Learning different types of new attributes by combining the neural network and iterative attribute construction

1997 ◽  
pp. 124-137
Author(s):  
Yuh-Jyh Hu
Keyword(s):  
Neural Network ◽  
The Neural Network ◽  
Different Types ◽  
Attribute Construction

scholarly journals The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 691 ◽  
Author(s):  
Irina Popova ◽  
Alexandr Rozhnoi ◽  
Maria Solovieva ◽  
Danila Chebrov ◽  
Masashi Hayakawa
Keyword(s):  
Neural Network ◽  
Radio Wave ◽  
Seismic Activity ◽  
Low Frequency ◽  
Magnetic Storms ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Different Types ◽  
Parameters Of Seismicity

The neural network approach is proposed for studying very-low- and low-frequency (VLF and LF) subionospheric radio wave variations in the time vicinities of magnetic storms and earthquakes, with the purpose of recognizing anomalies of different types. We also examined the days with quiet geomagnetic conditions in the absence of seismic activity, in order to distinguish between the disturbed signals and the quiet ones. To this end, we trained the neural network (NN) on the examples of the representative database. The database included both the VLF/LF data that was measured during four-year monitoring at the station in Petropavlovsk-Kamchatsky, and the parameters of seismicity in the Kuril-Kamchatka and Japan regions. It was shown that the neural network can distinguish between the disturbed and undisturbed signals. Furthermore, the prognostic behavior of the VLF/LF variations indicative of magnetic and seismic activity has a different appearance in the time vicinity of the earthquakes and magnetic storms.

scholarly journals A Precision Agricultural Application: Manggis Fruit Classification Using Hybrid Deep Learning

2021 ◽  
Vol 35 (5) ◽  
pp. 375-381
Author(s):  
Putra Sumari ◽  
Wan Muhammad Azimuddin Wan Ahmad ◽  
Faris Hadi ◽  
Muhammad Mazlan ◽  
Nur Anis Liyana ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stochastic Gradient Descent ◽  
Traditional Methods ◽  
High Performing ◽  
The Neural Network ◽  
Different Types ◽  
Filter Size ◽  
Fully Connected ◽  
Convolution Filters

Fruits come in different variants and subspecies. While some subspecies of fruits can be easily differentiated, others may require an expertness to differentiate them. Although farmers rely on the traditional methods to identify and classify fruit types, the methods are prone to so many challenges. Training a machine to identify and classify fruit types in place of traditional methods can ensure precision fruit classification. By taking advantage of the state-of-the-art image recognition techniques, we approach fruits classification from another perspective by proposing a high performing hybrid deep learning which could ensure precision mangosteen fruit classification. This involves a proposed optimized Convolutional Neural Network (CNN) model compared to other optimized models such as Xception, VGG16, and ResNet50 using Adam, RMSprop, Adagrad, and Stochastic Gradient Descent (SGD) optimizers on specified dense layers and filters numbers. The proposed CNN model has three types of layers that make up its model, they are: 1) the convolutional layers, 2) the pooling layers, and 3) the fully connected (FC) layers. The first convolution layer uses convolution filters with a filter size of 3x3 used for initializing the neural network with some weights prior to updating to a better value for each iteration. The CNN architecture is formed from stacking these layers. Our self-acquired dataset which is composed of four different types of Malaysian mangosteen fruit, namely Manggis Hutan, Manggis Mesta, Manggis Putih and Manggis Ungu was employed for the training and testing of the proposed CNN model. The proposed CNN model achieved 94.99% classification accuracy higher than the optimized Xception model which achieved 90.62% accuracy in the second position.

A Data-Driven Approach to Predict the Breakdown Pressure of the Tight and Unconventional Formation

2021 ◽  
Author(s):  
Zeeshan Tariq ◽  
Murtada Saleh Aljawad ◽  
Mobeen Murtaza ◽  
Mohamed Mahmoud ◽  
Dhafer Al-Shehri ◽  
...  
Keyword(s):  
Neural Network ◽  
Hydraulic Fracturing ◽  
Rock Properties ◽  
Breakdown Pressure ◽  
Rock Samples ◽  
In Situ Stresses ◽  
Fracturing Fluids ◽  
The Neural Network ◽  
Different Types

Abstract Unconventional reservoirs are characterized by their extremely low permeabilities surrounded by huge in-situ stresses. Hydraulic fracturing is a most commonly used stimulation technique to produce from such reservoirs. Due to high in situ stresses, breakdown pressure of the rock can be too difficult to achieve despite of reaching maximum pumping capacity. In this study, a new model is proposed to predict the breakdown pressures of the rock. An extensive experimental study was carried out on different cylindrical specimens and the hydraulic fracturing stimulation was performed with different fracturing fluids. Stimulation was carried out to record the rock breakdown pressure. Different types of fracturing fluids such as slick water, linear gel, cross-linked gels, guar gum, and heavy oil were tested. The experiments were carried out on different types of rock samples such as shales, sandstone, and tight carbonates. An extensive rock mechanical study was conducted to measure the elastic and failure parameters of the rock samples tested. An artificial neural network was used to correlate the breakdown pressure of the rock as a function of fracturing fluids, experimental conditions, and rock properties. Fracturing fluid properties included injection rate and fluid viscosity. Rock properties included were tensile strength, unconfined compressive strength, Young's Modulus, Poisson's ratio, porosity, permeability, and bulk density. In the process of data training, we analyzed and optimized the parameters of the neural network, including activation function, number of hidden layers, number of neurons in each layer, training times, data set division, and obtained the optimal model suitable for prediction of breakdown pressure. With the optimal setting of the neural network, we were successfully able to predict the breakdown pressure of the unconventional formation with an accuracy of 95%. The proposed method can greatly reduce the prediction cost of rock breakdown pressure before the fracturing operation of new wells and provides an optional method for the evaluation of tight oil reservoirs.

scholarly journals Enhancement of damaged-image prediction through Cahn–Hilliard image inpainting

2021 ◽  
Vol 8 (5) ◽  
pp. 201294
Author(s):  
José A. Carrillo ◽  
Serafim Kalliadasis ◽  
Fuyue Liang ◽  
Sergio P. Perez
Keyword(s):  
Neural Network ◽  
Computational Cost ◽  
Image Inpainting ◽  
Finite Volume Scheme ◽  
Binary Images ◽  
Hilliard Equation ◽  
The Neural Network ◽  
Image Prediction ◽  
Different Types ◽  
Cahn Hilliard Equation

We assess the benefit of including an image inpainting filter before passing damaged images into a classification neural network. We employ an appropriately modified Cahn–Hilliard equation as an image inpainting filter which is solved numerically with a finite-volume scheme exhibiting reduced computational cost and the properties of energy stability and boundedness. The benchmark dataset employed is Modified National Institute of Standards and Technology (MNIST) dataset, which consists of binary images of handwritten digits and is a standard dataset to validate image-processing methodologies. We train a neural network based on dense layers with MNIST, and subsequently we contaminate the test set with damages of different types and intensities. We then compare the prediction accuracy of the neural network with and without applying the Cahn–Hilliard filter to the damaged images test. Our results quantify the significant improvement of damaged-image prediction by applying the Cahn–Hilliard filter, which for specific damages can increase up to 50% and is advantageous for low to moderate damage.

Use of Neural Networks in Road Recognition by Vibration Data

2010 ◽  
Author(s):  
Giacomo Reggiani ◽  
Marco Cocconcelli ◽  
Riccardo Rubini ◽  
Francesco Lolli
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sensitivity Analysis ◽  
Vibration Signal ◽  
Accelerometer Sensor ◽  
Vibration Signals ◽  
Vibration Data ◽  
The Neural Network ◽  
Different Types ◽  
Control Set

This paper deals with road recognition by the use of neural networks on vibration signals. In particular, an accelerometer sensor is used and it acquires the vibrations transmitted in the contact between the tyres and the ground. Twelve different types of road have been tested at different speed of the car. Based on these data a neural network has been proposed to correlate a set of suitable characteristics of the vibration signal with the type of road. The effectiveness of the resulting neural network has been proved on a control set of data. Moreover the paper reports a sensitivity analysis of the neural network in order to minimize the number of inputs needed and to make it rugged.

scholarly journals Real-Time Dispatching Performance Improvement of Multiple Multi-Energy Supply Microgrids Using Neural Network Based Approximate Dynamic Programming

2021 ◽  
Vol 2 ◽  
Author(s):  
Bei Li ◽  
Robin Roche
Keyword(s):  
Neural Network ◽  
Dynamic Programming ◽  
Real Time ◽  
Energy Supply ◽  
Approximate Dynamic Programming ◽  
Utilization Efficiency ◽  
Decision Algorithm ◽  
The Neural Network ◽  
Different Types ◽  
The Impact

In the multi-energy supply microgrid, different types of energy can be scheduled from a “global” view, which can improve the energy utilization efficiency. In addition, hydrogen storage system performs as the long-term storage is considered, which can promote more renewable energy installed in the local consumer side. However, when there are large numbers of grid-connected multi-energy microgrids, the scheduling of these multiple microgrids in real-time is a problem. Because different types of devices, three types of energy, and three types of utility grid networks are considered, which make the dispatching problem difficult. In this paper, a two-stage coordinated algorithm is adopted to operate the microgrids: day-ahead scheduling and real-time dispatching. In order to reduce the time taken to solve the scheduling problem, and improve the scheduling performance, approximate dynamic programming (ADP) is used in real-time operation. Different types of value function approximations (VFA), i.e., linear function, nonlinear function, and neural network are compared to study about the influence of the VFA on the decision results. Offline and online processes are developed to study the impact of the historical data on the regression of VFA. The results show that the neural network based ADP one-step decision algorithm has almost the same performance as the Global optimization algorithm, and the highest performance among all others Local optimization algorithms. The total operation cost relative error is less than 3%, while the running time is only 31% of the Global algorithm. In the neural network based ADP, the key technology is continuously updating the training dataset online, and adopting an appropriate neural network structure, which can at last improve the scheduling performance.

scholarly journals Application of artificial neural networks for irrigation control

2021 ◽  
pp. 84-88
Author(s):  
Galina Nickolaevna Kamyshova
Keyword(s):  
Neural Network ◽  
Irrigation Management ◽  
Small Error ◽  
The Neural Network ◽  
Different Types ◽  
Neural Network Algorithm ◽  
Irrigation Control ◽  
Artificial Neural ◽  
Spatio Temporal ◽  
Intelligent Methods

Modern methods of precision farming, based on the requirements of the spatio-temporal optimality of irrigation of agricultural crops, require new approaches, since achieving the required accuracy is impossible without the use of modern digital technologies and intelligent methods. The article presents a model of operational irrigation management based on an artificial neural network. The advantage is the small error of the neural network algorithm and its ability to adapt to changing conditions, in contrast to traditional methods, which makes it possible to provide optimal results for different types of soils and types of crops.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

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