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The main notion of this paper is to identify the cognitive load during a mental arithmetic task experiment using fNIRS signals. The first objective is to classify the difficulty level and the state of inactivity during the given task. To identify the classes, the feature vectors have to undergo all the possible steps of a pattern classification problem. In this paper, we have developed a novel Feature...
As the number of features in pattern recognition applications continuously grows, new algorithms are necessary to reduce the dimensionality of the feature space while producing comparable results. For example, a dynamic area of research, activity recognition, produces large quantities of high-velocity, high-dimensionality data that require real time classification. While dimensionality reduction approaches...
In this paper the Supervised Locally Linear Embedding (SLLE) algorithm is introduced into polarimetric SAR (PolSAR) feature dimensionality reduction (DR) and land cover classification. SLLE technique, as a supervised nonlinear manifold learning method, can obtain a low-dimensional embedding space which preserves both the local geometric property of high-dimensional data and discriminative information...
Clustering analysis is one of the key issues in the data mining technology. This paper proposes a method of spectral clustering based on the sparse samples (SCSS) that solves the problem of sparse sampling density. The algorithm firstly makes the data points into N times the original points in the l-nearest neighbors of each data point at random, increases the sampling density and selects optimal...
Emotion has a greater importance in communication, perception and decision making. The ability to identify the emotional states of people surrounding us is an essential part of natural communication. Human brain activity is used to analyze the emotions in different situations and brain activity is measured by EEG signal. The existing system has concluded a 6 layer biologically tested feed forward...
In the classification of the heart disease data set a high dimensional data set is used in the pre processing stage of data mining process. This raw dataset consist of redundant and inconsistent data thereby increasing the search space and storage of the data. To achieve the classification accuracy we need to remove the redundant and the irrelevant data present. The dimensionality reduction technique...
Establishing the identity of a person with the use of individual biometric features has become the need for the present technologically advancing world. Due to rise in data thefts and identity hijacking, there is a critical need for providing user security using biometric authentication techniques. A unimodal biometric system is known to have many disadvantages with regard to accuracy, reliability...
Aiming at disadvantages of labor and time consuming of traditional tea classification method, this paper proposes a tea variety classification algorithm that integrates spectral and image characteristics with Mengding Mountain Huangya tea, Zhuyeqing tea and Ganlu tea of Ya'an City, Sichuan Province as objects. It firstly collected the hyperspectral images of tea samples with “GaiaSorter” hyperspectral...
Hyperspectral imaging is employed in a broad array of applications. The usual idea in all of these applications is the requirement for classification of a hyperspectral image data. Where Hyperspectral data consists of many bands - up to hundreds of bands - that cover the electromagnetic spectrum. This results in a hyperspectral data cube that contains approximately hundreds of bands - which means...
Raman spectroscopy (RS) of Nasopharyngeal carcinoma (NPC) tissue contained various biomedicine features. These features indicated molecular-level information of tissue at different carcinoma development-level. This study suggested an automatic and quick method for the NPC Raman spectra classification at different stages by multivariate statistical analysis. In the RS measurement, high quality Raman...
As traditional media and new media such as Weibo and WeChat are increasingly used, Internet has become the main supporter of thoughts of groups or individuals, which plays a key role in guidance of our daily life and society development. This study aims to investigate/propose a note feature extraction algorithm of data processing in massive news data, extracting the key words in the news and clarifying...
In the world we live in, people from different professions are at increased risk for depressive symptoms and posttraumatic stress disorder (PTSD) due to hard working or extreme environmental conditions. Accurate diagnosis and determining the causes are very important to solve these kinds of psychological problems. Machine learning (ML) techniques are gaining popularity in neuroscience due to their...
Internet of Vehicles is new concept proposed in recently year extended from Internet of Things. It plays an important role in improving traffic situation. This system is respected as a part of Internet of Vehicles aimed to identify road rage, fatigue driving or drunk driving and so on by recognizing drivers' facial expression. With Internet of Things architecture, this system has a multi-function...
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...
In this paper, Improved Particle Swarm Optimization (IPSO) approach is used in feature selection process - Linear Discriminant Analysis (LDA). This evolutionary random search method enhanced the classification rate with less computational time. The effectiveness of the proposed IPSO-LDA method is verified by employed Indian Face Database (IFD) and compare the results with existing methods.
Gabor filters are used to extract holistic feature in facial expression recognition. However, local subtle features can't be extracted effectively and it results in large amounts of data redundancy. In this paper, we proposed a novel facial expression recognition method based on the selection of local Gabor features and the extended nearest neighbor algorithm. The Gabor filter and radial encode is...
Several challenges accompanied the growth of online social networks, such as grouping people with similar interest. Grouping like-minded people is of a high importance. Indeed, it leads to many applications like link prediction and friend or product suggestion, and explains various social phenomenon. In this paper, we present two methods of grouping like-minded people based on their textual posts...
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...
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