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Urban expansion monitoring and organization can be performed through space-based observation thanks to the revisit time and level of details guaranteed by satellite remote sensing. In particular, the Landsat mission products are the most used thanks to the long time coverage and open access policy. This paper proposed a hybrid method — combination of pixel- and object-based analysis — in order to...
In data mining, a well known problem of “Curse of Dimensionality” occurs due to presence of large number of dimensions in a dataset. This problem leads to reduced accuracy of machine learning classifiers because of presence of many insignificant and irrelevant dimensions or features in the dataset. Data mining applications such as bioinformatics, risk management, forensics etc., generally involves...
The data mining applications such as bioinformatics, risk management, forensics etc., involves very high dimensional dataset. Due to large number of dimensions, a well known problem of “Curse of Dimensionality” occurs. This problem leads to lower accuracy of machine learning classifiers due to involvement of many insignificant and irrelevant dimensions or features in the dataset. There are many methodologies...
As increase in the internet services and usage with open access to sensitive data, necessity of security to these systems had become a need of the hour. Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more crucial issue. To detect the attacks hitting the network it is very obligatory to properly monitor the flow...
Dimensionality reduction algorithms help reduce the classification time and sometimes the classification error. For time critical applications, in order to have reduction in the feature acquisition phase, feature selection is preferable to dimensionality reduction, which requires measurement of all inputs. Traditional feature selection methods, such as forward or backward selection, are costly to...
Facial expression is a prominent posture beneath the skin of the face. They are the way of communication in humans which convey many things non-verbally. During the past years face recognition has received significant attention as one of the most important applications of image understanding and analysis. Many algorithms have been implemented on different static and non-static conditions. Static conditions...
In recent years, electronic commerce and online social networks (OSNs) have experienced fast growth, and as a result, recommendation systems (RSs) have become extremely common. Accuracy and robustness are important performance indexes that characterize customized information or suggestions provided by RSs. However, nefarious users may be present, and they can distort information within the RSs by...
This paper presents the process of Quranic Accent Automatic Identification. Recent feature extraction technique that is used for Quranic verse rule identification/Tajweed include Mel Frequency Cepstral Coefficients (MFCC) which prone to additive noise and may reduce the classification result. Therefore, to improve the performance of MFCC with addition of Spectral Centroid features and is proposed...
The ULTRA-WIDE BAND (UWB) signals transmit a large amount of information over a short distance with low power and the signals reflected by the inspected materials can be obtained without contacts of the materials. As a result, the reflected UWB signals offer us one potential contactless material identification or classification tool. In this paper, we study the UWB signals collected in a series of...
Data compression algorithms are used in process plants to store and transmit data for analysis purposes. Amount of data is increasing in process plants due to advances in automation and process monitoring technologies. Process data historians are used in plants to store, manage, retrieve and analyze process data. Process data historians use data compression algorithms to effectively manage large amount...
For hyperspectral data classification, feature reduction techniques have become an apparent need to extract information from original data. In this paper, we introduce Locality Sensitive Discriminant Analysis (LSDA) to perform feature reduction for classification of hyperspectral imagery. By preserving both the discriminant and local geometrical structure in the data, the proposed method can obtain...
Stable local feature detection and representation are the fundamental components of target recognition and image retrieval. The traditional SIFT algorithm's descriptor of the feature points is a 128-element vector, and a lot of redundant information is presence. So the brief and effective expression of the image feature information is the key to improve the performance of the algorithm. This paper...
Mass spectrometry is a powerful tool in chemistry research. A primary aim of data mining in chemistry is to try to obtain useful information from chemistry databases, and then classify the compounds using the useful samples features. Suffering from the traits of high dimension, and small sample in mass spectrometry data, in order to create models, it will be first to provide useful features which...
Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification...
Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field...
The aim of this research is to reduce data for a P300 spelling system by using downsampling algorithm to reduce the sampling rate of the system. The comparison between this method and standard algorithms such as Discrete Wavelet Transforms (DWT) and Principal Components Analysis (PCA) is also discussed in this paper. While downsampling algorithm is used to reduce sampled data, Ensemble of Support...
The high computational complexity of text classification is a significant problem with the growing surge in text data. An effective but computationally expensive classification is the k-nearest-neighbor (kNN) algorithm. Principal Component Analysis (PCA) has commonly been used as a preprocessing phase to reduce the dimensionality followed by kNN. However, though the dimensionality is reduced, the...
Detecting and identification the network traffic attracts many attentions in recent years. Statistical approach using the machining learning algorithm can classify the network traffic efficiently without detecting the payload of every packet. At the same time, the accuracy depends on the statistical features of the training set. However, the traditional process without pre-treatment of the statistical...
In recent years, spike sorting has become an emerging technique in multi-channel recording for neuroprosthetic applications. To achieve on-chip real-time processing, it is necessary to design reliable yet low complexity feature extraction and dimensionality reduction to suit low power hardware resources. To satisfy this criterion, this paper proposes asynchronous sampling of first derivative spike...
Traditional dimension reduction methods reduces noises by explicit rank reduction and dimension reduction simultaneously. In this paper, we propose a method by a robust formulation using L2, 1 norm together with rank reduction without dimension reduction using trace norm regularization. We derive an efficient algorithm for the nonlinear optimizations of proposed objective function. Extensive experiments...
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