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As one of the most popular financial market instruments, the stock has formed one of the most massive and complex financial markets in the world. It could handle millions of transactions within a short period ...
Arguments facilitate humans to deliver their ideas. The outcome of the discussion heavily relies on the validity of the argument. If an argument is well-composed, it is more effective to grasp the core idea be...
Road traffic accidents (RTA) are commonly encountered incidents that can cause injuries, death, and property damage to members of society. Ethiopia is one of the highest incident rates of road traffic accident...
Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with...
In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and ...
The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people’s lives and restart the economy quickly and safely. People’s social behavior, reflected in their mobility dat...
Recent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to informati...
Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This ...
Nowadays, urban data such as demographics, infrastructure, and criminal records are becoming more accessible to researchers. This has led to improvements in quantitative crime research for predicting future cr...
This paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social medi...
Large data requirements are often the main hurdle in training neural networks. Convolutional neural network (CNN) classifiers in particular require tens of thousands of pre-labeled images per category to appro...
To address the challenges of big data analytics, several works have focused on big data optimization using metaheuristics. The constraint satisfaction problem (CSP) is a fundamental concept of metaheuristics t...
Nowadays a large amount of data is originated by complex systems, such as social networks, transportation systems, computer and service networks. These systems can be modeled by using graphs and studied by exp...
A key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for acci...
Computer networks target several kinds of attacks every hour and day; they evolved to make significant risks. They pass new attacks and trends; these attacks target every open port available on the network. Se...
The public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big d...
Evaluating the exact first derivative of a feedforward neural network (FFNN) output with respect to the input feature is pivotal for performing the sensitivity analysis of the trained neural network with respe...
With the rapid growth in the amount of video data, efficient video indexing and retrieval methods have become one of the most critical challenges in multimedia management. For this purpose, Content-Based Video...
Many algorithms have proposed to solve the association rule learning problem. However, most of these algorithms suffer from the problem of scalability either because of tremendous time complexity or memory usa...
Brain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However,...
Feature selection is a pre-processing technique used to remove unnecessary characteristics, and speed up the algorithm's work process. A part of the technique is carried out by calculating the information gain...
Social media postings are increasingly being used in modern days disaster management. Along with the textual information, the contexts and cues inherent in the images posted on social media play an important r...
Data-driven innovation is propelled by recent scientific advances, rapid technological progress, substantial reductions of manufacturing costs, and significant demands for effective decision support systems. T...
The process of big data handling refers to the efficient management of storage and processing of a very large volume of data. The data in a structured and unstructured format require a specific approach for ov...
Detection and removal of outliers in a dataset is a fundamental preprocessing task without which the analysis of the data can be misleading. Furthermore, the existence of anomalies in the data can heavily degr...
Social media platforms have become fundamental tools for sharing information during natural disasters or catastrophic events. This paper presents SEDOM-DD (Sub-Events Detection on sOcial Media During Disasters...
The Covid-19 pandemic that began in the city of Wuhan in China has caused a huge number of deaths worldwide. Countries have introduced spatial restrictions on movement and social distancing in response to the ...
The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-touris...
Big Data is an essential research area for governments, institutions, and private agencies to support their analytics decisions. Big Data refers to all about data, how it is collected, processed, and analyzed ...
Class imbalance is an important consideration for cybersecurity and machine learning. We explore classification performance in detecting web attacks in the recent CSE-CIC-IDS2018 dataset. This study considers ...
New dipeptidyl peptidase-4 (DPP-4) inhibitors need to be developed to be used as agents with low adverse effects for the treatment of type 2 diabetes mellitus. This study aims to build quantitative structure-a...
Social media is popular in our society right now. People are using social media platforms to purchase various products. We collected the data from various social media platforms. We analyzed the data for predi...
This study explores tweets from Oct 2008 to Oct 2018 related to halal tourism. The tweets were extracted from twitter and underwent various cleaning processes. A total of 33,880 tweets were used for analysis. ...
Class rarity is a frequent challenge in cybersecurity. Rarity occurs when the positive (attack) class only has a small number of instances for machine learning classifiers to train upon, thus making it difficu...
In this study, we present the detection of the up-downward as well as the right- leftward motion of cursor based on feature extraction. In this algorithm, the K-means clustering method is used to recognize the...
With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statist...
The ever-increasing social media users has dramatically contributed to significant growth as far as the volume of online information is concerned. Often, the contents that these users put in social media can g...
The emergence of collaborative interactions has empowered users by enabling their interactions through tagging practices that create a folksonomy, also called, classification of the shared resources, any ident...
A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 ma...
An intrusion detection system (IDS) is a device or software application that monitors a network for malicious activity or policy violations. It scans a network or a system for a harmful activity or security br...
Clustering algorithm analysis, including time and space complexity analysis, has always been discussed in the literature. The emergence of big data has also created a lot of challenges for this issue. Because ...
The rise of unstructured data leads to unprecedented opportunities for marketing applications along with new methodological challenges to leverage such data. In particular, redundancy among the features extrac...
With the development of peta- and exascale size computational systems there is growing interest in running Big Data and Artificial Intelligence (AI) applications on them. Big Data and AI applications are imple...
The analysis of various data is a challenging issue in the field of air traffic flow management (ATFM). Managing a large volume of data and their correct interpretation plays a key role in the prevention of ai...
Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide (and growing) library of algorithms that could be applied ...
A new data-driven programming model is defined by the deep learning (DL) that makes the internal structure of a created neuron system over a fixed of training data. DL testing structure only depends on the dat...
Smoking invariably has environmental, social, economic and health consequences in Ethiopia. Reducing and quitting cigarette smoking improves individual health and increases available household funds for educat...
Label noise is an important data quality issue that negatively impacts machine learning algorithms. For example, label noise has been shown to increase the number of instances required to train effective predi...
An amendment to this paper has been published and can be accessed via the original article.
The original article was published in Journal of Big Data 2020 7:109
The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. For the use-case...
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