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The paper presents further research on neural engineering that focuses on the classification of emotional, mental, physical and no stress through the use of Electroencephalography (EEG) signal analysis. Stress is one of the leading causes of several health-related problems and diseases. Therefore, it becomes necessary for people to monitor their stress. The human body acquires and responds to stress...
Patient admitted with acute decompensated heart failure (ADHF) facing with high risk of mortality where 30 day mortality rates are reaching 10%. Identifying patient with high and low risk of mortality could improve clinical outcomes and hospital resources allocation. This paper proposed the use of artificial neural network to predict mortality for the patient admitted with ADHF. Results show that...
The goals of this paper were twofold: to continue and refine previous research in the topic of tree cover type classification by harnessing modern machine learning models, and to extend the conclusions of that work to demonstrate that results gained from such models can be used to assist U.S. land management agencies in current challenges they face. Using the same dataset as the past study, an artificial...
Temperature resolution is a key factor for the performance of a Distributed Temperature Sensor (DTS). One can define the resolution as the degree of uncertainty in the temperature information. Thus, the temperature measured in a steady-state condition at a given point in the fiber will vary between successive measurements and between adjacent points that are at the same temperature. Temperature resolution...
In this paper, artificial neural networks are modeled to predict complete band-gaps of bi-dimensional photonic crystals. The available data-set has been generated by an integrated artificial immune network and MPB (MIT Photonic Bands) optimization procedure. Two case studies were carried out, considering square lattice photonic crystals composed of two and three silicon round rods embedded in air...
In recent years due to increased competition between companies in the services sector, predict churn customer in order to retain customers is so important. The impact of brand loyalty and customer churn in an organization as well as the difficulty of attracting a new customer per lost customer is very painful for organizations. Obtaining a predictive model customer behaviour to plan for and deal with...
Programming languages are the primary tools of the software development industry. As of today, the programming language of the vast majority of the published source code is manually specified or programmatically assigned based solely on the respective file extension. This work shows that the identification of the programming language can be done automatically by utilizing an artificial neural network...
This paper deals with TanDEM-X and Cartosat-1 DEM fusion over urban areas with support of weight maps predicted by an artificial neural network (ANN). Although the TanDEM-X DEM is a global elevation dataset of unprecedented accuracy (following HRTI-3 standard), its quality decreases over urban areas because of artifacts intrinsic to the SAR imaging geometry. DEM fusion techniques can be used to improve...
In the electricity sector, new sides have emerged with the development of technology and the increasing the electric energy need. Today, electricity has become a product that is bought and sold in the market environment. Forecasting which is the first step of plans and planning have become much more important and have been made mandatory for the market participants by energy market regulators. In...
Credit scoring is an important process in every financial institution and bank. Its high accuracy in classifying customers helps decrease the credit risk and increase reliability and profit. In this paper, we propose a binary classification approach that can classify customers who apply for loans. A statistical technique called Stepwise Regression (SR) is used as a pre-process to select important...
In recent years, the strong growth in solar power generation industries is requiring an increasing need to predict the profile of solar power production over the day, in order to develop high efficient and optimized stand-alone and grid connected photovoltaic systems. Moreover, the opportunities offered by battery energy storage systems coupled with PV systems, require the load power to be forecasted...
Impacts are one of the main causes of damage in composite panels. The determination of the impact location and the reconstruction of impact force are necessary to evaluate the health of the structure. These data may be measured indirectly from the measurements of responses of sensors located on the system subjected to the impact. In this study, a composite panel model developed in Abaqus/CAE is first...
The class imbalance problem occurs when instances in one class are more than that in another. It has been reported to severely hinder classification performance of many traditional classification algorithms and many researchers have paid a great deal of attention to this field. Different kinds of methods have been pro-posed to solve the problem these years, such as resampling methods, integrated learning...
This study aims to present time series-based forecasting for Malaysian crude palm oil prices using neural network algorithms. Daily prices of soy bean oil and currency exchange rates are tested as input features, in addition to crude palm oil prices. Efforts are focused on finding the optimal network structures for the modelling of crude palm oil price forecasting. Neural network structures with an...
Personality is the defining essence of an individual as it guides the way we think, act and interpret external stimuli. Classification of personality is important as it can serves as a framework in the job assignment task, particularly, in the high risk job including the Police Force. There are many attributes of individual traits but not all of them can be used to indicate individual personality...
Hardware implementation of deep machine learning using the convolutional neural network has been successfully demonstrated using array architecture with non-volatile storage elements such as floating-gate MOS transistor, resistive memory, phase change memory, etc. We present a new simulation platform, NVMLearn, to aid the design, verification, and system-level power and performance estimation for...
Examines the use of artificial feedforward neural networks and GRNN type networks in solving prediction problems. Exemplified by student research, the authors conducted the study on the choice of the type of neural network that is most suitable for the solution of such problems. The article desctibes the set-up and the training of generalized regression neural network of and feedforward neural network...
The purpose of this work is to develop devices capable of identifying sign language characters and comparing them in order to verify layout with better accuracy and robustness. The recognition is performed using Artificial Neural Networks and all the input data are signals from flex sensors, accelerometers and gyroscopes, positioned differently on each device. After being trained, validated and tested,...
Meta search engine is an effective tool for searching information online. In comparison with independent search engine like Google, Bing, and etc., meta search engine has a wider coverage and can meet the requirement of information retrieval in a better manner. In particular, when a query is received from the user, the meta search engine sends it to some proper candidate member engines, collects results...
Objectives: Nowadays breath test, using the Ghoss method for the calculation of gastric emptying of solids, is characterized by a very high number of expirations in a very long time (about 4 hours). In this work, a simplified model aiming to the reduction of the number of expirations during this time was designed, preserving high levels of accuracy, sensitivity and specificity in the classification...
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