Identification of Covid’19 Vaccinator by Deep Learning Approach Using Contactless Palmprints

The invention of the first vaccine has also raised several anti-vaccination views among people. Vaccine reluctance may be exacerbated by the growing reliance on social media, which is considered as a source of health information. During this COVID'19 scenario, the verification of non-vaccinators via the use of biometric characteristics has received greater attention, especially in areas such as vaccination monitoring and other emergency medical services, among other things. The traditional digital camera utilizes the middle-resolution images for commercial applications in a regulated or contact-based environment with user participation, while the latter uses high-resolution latent palmprints. This research study attempts to utilize convolutional neural networks (CNN) for the first time to perform contactless recognition. To identify the COVID '19 vaccine using the CNN technique, this research work has used the contactless palmprint method. Further, this research study utilizes the PalmNet structure of convolutional neural network to resolve the issue. First, the ROI region of the palmprint was extracted from the input picture based on the geometric form of the print. After image registration, the ROI region is sent into a convolutional neural network as an input. The softmax activation function is then used to train the network so that it can choose the optimal learning rate and super parameters for the given learning scenario. The neural networks of the deep learning platform were then compared and summarized.

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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽

10.3390/s21082852 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2852

Author(s):

Parvathaneni Naga Srinivasu ◽

Jalluri Gnana SivaSai ◽

Muhammad Fazal Ijaz ◽

Akash Kumar Bhoi ◽

Wonjoon Kim ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Skin Disease ◽

Network Architecture ◽

Large Scale ◽

Short Term Memory ◽

Convolutional Networks ◽

Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Hyperparameters optimization for ResNet and Xception in the purpose of diagnosing COVID-19

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210925 ◽

2021 ◽

pp. 1-17

Author(s):

Hania H. Farag ◽

Lamiaa A. A. Said ◽

Mohamed R. M. Rizk ◽

Magdy Abd ElAzim Ahmed

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Random Search ◽

Learning Networks ◽

Residual Network ◽

Global Pandemic ◽

Search Optimization

COVID-19 has been considered as a global pandemic. Recently, researchers are using deep learning networks for medical diseases’ diagnosis. Some of these researches focuses on optimizing deep learning neural networks for enhancing the network accuracy. Optimizing the Convolutional Neural Network includes testing various networks which are obtained through manually configuring their hyperparameters, then the configuration with the highest accuracy is implemented. Each time a different database is used, a different combination of the hyperparameters is required. This paper introduces two COVID-19 diagnosing systems using both Residual Network and Xception Network optimized by random search in the purpose of finding optimal models that give better diagnosis rates for COVID-19. The proposed systems showed that hyperparameters tuning for the ResNet and the Xception Net using random search optimization give more accurate results than other techniques with accuracies 99.27536% and 100 % respectively. We can conclude that hyperparameters tuning using random search optimization for either the tuned Residual Network or the tuned Xception Network gives better accuracies than other techniques diagnosing COVID-19.

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Convolutional Neural Network for Iris Recognition

10.21203/rs.3.rs-244624/v1 ◽

2021 ◽

Author(s):

Wael Alnahari

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Iris Recognition ◽

Recognition System ◽

High Accuracy ◽

Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

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Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9961.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 608-612

Keyword(s):

Neural Network ◽

Resource Allocation ◽

Deep Learning ◽

Convolutional Neural Network ◽

Data Storage ◽

Memory Management ◽

Research Work ◽

Dynamic Resource Allocation ◽

Mobile Platform ◽

Dynamic Resource

Memory management is very essential task for large-scale storage systems; in mobile platform generate storage errors due to insufficient memory as well as additional task overhead. Many existing systems have illustrated different solution for such issues, like load balancing and load rebalancing. Different unusable applications which are already installed in mobile platform user never access frequently but it allocates some memory space on hard device storage. In the proposed research work we describe dynamic resource allocation for mobile platforms using deep learning approach. In Real world mobile systems users may install different kind of applications which required ad-hoc basis. Such applications may be affect to execution performance of system as well space complexity, sometime they also affect another runnable applications performance. To eliminate of such issues, we carried out an approach to allocate runtime resources for data storage for mobile platform. When system connected with cloud data server it store complete file system on remote Virtual Machine (VM) and whenever a single application required which immediately install beginning as remote server to local device. For developed of proposed system we implemented deep learning base Convolutional Neural Network (CNN), algorithm has used with tensorflow environment which reduces the time complexity for data storage as well as extraction respectively.

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Using Convolutional Neural Networks for the Detection of Spinal Transverse Processes

Inquiry@Queen's Undergraduate Research Conference Proceedings ◽

10.24908/iqurcp.13355 ◽

2019 ◽

Author(s):

Victoria Wu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transverse Process ◽

Classification Model ◽

Ultrasound Images ◽

Ultrasound Scan ◽

X Ray ◽

Scoliosis Screening

Introduction: Scoliosis, an excessive curvature of the spine, affects approximately 1 in 1,000 individuals. As a result, there have formerly been implementations of mandatory scoliosis screening procedures. Screening programs are no longer widely used as the harms often outweigh the benefits; it causes many adolescents to undergo frequent diagnosis X-ray procedure This makes spinal ultrasounds an ideal substitute for scoliosis screening in patients, as it does not expose them to those levels of radiation. Spinal curvatures can be accurately computed from the location of spinal transverse processes, by measuring the vertebral angle from a reference line [1]. However, ultrasound images are less clear than x-ray images, making it difficult to identify the spinal processes. To overcome this, we employ deep learning using a convolutional neural network, which is a powerful tool for computer vision and image classification [2]. Method: A total of 2,752 ultrasound images were recorded from a spine phantom to train a convolutional neural network. Subsequently, we took another recording of 747 images to be used for testing. All the ultrasound images from the scans were then segmented manually, using the 3D Slicer (www.slicer.org) software. Next, the dataset was fed through a convolutional neural network. The network used was a modified version of GoogLeNet (Inception v1), with 2 linearly stacked inception models. This network was chosen because it provided a balance between accurate performance, and time efficient computations. Results: Deep learning classification using the Inception model achieved an accuracy of 84% for the phantom scan. Conclusion: The classification model performs with considerable accuracy. Better accuracy needs to be achieved, possibly with more available data and improvements in the classification model. Acknowledgements: G. Fichtinger is supported as a Canada Research Chair in Computer-Integrated Surgery. This work was funded, in part, by NIH/NIBIB and NIH/NIGMS (via grant 1R01EB021396-01A1 - Slicer+PLUS: Point-of-Care Ultrasound) and by CANARIE’s Research Software Program. Figure 1: Ultrasound scan containing a transverse process (left), and ultrasound scan containing no transverse process (right). Figure 2: Accuracy of classification for training (red) and validation (blue). References: Ungi T, King F, Kempston M, Keri Z, Lasso A, Mousavi P, Rudan J, Borschneck DP, Fichtinger G. Spinal Curvature Measurement by Tracked Ultrasound Snapshots. Ultrasound in Medicine and Biology, 40(2):447-54, Feb 2014. Krizhevsky A, Sutskeyer I, Hinton GE. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25:1097-1105.

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Convolutional Neural Network

10.4018/978-1-6684-2408-7.ch077 ◽

2022 ◽

pp. 1559-1575

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

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Research on improved convolutional wavelet neural network

Scientific Reports ◽

10.1038/s41598-021-97195-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jingwei Liu ◽

Peixuan Li ◽

Xuehan Tang ◽

Jiaxin Li ◽

Jiaming Chen

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Radial Basis Function ◽

Error Rate ◽

Basis Function ◽

Activation Function ◽

Wavelet Neural Network ◽

Mean Square ◽

The Mean

AbstractArtificial neural networks (ANN) which include deep learning neural networks (DNN) have problems such as the local minimal problem of Back propagation neural network (BPNN), the unstable problem of Radial basis function neural network (RBFNN) and the limited maximum precision problem of Convolutional neural network (CNN). Performance (training speed, precision, etc.) of BPNN, RBFNN and CNN are expected to be improved. Main works are as follows: Firstly, based on existing BPNN and RBFNN, Wavelet neural network (WNN) is implemented in order to get better performance for further improving CNN. WNN adopts the network structure of BPNN in order to get faster training speed. WNN adopts the wavelet function as an activation function, whose form is similar to the radial basis function of RBFNN, in order to solve the local minimum problem. Secondly, WNN-based Convolutional wavelet neural network (CWNN) method is proposed, in which the fully connected layers (FCL) of CNN is replaced by WNN. Thirdly, comparative simulations based on MNIST and CIFAR-10 datasets among the discussed methods of BPNN, RBFNN, CNN and CWNN are implemented and analyzed. Fourthly, the wavelet-based Convolutional Neural Network (WCNN) is proposed, where the wavelet transformation is adopted as the activation function in Convolutional Pool Neural Network (CPNN) of CNN. Fifthly, simulations based on CWNN are implemented and analyzed on the MNIST dataset. Effects are as follows: Firstly, WNN can solve the problems of BPNN and RBFNN and have better performance. Secondly, the proposed CWNN can reduce the mean square error and the error rate of CNN, which means CWNN has better maximum precision than CNN. Thirdly, the proposed WCNN can reduce the mean square error and the error rate of CWNN, which means WCNN has better maximum precision than CWNN.

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Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input

Sensors ◽

10.3390/s20144017 ◽

2020 ◽

Vol 20 (14) ◽

pp. 4017 ◽

Cited By ~ 3

Author(s):

Davor Kolar ◽

Dragutin Lisjak ◽

Michał Pająk ◽

Danijel Pavković

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Input Matrix ◽

Accelerometer Signal ◽

Rotary Machinery ◽

Signal Input

Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions 6400 × 1 × 3 is used as input for convolutional neural network training. Since convolutional neural networks can recognize patterns across input matrix, it is expected that wide input matrix containing vibration data should yield good classification performance. Using convolutional neural networks (CNN) trained model, classification in one of the four classes can be performed. Additionally, number of kernels of CNN is optimized using grid search, as preliminary studies show that alternating number of kernels impacts classification results. This study accomplished the effective classification of different rotary machinery states using convolutional artificial neural network for classification of raw three axis accelerometer signal input.

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Dental Caries Classification System Using Deep Learning Based Convolutional Neural Network

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9295 ◽

2020 ◽

Vol 17 (9) ◽

pp. 4660-4665

Author(s):

L. Megalan Leo ◽

T. Kalpalatha Reddy

Keyword(s):

Neural Network ◽

Deep Learning ◽

Dental Caries ◽

Convolutional Neural Network ◽

Median Filter ◽

Activation Function ◽

Radiographic Examination ◽

Testing Dataset ◽

The People ◽

Deep Learning Model

In the modern times, Dental caries is one of the most prevalent diseases of the teeth in the whole world. Almost 90% of the people get affected by cavity. Dental caries is the cavity which occurs due to the remnant food and bacteria. Dental Caries are curable and preventable diseases when it is identified at earlier stage. Dentist uses the radiographic examination in addition with visual tactile inspection to identify the caries. Dentist finds difficult to identify the occlusal, pit and fissure caries. It may lead to sever problem if the cavity left untreated and not identified at the earliest stage. Machine learning can be applied to solve this issue by applying the labelled dataset given by the experienced dentist. In this paper, convolutional based deep learning method is applied to identify the cavity presence in the image. 480 Bite viewing radiography images are collected from the Elsevier standard database. All the input images are resized to 128–128 matrixes. In preprocessing, selective median filter is used to reduce the noise in the image. Pre-processed inputs are given to deep learning model where convolutional neural network with Google Net inception v3 architecture algorithm is implemented. ReLu activation function is used with Google Net to identify the caries that provide the dentists with the precise and optimized results about caries and the area affected. Proposed technique achieves 86.7% accuracy on the testing dataset.

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Investigation of optimal configurations of a convolutional neural network for the identification of objects in real-time

Information Technology and Nanotechnology ◽

10.18287/1613-0073-2019-2416-417-423 ◽

2019 ◽

pp. 417-423

Author(s):

M A Isayev ◽

D A Savelyev

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Real Time ◽

State Of The Art ◽

Average Precision ◽

The Core ◽

Particular Solution ◽

Optimal Configurations

The comparison of different convolutional neural networks which are the core of the most actual solutions in the computer vision area is considers in hhe paper. The study includes benchmarks of this state-of-the-art solutions by some criteria, such as mAP (mean average precision), FPS (frames per seconds), for the possibility of real-time usability. It is concluded on the best convolutional neural network model and deep learning methods that were used at particular solution.

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