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The power output of solar energy conversion facilities such as photovoltaic systems is highly dependent and proportional to the amount of solar radiation absorbed on the collecting surface. In order to have an efficient design of these systems, it is essential to perform solar resource assessment on the intended location prior to installation. Advancements in computational intelligence led to applications...
Background: Many relevancy filters have been proposed to select training data for building cross-project defect prediction (CPDP) models. However, up to now, there is no consensus about which relevancy filter is better for CPDP. Goal: In this paper, we conduct a thorough experiment to compare nine relevancy filters proposed in the recent literature. Method: Based on 33 publicly available data sets,...
We develop an intelligent credit rating system that can provide debtors' rating information without involving credit rating agencies. Several models are used for credit scoring in our work, including the Duffie's model, logistic regression, and random forest. We compare the performance of these models and build an in-depth understanding of the evaluation of credit rating. Furthermore, we propose a...
This paper presents a generalized modeling framework of fault detection and correction processes with bivariate distributions. The presented framework includes almost all existing software reliability growth models, namely the models in which both fault detection and correction processes are described by non-homogeneous Poisson processes. In our framework, the time dependency of fault correction time...
In this paper, a text independent speaker recognition system based on Gaussian mixture models (GMM) was developed with a specific focus on the use of a voice activated detector (VAD) algorithm in the training and testing. At the training level, a modified estimation/maximization (EM) algorithm is used. It is less prone to get trapped around a local maximum and so, it will have more chance to converge...
Based on the analysis of current conditions and shortages of the configuration files management in smart substation, the problems existing in current conditions are proposed, such as: ICD test technology, SCD test technology, SCD difference comparison technology. In order to meet the demand of the large scale smart substation construction, a solution of building the smart substation configuration...
Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when...
In this paper, we propose a classification model for learning state based on individual biometric data. In particular, we use the pupil size as a biometric data and the data has been collected from 72 participants. We also deploy the support vector machine (SVM) in conjunction with k-fold validation as an analysis tool. In order to improve the performance of the SVM, the we remove outliers from the...
Model-Based Testing rises hopes of project teams of meeting both eager time and budget constraints as well as achieving better system quality by thorough testing. However, toolchain and method impose a certain skill set on the project engineer. This paper presents a possible way forward and introduces the constraints to the system architecture.
The Automated Systems of Technical Diagnostics (ASTD) of the dynamic distributed information systems (DIS) need the operational reconfiguration corresponding to changes of DIS. As a result, support of ASTD also requires the up-dating, advancing changes of DIS. In this work, development of the network model of testing is provided, allowing to reduce time of synthesis of tests for DIS. The Model is...
In this work, we present a performance comparison of the Multi Layer Perceptron (MLP), Support Vector Machines (SVM) and Voted Perceptron (VP) when applied to a social signal processing task. The signal processing task is in the field of computational politics where the aim is to predict the political parties of American congress members based on their response to certain questions. Using this dataset...
The paper presents a framework for model-based testing of automotive distributed system and a method of automatic assignment of testing priorities used within the framework. The proposed method utilizes classifiers for automatic assignment of testing priorities to specific parts of the tested system. The paper also introduces a set of extraneous data accompanying the modeling language that are exploited...
Radio frequency interference (RFI) is electromagnetic interference (EMI) from signals in the radio frequencies of the electromagnetic spectrum. RFI reduces the sensitivity of radio telescope and produces artefacts in the observed data. We present the result of applying machine learning techniques to detect confidently man made RFI. We confirm that not all the features selected to characterise RFI...
Outlier detection is a primary step in many data mining applications. An outlier is an abnormal individual from a population, which usually leads poor accuracy in models. Medical literatures are the most reliable resources for researchers to know the progress in their research areas and latest contributions from others. Traditional keyword search retrieves all the text data that contain the keywords...
In this paper, we address the problem of fault detection (FD) of chemical processes using improved generalized likelihood ratio test. The improved GLRT is the method that combines the advantages of the exponentially weighted moving average (EWMA) filter with those of the GLRT method. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data...
There are so many applications using Crowdsource techniques in retrieving data, especially data on the traffic density of vehicles on certain roads, sample applications that already implementing the concept Crowdsourced data such as Waze, Google Maps, Bing Maps, etc. However, from that Crowdsouced data it will arise the question, whether the given data valid? Or how accurate the data provided by the...
Color is one of the attributes that play a role in identifying specific objects, color processing including the extraction of information about the spectral properties of the object's surface and look for the best similarity of a set of descriptions which have been known to do an introduction. Therefore, the classification is needed right fuji apples to obtain good quality fruit. Fuzzy model is one...
Fraud, waste, and abuse in medical insurance contributes to significant increases in costs for providers and patients. One way to reduce costs is through the detection of abnormal medical practices that could indicate possible fraud. In this paper, we expand upon our previous research into medical specialty anomaly detection by validating the efficacy of our model using real-world fraud cases, and...
This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem...
Agriculture is one of primary sectors of the national economy and is receiving more attention from government annually in order to increase productions and boost national economy. Agriculture, especially rice cultivation, has been challenged with various issues for the past decades such as extreme weather (global warming) which could result in crop failure. From the weather aspect, this paper aims...
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