scholarly journals Deep learning assisted cognitive diagnosis for the D-Riska application

2021 ◽  
Author(s):  
Cristian Cuerda ◽  
Alejandro Zornoza ◽  
Jose A. Gallud ◽  
Ricardo Tesoriero ◽  
Dulce Romero Ayuso
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Brain Injury ◽  
Diagnostic Tests ◽  
Acquired Brain Injury ◽  
Cognitive Diagnosis ◽  
Neural Network Classifier ◽  
Proposed Model ◽  
Final Score

AbstractIn this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 % of Accuracy, 94 % of Precision, 91 %, of Recall and 92% of F1-Score.

scholarly journals Deep Learning Assisted Cognitive Diagnosis for the D-Riska Application

2021 ◽  
Author(s):  
Cristian Cuerda ◽  
Alejandro Zornoza ◽  
Ricardo Tesoriero ◽  
Jose A. Gallud ◽  
Dulce Romero-Ayuso
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Brain Injury ◽  
Diagnostic Tests ◽  
Acquired Brain Injury ◽  
Cognitive Diagnosis ◽  
Neural Network Classifier ◽  
Proposed Model ◽  
Final Score

Abstract In this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class. Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 \% of Accuracy, 94 \% of Precision, 91 \%, of Recall and 92\% of F1-Score.

scholarly journals Convolution Based Neural Network Model to Prevent Diseases Classification and Prediction Using Deep Learning

2021 ◽  
Vol 10 (8) ◽  
pp. 46-50
Author(s):  
D J Samatha Naidu ◽  
M.Gurivi Reddy
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
The Internet ◽  
Learning Models ◽  
Data Set ◽  
Proposed Model ◽  
Recent Developments ◽  
Automatic Image Recognition ◽  
Recognition Systems

The farmer is a backbone to nation, but majority of the cultivated crops in india affecting by various diseases at various stages of its cultivation. Recent research works shows that diseases are not providing accurate results and few identifying but not providing optimized solutions to the system. In proposed work, the recent developments of Artificial intelligence through Deep Learning show that AIR (Automatic Image Recognition systems) using CNN algorithm models can be very beneficial in such scenarios. The Rice leaf diseases images related dataset is not easily available to automate , so that we have created our own trained data set which is small in size hence we have used transfer learning to develop our Proposed model which supports deep learning models. The Proposed CNN architecture illustrated based on VGG-16 model and it is trained, tested on given dataset collected from rice fields and the internet. The accuracy of the proposed model is moderately accurate with 92.46%.

scholarly journals Classification and Specific Primer Design for Accurate Detection of SARS-CoV-2 Using Deep Learning

2020 ◽  
Author(s):  
Alejandro Lopez-Rincon ◽  
Alberto Tonda ◽  
Lucero Mendoza-Maldonado ◽  
Daphne G.J.C. Mulders ◽  
Richard Molenkamp ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Diagnostic Methods ◽  
Neural Network Classifier ◽  
Explainable Artificial Intelligence ◽  
Primer Sets ◽  
Virus Strains

ABSTRACTIn this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from available repositories, separating the genome of different virus strains from the Coronavirus family with considerable accuracy. The network’s behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are first validated on samples from other repositories, and proven able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets on existing datasets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n=6 previously tested positive), delivering a sensibility similar to routine diagnostic methods, and 100% specificity. In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from NGDC, separating the genome of different virus strains from the Coronavirus family with accuracy 98.73%. The network’s behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from NCBI and GISAID, and proven able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n=6 previously tested positive), delivering a sensibility similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.

scholarly journals Coronavirus 2019 (COVID-19) Detection Based on Deep Learning

2020 ◽  
Vol 23 (4) ◽  
pp. 408-415
Author(s):  
Toqa Abd Ul-Mohsen Sadoon ◽  
Mohammed Hussein Ali
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Imaging Techniques ◽  
Treatment Decision ◽  
High Accuracy ◽  
The Other ◽  
Test Accuracy ◽  
Proposed Model ◽  
Deep Learning Model

Deep learning modeling could provide to detected Corona Virus 2019 (COVID-19) which is a critical task these days to make a treatment decision according to the diagnostic results. On the other hand, advances in the areas of artificial intelligence, machine learning, deep learning, and medical imaging techniques allow demonstrating impressive performance, especially in problems of detection, classification, and segmentation. These innovations enabled physicians to see the human body with high accuracy, which led to an increase in the accuracy of diagnosis and non-surgical examination of patients. There are many imaging models used to detect COVID-19, but we use computerized tomography (CT) because is commonly used. Moreover, we use for detection a deep learning model based on convolutional neural network (CNN) for COVID-19 detection. The dataset has been used is 544 slice of CT scan which is not sufficient for high accuracy, but we can say that it is acceptable because of the few datasets available in these days. The proposed model achieves validation and test accuracy 84.4% and 90.09%, respectively. The proposed model has been compared with other models to prove superiority of our model over the other models.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

A Framework for Extractive Text Summarization based on Deep Learning Modified Neural Network Classifier

2020 ◽  
Author(s):  
BalaAnand Muthu ◽  
Sivaparthipan CB ◽  
Priyan Malarvizhi Kumar ◽  
Seifedine Nimer Kadry ◽  
Ching-Hsien Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Text Summarization ◽  
Neural Network Classifier

scholarly journals Artificial Intelligence Sebagai Penggerak Industri 4.0 dan Tantangannya Bagi Sektor Pemerintah dan Swasta

2019 ◽  
Vol 10 (1) ◽  
pp. 68
Author(s):  
Reza Yogaswara
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Competitive Advantage ◽  
Smart City ◽  
Chief Information Officer ◽  
Deep Learning Neural Network

Artificial Intelligence (AI) atau kecerdasan buatan menjadi penggerak revolusi industri 4.0 yang menjanjikan banyak kemudahan bagi sektor pemerintah maupun industri. Internet of Things (IoT) dan big data contohnya dimana AI dapat diimplementasikan, teknologi yang telah banyak diadopsi di era industri 4.0 ini mampu menghubungkan setiap perangkat, seseorang dapat mengotomatisasi semua perangkat tanpa harus berada di lokasi, lebih dari itu, saat ini telah banyak mesin yang dapat menginterprestasi suatu kondisi atau kejadian tertentu dengan bantuan AI, sebagaimana telah kamera cerdas pendeteksi kepadatan volume kendaraan di jalan raya menggunakan teknologi Deep Learning Neural Network, yang telah diimplementasikan pada beberapa Pemerintah Daerah Kabupaten dan Kota dalam mendukung program Smart City yang telah dicanangkan. Pada sektor industri, banyak juga dari mereka yang telah mengotomatisasi mesin produksi dan manufaktur menggunakan robot dan Artificial Intelligence, sehingga Industri 4.0 akan meningkatkan daya saing melalui perangkat cerdas, setiap entitas yang mampu menguasai teknologi ini disitulah keunggulan kompetitifnya (competitive advantage). Namun ditengah perkembangan industri 4.0 yang cukup masif pemerintah harus bergerak cepat dalam mengadopsi platform ini, jika tidak, mereka akan menurunkan efisiensi proses bisnis untuk menjaga stabilitas layanan publik. Oleh sebab itu diperlukan keilmuan dan pemahaman yang benar bagi pemerintah dalam menghadapai era Industri 4.0, dimana Chief Information Officer (CIO) dapat mengambil peranan penting dalam memberikan dukungan yang didasari atas keilmuan mereka terkait tren teknologi industri 4.0, khususnya AI yang telah banyak diadopsi di berbagai sektor.

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2618-2625
Author(s):  
R. T. Subhalakshmi ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Superior Performance ◽  
Multi Scale ◽  
Proposed Model ◽  
Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

2019 ◽  
pp. 159-173
Author(s):  
Jay Rodge ◽  
Swati Jaiswal
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Activation Functions ◽  
Comprehensive Overview ◽  
Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

scholarly journals Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5609 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Deep Learning Neural Network

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

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