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 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.

scholarly journals Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kangrok Oh ◽  
Hae Min Kang ◽  
Dawoon Leem ◽  
Hyungyu Lee ◽  
Kyoung Yul Seo ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Low Income ◽  
Detection System ◽  
Learning Technologies ◽  
Fundus Photography ◽  
Wide Field ◽  
Middle Income ◽  
Middle Income Countries ◽  
Automated Screening

AbstractVisually impaired and blind people due to diabetic retinopathy were 2.6 million in 2015 and estimated to be 3.2 million in 2020 globally. Though the incidence of diabetic retinopathy is expected to decrease for high-income countries, detection and treatment of it in the early stages are crucial for low-income and middle-income countries. Due to the recent advancement of deep learning technologies, researchers showed that automated screening and grading of diabetic retinopathy are efficient in saving time and workforce. However, most automatic systems utilize conventional fundus photography, despite ultra-wide-field fundus photography provides up to 82% of the retinal surface. In this study, we present a diabetic retinopathy detection system based on ultra-wide-field fundus photography and deep learning. In experiments, we show that the use of early treatment diabetic retinopathy study 7-standard field image extracted from ultra-wide-field fundus photography outperforms that of the optic disc and macula centered image in a statistical sense.

scholarly journals OBJECT DETECTION SYSTEM FOR THE BLIND WITH VOICEGUIDANCE

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Rajeshvaree Ravindra Karmarkar ◽  
Prof.V.N Honmane
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Object Detection ◽  
Visually Impaired ◽  
Detection System ◽  
Learning Model ◽  
Learning Technologies ◽  
Industrial Facilities ◽  
Guidance Technique ◽  
Deep Learning Model

—As object recognition technology has developed recently, various technologies have been applied to autonomousvehicles, robots, and industrial facilities. However, the benefits ofthese technologies are not reaching the visually impaired, who need it the most. This paper proposed an object detection system for the blind using deep learning technologies. Furthermore, a voice guidance technique is used to inform sight impaired persons as to the location of objects. The object recognition deep learning model utilizes the You Only Look Once(YOLO) algorithm and a voice announcement is synthesized using text-tospeech (TTS) to make it easier for the blind to get information about objects. Asa result, it implements an efficient object-detection system that helps the blind find objects in a specific space without help from others, and the system is analyzed through experiments to verify performance.

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.

A Deep Learning based Accident Detection System

2020 ◽  
Author(s):  
Gokul Rajesh ◽  
Amitha Rossy Benny ◽  
A. Harikrishnan ◽  
James Jacob Abraham ◽  
Nithin Prince John
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Accident Detection

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.

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