scholarly journals Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks

2020 ◽  
Author(s):  
Mohammad Ahmadlou ◽  
A'kif Al‐Fugara ◽  
Abdel Rahman Al‐Shabeeb ◽  
Aman Arora ◽  
Rida Al‐Adamat ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Multilayer Perceptron ◽  
Learning Model ◽  
Susceptibility Mapping ◽  
Flood Susceptibility ◽  
Model Combining ◽  
Flood Susceptibility Mapping ◽  
Deep Learning Model

scholarly journals Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2664
Author(s):  
Sunil Saha ◽  
Jagabandhu Roy ◽  
Tusar Kanti Hembram ◽  
Biswajeet Pradhan ◽  
Abhirup Dikshit ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Landslide Susceptibility ◽  
Learning Model ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Learning Approaches ◽  
Statistical Measures ◽  
Deep Learning Model

The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning.

scholarly journals A Novel Deep Convolutional Neural Network for Tuberculosis Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 1135-1139
Author(s):  
Syed Farhan Hyder Abidi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mycobacterium Tuberculosis ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Model

India accounts for the world’s largest number of cases in TB, with 2.8 million cases annually, and accounts for more than a quarter of the global TB burden. Tuberculosis (TB) is caused by the bacterium (Mycobacterium tuberculosis) which most commonly affects the lungs. TB is transmitted from person to person through the air. When people with TB cough, sneeze or spit, the germs are propelled into the air. This paper showcases a methodology which uses a Deep Learning Model (dCNN) for the detection of Tuberculosis in the lungs. The accuracy obtained by the methods for the model is desirable and dependable, which is increasingly productive in contrast to the accuracy shown by other neural networks.

scholarly journals Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks

2020 ◽  
Vol 10 (24) ◽  
pp. 8934
Author(s):  
Yan He ◽  
Bin Fu ◽  
Jian Yu ◽  
Renfa Li ◽  
Rucheng Jiang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Learning Model ◽  
Streaming Data ◽  
Smart Devices ◽  
Generative Adversarial Network ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Iot Devices ◽  
Deep Learning Model

Wireless and mobile health applications promote the development of smart healthcare. Effective diagnosis and feedbacks of remote health data pose significant challenges due to streaming data, high noise, network latency and user privacy. Therefore, we explore efficient edge and cloud design to maintain electrocardiogram classification performance while reducing the communication cost. These contributions include: (1) We introduce a hybrid smart medical architecture named edge convolutional neural networks (EdgeCNN) that balances the capability of edge and cloud computing to address the issue for agile learning of healthcare data from IoT devices. (2) We present an effective deep learning model for electrocardiogram (ECG) inference, which can be deployed to run on edge smart devices for low-latency diagnosis. (3) We design a data enhancement method for ECG based on deep convolutional generative adversarial network to expand ECG data volume. (4) We carried out experiments on two representative datasets to evaluate the effectiveness of the deep learning model of ECG classification based on EdgeCNN. EdgeCNN shows superior to traditional cloud medical systems in terms of network Input/Output (I/O) pressure, architecture cost and system high availability. The deep learning model not only ensures high diagnostic accuracy, but also has advantages in aspect of inference time, storage, running memory and power consumption.

scholarly journals Deep Learning Model for Plant Disease Detection

2020 ◽  
Vol 9 (1) ◽  
pp. 750-754
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Early Detection ◽  
Plant Disease ◽  
Early Stage ◽  
Learning Model ◽  
Plant Diseases ◽  
Convolution Neural Networks ◽  
Deep Learning Model

Food is one of the basic needs of human being. We know that the population is rising enormously.so it is more important to feed such a huge population. But nowadays plants are largely affected with various types of diseases. If proper care should not be taken then it will show effect on quality of food products, quantity and finally on productivity of crops.. so, Early detection of plant disease is very essential, but it is very hard to farmers to monitor the crops manually it takes more processing time, huge amount of work, expensive and need expertised persons. Automatic detection of plant diseases helps the farmers to monitor the large fields easily,because our approach of using convolution neural networks provides a chance to discover diseases at the very early stage. By using Image Processing and machine learning models we can detect the plant diseases automatically but the accuracy is very less, early detection is also a major challenge. With the modern advanced developments in deep learning, in our project we have implemented the convolution neural networks(CNN) which comprises of different layers,by using those layers we can automatically detect and classify the diseases present in the plants. High Classification accuracy and more processing speed are the main advantages of our approach. After training the model on color, grayscale and segmented datasets our deep learning model will be capable of classifying a large number of different diseases and our project gives us the name of the disease that the plant has with its confidence level and also provides remedies for corresponding diseases

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