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In this study, Kernel Principal Component Analysis is applied to understand and visualize non-linear variation patterns by inverse mapping the projected data from a high-dimensional feature space back to the original input space. Performance Evaluation of Random Forest on various data sets has been compared to understand accuracy and various statistical measures of interest.
Gender classification has found application in a myriad of fields such as surveillance, interaction between humans and computers, face recognition and most recently in digital signage for gender-targeted advertising. There are several existing methods of gender classification such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All these...
Network security has become one of the well-known concerns in the last decades. Machine learning techniques are robust methods in detecting malicious activities and network threats. Most previous works learn offline supervised classifiers while they require large amounts of labeled examples and also should update models because the data change over time in real world applications. To alleviate these...
Chronic kidney disease is a universal common obstacle which its outcomes can be prevented or delayed by early detection and cure. Classification of kidney disease is vital for global improvement and accomplishment of practical guidance. Therefore, data mining and machine learning techniques can be used to discover knowledge and identify patterns for classification. Since there exist features that...
Empirical studies in software reliability research have predominantly focused on end-user applications. In this paper we present an empirical study on operating system reliability, which considered a sample of 7,007 real operating system failures collected from 566 computers. Our approach considered the clustering of different types of failures, as well as used an algorithm we created to search for...
Several programs demand large memory allocation to execute their tasks. Normally, the demands are based on intentions of program designers, users, and system administrators. Sometimes, however, faulty programs or malicious programs demand large memory without the intentions. These unexpected large memory demands may cause system instability. Generally, operating systems have resource limitation mechanisms...
There exist large datasets containing the sequences of points that moving objects occupy in space as time goes by. Such sequences of moving objects are known as trajectories. Being able to issue queries that allow the extraction of patterns from the movements of these objects is important to many real world applications, such as urban planning in transportation and bird migration tracking in ecology...
Designed with the goal of mimicking key features of real HPC workloads, mini-apps have become an important tool for co-design. An investigation of mini-app behavior can provide system designers with insight into the impact of architectures, programming models, and tools on application performance. Mini-apps can also serve as a platform for fast algorithm design space exploration, allowing the application...
Since its invention, Particle Swarm Optimization (PSO) has been used to solve difficult problems, some of which can take large amounts of time and resources. In recent years, general purpose computing on a graphic processing unit (GPGPU) has arisen to enable scientists and engineers to take advantage of the sometimes enormous speed gains made by parallel programming. While placing a human in the loop...
P300 speller for Brain-Computer Interface systems aim to provide a direct communication between computer - machine and human brain, without any muscular activity. The communication is provided by detecting the presence of P300 Event Related Potentials (ERPs) in the electroencophelogram (EEG) signals, recorded from scalp. The major problem associated with P300 spellers is the stratification of EEGs...
Multiclass classification is the task of classifying the samples into more than two classes. Generally multi-classifiers face difficulty in classifying samples those are very close to the separating hyperplane, known as Generalization error. Generalization error can be reduced by maximizing the margin of the separating hyperplanes. Support Vector Machine (SVM) is a maximum-margin classifier, its aim...
The paper studies a method for recommendation based on community partition applying for user in social network. Firstly, the largest connected component in friend-relationship complex network are taken as the logic unit, and divide up the largest connected component into non-intersect kernel sub-network, the kernel sub-network based on The maximum complete sub-graph which has the mathematics foundation...
In this paper, we would like to introduce a GPU accelerated solver for systems of linear equations with an infinite precision. The infinite precision means that the system can provide a precise solution without any rounding error. These errors usually come from limited precision of floating point values within their natural computer representation. In a simplified description, the system is using...
Self Organizing Maps perform clustering of data based on unsupervised learning. It is of concern that initialization of the weight vector contributes significantly to the performance of SOM and since real world datasets being high-dimensional, the complexity of SOM tend to increase tremendously leading to increased time consumption as well. Our work focuses on the analysis of different weight initialization...
In this paper, first, we point out that fuzzy integers are not special cases of fuzzy numbers. Then, we define the concept of fuzzy integers, develop the methods for establishing rankings and total orderings on sets of fuzzy integers, and discuss the interrelationships between rankings and total orderings.
This paper summarizes a process of operating system adaptation to an Intel Atom processor. The main objectives of this project was to adapt a simple micro kernel embedded operating system to a more complicated processor family, without destroying the original modules of system or changing their functionality. Our motivation was the lack of information or techniques regarding operating system migration...
Image classification is currently a vital and challenging topic in computer vision. Although it has been achieved many classification algorithms so far, the classification of natural images still remains great difficulties in image processing. In this paper, we propose a semantic linear-time graph kernel for image classification. Each image is represented by a graph and the vertex of each graph corresponds...
Prefetching disk blocks reduces subsequent disk access times, allowing applications to load and run more quickly. Successful prefetching depends on the accuracy with which upcoming disk I/O can be predicted, and many techniques are not particularly accurate, while incurring significant memory and CPU overheads. A new lightweight prefetching technique for general applications performs off-line analysis...
Understanding network workload through the characterization of network flows, being essential for assisting network management tasks, can benefit largely from traffic sampling as long as an accurate snapshot of network behavior is captured. This paper is devoted to evaluate the real applicability of using sampling to support flow analysis. Considering both classical and emerging sampling techniques,...
Implementation of a frequency measurement device based on ARM-embedded computer viz. Raspberry Pi is presented. This device is used as a part of a wide area frequency measurement system implemented at Indian Institute of Technology Bombay. Free and open source software is used including Linux based operating system patched with a realtime development framework. This serves as a low-cost high performance...
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