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Mining provides useful information from the huge volume of the data stored in repositories. The present study focus on implementing five different algorithms using the data mining WEKA. The algorithm in the study includes Naive Bayes, Zero R, One R, J48 and Random Tree algorithm. All these well-known familiar algorithms are used in classification rule mining techniques. Datasets are collected from...
There is a recent interest in using functional magnetic resonance imaging (fMRI) for decoding more naturalistic, cognitive states, in which subjects perform various tasks in a continuous, self-directed manner. In this setting, the set of brain volumes over the entire task duration is usually taken as a single sample with connectivity estimates, such as Pearson's correlation, employed as features....
Due to the complexity of human languages, most of sentiment classification algorithms are suffered from a huge-scale dimension of vocabularies which are mostly noisy and redundant. Deep Belief Networks (DBN) tackle this problem by learning useful information in input corpus with their several hidden layers. Unfortunately, DBN is a time-consuming and computationally expensive process for large-scale...
Texture can be characterized in different ways. Local texture facets are considered to be one of the useful approaches for texture analysis. Local texture facets comprise data about the texture behavior. The present approach extracts the texture primitive units (TPU) and texture primitive spectrum (TPS) for classification of the textures. The present paper derives a feature extraction algorithm based...
The development needs the internet and cable television entertainment increase per year that affect popping up various multimedia service provider company which is offered a lot of services to win the market. This makes customer has a lot of company choices and makes customer to be more demanded and move easily from a provider to other provider, where company knows that keep customer has the cost...
In the communication systems, Adaptive Neuro-Fuzzy Interface Systems (ANFIS) are used for the detection of unauthorized user access and forecasting product demand for 3G and 4G mobile phones. The Paper proposes ANFIS for identification of quantity of servers in a teletraffic system. Combinations of teletraffic performance parameters are defined for synthesis of the structure of the neuro-fuzzy classifier...
In this paper various types of classifiers for quantitatively identify teletraffic service devices are proposed. The classification method “K — Nearest Neighbors With Defined Cityblock Metric Distance At Three Nearest Neighbors” is selected. A classifier structure is synthesized based on Adaptive Neuro-Fuzzy Interface Systems (ANFIS) in hybrid learning algorithm and Gaussian type membership function...
In the current era, there is a high demand of accurate text identification and categorization methods in N - Lingual non-scanned and scanned machine printed documents, where N represents mono, bi, tri or multi mode. In this paper, a technical study and analysis is presented to show N-lingual document classification for normal text, printed and handwritten documents. Text classification for normal...
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one...
To ensure the protection of computer networks, an intrusion detection system (IDS) should be integrated in the security infrastructure. However, IDSs generate a high amount of false alerts exceeding the administrator ability for analysis and omit several attacks which can threaten the network security. In this paper, a two-stage process based on data mining and optimization is proposed having as input...
In recent years, flow-based anomaly detection has been used as a scalable method for high-speed networks. The application of supervised learning in flow-based anomaly detection has been considered in a number of studies. However, supervised methods are not very useful as they are only trained with labelled data. Therefore, in this study, we use a semi-supervised method to address the problem of the...
Named Entity Recognition or NER is one of the sub-research field of Information Extraction which can be used for machine translation, question answering, semantic web, etc. One of the biggest challenge of NER is the adversity to construct a manually labeled training data. In this work, we present a semi-supervised approach for Indonesian language NER which is capable of creating high quality training...
In this paper, we propose a new high quality pseudo-relevance feedback documents selection approach that uses machine learning based classifier for selecting a set of good feedback documents for boosting the effectiveness of Query Expansion (QE). Our proposed classification technique utilizes very small amount of labelled data set for training purpose that is very appropriate to select a set of good...
The efficiency research about protein sub-cellular localization has become a hot topic recently. Feature extraction plays an important role in the accurate classification or location of proteins. Since the contribution of each feature dimension is different, this paper enlarges the contribution of feature dimensions which have great effect on classification by weighting with its Fisher linear discriminant...
Diabetes mellitus is considered to be a severe health issue which is caused due to the presence of higher amount of plasma/glucose in the blood. A number of decision support systems were introduced to help medical experts for analyzing different factors that cause diabetes. Here a computerized information system is designed using Stacked Generalization for predicting diabetes. The classifiers under...
Haze and mist always affect the quality of vision. If an image is suffered from haze or mist, then the object is unclear and the image seems whiter than the original one. There are several haze removal algorithms that can reduce the effect of haze and mist. However, if an image is not suffered from the haze and mist, applying the haze removal algorithm may darken the image. Therefore, in computer...
Classifiers have known to be used in various fields of applications. However, the main problem usually found recently is about applying a classifier to large datasets. Thus, the process of reducing size of the training set becomes necessary especially to accelerate the processing time of the classifier. Concerning the problem, this paper proposes a new method which can reduce size of the training...
k-mer frequency has been widely used as digital features of DNA fragments in microbial DNA recognition. However, to achieve ideal identification accuracy, it often needs to extract a nearly ten thousand-dimensional vector from DNA fragments as species labels. The high dimension of the feature vector will lead to excessive calculation loss. Rough set theory is a good method for attitude reduction but...
In this paper, we study the testing flow in fabrication process of stacked-die products. We discuss the difficulties of traditional testing mechanism. To improve production yield, a contactless testing with cyber physical system (CPS) for pre-bond interposers is proposed in this paper. We propose a testing framework comprising a heating laser and an infrared-radiation camera. In addition, we also...
Linkage of routine and administrative databases from multiple sources provides an advantageous form of understanding chronic diseases, such as arthropathy conditions. Data mining classification algorithms can be a cost-effective approach to identify patients' cohorts with certain disorders within these complex databases. However, selecting good potential predictors, given a certain condition from...
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