scholarly journals A Fusion-Based Hybrid-Feature Approach for Recognition of Unconstrained Offline Handwritten Hindi Characters

2021 ◽  
Vol 13 (9) ◽  
pp. 239
Author(s):  
Danveer Rajpal ◽  
Akhil Ranjan Garg ◽  
Om Prakash Mahela ◽  
Hassan Haes Alhelou ◽  
Pierluigi Siano
Keyword(s):  
Character Recognition ◽  
Large Population ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Hybrid Features ◽  
Efficient Management ◽  
Novel Approach ◽  
Proposed Model ◽  
Machine Learning Approach

Hindi is the official language of India and used by a large population for several public services like postal, bank, judiciary, and public surveys. Efficient management of these services needs language-based automation. The proposed model addresses the problem of handwritten Hindi character recognition using a machine learning approach. The pre-trained DCNN models namely; InceptionV3-Net, VGG19-Net, and ResNet50 were used for the extraction of salient features from the characters’ images. A novel approach of fusion is adopted in the proposed work; the DCNN-based features are fused with the handcrafted features received from Bi-orthogonal discrete wavelet transform. The feature size was reduced by the Principal Component Analysis method. The hybrid features were examined with popular classifiers namely; Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). The recognition cost was reduced by 84.37%. The model achieved significant scores of precision, recall, and F1-measure—98.78%, 98.67%, and 98.69%—with overall recognition accuracy of 98.73%.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

A COMPARITIVE ANALYSIS AND TIME SERIES FORECASTING OF MONTHLY STREAM FLOW DATA USING HYBRID MODEL

2015 ◽  
Vol 76 (13) ◽  
Author(s):  
Siraj Muhammed Pandhiani ◽  
Ani Shabri
Keyword(s):  
Hybrid Model ◽  
Stream Flow ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Flow Data ◽  
Indus River ◽  
Proposed Model

In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.

Biomimetic Pattern Recognition for Classification of Proteomic Profile

2013 ◽  
Vol 791-793 ◽  
pp. 1961-1964
Author(s):  
Xiao Li Yang ◽  
Qiong He
Keyword(s):  
Pattern Recognition ◽  
Cancer Diagnosis ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Proteomic Profile ◽  
Satisfactory Model ◽  
Over Time

We propose a biomimetic pattern recognition (BPR) approach for classification of proteomic profile. The proposed approach preprocess profile using iterative minimum in adaptive setting window (IMASW) method for baseline correction, discrete wavelet transform (DWT) for fitting and smoothing, and average total ion normalization (ATIN) for remove the influence of vary amount of sample and degradation over time. Then principal component analysis (PCA) and BPR build classification model. With an optimization of the parameters involved in the modeling, we obtain a satisfactory model for cancer diagnosis in three proteomic profile datasets. The predicted results show that BPR technique is more reliable and efficient than support vector machine (SVM) method.

BASIC HANDWRITTEN CHARACTER RECOGNITION FROM MULTI-LINGUAL IMAGE DATASET USING MULTI-RESOLUTION AND MULTI-DIRECTIONAL TRANSFORM

2012 ◽  
Vol 10 (05) ◽  
pp. 1250046 ◽  
Author(s):  
SHITALA PRASAD ◽  
GYANENDRA K. VERMA ◽  
BHUPESH KUMAR SINGH ◽  
PIYUSH KUMAR
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Morphological Alteration ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Novel Approach ◽  
Pros And Cons ◽  
Resolution Algorithm ◽  
Level Fusion

This paper, proposes a novel approach for feature extraction based on the segmentation and morphological alteration of handwritten multi-lingual characters. We explored multi-resolution and multi-directional transforms such as wavelet, curvelet and ridgelet transform to extract classifying features of handwritten multi-lingual images. Evaluating the pros and cons of each multi-resolution algorithm has been discussed and resolved that Curvelet-based features extraction is most promising for multi-lingual character recognition. We have also applied some morphological operation such as thinning and thickening then feature level fusion is performed in order to create robust feature vector for classification. The classification is performed with K-nearest neighbor (K-NN) and support vector machine (SVM) classifier with their relative performance. We experiment with our in-house dataset, compiled in our lab by more than 50 personnel.

scholarly journals Hyperspectral image classification using support vector machines

2020 ◽  
Vol 9 (4) ◽  
pp. 684
Author(s):  
Jonnadula Dr.J.Harikiran Harikiran
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Bidimensional Empirical Mode Decomposition

In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

An Artificial Intelligent Centered Object Inspection System Using Crucial Images

2018 ◽  
Vol 5 (1) ◽  
pp. 44-57 ◽  
Author(s):  
Santosh Kumar Sahoo ◽  
B. B. Choudhury
Keyword(s):  
Principal Component ◽  
Cuckoo Search ◽  
Basic Function ◽  
Least Square ◽  
Support Vector ◽  
Inspection System ◽  
Bench Mark ◽  
Test Functions ◽  
Proposed Model ◽  
Object Inspection

This article proposes a unique optimization algorithm like Adaptive Cuckoo Search (AdCS) algorithm followed by an Intrinsic Discriminant Analysis (IDA) to design an intelligent object classifier for inspection of defective object like bottle in a manufacturing unit. By using this methodology the response time is very faster than the other techniques. The projected scheme is authenticated using different bench mark test functions along with an effective inspection procedure for identification of bottle by using AdCS, Principal-Component-Analysis (PCA) and IDA. Due to this the projected procedures terms as PCA+IDA for dimension reduction in addition to this AdCS-IDA for classification or identification of defective bottles. The analyzed response obtained from by an application of AdCS algorithm followed by IDA and compared to other algorithm like Least-Square-Support-Vector-Machine (LSSVM), Linear Kernel Radial-Basic-Function (RBF) to the proposed model, the earlier applied scheme reveals the remarkable performance.

scholarly journals Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 218 ◽  
Author(s):  
Nan Wei ◽  
Changjun Li ◽  
Jiehao Duan ◽  
Jinyuan Liu ◽  
Fanhua Zeng
Keyword(s):  
Deep Learning ◽  
Natural Gas ◽  
Load Forecasting ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Weather Factors ◽  
Original Dataset ◽  
Proposed Model

Forecasting daily natural gas load accurately is difficult because it is affected by various factors. A large number of redundant factors existing in the original dataset will increase computational complexity and decrease the accuracy of forecasting models. This study aims to provide accurate forecasting of natural gas load using a deep learning (DL)-based hybrid model, which combines principal component correlation analysis (PCCA) and (LSTM) network. PCCA is an improved principal component analysis (PCA) and is first proposed here in this paper. Considering the correlation between components in the eigenspace, PCCA can not only extract the components that affect natural gas load but also remove the redundant components. LSTM is a famous DL network, and it was used to predict daily natural gas load in our work. The proposed model was validated by using recent natural gas load data from Xi’an (China) and Athens (Greece). Additionally, 14 weather factors were introduced into the input dataset of the forecasting model. The results showed that PCCA–LSTM demonstrated better performance compared with LSTM, PCA–LSTM, back propagation neural network (BPNN), and support vector regression (SVR). The lowest mean absolute percentage errors of PCCA–LSTM were 3.22% and 7.29% for Xi’an and Athens, respectively. On these bases, the proposed model can be regarded as an accurate and robust model for daily natural gas load forecasting.

SVM and HMM Classifier Combination Based Approach for Online Handwritten Indic Character Recognition

2020 ◽  
Vol 13 (2) ◽  
pp. 200-214
Author(s):  
Rajib Ghosh ◽  
Prabhat Kumar
Keyword(s):  
Character Recognition ◽  
Text Recognition ◽  
Support Vector ◽  
Present System ◽  
Novel Approach ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Shafer Theory ◽  
Public Datasets ◽  
Indic Scripts

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

CARDIAC ADVANCED ARRYTHMIA PREDICTION SYSTEMS- CAAPS

2021 ◽  
pp. 54-55
Author(s):  
Pradeep Kumar Radhakrishnan ◽  
Gayathri Ananyajyothi Ambat ◽  
Saihrudya Samhita ◽  
Murugan U S ◽  
Tarig Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Ventricular Arrhythmias ◽  
Deep Neural Network ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Term Memory

There is a constant search for novel methods of classication and predicting cardiac rhythm disorders or arrhythmias. We prefer to classify them as wide complex tachyarrhythmia's or ventricular arrhythmias inclusive of malignant ventricular arrhythmias which with hemodynamic compromise is usually life threatening. Long term and fatality predictions warranting AICD implantation are already available. We have a novel method and robust algorithm with preprocessing and optimal feature selection from ECG signal analysis for such rhythm disorders. Variability of ECG recording makes predictability analysis challenging especially when execution time is of prime importance in tackling resuscitative attempts for MVA. Noisy data needs ltering and preprocessing for effective analysis. Portable devices need more of this ltering prior to data input. Deterministic probabilistic nite state automata (DPFA) which generates a probability strings from the broad morphologic patterns of an ECG can generate a classier data for the algorithm without preprocessing for atrial high rate episodes (AHRE). DPFA can be effectively used for atrial tachyarrhythmias for predictive analysis. The method we suggest is use of optimal classier set for prediction of malignant ventricular arrhythmias and use of DFPA for atrial arrhythmias. Here traditional practices of heart rate variability based support vector machine (SVM), discrete wavelet transform (DWT), principal component analysis (PCA), deep neural network (DNN), convoutional neural network (CNN) or CNN with long term memory (LSTM) can be outperformed. AICD - automatic implantable cardiac debrillator, MVA - Malignant Ventricular Arrhythmias, VT - ventricular tachycardia, VF - ventricular brillation,DFPA deterministic probabilistic nite state automata, SVM -Support Vector Machine, DWT discrete wavelet transform, PCA principal component analysis, DNN deep neural network, CNN convoutional neural network, Convoutional LSTM Long short term memory,RNN recurrent neural network

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