarity with typhoid fever, it often occur wrong handling. Therefore we need a system that is able to diagnose the disease suffered by patients, so that they can recognize whether the patient has DHF or Typhoid. The system will be built using Neural Network Learning Vector Quantization (LVQ) based on the best training results. This research is to diagnose Dengue Hemorrhagic Fever using LVQ with input parameters are hemoglobin, leukocytes, platelets, and heritrocytes. Based on result, the best accuracy is 97,14% with Mean Square Error (MSE) is 0.028571 with 84 train data and 36 test data. Conclution from the research is LVQ method can diagnose DHF Keywords: Dengue Hemorrhagic Fever; Learning Vector Quantization; classification; Neural Network;

scholarly journals Financial Time Series Image Algorithm Based on Wavelet Analysis and Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wuwei Liu ◽  
Jingdong Yan
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Mean Square Error ◽  
Financial Time Series ◽  
Prediction Method ◽  
Mean Square ◽  
Rbf Network ◽  
Network Prediction ◽  
The Mean ◽  
Hidden Layer

In recent years, people are more and more interested in time series modeling and its application in prediction. This paper mainly discusses a financial time series image algorithm based on wavelet analysis and data fusion. In this research, we conducted an in-depth study on the scale decomposition sequence and wavelet transform sequence in different scale domains of wavelet transform according to the scale change rule based on wavelet transform. We use wavelet neural network with different input neurons and hidden neurons to predict, respectively. Finally, the prediction results are integrated into the final prediction results based on the original time series by using wavelet reconstruction technology. Using RBF algorithm in neural network and SPSS Clementine, the wavelet transform sequences on five scales are modeled. Each network model has three layers: one input layer, one hidden layer, and one output layer, and each output layer has only one output element. In order to compare the prediction effect of the model proposed in this study, the ordinary RBF network is used to model and predict the log yield itself. When the input sample is 5, the minimum mean square error is obtained when the hidden layer is 6, and the mean square error is 1.6349. The mean square error of the training phase is 0.0209, and the validation error is 1.6141. The results show that the prediction results of the wavelet prediction method combined with the RBF network prediction method are better than those of wavelet prediction or RBF network prediction.

scholarly journals Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography

2020 ◽  
Author(s):  
Víctor de la Fuente Castillo ◽  
Alberto Díaz-Álvarez ◽  
Miguel-Ángel Manso-Callejo ◽  
Francisco Serradilla García
Keyword(s):  
Genetic Programming ◽  
Network Architecture ◽  
Structural Diversity ◽  
Network Architectures ◽  
Complex Structures ◽  
Road Detection ◽  
Learning Techniques ◽  
Candidate Network ◽  
Optimal Network ◽  
Hidden Layer

Photogrammetry involves aerial photography of the earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It’s used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep Learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our system applies grammar guided genetic programming to the search of deep neural network architectures. In this kind of evolutive algorithm all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g. Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state of the art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

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