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With a large amount of industrial data available, it is of considerable interest to develop data-based models. The challenge lies in the significant noises that appear in all data collected from industry. The errors-in-variables (EIV) model is a model that accounts for measurement noises in all observations (both input and output). In most of the traditional EIV identification methods, the input generation...
SVM (Support Vector Machine), a state of the art classifier model is implemented on a computational mobile platform and its performances are evaluated against a low complexity classifier such as SFSVC (Super Fast Vector Support Classifier) on the same platform. For a better comparison, similar implementation for the two architectures are considered, such as using the same basic linear algebra library...
Digital image processing is shifting the information analysis paradigms in a variety of systems whereas that it is becoming highly viable. Medicine has made the most of image processing by enhancing decision-making through computer-aided diagnosis (CAD) systems. CAD image models support medical inferences by extracting key visual features and classifying regions of interest. Linear parametric system...
This paper presents a design for a High Performance Machine Learning (HPML) framework to support DDDAS decision processes. The HPML framework can provide a high performance computing environment to implement large scale machine learning algorithms that leverages Big Data tools (e.g., SPARK, Hadoop), parallel algorithms, and MapReduce programming paradigm. The framework provides the following capabilities:...
Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this paper we show how Inductive algorithms constructed from building blocks on small data sub-sample can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art...
In this paper, we present a system that supports the design of web graphical user interface by finding the optimal placement of interactive elements. The definition of optimal placement is context specific; it aims at maximizing measurable aspects of the user experience, and it is derived using expert knowledge embedded in the system, which is based on HCI principles, user studies, and data analytics...
Modeling of data is an important step in process of interpreting the data and to understand the desired situation more clearly. The topic of social network structures is one of the highly studied subject and modeling is very important for social network mining. One of the modeling tools for such structures is Graphs. Graphs have been used for modeling and visualization tool of many structures such...
Presence detection is used in occupancy control to dynamically adjust energy-related appliances in smart building applications. Yet, practical applications typically suffer from high sensor unreliability. We propose a computationally efficient approach, based on Hidden Markov Models, to fuse sensor observations from multiple sensors to better estimate user state (presence/absence). Our model considers...
Trust and reputation management is introduced tothe Online Social networks (OSNs) as a solution to promote ahealthy collaboration relationship among participants. Currently, most trust and reputation systems focus on evaluating thecredibility of the users. The reputation systems in OSNs have asobjective to help users to make difference between trustworthyand untrustworthy, and encourage honest users...
An improved KNN text classification algorithm based on Simhash has been proposed by introducing Simhash and the average Hamming distance of adjacent texts as a unit, which solves the problems caused by data imbalance and the large computational overhead in the traditional KNN text classification algorithms. Experimental results demonstrate that the proposed algorithm performs a higher precision, a...
This paper presents a solution to classifying sentences with multi-labels. This problem is an essential part to a semantic search process. Sentences or keywords with correctly automated labelling can enhance the efficiency and performance of the search. The technique introduces a vector space of relevance for keywords and sentences with necessary operations. Concepts and motivation are explained with...
Efficient processing of spectral unmixing is a challenging problem in high-resolution satellite data analysis. The decomposition of a pixel into a linear combination of pure spectra into their corresponding proportions is often very time-consuming. In this paper, a fast unmixing algorithm is proposed based on classifying pixels into a full unmixing group for subset selection requiring intensive computational...
This paper presents an experimental design for building an efficient energy disaggregation system through multi-label classification approach. The proposed system requires a single point measurement of common electrical parameter data at aggregate electric circuit to identify the operating status of multiple appliances. Some multi-label classification algorithms were evaluated to select the best one...
Membrane computing also called P system, seeks to discover new computational models from the study of cellular membranes. In this study, we reported our initial efforts to classify Macao visitor expenditure profile using a membrane computing approach. Specifically, we designed a novel P system including specific membrane structure and membrane rules to realize an improved k-medoids clustering algorithm...
The study of flower classification system is a very important subject in the field of Botany. A classifier of flowers with high accuracy will also bring a lot of fun to people's lives. However, because of the complex background of flowers, the similarity between the different species of flowers, and the differences among the same species of flowers, there are still some challenges in the recognition...
Anomaly detection involves way towards finding the example in the information that violates ordinary conduct. The choice of anomaly detection algorithm can to a great extent affect the undertaking of anomaly identification. The decision of abnormality revelation calculation can influence complexity and correctness of the process. The choice of anomaly recognition calculations may increase the occurrence...
Document similarity is the foundation of many intelligent data processing systems, such as information retrieval, text classification and clustering. However, traditional document similarity algorithms are challenged by the privacy-preserving problem. Recently, privacy-preserving document similarity approaches are provided to solve this problem and there are two kinds of approaches which are vector...
The effective management of scalable applications for solving large problems in a heterogeneous distributed computing environment is the non-trivial problem. Scalable applications generate competitive job flows that have be executed with the help of shared resources of the environment. The promising approach to solve this problem is to use multi-agent technologies. To this end, we develop a multi-agent...
Performance monitoring is essential for all subsystems, especially high performance computing systems. These systems are sensitive to errors and failures which lead to data losses and then severely impact on the organizations. Consequently, resource information in the systems (e.g., CPU usage, memory usage, disk I/O usage, etc.) during the operations must be collected through the system monitoring...
The subpixel mapping technique can obtain a fine-resolution map of target classes in the hyperspectral remote sensing image based on the spatial dependence. In recent years, the subpixel mapping methods based on Maximum A Posterior framework and Total Variation prior (MAP-TV) has received extensive attention because of its unified framework. However, due to the inherent nonlinearity of the TV prior,...
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