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Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). For solving this high dimensional classification problem, the widely used algorithm remains to be Support Vector Machines (SVM). But due to the high variance of the data, the classification performance of SVM remains...
Positioning applications become more popular with the advancement of location aware services. Global Positioning System is a successful solution for outdoors whereas it is not suitable for indoor environments due to the lack of line of sight for radio frequency signals. Therefore, various systems have been developed to solve the indoor positioning problem. Enhancing the performance of these systems...
Dictionary Learning is a method used in signal and image processing. In this study, classification of mammogram images were realized by using dictionary learning and sparse representation algorithms. The attributes of the images were detected with Wavelet Transform and PCA, and the new dataset which was created by the obtained attributes were classified by Dictionary Learning. Moreover, the classification...
Ethnicity is one of the most salient clues to face identity. Analysis of ethnicity-specific facial data is a challenging problem and predominantly carried out using computer-based algorithms. Current published literature focusses on the use of frontal face images. We addressed the challenge of binary (British Pakistani or other ethnicity) ethnicity classification using profile facial images. The proposed...
The papers in this special section focus on multimedia data retrieval and classification via large-scale systems. Today, large collections of multimedia data are explosively created in different fields and have attracted increasing interest in the multimedia research area. Large-scale multimedia data provide great unprecedented opportunities to address many challenging research problems, e.g., enabling...
A large number of text data are regularly published in social networks and the media. Processing and analysis of such information is an highly required direction. This paper focuses on the way to use the entropy measure when dealing with big volumes of text data in classification. The used entropy measure stands for algorithm quality criteria when defining a class in a set of data. The work also features...
This paper aims to investigate the neural networking system. The signals to be studied have been taken from photonic sensors. For classification, a given signal is first transformed into different feature domains and then neural network is used to train the given dataset to form the network. Wavelet transform is used to extract the signal properties-skewness, kurtosis and entropy and Fourier Transform...
Support Vector Machine (SVM) is one of the most popular machine learning algorithm to perform classification tasks and help organizations in different ways to improve their efficiency. A lot of studies have been made to improve SVM including speed, accuracy, and/or scalability. The algorithm possesses parameters that need precision tuning to perform well. This work proposes a novel parallelized parameter...
With the development of the Internet, and the increase in the online storage space, there has been an explosion in the volume of videos and images circulating online. An important part of the digital forensics' tasks is to scrutinise part of these images to make important decisions. Digital tampering of images can impede reliability of these decisions. Through this paper we attempt to improve the...
When High-speed train runs, the strength of data field signal determines whether the CIR device on the High-speed train can work normally. While proper functioning of the CIR device affects the normal operation of the train deeply. The K-MEANS-analysis-model proposed in this paper, applies the K-MEANS method of machine learning to train numerous data duplicate which generated by different locomotives...
Load profiling refers to a procedure which leads to the formulation of daily load curve and consumer classes regarding the similarity of the curves shapes. This procedure incorporates a set of unsupervised machine learning algorithms. Various researches propose clustering algorithms for grouping together load curves with high degree of similarity. K-means is the most common algorithm in the load profiling...
Science and technology (S&T) linkages have been studied extensively using patent and scientific publication databases. Existing methods used to track S&T linkages, such as analysis of non-patent literature (NPL) or author-inventor matching offer a narrow window for industry level analysis of the data. This paper examines the application of a machine learning algorithm, namely Latent Dirichlet...
This paper presents an object detection accelerator that features many-scale (17), many-object (up to 50), multi-class (e.g., face, traffic sign), and high accuracy (average precision of 0.79/0.65 for AFW/BTSD datasets). Employing 10 gradient/color channels, integral features are extracted, and the results of 2,000 simple classifiers for rigid boosted templates are adaptively combined to make a strong...
Hyperspectral remote sensing is becoming an active research field in the last decades thanks to the availability of efficient machine learning algorithms and also to the ever-increasing computation power. However, there exist application domains (e.g., embedded applications) in which the deployment of this kind of systems becomes unfeasible due to the high requirements related to the size, power consumption...
In practice, unlabeled data can be cheaply and easily collected from target domain, but it is quite difficult and expensive to obtain a large amount of labeled data. Therefore how to use both of labeled and unlabeled data to improve the learning performance becomes critical issue for many real-world applications. Active Learning and Semi-supervised Learning are right solutions to such problem, and...
Feature selection is an important tool used in data reduction; it aims at improving efficiency in many machine-learning algorithms by choosing a small set of informative features among the whole dataset. Feature selection algorithms can be classified in three major categories: Filter, Wrapper and Embedded. In this paper, we proposed a new hybrid filter-wrapper algorithm of feature selection based...
Innovation in the public-sector refers to the development of important improvements in the public administration and their corresponding services. One of such public services is the social security, of which central process has been the information security of their offered services. The aim of the present study has been the analysis of the trends and the discovery of behavioural patterns in the attacks...
Multi-label learning aims to predict the label sets an instance belongs to. Extreme Learning Machine (ELM), as a single-hidden layer feedforward neural network algorithm, has been extended to multi-label scenarios because of faster learning speed and less human intervention. Aiming at dealing with the problem of ignoring the inter-label dependencies, the proposed method, ELM-LMF, decomposes the label...
Data mining algorithms are used to analyze and discover useful information from data. This paper presents an experiment that applies Combinatorial Testing (CT) to five data mining algorithms implemented in an open-source data mining software called WEKA. For each algorithm, we first run the algorithm with 51 datasets to study the impact different datasets have on the test coverage. We select one dataset...
From the day internet came into existence, the era of social networking sprouted. In the beginning, no one may have thought internet would be a host of numerous amazing services like the social networking. Today we can say that online applications and social networking websites have become a non separable part of one's life. Many people from diverse age groups spend hours daily on such websites. Despite...
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