scholarly journals Quantification of Hydrocarbon Abundance in Soils Using Deep Learning with Dropout and Hyperspectral Data

2019 ◽  
Vol 11 (16) ◽  
pp. 1938 ◽  
Author(s):  
Asmau M. Ahmed ◽  
Olga Duran ◽  
Yahya Zweiri ◽  
Mike Smith
Keyword(s):  
Deep Learning ◽  
Contaminated Soils ◽  
Early Stage ◽  
Imaging Techniques ◽  
Rapid Identification ◽  
Hyperspectral Data ◽  
Learning Approach ◽  
Backpropagation Algorithm ◽  
Average Mean Square Error ◽  
Deep Learning Neural Network

Terrestrial hydrocarbon spills have the potential to cause significant soil degradation across large areas. Identification and remedial measures taken at an early stage are therefore important. Reflectance spectroscopy is a rapid remote sensing method that has proven capable of characterizing hydrocarbon-contaminated soils. In this paper, we develop a deep learning approach to estimate the amount of Hydrocarbon (HC) mixed with different soil samples using a three-term backpropagation algorithm with dropout. The dropout was used to avoid overfitting and reduce computational complexity. A Hyspex SWIR 384 m camera measured the reflectance of the samples obtained by mixing and homogenizing four different soil types with four different HC substances, respectively. The datasets were fed into the proposed deep learning neural network to quantify the amount of HCs in each dataset. Individual validation of all the dataset shows excellent prediction estimation of the HC content with an average mean square error of ~ 2 . 2 × 10 - 4 . The results with remote sensed data captured by an airborne system validate the approach. This demonstrates that a deep learning approach coupled with hyperspectral imaging techniques can be used for rapid identification and estimation of HCs in soils, which could be useful in estimating the quantity of HC spills at an early stage.

A Comparative Analysis of Early Stage Diabetes Prediction using Machine Learning and Deep Learning Approach

2021 ◽  
Author(s):  
Md Abu Rumman Refat ◽  
Md. Al Amin ◽  
Chetna Kaushal ◽  
Mst Nilufa Yeasmin ◽  
Md Khairul Islam
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Early Stage ◽  
Learning Approach ◽  
Diabetes Prediction

scholarly journals A Deep Learning Method for Prediction of Cardiovascular Disease Using Convolutional Neural Network

2020 ◽  
Vol 34 (5) ◽  
pp. 601-606
Author(s):  
Tulasi Krishna Sajja ◽  
Hemantha Kumar Kalluri
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Cardiovascular Disease ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Traditional Approaches

Heart disease is a very deadly disease. Worldwide, the majority of people are suffering from this problem. Many Machine Learning (ML) approaches are not sufficient to forecast the disease caused by the virus. Therefore, there is a need for one system that predicts disease efficiently. The Deep Learning approach predicts the disease caused by the blocked heart. This paper proposes a Convolutional Neural Network (CNN) to predict the disease at an early stage. This paper focuses on a comparison between the traditional approaches such as Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Neural Networks (NN), and the proposed prediction model of CNN. The UCI machine learning repository dataset for experimentation and Cardiovascular Disease (CVD) predictions with 94% accuracy.

A multi-task deep learning neural network for predicting flammability-related properties from molecular structures

2021 ◽  
Author(s):  
Ao Yang ◽  
Yang Su ◽  
Zihao Wang ◽  
Saimeng Jin ◽  
Jingzheng Ren ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Process Design ◽  
Early Stage ◽  
Molecular Structures ◽  
Product And Process ◽  
Deep Learning Neural Network

It is significant that the hazardous properties of chemicals including replacements for banned or restricted products are assessed at an early stage of product and process design. This work proposes...

scholarly journals A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 748
Author(s):  
Mehedi Masud ◽  
Niloy Sikder ◽  
Abdullah-Al Nahid ◽  
Anupam Kumar Bairagi ◽  
Mohammed A. AlZain
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Diagnostic Methods ◽  
Learning Approach ◽  
Reliable System ◽  
Colon Cancers ◽  
Classification Framework ◽  
Machine Learning Approach ◽  
Histopathological Images ◽  
Cancer Tissues

The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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