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

Processing Times ◽

Learning Technique ◽

Neural Network Algorithm ◽

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.

Efficient CNN Approach for Facial Expression Recognition

Journal of Physics Conference Series ◽

10.1088/1742-6596/2129/1/012083 ◽

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 ◽

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.

Case Study of Deep Learning Model of Temperature-Induced Deflection of a Cable-Stayed Bridge Driven by Data Knowledge

Symmetry ◽

10.3390/sym13122293 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2293

Author(s):

Zixiang Yue ◽

Youliang Ding ◽

Hanwei Zhao ◽

Zhiwen Wang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Prior Knowledge ◽

Learning Model ◽

Bridge Management ◽

Learning Network ◽

Cable Stayed Bridge ◽

Main Girder ◽

A cable-stayed bridge is a typical symmetrical structure, and symmetry affects the deformation characteristics of such bridges. The main girder of a cable-stayed bridge will produce obvious deflection under the inducement of temperature. The regression model of temperature-induced deflection is hoped to provide a comparison value for bridge evaluation. Based on the temperature and deflection data obtained by the health monitoring system of a bridge, establishing the correlation model between temperature and temperature-induced deflection is meaningful. It is difficult to complete a high-quality model only by the girder temperature. The temperature features based on prior knowledge from the mechanical mechanism are used as the input information in this paper. At the same time, to strengthen the nonlinear ability of the model, this paper selects an independent recurrent neural network (IndRNN) for modeling. The deep learning neural network is compared with machine learning neural networks to prove the advancement of deep learning. When only the average temperature of the main girder is input, the calculation accuracy is not high regardless of whether the deep learning network or the machine learning network is used. When the temperature information extracted by the prior knowledge is input, the average error of IndRNN model is only 2.53%, less than those of BPNN model and traditional RNN. Combining knowledge with deep learning is undoubtedly the best modeling scheme. The deep learning model can provide a comparison value of bridge deformation for bridge management.

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

Water ◽

10.3390/w13192664 ◽

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 ◽

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.

Advanced Deep Learning Model

10.5121/csit.2021.111707 ◽

2021 ◽

Author(s):

Yew Kee Wong

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Deep Learning ◽

Learning Algorithms ◽

Learning Model ◽

Intelligence Analysis ◽

Basic Parameters ◽

Deep Learning Model ◽

Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

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

Open Forum Infectious Diseases ◽

10.1093/ofid/ofy210.1686 ◽

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 ◽

Learning Technique ◽

Artificial Neural

2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies ◽

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

10.1109/act.2009.34 ◽

2009 ◽

Cited By ~ 2

Author(s):

Sandeep Kumar Kushwaha ◽

Madhvi Shakya

Keyword(s):

Neural Network ◽

Machine Learning ◽

Structure Prediction ◽

Tertiary Structure ◽

Tertiary Structure Prediction ◽

Learning Technique

Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices

10.1101/744789 ◽

2019 ◽

Author(s):

Ananya Bhattacharjee ◽

Md. Shamsuzzoha Bayzid

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Missing Data ◽

Phylogenetic Trees ◽

Large Scale ◽

Missing Values ◽

Gene Tree ◽

Estimation Methods ◽

Learning Technique ◽

Distance Matrices

AbstractBackgroundDue to the recent advances in sequencing technologies and species tree estimation methods capable of taking gene tree discordance into account, notable progress has been achieved in constructing large scale phylogenetic trees from genome wide data. However, substantial challenges remain in leveraging this huge amount of molecular data. One of the foremost among these challenges is the need for efficient tools that can handle missing data. Popular distance-based methods such as neighbor joining and UPGMA require that the input distance matrix does not contain any missing values.ResultsWe introduce two highly accurate machine learning based distance imputation techniques. One of our approaches is based on matrix factorization, and the other one is an autoencoder based deep learning technique. We evaluate these two techniques on a collection of simulated and biological datasets, and show that our techniques match or improve upon the best alternate techniques for distance imputation. Moreover, our proposed techniques can handle substantial amount of missing data, to the extent where the best alternate methods fail.ConclusionsThis study shows for the first time the power and feasibility of applying deep learning techniques for imputing distance matrices. The autoencoder based deep learning technique is highly accurate and scalable to large dataset. We have made these techniques freely available as a cross-platform software (available at https://github.com/Ananya-Bhattacharjee/ImputeDistances).

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

Multi-modal infusion pump real-time monitoring technique for improvement in safety of intravenous-administration patients

10.1177/0954411920960260 ◽

2020 ◽

Vol 235 (1) ◽

pp. 73-81

Author(s):

Young Jun Hwang ◽

Gun Ho Kim ◽

Eui Suk Sung ◽

Kyoung Won Nam

Keyword(s):

Neural Network ◽

Performance Evaluation ◽

Deep Learning ◽

Real Time ◽

Infusion Pump ◽

Medication Administration ◽

Thin Membrane ◽

Monitoring Technique ◽

Learning Technique ◽

Intravenous (IV) medication administration processes have been considered as high-risk steps, because accidents during IV administration can lead to serious adverse effects, which can deteriorate the therapeutic effect or threaten the patient’s life. In this study, we propose a multi-modal infusion pump (IP) monitoring technique, which can detect mismatches between the IP setting and actual infusion state and between the IP setting and doctor’s prescription in real time using a thin membrane potentiometer and convolutional-neural-network-based deep learning technique. During performance evaluation, the percentage errors between the reference infusion rate (IR) and average estimated IR were in the range of 0.50–2.55%, while those between the average actual IR and average estimated IR were in the range of 0.22–2.90%. In addition, the training, validation, and test accuracies of the implemented deep learning model after training were 98.3%, 97.7%, and 98.5%, respectively. The training and validation losses were 0.33 and 0.36, respectively. According to these experimental results, the proposed technique could provide improved protection functions to IV-administration patients.

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 ◽

10.3390/s18113704 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3704 ◽

Cited By ~ 30

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 ◽

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.

Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat

Symmetry ◽

10.3390/sym13112012 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2012

Author(s):

Jiameng Gao ◽

Chengzhong Liu ◽

Junying Han ◽

Qinglin Lu ◽

Hengxing Wang ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Wheat Variety ◽

Learning Model ◽

Wheat Cultivars ◽

Learning Methods ◽

Wheat Varieties ◽

Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production.