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Machine Leaning (ML) plays an important role in the electronic data management. It is always costly and difficult to manage the data manually without adopting ML or with ML using metadata. Many ML algorithms have been proposed to solve different data management issues, but the prediction of the confidential data and non- confidential data in a data file is still a challenging research gap. A file...
The performance of network intrusion detection systems based on machine learning techniques largely depends on the selected features. However, choosing the optimal subset of features from a given feature set requires extensive computing resources. To tackle this problem we propose an optimal feature selection algorithm based on a local search algorithm. In order to evaluate the performance of our...
Associative Classification is a recent and rewarding approach which combines associative rule mining and classification. This technique has attracted many researchers as it derives accurate classifier with effective rules. Associative classifiers are useful for application where maximum predictive accuracy is desired. Increasing access to huge datasets and corresponding demands to analyze these data...
Drug trafficking organizations are using “go-fast” boats, small fishing boats and commercial containers to smuggle illegal drugs. Because that, detection, classification, and tracking of small vessels are important tasks for improving the security of complex maritime systems. This paper presents a simple method based on the least square method, averaging operator, instead of T-norm operator, and triangular...
Terrain classification in field environment for mobile robot is affected by weather conditions among which illumination diversification plays a major role. With the changes in feature extraction method, classification result will vary significantly in the changing environment. This paper introduces a method of illumination recognition by analyzing the illumination distribution in visual space and...
Rainfall prediction is an important part of weather prediction. Compared to conventional methods predicting rainfall rate, the approach applying historical records and data mining technology shows obviously advantage in computing cost. Many excellent works have been done attempting to build predicting model with data mining methods, however, most of them just test the predicting accuracy on data set...
During the last few years, imbalanced data classification issue has gained a great deal of attention. Many real life applications suffer from imbalanced distribution of data that can be handled by using different approaches such as data level, algorithm level or classifier ensembles. Single level as well as multi level classifier ensemble technique has shown improvement in classification performance...
This paper introduces a novel approach to predict human motion for the Non-binding Lower Extremity Exoskeleton (NBLEX). Most of the exoskeletons must be attached to the pilot, which exists potential security problems. In order to solve these problems, the NBLEX is studied and designed to free pilots from the exoskeletons. Rather than applying Electromyography (EMG) and Ground Reaction Force (GFR)...
Occurrence of multiple seizures is a common phenomenon observed in patients with epilepsy: a neurological malfunction that affects approximately 50 million people in the world. Seizure prediction is widely acknowledged as an important problem in the neurological domain, as it holds promise to improve the quality of life for patients with epilepsy. A noticeable number of clinical studies showed evidence...
NeuroinformaticsNatural Language Processing (NeuroNLP) relies on clustering and classification for information categorization of biologically relevant extraction targets and for interconnections to knowledge-related patterns in event and text mined datasets. The accuracy of machine learning algorithms depended on quality of text-mined data while efficacy relied on the context of the choice of techniques...
The traffic identification is a preliminary and essential step for stable network service provision and efficient network resource management. While a number of identification methods have been introduced in literature, the payload signature-based identification method shows the highest performance in terms of accuracy, completeness, and practicality. However, the payload signature-based method's...
Classification is an important technique in data mining. The K-Nearest neighbor (K-NN) algorithm is a memory based algorithm and is capable of producing satisfactory results when applied on certain data but the distance measures used in this algorithm is not capable of handling the data sets containing the uncertain attribute values. Data uncertainty is common in real word applications. In this paper...
This paper describes a novel post-processing step for an electromyogram (EMG) pattern classification algorithm that is used in the control of myoelectric prosthetic hands. Amputees often find it difficult to control multiple degrees of freedom, but increasing numbers of prosthetic hands have multiple degrees of freedom. In general, larger numbers of classes tend to reduce the classification accuracy,...
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...
Laser induced breakdown spectroscopy (LIBS) is an atomic emission based spectroscopy that uses a laser pulse as the source of excitation. The laser is focused to form hot plasma, which atomizes and excites the sample. In the LIBS spectrum each “feature” is the amplitude or intensity detected at different wavelengths in the range of 200–1000 nm. Pattern recognition techniques were applied on samples...
The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome...
Generalizing hypotheses based on the past data in order to predict the future is the essential core of human learning. Various successful methods and techniques have been developed so far that perform some sort of classification of current data in order to predict future unseen cases. Multi class classification problems are among them as well. In many domains in spite of these automatic techniques,...
With multiple channels, Polarimetric SAR (PolSAR) contains abundant target information and anti-jamming ability, which can improve the ability of target discrimination and image interpretation. The classification problem of PolSAR has become one of the most urgent problems to be solved in PolSAR application with the improvement of PolSAR technology. Due to the complexity of multiple-dimensional classification,...
Panchromatic (PAN) satellite imagery comprises only a single band but it has finer resolution in comparison to the multi-spectral band imagery. In the case of feature extraction and classification, although the multi-spectral imagery has an advantage in availability of the different aspect of spectral properties of the ground coverage, the recent studies introducing intelligent classification and...
The analysis of the seafloor in shallow waters using remote sensing imagery at very high spatial resolution is a very challenging topic due to the minimum signal level received; the presence of noisy contributions from the atmosphere, solar reflection, foam, turbidity and water column; and the limited spectral information available for the classification at such depths that impedes, for example, the...
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