Neural Network: A Machine Learning Technique for Tertiary Structure Prediction of Proteins from Peptide Sequences

2009 ◽  
Author(s):  
Sandeep Kumar Kushwaha ◽  
Madhvi Shakya
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Tertiary Structure Prediction ◽  
Machine Learning Technique ◽  
Learning Technique

scholarly journals 2030. How Machine-Learning Technique Using Artificial Neural Network Determines Whether the Fever Is Actually Related to the Bacteremia

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S591-S591
Author(s):  
Kyoung Hwa Lee ◽  
Seul Gi Yoo ◽  
Da Eun Kwon ◽  
Soon Young Park ◽  
Jae June Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Artificial Neural

Effectiveness of a Hybrid Deep Learning Model Integrated with a Hybrid Parameterisation Model in Decision-Making Analysis

2020 ◽  
Author(s):  
Masurah Mohamad ◽  
Ali Selamat
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Missing Data ◽  
Machine Learning Technique ◽  
Processing Times ◽  
Learning Technique ◽  
Neural Network Algorithm ◽  
Deep Learning Model

Deep learning has recently gained the attention of many researchers in various fields. A new and emerging machine learning technique, it is derived from a neural network algorithm capable of analysing unstructured datasets without supervision. This study compared the effectiveness of the deep learning (DL) model vs. a hybrid deep learning (HDL) model integrated with a hybrid parameterisation model in handling complex and missing medical datasets as well as their performance in increasing classification. The results showed that 1) the DL model performed better on its own, 2) DL was able to analyse complex medical datasets even with missing data values, and 3) HDL performed well as well and had faster processing times since it was integrated with a hybrid parameterisation model.

scholarly journals A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3704 ◽  
Author(s):  
Phuong-Thao Ngo ◽  
Nhat-Duc Hoang ◽  
Biswajeet Pradhan ◽  
Quang Nguyen ◽  
Xuan Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Flash Flood ◽  
Spatial Prediction ◽  
Flash Floods ◽  
Flood Inundation ◽  
Machine Learning Technique ◽  
Flood Model ◽  
Learning Technique ◽  
Sar Imagery

Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.

scholarly journals A Machine Learning Technique to Analyze Depressive Disorders

2021 ◽  
Author(s):  
Dixita Mali ◽  
Kritika Kumawat ◽  
Gaurav Kumawat ◽  
Prasun Chakrabarti ◽  
Sandeep Poddar ◽  
...  
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Machine Learning ◽  
Depressive Disorders ◽  
Random Trees ◽  
Machine Learning Technique ◽  
Machine Learning Classifiers ◽  
Large Numbers ◽  
Learning Technique ◽  
The Impact

Abstract Depression is an ordinary mental health care problem and the usual cause of disability worldwide. The main purpose of this research was to determine that how depression affects the life of an individual. It is a leading cause of morbidity and death. Over the last 50–60 years, large numbers of studies published various aspects including the impact of depression. The main purpose of this research is to determine whether the person is suffering from depression or not. The dataset of Depression has been taken from the Kaggle website. Guided Machine Learning classifiers have helped in the highest accuracy of a dataset. Classifiers like XGBoost Tree, Random Trees, Neural Network, SVM, Random Forest, C5.0, and Bay Net. From the result, it is evident that the C5.0 classifier is giving the highest accuracy with 83.94 % and for each classifier, the result is derived based without pre-processing.

scholarly journals Efficient CNN Approach for Facial Expression Recognition

2021 ◽  
Vol 2129 (1) ◽  
pp. 012083
Author(s):  
Gheyath Mustafa Zebari ◽  
Dilovan Asaad Zebari ◽  
Diyar Qader Zeebaree ◽  
Habibollah Haron ◽  
Adnan Mohsin Abdulazeez ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Deep Neural Network ◽  
Learning Technology ◽  
Expression Recognition ◽  
Machine Learning Technique ◽  
Learning Technique

Abstract In the last decade, the Facial Expression Recognition field has been studied widely and become the base for many researchers, and still challenging in computer vision. Machine learning technique used in facial expression recognition facing many problems, since human emotions expressed differently from one to another. Nevertheless, Deep learning that represents a novel area of research within machine learning technology has the ability for classifying people’s faces into different emotion classes by using a Deep Neural Network (DNN). The Convolution Neural Network (CNN) method has been used widely and proved as very efficient in the facial expression recognition field. In this study, a CNN technique for facial expression recognition has been presented. The performance of this study has been evaluated using the fer2013 dataset, the total number of images has been used. The accuracy of each epoch has been tested which is trained on 29068 samples, validate on 3589 samples. The overall accuracy of 69.85% has been obtained for the proposed method.

IMPROVED MACHINE LEARNING TECHNIQUE FOR SOLVING HAUSDORFF DERIVATIVE DIFFUSION EQUATIONS

Fractals ◽  
2020 ◽  
Vol 28 (04) ◽  
pp. 2050071
Author(s):  
JIANG WANG ◽  
YINGJIE LIANG ◽  
LIN QIU ◽  
XU YANG
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Error Function ◽  
Diffusion Equations ◽  
Computational Accuracy ◽  
Feed Forward Neural Network ◽  
Machine Learning Technique ◽  
Trial Solution ◽  
Gradient Descent Algorithm ◽  
Learning Technique

This study aims at combining the machine learning technique with the Hausdorff derivative to solve one-dimensional Hausdorff derivative diffusion equations. In the proposed artificial neural network method, the multilayer feed-forward neural network is chosen and improved by using the Hausdorff derivative to the activation function of hidden layers. A trial solution is a combination of the boundary and initial condition terms and the network output, which can approximate the analytical solution. To transform the original Hausdorff derivative equation into a minimization problem, an error function is defined, where the coefficients are approximated by using the gradient descent algorithm in the back-propagation process. Two numerical examples are given to illustrate the accuracy and the robustness of the proposed method. The obtained results show that the improved machine learning technique is efficient in computing the Hausdorff derivative diffusion equations both from computational accuracy and stability.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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