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Machine learning is a subdivision of Artificial Intelligence (AI) that is concerned with the design and development of intelligent algorithms that enables machines to learn from data without being programmed. Machine learning mainly focus on how to automatically recognize complex patterns among data and make intelligent decisions. In this paper, intelligent machine learning algorithms are used to...
Classification is the process of finding a model or function that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The goal of classification is to accurately predict the target class for each case in the data. In sequence database having sequences, in which each sequence is a list of...
Opinions about the data has been an important part of analyzing the opinions and sentiments. The sentiment analysis is a major part of data mining that has important applications in various fields. The novice customers get into any field by only getting reviews from the various websites or reviewers. The reviews are not necessarily correct all the time. So, we need to first analyze them and then put...
Breast cancer is a major threat for middle aged women throughout the world and currently this is the second most threatening cause of cancer death in women. But early detection and prevention can significantly reduce the chances of death. An important fact regarding breast cancer prognosis is to optimize the probability of cancer recurrence. This paper aims at finding breast cancer recurrence probability...
Utilization of machine learning algorithms in time-series data analysis is crucial to effective decision making in today's dynamic and competitive environment. One data type of growing interest is the electricity consumer load profile (LP) data. Owing to advances in the smart grid, immense amount of LP data became available to policymakers as potential to improving the electricity sector. Due to the...
The massive amount of data collected by healthcare sector can be effective for analysis, diagnosis and decision making if it is mined properly. Hidden information extracted from the voluminous data can provide help and remedy to handle critical healthcare situations. Chronic kidney disease is a fatal illness of kidney which can be prevented with early correct predictions and proper precautions. Data...
Data mining can be used in various fields' i.e. mobile computing, web mining, expert predictions, crime analysis, engineering, management and medicine. In medical field, data mining techniques can be used by the researchers for the diagnosis and prediction of various diseases. A framework is proposed to predict Syncope Disease using Ensemble technique that contains Naïve Bayes, Gini Index and Support...
Cardiovascular disease is a worldwide health problem and according to American Heart Association (AHA), it also causes an approximate death of 17.3 million each year. Therefore early detection and treatment of asymptomatic cardiovascular disease which can significantly reduce the chances of death. An important fact regarding such life-threatening disease prognosis is to identify the patient's physical...
In this article, we present an application of metaheuristics optimization approaches to improve medical classifier performance. Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) have been applied in conjunction with Least Square Support Vector Machine (LS-SVM) approach to optimize the total misclassification error in term of False Positive and False Negative rates...
The feature subset selection, along with the parameters of classifier significantly influences the classification accuracy. In order to ensure the optimal classification performance, the artificial bee colony (ABC) algorithm is proposed to simultaneously optimize the feature subset and the parameters of support vector machines (SVM), meanwhile for improving the optimizing performance of ABC algorithm,...
The Sentiment discovery from data sources of social media is a challenging task. Since the data is available not only in the structured format but also exists in un structured and semi structured as users are expressing freely their opinions in their desired style. Opinion mining is an important research area for many domains. It has most feasible approaches for business Intelligance. In this review...
Multiple time series clinical data are very sensitive to analysis and predict the disease. In multiple time series clinical data contains multiple measurement data are collected from different time interval and different dataset are merged using merging algorithm and statistical measurement are used to determine the distribution of data then those data are given to classifier to predict Hepatocellular...
Data classification in medical field is distinct from that in other fields, because the medical data are heterogeneous, skewed and complex in nature and medical data classification involves multi class classification. In this paper we present the experimental analysis of well-known traditional classification algorithms on bio-medical datasets in order to observe their performance. This experimental...
In the recent few years several efforts were dedicated for mining opinions and sentiment automatically from natural language in online networking messages, news and business product reviews. In this paper, we have explored sentiment orientation considering the positive and negative sentiments using film user reviews. We applied the technique Naive Bayes' classifier.). We have performed the sentiment...
Cardiovascular risk prediction is a vital aspect of personalized health care. In this study, retinal vascular function is assessed in asymptomatic participants who are classified into risk groups based on Framingham Risk Score. Feature selection, oversampling and state-of-the-art classification methods are applied to provide a sound individual risk prediction based on Retinal Vessel Analysis (RVA)...
Class noise elimination in large databases is a real issue in data mining processing. In fact, class noise may sometimes lead to distortion or inaccuracy. So to overcome this problem, many techniques have been proposed. However, most of them don't have the capacity to deal with huge volume. In this context, this paper presents an architecture for class noise detection and elimination in large datasets...
Medical data mining is one of the significant research field as medical organizations produce large volume of data on daily basis. Handling this vast amount of data in medical field is challenging, so there is a need to mine this data in order to extract useful patterns for disease prediction. A hybrid K-means and Support Vector Machine algorithm for disease prediction is proposed in this paper. The...
To address the lack of health status identification and poor stability problems in the rotating machinery equipment, this paper proposes a new method for health status identification of rolling bearing based on SVM and improved evidence theory. Firstly, in order to reflect the rolling health condition, we use the empirical mode decomposition (EMD) to extract energy value and the original part of the...
In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both...
In these days, Employee turnover has become a major challenge in many software industries. Most often, people suffer from stress due to heavy work pressures imposed on them and competitive spirit of the work completed in their day to day lives. A survey was conducted by software professionals who work for various companies and stress on them was investigated. For this study, the PEGASOS optimization...
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