Social media has become a popular means for people to consume and share the news. At the same time, however, it has also enabled the wide dissemination of fake news, that is, news with intentionally false information, causing significant negative effects on society. To mitigate this problem, the research of fake news detection has recently received a lot of attention. Despite several existing computational

At present, due to the introduction of the big data era, numerous numbers of data are generated consistently. Many applications utilize big data platforms, namely Spark, Hadoop, Amazon web services, and so on, since these platforms use several parameters for tuning that further enhance the operating performances. It requires a long duration of time to tune the parameters because of the complex relationship

Modern organizations typically store their data in a raw format in data lakes. These data are then processed and usually stored under hybrid layouts, because they allow projection and selection operations. Thus, they allow (when required) to read less data from the disk. However, this is not very well exploited by distributed processing frameworks (e.g., Hadoop, Spark) when analytical queries are posed

Community detection problem is a projection of data clustering where the network's topological properties are only considered for measuring similarities among nodes. Also, finding communities' kernel nodes and expanding a community from kernel will certainly help us to find optimal communities. Among the existing community detection approaches, the affinity propagation (AP)-based method has been showing

The technical advancements in big data have become popular and most desirable among users for storing, processing, and handling huge data sets. However, clustering using these big data sets has become a major challenge in big data analysis. The conventional clustering algorithms used scalable solutions for managing huge data sets. Thus, this study proposes a technique for big data clustering using

This study aims to reveal the evolution of publication hotspots in the field of electronic health records (EHRs) and differences among countries. We applied keyword frequency analysis, keyword co-occurrence analysis, principal component analysis, multidimensional scaling analysis, and visualization technology to compare the high-frequency Medical Subject Heading (MeSH) terms in six countries during

With the advent of the new information technologies, the growth of online reviews regarding an organization or a company or any other sector has been playing a vital role in improving the sector plans and decisions. The vast significance of the online reviews that determine the sentiment polarity is the hectic challenge of the current scenario. Sentiment classification is a process of classifying the

With the rapid growth of storage providers, data deduplication is an essential storage optimization technique that greatly minimizes data storage costs by storing a unique copy of duplicate data. Nowadays, deduplication introduces various new challenges such as security and insufficient space issue. Hence, in this article, we propose a secure data deduplication with access control of big data over

In this article, the proposed method develops a big data classification model with the aid of intelligent techniques. Here, the Parallel Pool Map reduce Framework is used for handling big data. The model involves three main phases, namely (1) feature extraction, (2) optimal feature selection, and (3) classification. For feature extraction, the well-known feature extraction techniques such as principle

The value of training for a data sciences professional is in the eye of the beholder. And dependent on the scope and breadth of that training and the cost and time frame of that training. Value for the employee may differ from value for the employer. The lens is different and value may depend on what lens you look through. Training can be online or on-site, short term with specific focus or longer

Stock market prediction acts as a challenging area for the investors for obtaining the profits in the financial markets. A greater number of models used in stock market forecasting is not capable of providing an accurate prediction. This article proposes a stock market prediction system that effectively predicts the state of the stock market. The deep convolutional long short-term memory (Deep-ConvLSTM)

The statistical analysis of basketball games is a fast-growing field. Certainly, basketball data are scientifically relevant because an appropriate analysis provides a great deal of information about the performance of both players and teams. The number of games played each season generates a large amount of data worth analyzing. Basketball analytics is well established in U.S. leagues. In Europe,

Mostly, sorting of data is a common operation in many applications, which causes the consumption of resources and thus leads to computation overheads. Regarding the context of Big Data multi-query, the shared sort operations are fairly large, which incur high-cost I/Os whether explicit or implicit. In particular, Big Data multi-query, including aggregation and sort operations, takes long execution

The MapReduce programming model was designed and developed for Google File System to efficiently process large-scale distributed data sets. The open source implementation of this Google project was called the Apache Hadoop. Hadoop architecture includes Hadoop MapReduce and Hadoop Distributed File System (HDFS). HDFS supports Hadoop in effectively managing data sets over the cluster and MapReduce programming

Experiences from various industries show that companies may have problems collecting customer invoice payments. Studies report that almost half of the small- and medium-sized enterprise and business-to-business invoices in the United States and United Kingdom are paid late. In this study, our aim is to understand customer behavior regarding invoice payments, and propose an analytical approach to learning

Classification of the privacy preserved medical data is the domain of the researchers as it stirs the importance behind hiding the sensitive data from the third-party authenticator. Ensuring the privacy of the medical records and using the disease prediction mechanisms played a remarkable role in peoples' lives such that the earlier detection of the diseases is required for earlier diagnosis. Accordingly

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested examples and the training examples. This raises a major question about which distance measures to be used for the KNN classifier

Studies found that a small portion of the population spent the majority of health care resources, and they highlighted the importance of predicting high-cost users in the health care management and policy. Most prior research on high-cost user prediction models are based on diagnosis data with additional cost and health care utilization data to improve prediction accuracy. To further improve the prediction

The Artificial Intelligence Writing Evaluation system is widely used in China College English writing. It provides for both teachers and the English learners services of automated composition evaluation on the net in order that teacher's working load can be reduced and they can learn directly about the students' English writing level and that the students' English writing will be improved. Juku automated

For the problems of abnormal values existing in the water intake monitoring data and centralized uploaded report, the abnormal data region discrimination (ADRD) algorithm and the cross-monitoring points historical correlation repair (CMHCR) method are proposed to discriminate and repair the abnormal data. The characteristics of abnormal data distribution are analyzed, and the ADRD algorithm is proposed

Extended binary phase shift keying (EBPSK) transmit-receive system is considered as a high-resolution radar tracking system. The target kinematic states can be estimated from a time series of target range and velocity measurements. The measurements usually have a huge amount of data. In this article, we present a big data-based tracking strategy comprising Doppler measurements as part of the data association

Rapid growth in the availability of behavioral big data (BBD) has outpaced the speed of updates to ethical research codes and regulation of data privacy and human subjects' data collection, storage, and use. The introduction of the European Union's (EU's) General Data Protection Regulation (GDPR) in May 2018 will have far-reaching effects on data scientists and researchers who use BBD, not only in

Many big data applications require real-time analysis of continuous data streams. Stream Processing Systems (SPSs) are designed to act on real-time streaming data using continuous queries consisting of interconnected operators. The dynamic nature of data streams, for example, fluctuation in data arrival rates and uneven data distribution, can cause an operator to be a bottleneck one. Scalability is

To improve the accuracy of midterm power load forecasting, a forecasting model is proposed by combing kernel principal component analysis (KPCA) with back propagation neural network. First, the dimension of the input space is reduced by KPCA, then input the data set to the neural network model, optimized by particle swarm optimization. The monthly average of daily peak loads is forecasted to modify

Particle filtering (PF) algorithm has found an increasingly wide utilization in many fields at present, especially in nonlinear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. This article proposed an improved PF algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. By

Social media provides a platform for individuals to craft personal brands and influence their perception by others, including potential employers. Yet there remains a need for more research investigating the relationship between individuals' online identities and offline outcomes. This study focuses on the context of college football recruiting, specifically on the relationship between recruits' Twitter

Predicting the results of sport matches and competitions is a growing research field, benefiting from the increasing amount of available data and novel data analytics techniques. Excellent forecasts can be achieved by advanced statistical and machine learning methods applied to detailed historical data, especially in very popular sports such as football (soccer). Here, we show that despite the large

Recently, professional team sport organizations have invested their resources to analyze their own and opponents' performance. So, developing methods and algorithms for analyzing team sports has become one of the most popular topics among data scientists. Analyzing football is hard because of its complexity, number of events in each match, and constant flow of circulation of the ball. Finding roles

We investigate how to learn functions that rate game situations on a soccer pitch according to their potential to lead to successful attacks. We follow a purely data-driven approach using techniques from deep reinforcement learning to valuate multiplayer positionings based on positional data. Empirically, the predicted scores highly correlate with dangerousness of actual situations and show that rating

We consider the task of determining the number of chances a soccer team creates, along with the composite nature of each chance-the players involved and the locations on the pitch of the assist and the chance. We infer this information using data consisting solely of attacking events, which the authors believe to be the first approach of its kind. We propose an interpretable Bayesian inference approach

Fantasy sports are a popular way for individuals to add another layer of enjoyment to their interest in sports. While fantasy sports have been around for many years, access to big data sets and computer power to process them is a relatively new phenomenon, as well as the ability to compete in daily competitions and not just season-long campaigns. We posit that access to new and yet unforeseen data

Football scores are an imperfect measure of a team's ability, and consequently exaggerate differences in abilities. Those teams that perform the best and the worst are not really so far from average in their ability; thus their future performances regress to the mean. Betting data indicate that gamblers do not fully account for this regression.

This article focuses on the performance of runners in official races. Based on extensive public data from participants of races organized by the Boston Athletic Association, we demonstrate how different pacing profiles can affect the performance in a race. An athlete's pacing profile refers to the running speed at various stages of the race. We aim to provide practical, data-driven advice for professional

Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing

We develop a number of data-driven investment strategies that demonstrate how machine learning and data analytics can be used to guide investments in peer-to-peer loans. We detail the process starting with the acquisition of (real) data from a peer-to-peer lending platform all the way to the development and evaluation of investment strategies based on a variety of approaches. We focus heavily on how

Ready data availability, cheap storage capacity, and powerful tools for extracting information from data have the potential to significantly enhance the human condition. However, as with all advanced technologies, this comes with the potential for misuse. Ethical oversight and constraints are needed to ensure that an appropriate balance is reached. Ethical issues involving data may be more challenging

In professional soccer, nowadays almost every team employs tracking technology to monitor performance during trainings and matches. Over the recent years, there has been a rapid increase in both the quality and quantity of data collected in soccer resulting in large amounts of data collected by teams every single day. The sheer amount of available data provides opportunities as well as challenges to

In this article we develop a novel online framework to visualize news data over a time horizon. First, we perform a Natural Language Processing analysis, wherein the words are extracted, and their attributes, namely the importance and the relatedness, are calculated. Second, we present a Mathematical Optimization model for the visualization problem and a numerical optimization approach. The model represents

The proliferation of new data sources, stemmed from the adoption of open-data schemes, in combination with an increasing computing capacity causes the inception of new type of analytics that process Internet of things with low-cost engines to speed up data processing using parallel computing. In this context, the article presents an initiative, called BIG-Boletín Oficial del Estado (BOE), designed

In this article, the application of the deep learning method based on Gaussian-Bernoulli type restricted Boltzmann machine (RBM) to the detection of denial of service (DoS) attacks is considered. To increase the DoS attack detection accuracy, seven additional layers are added between the visible and the hidden layers of the RBM. Accurate results in DoS attack detection are obtained by optimization

Computational propaganda deploys social or political bots to try to shape, steer, and manipulate online public discussions and influence decisions. Collective behavior of populations of social bots has not been yet widely studied, although understanding of collective patterns arising from interactions between bots would aid social bot detection. In this study, we show that there are significant differences

MapReduce (MR) computing paradigm and its open source implementation Hadoop have become a de facto standard to process big data in a distributed environment. Initially, the Hadoop system was homogeneous in three significant aspects, namely, user, workload, and cluster (hardware). However, with growing variety of MR jobs and inclusion of different configurations of nodes in the existing cluster, heterogeneity

This article proposes a novel approach, called data snapshots, to generate real-time probabilities of winning for National Basketball Association (NBA) teams while games are being played. The approach takes a snapshot from a live game, identifies historical games that have the same snapshot, and uses the outcomes of these games to calculate the winning probabilities of the teams in this game as the

Nonstandard insurers suffer from a peculiar variant of fraud wherein an overwhelming majority of claims have the semblance of fraud. We show that state-of-the-art fraud detection performs poorly when deployed at underwriting. Our proposed framework "FraudBuster" represents a new paradigm in predicting segments of fraud at underwriting in an interpretable and regulation compliant manner. We show that