An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques

The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.

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An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

Complexity ◽

10.1155/2020/5843932 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 9

Author(s):

Syed Atif Ali Shah ◽

Irfan Uddin ◽

Furqan Aziz ◽

Shafiq Ahmad ◽

Mahmoud Ahmad Al-Khasawneh ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Learning Algorithms ◽

Single Layer ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Proposed Model ◽

The Cost

Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism.

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An Efficient Method for Detection of DDoS Attacks on the Web Using Deep Learning Algorithms

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/271042021 ◽

2021 ◽

Vol 10 (4) ◽

pp. 2821-2829

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

State Of The Art ◽

Ddos Attacks ◽

Problem Statement ◽

Neural Network Approach ◽

Learning Techniques ◽

Attack Data ◽

Deep Learning Neural Network

Recently, DDoS attacks is the most significant threat in network security. Both industry and academia are currently debating how to detect and protect against DDoS attacks. Many studies are provided to detect these types of attacks. Deep learning techniques are the most suitable and efficient algorithm for categorizing normal and attack data. Hence, a deep neural network approach is proposed in this study to mitigate DDoS attacks effectively. We used a deep learning neural network to identify and classify traffic as benign or one of four different DDoS attacks. We will concentrate on four different DDoS types: Slowloris, Slowhttptest, DDoS Hulk, and GoldenEye. The rest of the paper is organized as follow: Firstly, we introduce the work, Section 2 defines the related works, Section 3 presents the problem statement, Section 4 describes the proposed methodology, Section 5 illustrate the results of the proposed methodology and shows how the proposed methodology outperforms state-of-the-art work and finally Section VI concludes the paper.

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Evaluation of Deep Neural Network in Directional Prediction of Forex Market

10.36227/techrxiv.12946994.v1 ◽

2020 ◽

Author(s):

Hamid Hassanpour

Keyword(s):

Neural Network ◽

Deep Learning ◽

Software Package ◽

Deep Neural Network ◽

Behavior Prediction ◽

Market Behavior ◽

Learning Techniques

This is a paper regarding application of deep neural network in prediction of Forex market. It utilized advanced deep learning techniques and software package in order ti evaluate capability of deep neural network in market behavior prediction.

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MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

10.1101/2021.08.04.21261235 ◽

2021 ◽

Author(s):

Liangrui Pan ◽

boya ji ◽

Xiaoqi wang ◽

shaoliang peng

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Test Accuracy ◽

Learning Networks ◽

X Ray ◽

Network Algorithm ◽

Life Saving ◽

Chest X Ray ◽

Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

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A Similarity-Based Object Classification Using Deep Neural Networks

Handbook of Research on Deep Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-7862-8.ch012 ◽

2019 ◽

pp. 197-219

Author(s):

Parvathi R. ◽

Pattabiraman V.

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Search Algorithm ◽

Random Projection ◽

Learning Networks ◽

Learning Models ◽

External Conditions ◽

And Training

This chapter proposes a hybrid method for classification of the objects based on deep neural network and a similarity-based search algorithm. The objects are pre-processed with external conditions. After pre-processing and training different deep learning networks with the object dataset, the authors compare the results to find the best model to improve the accuracy of the results based on the features of object images extracted from the feature vector layer of a neural network. RPFOREST (random projection forest) model is used to predict the approximate nearest images. ResNet50, InceptionV3, InceptionV4, and DenseNet169 models are trained with this dataset. A proposal for adaptive finetuning of the deep learning models by determining the number of layers required for finetuning with the help of the RPForest model is given, and this experiment is conducted using the Xception model.

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Human Activity Recognition Based on Deep Learning Techniques

Proceedings ◽

10.3390/ecsa-6-06539 ◽

2019 ◽

Vol 42 (1) ◽

pp. 15

Author(s):

Manuel Gil-Martín ◽

Marcos Sánchez-Hernández ◽

Rubén San-Segundo

Keyword(s):

Neural Network ◽

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Deep Neural Network ◽

Human Activity Recognition ◽

Support Vector ◽

Daily Life Activities ◽

Learning Techniques ◽

The Fourier Transform

Deep learning techniques are being widely applied to Human Activity Recognition (HAR). This paper describes the implementation and evaluation of a HAR system for daily life activities using the accelerometer of an iPhone 6S. This system is based on a deep neural network including convolutional layers for feature extraction from accelerations and fully-connected layers for classification. Different transformations have been applied to the acceleration signals in order to find the appropriate input data to the deep neural network. This study has used acceleration recordings from the MotionSense dataset, where 24 subjects performed 6 activities: walking downstairs, walking upstairs, sitting, standing, walking and jogging. The evaluation has been performed using a subject-wise cross-validation: recordings from the same subject do not appear in training and testing sets at the same time. The proposed system has obtained a 9% improvement in accuracy compared to the baseline system based on Support Vector Machines. The best results have been obtained using raw data as input to a deep neural network composed of two convolutional and two max-pooling layers with decreasing kernel sizes. Results suggest that using the module of the Fourier transform as inputs provides better results when classifying only between dynamic activities.

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A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

Nature Communications ◽

10.1038/s41467-019-13749-3 ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 2

Author(s):

Jin-Woong Lee ◽

Woon Bae Park ◽

Jin Hee Lee ◽

Satendra Pal Singh ◽

Kee-Sun Sohn

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Inorganic Compounds ◽

Phase Identification ◽

X Ray Diffraction ◽

X Ray ◽

Learning Techniques ◽

Learning Technique ◽

Xrd Patterns

AbstractHere we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.

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MajraDoc an Image based Disease Detection App for Agricultural Plants using Deep Learning Techniques

10.5121/csit.2021.112402 ◽

2021 ◽

Author(s):

Sara Saleh Alfozan ◽

Mohamad Mahdi Hassan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Food Safety ◽

Early Detection ◽

Deep Neural Network ◽

High Accuracy ◽

Vital Role ◽

Mobile App ◽

Learning Techniques ◽

Agricultural Plants

Infection of agricultural plants is a serious threat to food safety. It can severely damage plants' yielding capacity. Farmers are the primary victims of this threat. Due to the advancement of AI, image-based intelligent apps can play a vital role in mitigating this threat by quick and early detection of plants infections. In this paper, we present a mobile app in this regard. We have developed MajraDoc to detect some common diseases in local agricultural plants. We have created a dataset of 10886 images for ten classes of plants diseases to train the deep neural network. The VGG-19 network model was modified and trained using transfer learning techniques. The model achieved high accuracy, and the application performed well in predicting all ten classes of infections.

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Comparison of machine learning and deep learning techniques in promoter prediction across diverse species

PeerJ Computer Science ◽

10.7717/peerj-cs.365 ◽

2021 ◽

Vol 7 ◽

pp. e365

Author(s):

Nikita Bhandari ◽

Satyajeet Khare ◽

Rahee Walambe ◽

Ketan Kotecha

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Short Term Memory ◽

Regulatory Elements ◽

Gene Promoters ◽

Promoter Prediction ◽

Promoter Sequences ◽

Learning Techniques

Gene promoters are the key DNA regulatory elements positioned around the transcription start sites and are responsible for regulating gene transcription process. Various alignment-based, signal-based and content-based approaches are reported for the prediction of promoters. However, since all promoter sequences do not show explicit features, the prediction performance of these techniques is poor. Therefore, many machine learning and deep learning models have been proposed for promoter prediction. In this work, we studied methods for vector encoding and promoter classification using genome sequences of three distinct higher eukaryotes viz. yeast (Saccharomyces cerevisiae), A. thaliana (plant) and human (Homo sapiens). We compared one-hot vector encoding method with frequency-based tokenization (FBT) for data pre-processing on 1-D Convolutional Neural Network (CNN) model. We found that FBT gives a shorter input dimension reducing the training time without affecting the sensitivity and specificity of classification. We employed the deep learning techniques, mainly CNN and recurrent neural network with Long Short Term Memory (LSTM) and random forest (RF) classifier for promoter classification at k-mer sizes of 2, 4 and 8. We found CNN to be superior in classification of promoters from non-promoter sequences (binary classification) as well as species-specific classification of promoter sequences (multiclass classification). In summary, the contribution of this work lies in the use of synthetic shuffled negative dataset and frequency-based tokenization for pre-processing. This study provides a comprehensive and generic framework for classification tasks in genomic applications and can be extended to various classification problems.

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Hyperspectral imaging and artificial intelligence to detect oral malignancy – part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network

Head & Face Medicine ◽

10.1186/s13005-021-00292-0 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Daniel G. E. Thiem ◽

Paul Römer ◽

Matthias Gielisch ◽

Bilal Al-Nawas ◽

Martin Schlüter ◽

...

Keyword(s):

Neural Network ◽

Computer Vision ◽

Deep Learning ◽

Hyperspectral Imaging ◽

Oral Mucosa ◽

Deep Neural Network ◽

Light Weight ◽

Tissue Classification ◽

Raw Data

Abstract Background Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle study was the classification of hyperspectral (HS)-reflectance values into the human-oral tissue types fat, muscle and mucosa using deep learning methods. Furthermore, the tissue-specific hyperspectral signatures collected will serve as a representative reference for the future assessment of oral pathological changes in the sense of a HS-library. Methods A total of about 316 samples of healthy human-oral fat, muscle and oral mucosa was collected from 174 different patients and imaged using a HS-camera, covering the wavelength range from 500 nm to 1000 nm. HS-raw data were further labelled and processed for tissue classification using a light-weight 6-layer deep neural network (DNN). Results The reflectance values differed significantly (p < .001) for fat, muscle and oral mucosa at almost all wavelengths, with the signature of muscle differing the most. The deep neural network distinguished tissue types with an accuracy of > 80% each. Conclusion Oral fat, muscle and mucosa can be classified sufficiently and automatically by their specific HS-signature using a deep learning approach. Early detection of premalignant-mucosal-lesions using hyperspectral imaging and deep learning is so far represented rarely in in medical and computer vision research domain but has a high potential and is part of subsequent studies.

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