AI-Driven Prognosis and Diagnosis for Personalized Healthcare Services

2020 ◽  
pp. 124-162
Author(s):  
Ritika Mehra ◽  
Mohit Iyer
Keyword(s):  
Deep Learning ◽  
Liver Diseases ◽  
Healthcare Services ◽  
Learning Technologies ◽  
Grid Search ◽  
Rapid Progress ◽  
Healthcare Data ◽  
Linear Svm ◽  
Psychological Capacities

Artificial intelligence (AI) seeks to replicate human psychological capacities. This is making an ideal change for healthcare services with the rapid availability of healthcare data and the rapid progress of analytics technology. AI tools are being used to diagnose major diseases such as cancer, neurology, liver diseases, cardiology, and so on. For the classification of the disease, several classification and dimensionality reduction algorithms are used. In this chapter, recent literature based on deep learning technologies has been reviewed to pursue healthcare domains and also various applications, and challenges of AI and deep learning that are used in this field have been discussed. Different AI algorithms (Linear SVM, SVM Grid Search, KNN, Logistic Regression, Decision Tree, Bagging, Boosting) have also been discussed in a nutshell. To predict diseases, all these algorithms will be implemented using various medical datasets, and also a comparison of all these algorithms will be shown.

scholarly journals Modern Approaches to Detect and Classify Comment Toxicity Using Neural Networks

2020 ◽  
Vol 27 (1) ◽  
pp. 48-61 ◽  
Author(s):  
Sergey V. Morzhov
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
Rapid Evolution ◽  
Learning Technologies ◽  
Training Set ◽  
Online Platforms ◽  
Processing Algorithms ◽  
New Algorithms

The growth of popularity of online platforms which allow users to communicate with each other, share opinions about various events, and leave comments boosted the development of natural language processing algorithms. Tens of millions of messages per day are published by users of a particular social network need to be analyzed in real time for moderation in order to prevent the spread of various illegal or offensive information, threats and other types of toxic comments. Of course, such a large amount of information can be processed quite quickly only automatically. that is why there is a need to and a way to teach computers to “understand” a text written by humans. It is a non-trivial task even if the word “understand” here means only “to classify”. the rapid evolution of machine learning technologies has led to ubiquitous implementation of new algorithms. A lot of tasks, which for many years were considered almost impossible to solve, are now quite successfully solved using deep learning technologies. this article considers algorithms built using deep learning technologies and neural networks which can successfully solve the problem of detection and classification of toxic comments. In addition, the article presents the results of the developed algorithms, as well as the results of the ensemble of all considered algorithms on a large training set collected and tagged by Google and Jigsaw.

Hybridizing Convolutional Neural Network for Classification of Lung diseases

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Learning Technologies ◽  
Data Set ◽  
Sample Data ◽  
Chest X Ray ◽  
The Cost ◽  
Image Orientation ◽  
Entire Dataset

Pulmonary disease is widespread worldwide. There is persistent blockage of the lungs, pneumonia, asthma, TB, etc. It is essential to diagnose the lungs promptly. For this reason, machine learning models were developed. For lung disease prediction, many deep learning technologies, including the CNN, and the capsule network, are used. The fundamental CNN has low rotating, inclined, or other irregular image orientation efficiency. Therefore by integrating the space transformer network (STN) with CNN, we propose a new hybrid deep learning architecture named STNCNN. The new model is implemented on the dataset from the Kaggle repository for an NIH chest X-ray image. STNCNN has an accuracy of 69% in respect of the entire dataset, while the accuracy values of vanilla grey, vanilla RGB, hybrid CNN are 67.8%, 69.5%, and 63.8%, respectively. When the sample data set is applied, STNCNN takes much less time to train at the cost of a slightly less reliable validation. Therefore both specialists and physicians are simplified by the proposed STNCNN System for the diagnosis of lung disease.

scholarly journals IoT Based Fire Accident Detection System with Deep Learning Intelligence

2021 ◽  
Vol 11 (1) ◽  
pp. 138-142
Author(s):  
Hitanshi Jain ◽  
◽  
Sai Teja Miyapuram ◽  
Sree Ranga Reddy ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Predictive Analytics ◽  
Detection System ◽  
Workplace Safety ◽  
Learning Technologies ◽  
Heat Index ◽  
Sensor Temperature ◽  
Accident Detection ◽  
Fire Accident

A fire accident can be caused by many hazards, such as a propane tank, a defective product, a vehicle crash, or poor workplace safety. Because accidents involving fire are often unexpected and sudden, there isn’t a standard legal process for dealing with them, other than filing a negligence or workers compensation claim. This project aims to detect and monitor Fire Accident incidents well in advance and alert the surroundings to minimize the losses. This is an integration of IoT and Deep Learning Technologies, where sensors are used to collect the relevant data under the supervision of a controller unit. The controller unit collects and sends this data to a cloud database, from where the data for the Deep Learning model is fetched. This data is then used for making some insights and further predictive analytics. From the insights, many variables were found to be one of the reasons for a fire accident to take place. We considered the information about variables like Flame sensor, Temperature, Heat Index, GPS coordinates, Smoke, Type of Gases, Date, and Time for feature set generation and fed the model to a deep neural network for making future predictions. Comparing to existing conventional methods, this proposed method is different in terms of integrating deep learning with IoT. This method of approach will predict the chance of accidents priorly by classification of data.

scholarly journals Applications of Deep Learning in News Text Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Menghan Zhang
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Recall Rate ◽  
Technological Advancement ◽  
Text Data ◽  
Healthcare Data ◽  
Feature Information ◽  
And Performance ◽  
The Relationship

The advancement in technology is taking place with an accelerating pace across the globe. With the increasing expansion and technological advancement, a vast volume of text data are generated everyday, in the form of social media platform, websites, company data, healthcare data, and news. Indeed, it is a difficult task to extract intriguing patterns from the text data, such as opinions, summaries, and facts, having varying length. Because of the problems of the length of text data and the difficulty of feature value extraction in news, this paper proposes a news text classification method based on the combination of deep learning (DL) algorithms. In order to classify the text data, the earlier approaches use a single word vector to express text information and only the information of the relationship between words were considered, but the relationship between words and categories was ignored which indeed is an important factor for the classification of news text. This paper follows the idea of a customized algorithm which is the combination of DL algorithms such as CNN, LSTM, and MLP and proposes a customized DCLSTM-MLP model for the classification of news text data. The proposed model is expressed in parallel with word vector and word dispersion. The relationship among words is represented by the word vector as an input of the CNN module, and the relationship between words and categories is represented by a discrete vector as an input of the MLP module in order to realize comprehensive learning of spatial feature information, time-series feature information, and relationship between words and categories of news text. To check the stability and performance of the proposed method, multiple experiments were performed. The experimental results showed that the proposed method solves the problems of text length, difficulty of feature extraction in the news text, and classification of news text in an effective way and attained better accuracy, recall rate, and comprehensive value as compared to the other models.

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

Sign in / Sign up

Export Citation Format

Share Document