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Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is help to make predictions on medical data. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. The methods strongly...
In this paper is introduced a facial recognition system, based on mathematical methods, developed to act as the presence of students identifier in a classroom. It uses a recognition process in which seeks to extract relevant information from an image, to encode and compare them with another images of faces stored in an image database. Such image information representing a set of characteristics showing...
The blockchain technology has emerged as an attractive solution to address performance and security issues in distributed systems. Blockchain's public and distributed peer-to-peer ledger capability benefits cloud computing services which require functions such as, assured data provenance, auditing, management of digital assets, and distributed consensus. Blockchain's underlying consensus mechanism...
API documentation is useful for developers to better understand how tocorrectly use the libraries. However, not all libraries provide gooddocumentation on API usages. To provide better documentation, existingtechniques have been proposed including program analysis-based anddata mining-based approaches. In this work, instead of mining, we aimto generate behavioral exception documentation for any given...
Metrology is a costly and time consuming activity in semiconductor fabrication; for this reason, Dynamic Sampling strategies and Virtual Metrology approaches have proliferated in the past recent years. Both Dynamic Sampling strategies and Virtual Metrology techniques aim at minimizing the amount of performed measures while keeping acceptable levels of production quality. In this work we study a Dynamic...
This paper aims to propose and discuss concepts of how users can recognise information seeking behaviour automatically and what implications such an automatic recognition can have. The authors develop the discussion around variables proposed in Wilson's second model of information behaviour and state how they can collect data necessary to recognise information behaviour automatically. The authors...
Modern mining approaches should be able to properly deal with the increased availability of structured data. Here we focus on the problem of processing streams of trees. Specifically, we cope with classification tasks. We show that by adopting a double concept drifting reaction mechanism in the context of a kernel-based ensemble of classifiers, it is actually possible to have an effective and efficient...
Unplanned hospital readmission is a costly problem in the United States. Patients treated and readmitted within 30 days cost tax payers up to $26 billion annually. In 2013 the U.S. federal government began to reduce payments to hospitals with excessive patient readmissions. Predictive modeling using machine learning can be a useful tool to help identify patients most likely to need readmission. However,...
As multi-dimensional text data are being generated at dazzling rate, topic modelling has become an important instrument for learning from large unstructured document sets. To focus on specific subsets of large document corpora, a user may specify various criteria to identify documents of interest before extracting topics from the documents. In this paper, we aim to accelerate the computation of topic...
Large-scale software systems like Amazon and healthcare.gov are used by thousands or millions of people every day. To ensure the quality of these systems, load testing is a required testing procedure in addition to the conventional functional testing techniques like unit and system integration testing. One of the important requirements of load testing is to create a field-like test environment. Unfortunately,...
Water quality assessment and prediction of Lake Michigan are becoming major challenges in Northwest Indiana, USA. Traditionally, mechanistic simulation models are employed for water quality modeling and prediction. However, given the complicate nature of Lake Michigan in Northwestern Indiana, the detailed simulation model is extremely simple in comparison and, at some point, additional detail exceeds...
Given an input tensor, its CANDECOMP/PARAFAC decomposition (or CPD) is a low-rank representation. CPDs are of particular interest in data analysis and mining, especially when the data tensor is sparse and of higher order (dimension). This paper focuses on the central bottleneck of a CPD algorithm, which is evaluating a sequence of matricized tensor times Khatri-Rao products (MTTKRPs). To speed up...
Based on core problems of personalized recommendation, traditional collaborative filtering recommendation algorithm and theories of AprioriAll algorithm based on association rule, it is proposed to build two-dimension user interest model combining user's implicit and explicit interests and increase the threshold value of third dimension time in this paper t o realize the real-time personalized recommendation...
The enormous amount of data being generated every day is a major issue for organisations. Analysing it and taking decisions from it is a major concern. Visual analytics can be a solution to visualize the data and draw better conclusions from data which was otherwise not possible. Instead of reports and written documents, graphics can play an important role in interpreting results. Visual analytics...
Dimensionality reduction is an essential pre-processing technique in many of the data analysis tasks. Popular approaches for dimensionality reduction are Feature Selection (FS) and Feature Extraction (FE). Till now, these approaches are often studied separately or independently so that the final result contains either original or transformed features. In our work, we propose to bridge these two approaches...
India has the second highest mobile phone users after China. In India using touch screen mobile device is fashion. All age group people now used touch screen handheld device to do shopping for daily need. Mobile commerce provides personalization and location based services to users. For those demographics of users plays an important role. In this paper authors try to find association between shopping...
In current times, there has been a surge in the amount of collected data from computational systems. The vast amount of data can be useful in many applications and fields, particularly so in Big Data Analytics. However with a large collection of data there is a difficulty discovering important information. Automatic Document Summarization (ADS) systems are suitable for the task of outlining useful...
Innovation in the public-sector refers to the development of important improvements in the public administration and their corresponding services. One of such public services is the social security, of which central process has been the information security of their offered services. The aim of the present study has been the analysis of the trends and the discovery of behavioural patterns in the attacks...
The study was a literature review to develop a new strategy to clustering data. This study is part of our ongoing main project to develop scientific community model based on scientific article publication. The concept of this strategy is utilizing scientific article metadata elements that has been published electronically to map scientists community respect to the topics that are developing. This...
In many application areas, data that is being generated and processed goes beyond the petabyte scale. Analyzing such an increasing massive volume of data faces computational, as well as, statistical challenges. In order to solve these challenges, distributed and parallel processing frameworks have been used for implementing scalable data analysis algorithms. Nevertheless, processing the whole big...
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