Identifying Community Fire Hazards from Citizen Communication by Applying Transfer Learning and Machine Learning Techniques

The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world’s healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak’s spread, and restore full functionality to the world’s healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models or hybridize them with powerful machine-learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID-19 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID-19 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID-19 classification and 97.8% for multiclass classification.

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Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Applied System Innovation ◽

10.3390/asi3020020 ◽

2020 ◽

Vol 3 (2) ◽

pp. 20 ◽

Cited By ~ 3

Author(s):

Aliyu Abubakar ◽

Mohammed Ajuji ◽

Ibrahim Usman Yahya

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Transfer Learning ◽

Fine Tuning ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Models ◽

Skin Injuries ◽

Learning Techniques ◽

Injured Skin

While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use transfer learning by leveraging pre-trained deep learning models due to deficient dataset in this paper, to discriminate two classes of skin injuries—burnt skin and injured skin. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies—fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy in categorizing burnt skin ad injured skin of approximately 99.9%.

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Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

10.20944/preprints202003.0204.v1 ◽

2020 ◽

Author(s):

Aliyu Abubakar ◽

Mohammed Ajuji ◽

Ibrahim Usman Yahya

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Strategies ◽

Transfer Learning ◽

Standard Technique ◽

Fine Tuning ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Models ◽

Learning Techniques

While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

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A General Survey on Plants Disease Detection Using Image Processing, Deep Transfer Learning and Machine Learning Techniques

Ubiquitous Networking - Lecture Notes in Computer Science ◽

10.1007/978-3-030-86356-2_18 ◽

2021 ◽

pp. 210-224

Author(s):

Houda Orchi ◽

Mohamed Sadik ◽

Mohammed Khaldoun

Keyword(s):

Machine Learning ◽

Image Processing ◽

Transfer Learning ◽

Machine Learning Techniques ◽

Disease Detection ◽

General Survey ◽

Learning Techniques

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Domain adaptation-based transfer learning using adversarial networks

The Knowledge Engineering Review ◽

10.1017/s0269888920000107 ◽

2020 ◽

Vol 35 ◽

Cited By ~ 1

Author(s):

Farzaneh Shoeleh ◽

Mohammad Mehdi Yadollahi ◽

Masoud Asadpour

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Transfer Learning ◽

Domain Adaptation ◽

Machine Learning Techniques ◽

Implicit Assumption ◽

Adversarial Networks ◽

Learning Techniques ◽

Speed Up ◽

Similar Domain

Abstract There is an implicit assumption in machine learning techniques that each new task has no relation to the tasks previously learned. Therefore, tasks are often addressed independently. However, in some domains, particularly reinforcement learning (RL), this assumption is often incorrect because tasks in the same or similar domain tend to be related. In other words, even though tasks are quite different in their specifics, they may have general similarities, such as shared skills, making them related. In this paper, a novel domain adaptation-based method using adversarial networks is proposed to do transfer learning in RL problems. Our proposed method incorporates skills previously learned from source task to speed up learning on a new target task by providing generalization not only within a task but also across different, but related tasks. The experimental results indicate the effectiveness of our method in dealing with RL problems.

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Cavitation Analysis in Centrifugal Pumps Based on Vibration Bispectrum and Transfer Learning

Shock and Vibration ◽

10.1155/2021/6988949 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Ali Hajnayeb

Keyword(s):

Machine Learning ◽

Transfer Learning ◽

Machine Learning Techniques ◽

Centrifugal Pumps ◽

Test Setup ◽

Learning Techniques ◽

Pump Test ◽

Processing Techniques ◽

Cavitation Detection

Detection of cavitation in centrifugal pumps is critical in their condition monitoring. In order to detect cavitation more accurately and confidently, more advanced signal processing techniques are needed. For the classification of a pump conditions based on the outputs of these techniques, advanced machine learning techniques are needed. In this research, an automatic system for cavitation detection is proposed based on machine learning. Bispectral analysis is used for analyzing the vibration signals. The resulting bispectrum images are given to convolutional neural networks (CNNs) as inputs. The CNNs are a pretrained AlexNet and a pretrained GoogleNet, which are used in this application through transfer learning. On the contrary, a laboratory test setup is used for generating controlled cavitation in a centrifugal pump. The suggested algorithm is implemented on the vibration dataset acquired from the laboratory pump test setup. The results show that the cavitation state of the pump can be detected accurately using this system without any need to image processing or feature extraction.

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Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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