Improving feature extraction in keystroke dynamics using optimization techniques and neural network

2011 ◽  
Author(s):  
M. Akila ◽  
S.S. Kumar
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Optimization Techniques ◽  
Keystroke Dynamics

scholarly journals Iris Recognition using Convolutional Neural Network Design

2019 ◽  
Vol 8 (9S) ◽  
pp. 672-678
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Iris Recognition ◽  
Recognition Rate ◽  
Optimization Techniques ◽  
Backpropagation Algorithm ◽  
Generalization Ability ◽  
Softmax Classifier ◽  
Random Patterns

Iris trait has gained the attention of many researchers recently as it consists of unique and highly random patterns. Many methods have been proposed for feature extraction and classification for iris trait but suffer from poor generalization ability. In this paper, a scratch convolutional neural network is designed in order to extract the iris features and softmax classifier is used for multiclass classification. The various optimization techniques with backpropagation algorithm are used for weight updating. The results show that the Convolutional Neural Network based feature extraction has proven to provide good generalization ability with improved recognition rate. The effect of various optimization techniques for generalization ability is also observed. The method is tested on IITD and CASIA-Iris-V3 database. The recognition rates obtained are comparable with state of art methods.

scholarly journals GA based Feature Selection for Multimodal Biometric Authentication

2021 ◽  
Vol 12 (2) ◽  
pp. 526-538
Author(s):  
A. Saravana Priya ◽  
◽  
Dr. Rajeswari Mukesh
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Identity Management ◽  
Vital Role ◽  
Optimization Techniques ◽  
Physical Contact ◽  
Biometric Authentication ◽  
Feature Extraction Method ◽  
Wide Range ◽  
Artificial Neural Network Ann

Multi-modal biometric authentication effectively replaces uni-modal biometric authentication system towards addressing a wide range of technical glitches in identity management and authentication. Legitimacy is playing a vital role in banking, military, and healthcare sectors where highly secure, strategic and confidential data transmission is involved. By integrating many independent biometric systems, one can overcome the problems of spoofing. However, there is lack of a simple, efficient and sufficient biometric authentication. Hence, the present study focuses on designing and implementing a multi-modal biometric authentication using a Genetic Algorithm (GA) based feature extraction method. The proposed research focuses on extracting human Skeleton and Human face feature using 3D Imaging technology. This modelling technique is used to capture human joints including the depth data to improve the efficiency of the system. The proposed research is subdivided into three phases. These are, image preprocessing (MinMax method), feature extraction using Heuristic Optimization Techniques (HOT), and Personnel recognition via the Artificial Neural Network (ANN). The Performance of the proposed method is evaluated based on the measure of FAR, FRR and accuracy. Finally, the performance of proposed approach is compared with existing techniques like GA, Neural network, etc. Combined Biometric is done in an unobtrusive way whereas other human recognition needs physical contact.

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

scholarly journals Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Aqeel Aslam ◽  
Cuili Xue ◽  
Yunsheng Chen ◽  
Amin Zhang ◽  
Manhua Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Feature Extraction ◽  
Early Gastric Cancer ◽  
Breath Analysis ◽  
Cancer Classification ◽  
Unlabeled Data ◽  
Softmax Classifier ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

AbstractDeep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from the breath samples. Precisely, we identified fifty peaks in each spectrum to distinguish the EGC, AGC, and healthy persons. This CAD system reduces the distance between the input and output by learning the features and preserve the structure of the input data set of breath samples. The features were extracted from the unlabeled data of the breath samples. After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and repeatable. The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result for recall, precision, and f score value, making it suitable for clinical application.

scholarly journals A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 319
Author(s):  
Yi Wang ◽  
Xiao Song ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Denoising ◽  
Color Image ◽  
Rapid Development ◽  
Similarity Index ◽  
Structural Similarity ◽  
Convolutional Network ◽  
Scale Feature ◽  
Multi Scale

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

scholarly journals Fuzzy enhancement and deep hash layer based neural network to detect Covid-19

2021 ◽  
pp. 1-11
Author(s):  
Amita Nandal ◽  
Marija Blagojevic ◽  
Danijela Milosevic ◽  
Arvind Dhaka ◽  
Lakshmi Narayan Mishra
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Performance Metrics ◽  
Regularization Parameter ◽  
Fuzzy Membership Function ◽  
Kappa Statistics ◽  
Pixel Intensity ◽  
Testing Sample ◽  
X Ray ◽  
Conventional Methods

This paper proposes a deep learning framework for Covid-19 detection by using chest X-ray images. The proposed method first enhances the image by using fuzzy logic which improvises the pixel intensity and suppresses background noise. This improvement enhances the X-ray image quality which is generally not performed in conventional methods. The pre-processing image enhancement is achieved by modeling the fuzzy membership function in terms of intensity and noise threshold. After this enhancement we use a block based method which divides the image into smooth and detailed regions which forms a feature set for feature extraction. After feature extraction we insert a hashing layer after fully connected layer in the neural network. This hash layer is advantageous in terms of improving the overall accuracy by computing the feature distances effectively. We have used a regularization parameter which minimizes the feature distance between similar samples and maximizes the feature distance between dissimilar samples. Finally, classification is done for detection of Covid-19 infection. The simulation results present a comparison of proposed model with existing methods in terms of some well-known performance indices. Various performance metrics have been analysed such as Overall Accuracy, F-measure, specificity, sensitivity and kappa statistics with values 93.53%, 93.23%, 92.74%, 92.02% and 88.70% respectively for 20:80 training to testing sample ratios; 93.84%, 93.53%, 93.04%, 92.33%, and 91.01% respectively for 50:50 training to testing sample ratios; 95.68%, 95.37%, 94.87%, 94.14%, and 90.74% respectively for 80:20 training to testing sample ratios have been obtained using proposed method and it is observed that the results using proposed method are promising as compared to the conventional methods.

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