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Social media is emerging rapidly on the internet. This media knowledge helps people, company and organizations to analyze information for important decision making. Opinion mining is also called as sentiment analysis which involves in building a system to gather and examine opinions about the product made in reviews or tweets, comments, blog posts on the web. Sentiment is classified automatically...
The World Wide Web plays an important role while searching for information in the data network. Users are constantly exposed to an ever-growing flood of information. Our approach will help in searching for the exact user relevant content from multiple search engines thus, making the search more efficient and reliable. Our framework will extract the relevant result records based on two approaches i...
Classification is the category that consists of identification of class labels of records that are typically described by set of features in dataset. The paper describes a system that uses a set of data pre-processing activities which includes Feature Selection and Discretization. Feature selection and dimension reduction are common data mining approaches in large datasets. Here the high data dimensionality...
While systematic reviews (SRs) are positioned as an essential element of modern evidence-based medical practice, the creation and update of these reviews is resource intensive. In this research, we propose to leverage advanced analytics techniques for automatically classifying articles for inclusion and exclusion for systematic review update. Specifically, we used the soft-margin Support Vector Machine...
Microblogging sites such as Twitter and Weibo are increasingly being used to enhance situational awareness during various natural and man-made disaster events such as floods, earthquakes, and bomb blasts. During any such event, thousands of microblogs (tweets) are posted in short intervals of time. Typically, only a small fraction of these tweets contribute to situational awareness, while the majority...
Classification of Videos based on their content is becoming more and more essential everyday because of the vast amount of video data becoming available. Various Feature Extraction and data mining techniques can be used to perform Video Classification. This paper uses edge detection techniques such as Object Extraction and Canny Edge Detection (using Sobel, Prewitt and Robert's operator) to extract...
As the need of internet is increasing day by day, the significance of security is also increasing. The enormous usage of internet has greatly affected the security of the system. Hackers do monitor the system minutely or keenly, therefore the security of the network is under observation. A conventional intrusion detection technology indicates more limitation like low detection rate, high false alarm...
In many real-world scenarios, predictive models need to be interpretable, thus ruling out many machine learning techniques known to produce very accurate models, e.g., neural networks, support vector machines and all ensemble schemes. Most often, tree models or rule sets are used instead, typically resulting in significantly lower predictive performance. The overall purpose of oracle coaching is to...
BRAIN computer interface (BCI) is a communication technique that aims to detect and identify brain intents and translate them into machine commands to control the operation of electrical and/or mechanical devices. Electroencephalography (EEG) is a widely used imaging technique for noninvasive BCI. Due to EEG non-stationarity, which is typically caused by variation of head size, electrode positions...
The aim of this study is to compares some classification techniques used to predict the performance of student. It is helps to analyse the slow leaner in the semester exams that are likely study in poor which are used to improve their skill as early to achieve the goal in end semester. The task can be processed based on the several attributes to predict the performance of the student activity respectively...
With the advances in communication and technologies, the World Wide Web is becoming an important and rich source for information. The amount and variety of information available makes customization and personalized recommendations of utter importance. In this paper, we present a framework for the next page prediction that exploits users' access history combined with his semantic interests to generate...
The analysis of various components of the Electroglottograph (EGG) signal, obtained after Ensemble Empirical Mode Decomposition (EEMD) is the primary objective of this paper. The ability of EEMD to detect intermittent high frequency data embedded in the data of lower frequency is exploited to segregate the Epoch locations and the Periodic nature of EGG signal. The dyadic filterbank property of EEMD...
Identification of root causes of a performance problem is very difficult in case of large scale IT environment. A model which is scalable and reasonably accurate is required for such complex scenarios. This paper proposes a hybrid model using random forest and statistical change point detection, for root cause localization. Based on impurity measure and change in error rates, random forest identifies...
Now a day's most of the people suffer from brain related neurodegenerative disorders. These disorders lead to various diseases. Dementia is one such disease. Dementia is a general term for a decline in mental ability severe enough to interfere with daily life. Alzheimer's disease is the most common type of dementia. Alzheimer's disease is one of the types of the dementia which accounts to 60–80% of...
In BioWorld, a medical intelligent tutoring system, novice physicians are tasked with solving virtual patient cases. Whilst the importance of modeling and predicting clinical reasoning is recognized, an important aspect of the learner contribution remains unexplored — the written case summary prepared by the learner. The premise of investigating the case summaries is that it captures the thought and...
Data mining approaches have been used in business purposes since its inception; however, at present it is used successfully in new and emerging areas like education systems. Government of Bangladesh emphasizes the need to improve the education system. In this research, we use data mining approaches to predict students' final outcome, i.e., final grade in a particular course by overcoming the problem...
Most stream classifiers need to detect and react to concept drifts, as traditional machine learning goes to big data machine learning. The most popular ways to adaptive to concept drifts are incrementally learning and classifier dynamic ensemble. Recent years, ensemble classifiers have become an established research line in this field, mainly due to their modularity which offers a natural way of adapting...
Data mining technique in the history of medical data found with enormous investigations found that the prediction of heart disease is very important in medical science. In medical history it is observed that the unstructured data as heterogeneous data and it is observed that the data formed with different attributes should be analyzed to predict and provide information for making diagnosis of a heart...
In order to extract the content information of Theme Web Pages more accurately, this paper proposes a self-learning method based on the tag information by calculating the information quantity of various tag indicators. This method predefines several tag information indexes and coefficients index to calculate a variety of tag information quantity of the web pages in turn, and then the candidate content...
The proposed methodology involves to compares classification techniques for predicting the cognitive skill of students which can be evaluate by conducting the online test. The paper focuses the comparative performance of C4.5 algorithm, Naïve Bayes classifier algorithm which one is well suited accuracy for predicting the skill of expertise by experimenting in Rapid miner.
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