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In this work a decision support system (DSS) for the conversion of Unified Parkinson's Disease Rating Scale (UPDRS) motor symptoms into a Hoehn & Yahr stage representation is proposed. Accurate estimation of a Parkinson's Disease patient's Hoehn & Yahr stage is of great importance since this single value is enough to represent condition, severity of symptoms and localization and disease progression...
A general approach is proposed to determine the occupancy in a room using sensor data and knowledge coming respectively from observation and questioning are determined. Means to estimate occupancy include motion detections, power consumption and and acoustic pressure rewarded by a micro-phone. The proposed approach is inspired from machine learning. It starts by determining the most useful measurements...
With the growing number of location-based SNS (Social Networking Service) users, the utilization of SNS data is getting more and more important. In this paper, we focus on the prediction of users' locations from location-based SNS data. The location-based SNS data consists of sequence of checkins which are too sparse to predict the users' locations. In our previous research we generated users' probability...
High utility itemset mining provides more useful and realistic results than frequent pattern mining because of its ability to consider statistical correlation and semantic significance among the items. The state of art algorithms designed for mining high utility itemsets always consider the database as static. If they are used for dynamic databases for the same purpose, database is rescanned from...
In this paper, we mine the US congress voting records to extract the latent information about the trust among congress members. In particular, we model the Senate as a social network and the voting process as a set of outcomes of the underlying opinion dynamics which we assume follow a corrupted DeGroot model. The transition matrix in the opinion dynamics model is the trust matrix among Senators that...
On social media platforms, companies, organizations and individuals are using the function of sharing or retweeting information to promote their products, policies, and ideas. While a growing body of research has focused on identifying the promoters from millions of users, the promoters themselves are seeking to know what strategies can improve promotional effectiveness, which is rarely studied in...
High-dimensional and sparse (HiDS) matrices are commonly encountered in many big data-related industrial applications like recommender systems. When acquiring useful patterns from them, non-negative matrix factorization (NMF) models have proven to be highly effective because of their fine representativeness of non-negative data. However, current NMF techniques suffer from a) inefficiency in addressing...
Social media interactions have become increasingly important in today's world. A survey conducted in 2014 among adult Americans found that a majority of those surveyed use at least one social media site. Twitter, in particular, serves 310 million active users on a monthly basis, and thousands of tweets are published every second. The public nature of this data makes it a prime candidate for data mining...
Hyperspectral technology has made significant advancements in the past two decades. Current sensors onboard airborne and space-borne platforms cover large areas of the Earth surface with unprecedented spectral resolutions. These characteristics enable a myriad of applications requiring fine identification of materials. Quite often, these applications rely on complicated methods of data analysis. In...
One of the more challenging real-world problems in computational intelligence is to learn from non-stationary streaming data, also known as concept drift. Perhaps even a more challenging version of this scenario is when - following a small set of initial labeled data - the data stream consists of unlabeled data only. Such a scenario is typically referred to as learning in initially labeled nonstationary...
The problem of distinct value estimation has many applications. Being a critical component of query optimizers in databases, it also has high commercial impact. Many distinct value estimators have been proposed, using various statistical approaches. However, characterizing the errors incurred by these estimators is an open problem: existing analytical approaches are not powerful enough, and extensive...
Limited access to supervised information may forge scenarios in real-world data mining applications, where training and test data are interconnected by a covariate shift, i.e., having equal class conditional distribution with unequal covariate distribution. Traditional data mining techniques assume that both training and test data represent an identical distribution, therefore suffer in presence of...
Algorithms for mining very large graphs, such as those representing online social networks, to discover the relative frequency of small subgraphs within them are of high interest to sociologists, computer scientists and marketeers alike. However, the computation of these network motif statistics via naive enumeration is infeasible for either its prohibitive computational costs or access restrictions...
Aspect Based Sentiment Analysis (ABSA) provides further insight into the analysis of social media. Understanding user opinion about different aspects of products, services or policies can be used for improving and innovating in an effective way. Thus, it is becoming an increasingly important task in the Natural Language Processing (NLP) realm. The standard pipeline of aspect-based sentiment analysis...
We propose estimators for popular clustering coefficient measures 1) network average clustering coefficient and 2) global clustering coefficient (aka transitivity). Unlike most of previous studies estimating clustering coefficients, we do not use independent vertex sampling as it is either unavailable or inefficient to implement in most Online Social Networks (OSNs). Instead, we propose estimators...
Analog-to-information converters and Compressed Sampling (CS) sensor front-ends try to only extract the relevant, information-bearing elements of an incoming data stream. Information extraction and recognition tasks can run directly on the compressed data stream without needing full signal reconstruction. The accuracy of the extracted information or classification is strongly determined by the front-end...
This paper presents an exploratory data analysis to evaluate how spatial information can be used to extract homogeneous regions in hyperspectral images. The basic assumption on the linear unmixing model applied to hyperspectral images, is that the pixels are in the convex hull of the cone with the endmembers at its vertices. Several spectral unmixing algorithms look for a single convex region to depict...
In response to globalization, International Financial Reporting Standards (IFRS) has become the norm of the global capital markets. Companies preparing financial statements using IFRS may make the financial situation fully disclosed. Nevertheless, an overestimated accrual expense of a balance sheet may not only underestimate the earnings data, but also increase the cash outflows of the statement of...
Long sequential pattern mining is of great importance. Many different algorithms have been proposed to get accurate and time-costing result. Under the background of big data, we consider the trade-off between the accuracy demand and fast estimation priority of mining result. Thus in this paper we proposed a method combining Conditional Random Field technique with pattern mining in order to get less...
Credit data, the data that describes the attributes of customer credit collected by enterprises or institutions, which contains a wealth of credit information, is the important basis of customer credit scoring. Using data mining technology to analyze the credit data and evaluate credit of customer has become a highly efficient method for customer credit estimation. Related research has become a hot...
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