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The aim of this study is to compare some classifiers' performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features...
Machine learning algorithm can be applied for automating soil type classification. This paper compares several machine learning algorithms for classifying soil type. Algorithms that involve support vector machine (SVM), neural network, decision tree, and naïve bayesian are proposed and assessed for this classification. Soil dataset is taken from the real data. Simulation is run by using RapidMiner...
This paper presents a novel framework for privacy aware collaborative information sharing for data classification. Data holders participating in this information sharing system, for global benefits are interested to model a classifier on whole dataset, but are ready to share their own table of data if a certain amount of privacy is guaranteed. To address this issue, we propose a privacy mechanism...
Classification is a well known of the significant tools used to recognize and examine most sharp information in images. Satellite image processing has become popular in these days because of benefits that those are giving. In any remote sensing particularly, the decision-making way mainly rely on the efficiency of the classification process. Image classification was performed generally and the classification...
Breast cancer is invasive cancer among world's women above 35 years of age. The most common symptoms of breast cancer are lumps, change in shape/skin colour and liquid oozing out from nipple. Breast cancer mostly starts from breast tissues that are either in lobules or in milk ducts. Ductal carcinoma is the common type of breast cancer starts from milk ducts and spread across the. Women between the...
Machine learning based classifiers used quite often for predicting forest cover types, are the Naïve Bayes classifier, the k-Nearest Neighbors classifier, and the Random forest classifier. This paper is directed towards examining all of these classifiers coupled with feature selection and attribute derivation in order to evaluate which one is best suited for forest cover type classification. Numerous...
Sentiment Analysis is the process of figuring out the emotions from a piece of writing that whether it is positive, negative or neutral and is used to tell the speaker's attitude. The trend, today, is to consider the opinions of a variety of individuals around the globe before purchasing an item using micro-blogging data. Customers tend to go over a lot of reviews about a particular item before buying...
Text classification is an important step in many data analysis procedures. The demand on text classification algorithm is booming due to the increase of the amount of digital data, especially in the healthcare field. A customizable and accurate algorithm is expected to produce positive impact on the efficiency of many data analysis procedures. In this work, we proposed a novel algorithm for accurately...
Analysis of a medical dataset having missing values and then filling the missing values through different approaches exists in the literature. However, the classification accuracies achieved using these approaches have not been so promising when analyzed. It is this reason; which implicitly motivated us to study and address new methods for imputation. In this paper, we propose an approach for efficient...
The data are generated very rapidly from different information sources. These generation of data is increasing day by day from various sources such as automated data collection tools, database systems, e-commerce and social media websites. There is an explosive growth of data from terabytes to petabytes. It is essential to extract valuable knowledge from these large data. Since large amount of data...
Lung Cancer is a disease of infection in lung due to uncontrolled cell growth which affects its functionality. It is mostly incurable due to that early detection of Lung Cancer is important. Early detection and treatment may help for patient's survival. Normally, diagnosis of lung cancer includes chest X-ray, ECG, Citi scan, MRI etc. In cancer diagnosis Artificial Neural Network and Fuzzy Min-Max...
Machine Learning plays very important role in processing of large amounts of structured and unstructured data. A set of algorithms can be used to get meaningful insights into the data that are helpful in making effective business decisions. Document clustering is one of the popular machine learning technique used to group unstructured data (text documents) based on its content and further analyze...
This paper reviews the comparative performance of Support Vector Machine (SVM) using four different kernels, i.e., Linear, Polynomial, Radial Basis Function (RBF) and Sigmoid. Overall accuracy (OA), Kappa Index Analysis (KIA), Receiver Operating Characteristic (ROC) and Precision (P) have been considered as evaluation parameters in order to assess the predictive accuracy of SVM. Both high resolution...
An Artificial Neural Network (ANN) is a statistical data modeling tool inspired by the functionality and the structure of the biological nervous system. An ANN consists of processing elements known as neurons that are interconnected to each other and work in unison to answer a particular problem. Neural networks can be used in places where detecting trends and extracting patterns are too complex to...
This paper examines the quality of feature set obtained from Wavelet based Energy-entropy with variation of scale and wavelet type. Here motor imagery of left-right hand movement classification problem has been studied. Elliptic bandpass filters are used to discard unwanted signals and also to extract alpha & beta rhythms. We have implemented wavelet-based energy-entropy with three level of decomposition...
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automated based Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. In this paper the authors have employed...
There exists a base classification system for classification of problem tickets in the Enterprise domain. Different deep learning algorithms (Gated Recursive Unit and Long Short Term Memory) were investigated for solving the classification problem. Experiments were conducted for different parameters and layers for these algorithms. Paper brings out the architectures tried, results obtained, our conclusions...
In Data Mining classification plays prominent role in predicting outcomes. One of the best supervised classification techniques in Data Mining is Naive Bayes Classification. Naive Bayes Classification is good at predicting outcomes and often outperforms other classification techniques. One of the reasons behind the strong performance of Naive Bayes Classification is due to the assumption of conditional...
Computers and Smartphone's becomes vital part of everyday life and hence use of internet becomes more and more. Due to internet, computers are becomes vulnerable of different kinds of security threats. Therefore it is required that we need to have efficient security method in order to avoid leakage of important data or misuse of data. This security method is called as Intrusion Detection System (IDS)...
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...
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