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Many systems can be represented as networks or graph collections of nodes joined by edges. The social structures in these networks can be investigated using graph theory through a process called social network...
The input to a machine learning model is a one-dimensional feature vector. However, in recent learning models, such as convolutional and recurrent neural networks, two- and three-dimensional feature tensors ca...
In this paper we are proposing an adaptive and real-time approach to resolve real-time financial data integration latency problems and semantic heterogeneity. Due to constraints that we have faced in some proj...
A scalable graphical method is presented for selecting and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation co...
Smartphones are considered as one of the most essential and highly personal devices of individuals in our current world. Due to the popularity of context-aware technology and recent developments in smartphones...
The Hadoop distributed file system (HDFS) is responsible for storing very large data-sets reliably on clusters of commodity machines. The HDFS takes advantage of replication to serve data requested by clients ...
Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured. The learning curve for using machine learning methods is rapidly dec...
In recent years, the rapid growth of the Internet has led to creation of massively large graphs. Since databases have become very large nowadays, they cannot be processed by a simple machine at an acceptable t...
Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted als...
One of the most common diseases among women is breast cancer, the early diagnosis of which is of paramount importance. Given the time-consuming nature of the diagnosis process of the disease, using new methods...
Nowadays, sharing moments on social networks have become something widespread. Sharing ideas, thoughts, and good memories to express our emotions through text without using a lot of words. Twitter, for instanc...
With the growing use of information technology in all life domains, hacking has become more negatively effective than ever before. Also with developing technologies, attacks numbers are growing exponentially e...
The trend of current software inevitably leads to the big data era. There are much of large software developed from hundreds to thousands of modules. In software development projects, finding the defect pronen...
The emergence and growth of connected technologies and the adaptation of big data are changing the face of all industries. In the insurance industry, Usage-Based Insurance (UBI) is the most popular use case of...
An important business of airlines is to get customer satisfaction. Due to bad weather, a mechanical reason, and the late arrival of the aircraft to the point of departure, flights delay and lead to customer di...
With the popularity of e-commerce, posting online product reviews expressing customer’s sentiment or opinion towards products has grown exponentially. Sentiment analysis is a computational method that plays an...
As social network structures evolve constantly, it is necessary to design an efficient mechanism to track the influential nodes and accurate communities in the networks. The attributed graph represents the inf...
Data mining and machine learning techniques for processing raster data consider a single spectral band of data at a time. The individual results are combined to obtain the final output. The essence of related ...
This study proposed an ECG (Electrocardiogram) classification approach using machine learning based on several ECG features. An electrocardiogram (ECG) is a signal that measures the electric activity of the he...
Economists are interested in more granular, more frequent data to aid in their understanding of the U.S. economy. The most frequent economic data currently available from the U.S. Census Bureau come from month...
In the era of accelerating growth of genomic data, feature-selection techniques are believed to become a game changer that can help substantially reduce the complexity of the data, thus making it easier to ana...
Apache Spark is one of the most widely used open source processing framework for big data, it allows to process large datasets in parallel using a large number of nodes. Often, applications of this framework u...
The DBSCAN algorithm is a prevalent method of density-based clustering algorithms, the most important feature of which is the ability to detect arbitrary shapes and varied clusters and noise data. Nevertheless...
The authors note a correction to the article [1]. Table 5 of the original article is incomplete. Few percentage values are missing. This article presents the corrected version of Table 5.
The original article was published in Journal of Big Data 2018 5:12
In this paper, we present a Hadoop implementation of the Apriori algorithm. Using Hadoop’s distributed and parallel MapReduce environment, we present an architecture to mine positive as well as negative associ...
Data scientists spend considerable amounts of time preparing data for analysis. Data preparation is labour intensive because the data scientist typically takes fine grained control over each aspect of each ste...
This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction...
This study investigates the effectiveness of multiple maxout activation function variants on 18 datasets using Convolutional Neural Networks. A network with maxout activation has a higher number of trainable p...
As cybersecurity threats increase in frequency and sophistication, organizations are realizing that one of their strongest resources to combat cyberattacks lies in the growing volume of data at their disposal....
In this paper, we describe the design of a machine learning-based classifier, tailored to predict whether a water meter will fail or need a replacement. Our initial attempt to train a recurrent deep neural net...
High class imbalance between majority and minority classes in datasets can skew the performance of Machine Learning algorithms and bias predictions in favor of the majority (negative) class. This bias, for cases ...
In this paper, we comprehensively explain how we built a novel implementation of the Random Forest algorithm on the High Performance Computing Cluster (HPCC) Systems Platform from LexisNexis. The algorithm was...
The integrity of modern network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are consistently shifting to stealthier and more complex forms of attacks in ...
Mobile phone and sensors have become very useful to understand and analyze human lifestyle because of the huge amount of data they can collect every second. This triggered the idea of combining benefits and ad...
Life in the city generates data on human behavior in many different ways. Measuring human behavior in terms of criminal offenses plays a critical role on identifying the most common crime type in each urban ar...
A conscious use of the battery is one of the key elements to consider while driving an electric vehicle. Hence, supporting the drivers, with information about it, can be strategic in letting them drive in a be...
Access to affordable healthcare is a nationwide concern that impacts a large majority of the United States population. Medicare is a Federal Government healthcare program that provides affordable health insura...
Customer’s experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study ...
The human brain is a complex system of neural tissue that varies significantly between individuals. Although the technology that delineates these neural pathways does not currently exist, medical imaging modal...
Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomen...
We are moving into an era where instrumentation, datafication, and computerization are routinely pervading the very fabric of cities, coupled with the interlinking, integration, and coordination of their syste...
We propose a Chernoff-bound approach and examine standard deviation value to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. It is a data stream m...
Due to the increasing popularity of recent advanced features and context-awareness in smart mobile phones, the contextual data relevant to users’ diverse activities with their phones are recorded through the d...
Deep neural networks have become a standard framework for image analytics. Besides the traditional applications, such as object classification and detection, the latest studies have started to expand the scope...
Sequence of graph snapshots have been commonly utilized in literature to represent changes in a dynamic graph. This approach may be suitable for small-size and slowly evolving graphs; however, it is associated...
‘Big data’ is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and priv...
This paper presents our research on the development of navigation systems of autonomous drone for delivering items that uses a GNSS (Global Navigation Satellite System) and a compass as the main tools in drone...
The widespread growth of Big Data and the evolution of Internet of Things (IoT) technologies enable cities to obtain valuable intelligence from a large amount of real-time produced data. In a Smart City, vario...
Adversarial attacks represent a serious evolving threat to the operation of deep neural networks. Recently, adversarial algorithms were developed to facilitate hallucination of deep neural networks for ordinar...
As a natural outcome of achieving equilibrium, digital economic progress will most likely be subject to increased cyber risks. Therefore, the purpose of this study is to present an algorithmic model that utili...
Speed
48 days to first decision for all manuscripts
40.7 days to first decision for reviewed manuscripts only
108 days from submission to acceptance
18 days from acceptance to publication
Citation Impact
2.501 - Source Normalized Impact per Paper (SNIP)
0.925 - SCImago Journal Rank (SJR)
6.1 - CiteScore
Usage
1,878,037 downloads (2021)
549 Altmetric mentions
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