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Cost-performance trade off is one of the critical challenges in cloud computing environments. Predictive auto-scaling systems mitigate this issue by scaling in/out system automatically based on performance prediction results. The goal of this research is to investigate the impact of different prediction results on the scaling actions generated by predictive auto-scaling systems. In this study, predictive...
This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this hypothesis, an experiment has been conducted...
Rainfall and river flow are one of the most difficult elements of hydrological cycle to predict. This is due to tremendous range of variability it displays over a wide range of scale both in terms of space and time. The situation is further aggravated by the fact that rainfall-runoff is also very difficult to measure at scales of interest to hydrology and climatologic. Computational intelligence techniques...
Outlier detection is a method to improve performances of machine learning models. In this paper, we use an outlier detection method to improve the performance of our proposed algorithm called decision boundary making (DBM). The primary objective of DBM algorithm is to induce compact and high performance machine learning models. To obtain this model, the DBM reconstructs the performance of support...
Previously, we investigated the prediction of total effort and errors for embedded software development projects using an artificial neural network (ANN). In addition, we proposed a method for reducing this margin of error. However, methods using ANNs have reached their improvement limits, since an appropriate value is estimated using what is known as point estimation in statistics. In this paper,...
The present study proposes prediction approaches of student's grade based on their comments data. Students describe their learning attitudes, tendencies and behaviors by writing their comments freely after each lesson. The main difficulty of this research is to predict students' performance by separately using two class data in each lesson. Although students learn the same subject, there exist differences...
Credit scoring is becoming a competitive issue with rapid growth and significant advance. Building a satisfactory credit model has attracted lots of researchers in the past decades and it is still one of the hottest research topics in the field of credit industry. This paper emphasizes on surveying the development of the artificial intelligence technologies for solving credit scoring. It covers algorithms...
Much of the research work on sentiment analysis has been carried out in the English language, but work in Bangla is limited to only news corpus and blogs. Microblogging sites are becoming a valuable source for publishing huge volumes of user-generated information, as users express their views, opinions, and sentiments over various topics. In this paper, we aim to automatically extract the sentiments...
This paper presents a comparative study of several classification methods for the task of recognizing traffic signs in urban areas. These classification methods are artificial neural network (ANN), k-nearest neighbors (kNN), support vector machine (SVM), and random forest (RF). First, HSI-based color segmentation process is applied to obtain candidate regions. Using centroid-based feature, these regions...
Digital visual media is one of the most commonly used means of communication. But, with the use of low-cost editing tools, tampering and counterfeiting visual contents are increasing enormously. In almost all the Image forensic application areas, the device used for capturing the image is of utmost importance as the origin of the particular image can act as a key evidence to substantiate the legitimacy...
Malware family identification is a complex process involving extraction of distinctive characteristics from a set of malware samples. Malware authors employ various techniques to prevent the identification of unique characteristics of their programs, such as, encryption and obfuscation. In this paper, we present n-gram based sequential features extracted from content of the files. N-grams are extracted...
This paper regards the exploitation of RSS in localization techniques within UWB networks. Both fingerprinting and model based approaches are studied and evaluated using a real UWB measurement campaign. As for fingerprinting approach, SVM, KNN, and ANN techniques are proposed and compared. As for model based approach, refined RSS models are proposed in order to better characterize the RSSI-distance...
A classification system that accurately categorizes caller behavior within Interactive Voice Response systems would assist in developing good automated self service applications. This paper details the implementation of such a classification system for a pay beneficiary application. Adaptive Neuro-Fuzzy Inference System (ANFIS), Feed forward Artificial Neural Network (ANN) and Support Vector Machine...
This This study aims to investigate the robustness of prediction model by comparing artificial neural networks (ANNs), and support vector machine (SVMs) model. The study employs ten years monthly data of six types of macroeconomic variables as independent variables and the average rate of return of one-month time deposit of Indonesian Islamic banks (RR) as dependent variable. Finally, the performance...
In order to improve the generalization performance of support vector machine (SVM), a support vector machine ensembling method based on independent component analysis (ICA) and fuzzy kernel clustering (FKC) was proposed. The ICA emphasizes the independence between the data characteristics and can effectively obtain a series of independent features, the performance of single SVM can be improved when...
The correct identification of two-phase flow regime is the basis for the accurate measurement of other flow parameters in two-phase flow measurement. A PSO-SVM(Particle Swarm Optimization and Support Vector Machine) model, which can overcome selecting parameters needed in SVM model, was developed to identify the flow regime. The application of PSO-SVM improves the accuracy of flow regime recognition...
Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address...
The importance to financial institutions of accurately evaluating the credit risk posed by their loan granting decisions cannot be underestimated; it is underscored by recent credit assessment failures that contributed greatly to the so-called "great recession" of the late 2000s. The paper compares the classification accuracy rates of several traditional and computational intelligence methods...
The analysis of mechanical properties of cement soil is the problem concerned greatly by the design-constructors. This paper uses SVM to establish the nonlinear relation between the parameters and unconfined compressive strength of cement soil. At the same time, this paper uses PSO to get the global optimization of SVM, which avoids the blindness of artificial selection and improves the prediction...
Credit scoring and behavioral scoring have become very important credit risk management tasks during the past few years due to the impact of several financial crises. The objective of the proposed study is to explore the performance of behavioral scoring using three commonly discussed data mining techniques-linear discriminant analysis (LDA), backpropagation neural networks (BPN), and support vector...
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