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Background Data mining techniques are used to mine unknown knowledge from huge data. Microarray gene expression (MGE) data plays a major role in predicting type of cancer. But as MGE data is huge in volume, applying traditional data mining approaches is time consuming. Hence parallel programming frameworks like Hadoop, Spark and Mahout are necessary to ease the task of computation. Objective Not...
Emergence of big data is directly proportional to the data shared in social media. Audio, video, text or the combination of all the above are the data shared in social media. Social networking is achieved by Social Networking Sites (SNS). In real world business, analysts use software tools to analyze product sales, promotion of brand and also tend to identify influential factors that impact their...
Experiments and generally data in the real world are unbalanced, that is the classification categories are not approximately equally presented because of subject mortality, non-response, etc. The term "Unbalanced" in this context is relative to the distribution of records among the target classes. The various limitations of working with an unbalanced data are discrepancies in calculating...
Healthcare Organizations have been dealing with rapidly growing Electronic Health Records (EHRs) and digital images. Maintaining high volumes of medical data leads to scalability issue. Cloud computing provides scalable resources on demand which includes computing and storage as a service. In this paper, the authors propose a model that would enable mining meaningful medical information from a community...
Intelligent Traffic Monitoring has been an area of avid research in the past decade. The aim of this research paper is to present a novel design of an adaptive traffic control system that ensures fair scheduling and provides dynamic suggestions of optimal routes to neighbourhoods based on traffic intensity information collected by piezoelectric strips installed on the road. The time slice for each...
Machine learning is a subfield of artificial intelligence that deals with the exploration and construction of systems that can learn from data. Machine learning trains the computers to manage the critical situations via examining, self-training, inference by observation and previous experience. This paper provides an overview of the development of an efficient classifier that represents the semantics...
Rootkits refer to software that is used to hide the presence and activity of malware and permit an attacker to take control of a computer system by affecting the kernel. This paper explores the application of data mining methods to predict rootkits based on the attributes extracted from the information contained in the log files. The rootkit records were categorized as Inline and Others based on the...
Data Mining Techniques help in discovering useful information from the available data. Classification, one of the data mining techniques finds its application in many areas, making rapid advancements in the field of biology and medicine. Diabetic Retinopathy, a threatening retinal disease places high necessity for computational approaches to automatically detect the disease. Shape related features...
The research in recent years emphasizes the application of computational techniques in the field of ophthalmology. Diabetic Retinopathy, a retinal disease is the major cause of blindness. Early detection can help in treatment but regular screening for early detection has been a highly labor — and resource-intensive task. Hence automatic detection of the diseases through computational techniques would...
Application of computational techniques in the field of medicine has been an area of intense research in recent years. Diabetic Retinopathy and Glaucoma are two retinal diseases that are a major cause of blindness. Regular Screening for early disease detection has been a highly labor - and resource- intensive task. Hence automatic detection of these diseases through computational techniques would...
Software defect detection has been an important topic of research in the field of software engineering for more than a decade. This research work aims to evaluate the performance of supervised machine learning techniques on predicting defective software through data mining algorithms. This paper places emphasis on the performance of classification algorithms in categorizing seven datasets (CM1, JM1,...
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