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Fundamental changes over time of surface EMG signal characteristics are a challenge for myocontrol algorithms controlling prosthetic devices. These changes are generally caused by electrode shifts after donning and doffing, sweating, additional weight or varying arm positions, which results in a change of the signal distribution—a scenario often referred to as covariate shift. A substantial decrease...
Band selection is an effective solutions for dimensionality reduction in hyperspectral imagery. In this paper, a novel band weighting and selection method is proposed based on maximizing margin in support vector machine (SVM). The goal is to reduce high dimensionality if hyperspectral data while achieving accuracy classification performance. This method computes the weights of the samples to maximize...
A method for sentiment polarity assignment for textual content written in Polish using supervised machine learning approach with transfer learning scheme is proposed in the paper. It has been shown that performing simple natural language processing steps prior to classification, provides inspiring results without redundant computation overhead. The documents containing subjective opinions were classified...
Improvement of classification accuracy is importance in data analysis problems. Enhancement of techniques have been proposed previously to address the problems as regard to classification performance, however, the issues of misclassification and noise elimination in the early stage of processing have been ignored by many researchers. If these problems were addressed, the performance of the classification...
This paper proposes an intellectual classification system to recognize normal and abnormal MRI brain images. Nowadays, decision and treatment of brain tumors is based on symptoms and radiological appearance. Magnetic resonance imaging (MRI) is a most important controlled tool for the anatomical judgment of tumors in brain. In the present investigation, various techniques were used for the classification...
Intrusion detection system (IDS) research field has grown tremendously in the past decade. Improving the detection rate of user to root (U2R) attack classes is an open research problem. Current IDS uses all data features to detect intrusions. Some of the features may be redundant to the detection process. The purpose of this empirical study is to identify important features to improve the detection...
In this paper, non-intrusive load monitoring using a single point sensing and wavelet-based classification is presented and applied to a test system feeding two dynamic and two static three-phase loads. The features in the three-phase voltage and current signals are extracted by wavelet transform to decompose the original signals. The energy of the obtained wavelet coefficients at the detail levels...
The abundant spectral and spatial information in the hyperspectral images (HSI) are largely used in the field of remote sensing. Though there are highly sophisticated sensors to capture the hyperspectral imagery, they suffer from issues like hyperspectral noise and spectral mixing. The major challenges encountered in this field, demands the use of preprocessing techniques prior to hyperspectral image...
Threats to computer networks are numerous and potentially devastating. Intrusion detection techniques provide protection to our data and track unauthorized access. Many algorithms and techniques have been proposed to improve the accuracy and minimize the false positive rate of the intrusion detection system (IDS). Statistical techniques, evolutionary techniques, and data mining techniques have also...
Intrusion detection system (IDS) research field has grown tremendously in the past decade. Improving the detection rate of user to root (U2R) attack class is an open research problem. Current IDS uses all data features to detect intrusions. Some of the features may be redundant to the detection process. The purpose of this empirical study is to identify the important features to improve the detection...
Automatic classification of electrocardiogram (ECG) signals is of Paramount importance in the detection of a wide range of heartbeat abnormalities as aid to improve the diagnostic achieved by cardiologists. In this paper an effective multi-class beat classifier, based on statistical identification of a minimum-complexity model, is proposed. The classifier is trained by extracting from the ECG signal...
Quran is the holy book for Muslims around the world. Since it was revealed to the Prophet Muhammad (PBUH) before about 14 hundreds years, Quran is preserved in all imaginable ways from distortion. The rapid and huge growth of digital media and internet usage, cause a wide spread of the Quranic knowledge as well as Quranic Verses, scripts, Translations, and many other Quranic sciences in its digital...
An accurate tumor classification is important to diagnosis and treatment cancers. The conventional methods for tumor classification include training and testing phases, which may cause over fitting. Although this problem can be avoided by using sparse representation classification, the existing sparse representation methods for tumor classification are inefficient. In this paper, an efficient and...
Brain computer interface (BCI) is a system that provide a direct communication between human brain and external devices. BCIs which based on mental tasks of users are widely used for disabled or paralyzed patients, in order to help their mobility. Preprocessing techniques have been extensively developed to increase the signal-to-noise ratio and spatial distribution of the signals. Common Spatial Pattern...
In this paper, a novel naïve Bayesian classifier based on the hybrid-weight feature attributes (short of "NBCHWFA") is proposed. NBCHWFA arranges a hybrid weight for each feature attribute by merging the effectiveness of feature attribute on classification and the dependence between feature attribute and class attribute. In order to demonstrate the feasibility and effectiveness of proposed...
The rapid growth in audio processing has given much help in advancing the development of digital music. It encourages the creation of method for the genre classification which is able to optimize the learning process to be done with ease, simple and has a good quality in a song search accuracy. Hence we need a development of the learning process with a variety of methods and better algorithms. This...
In the present paper we describe a recent approach of probabilistic self-organizing maps (PRSOM). The PRSOM become more and more interesting in many fields such as: pattern recognition, clustering, classification, speech recognition, data compression, medical diagnosis… The PRSOM give an estimation of the density probability function of the data, this density dependent on the parameters of the PRSOM,...
The DNA microarray classification is one of the most popular technique among researchers and practitioners. In microarray data analysis, huge useful information may be lost due to irrelevant and insignificant features of the dataset. To overcome this drawback of the data set, only those features are selected which have high relevance with the classes and high significance in the feature set. In this...
We explore the application of Kernel Support Vector Machines (SVM) to the realm of text messages. Our intent is to classify the author of a text message based on usage patterns present in a training set of text messages. We achieve between 57% and 96% accuracy in determining the author of unknown samples.
In this paper, classifying and indexing video genres using Support Vector Machines (SVMs) are based on only audio features. In fact, those segmentation parameters are extracted at block levels, which have a major benefit by capturing local temporal information. The main contribution of our study is to present a powerful combination between the two employed audio descriptors Mel Frequency Cepstral...
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