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Diagnosing liver disease is the challenging task for many public health physicians. In this study, we propose the framework to diagnose the hepatitis disease. For this study the adaptive rule based induction were formulated and the adaptive rule implemented in combined Robust BoxCox Transformation (RBCT) and Neural Network (NN) methods. The performance of proposed model is compared and results are...
We investigated whether men and women use different scanning strategies to extract the visual information relevant for the recognition of female and male faces. We used an old-new recognition task during which observers were asked to identify previously learned faces. Data from two groups of observers (male, female) revealed a more accurate, but not faster, recognition of male face stimuli. Interestingly,...
Predicting stock market accurately has always fascinated the market analysts. During the previous few decades assorted machine learning techniques (Regression, RBFN, SOM, BN and SVM) have been applied to examine the highly debatable nature of stock market by capturing and using repetitive patterns. Our main aim is to accurately predict value for the future and maximum amount of profit for a holder...
On an average 9 out of 10 startups fail(industry standard). Several reasons are responsible for the failure of a startup including bad management, lack of funds, etc. This work aims to create a predictive model for startups based on many key things involved at various stages in the life of a startup. It is highly desirable to increase the success rate of startups and not much work have been done to...
With the large-scale photovoltaic power plants and centralized network, more and more new energy grid problem arises. Distributed photovoltaic power plants connected to the grid, and can lead to accidents such as power grid voltage instability occurs at a certain probability. The fundamental reason is the unreliability of distributed photovoltaic power generation. Solar irradiance and ambient temperature...
There exists a base classification system for classification of problem tickets in the Enterprise domain. Different deep learning algorithms (Gated Recursive Unit and Long Short Term Memory) were investigated for solving the classification problem. Experiments were conducted for different parameters and layers for these algorithms. Paper brings out the architectures tried, results obtained, our conclusions...
Reliability is a software quality characteristic that refer to the probability a system will work correctly over a period of time. Reliability prediction is important as it can be used to plan deployment, maintenance and test activities. This study assesses the efficiency of several techniques in software reliability model (SRM) selection and aims to find out the possible enhancement to improve software...
This paper presents regional Support Vector Machine (SVM) classifiers with a spatial model for object detection. The conventional SVM maps all the features of training examples into a feature space, treats these features individually, and ignores the spatial relationship of the features. The regional SVMs with a spatial model we propose in this paper take into account a 3-dimentional relationship...
Process nonlinearity and time-varying behavior of industrial systems are the main factors for poor performance of online soft sensors. To ensure high predictive accuracy, adaptive soft sensor is a common practice. In this paper, an adaptive soft sensor based on moving window Gaussian process regression (GPR) is presented. To make the moving window strategy more efficient, a just-in-time learning (JITL)...
In this paper, D-Wave quantum computing Ising model is employed and evaluated for the forecasting of positive extremes of daily mean air temperature. Forecast models are designed with two to five qubits, which represent 2-, 3-, 4-, and 5-day historical data respectively. Ising model's real-valued weights and dimensionless coefficients are calculated using daily mean air temperatures from 119 places...
Research that explores the use of machine learning for automatic security classification of information objects is about to emerge. In this paper we investigate the opportunity to increase the machine learning performance by taking advantage from time information that is "hidden" in the documents of the training set. This paper presents a technique to do so, and confirms that this is a promising...
Even though wine-drinkers generally agree that wines may be ranked by quality, wine-tasting is famously subjective. There have been many attempts to construct a more methodical approach to the assessment of wines. We propose a method of assessing wine quality using a decision tree, and test it against the wine-quality dataset from the UC Irvine Machine Learning Repository. Results are 60% in agreement...
A moving mesh method for semiconductor device simulation is developed which effectively compromises accuracies without increasing mesh number. In this method, mesh positions are shifted referring to the solution of the previous bias condition, or to the Newton corrections. The method is applied to solve PN-junctions and MOSFETs. The method provides an effective way to cover the changes of carrier...
Online recommendation systems provide useful information to users on various products and also allow the users to rate the products. However, they do not usually consider the fact that users trust their connections more than others and that the trusts vary from connection to connection i.e., we value the opinions of our connections differently. Moreover, the importance of connections' opinion changes...
The tracking and forecasting of cloud is very important for ultra-short term photovoltaic power forecast with sky images. To recognize clouds deformation and tracking clouds motion in sky images, an optimization model is constructed in this paper. In a binary sky image, the shape and position of a cloud can be represented by a set of coordinates of cloud border points, and the deformation and motion...
The collective communication operations, which are widely used in parallel applications for global communication and synchronization are critical for application's performance and scalability. However, how faulty collective communications impact the application and how errors propagate between the application processes is largely unexplored. One of the critical reasons for this situation is the lack...
A research approach of crack detection of rotating shafts based on acoustic emission (AE) signals and machine learning is proposed in this paper. The relationship between crack intensity and domain features are investigated, and the features which could well indicate the crack condition are selected for modelling and crack prediction. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN)...
In this paper, we develop the max-margin similarity preserving factor analysis (MMSPFA) model. MMSPFA utilizes the latent variable support vector machine (LVSVM) as the classification criterion in the latent space to learn a discriminative subspace with max-margin constraint. It jointly learns factor analysis (FA) model, similarity preserving (SP) term and max-margin classifier in a united Bayesian...
Lending loans to borrowers is considered one of the main profit sources for banks and financial institutions. Thus, careful assessment and evaluation should be taken when deciding to grant credit to potential borrowers. With the rapid growth of credit industry and the massive volume of financial data, developing effective credit scoring models is very crucial. The literature in this area is very dense...
This paper proposes a novel model for predicting the outdoor to indoor radio signal coverage. This model is based on a joint ray launching algorithm that adapts different resolutions for outdoor and indoor simulations. The performance of the joint ray launching method is evaluated by a measurement at 3.5 GHz frequency. This model appears to be efficient for a scenario with mixed resolutions, in terms...
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