Page 10 of 19
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...
Nowadays this concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various method...
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational a...
Collaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation...
Tourism is one of the world’s largest industries fundamentally arising from mobility as a form of capital. In destination islands that have a delicate ecosystem to maintain, this source of income can become pr...
Recently unique spans of genetic data are produced by researchers, there is a trend in genetic exploration using machine learning integrated analysis and virtual combination of adaptive data into the solution ...
The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve comp...
The main obstacle for local and daily or weekly time-series mapping using very high-resolution satellite imagery is the high price and availability of data. These constraints are currently obtaining solutions ...
Stock market process is full of uncertainty; hence stock prices forecasting very important in finance and business. For stockbrokers, understanding trends and supported by prediction software for forecasting i...
Database queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature ...
Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variat...
Finding the most suitable co-author is one of the most important ways to conduct effective research in scientific fields. Data science has contributed to achieving this possibility significantly. The present s...
At present, voice biometrics are commonly used for identification and authentication of users through their voice. Voice based services such as mobile banking, access to personal devices, and logging into soci...
This paper explores the public perception of economic measures implemented as a reaction to the COVID-19 pandemic in Poland in March–June 2020. A mixed-method approach was used to analyse big data coming from ...
The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations ob...
In the era of global-scale services, organisations produce huge volumes of data, often distributed across multiple data centres, separated by vast geographical distances. While cluster computing applications, ...
Deep learning models are tools for data analysis suitable for approximating (non-linear) relationships among variables for the best prediction of an outcome. While these models can be used to answer many importan...
Machine learning algorithms efficiently trained on intrusion detection datasets can detect network traffic capable of jeopardizing an information system. In this study, we use the CSE-CIC-IDS2018 dataset to in...
A significant advancement that occurs during the data cleaning stage is estimating missing data. Studies have shown that improper data handling leads to inaccurate analysis. Furthermore, most studies indicate ...
Value-Added Services at a Mobile Telecommunication company provide customers with a variety of services. Value-added services generate significant revenue annually for telecommunication companies. Providing so...
Various recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. H...
Maternal mortality is one of the socio-economic problems and widely considered a serious indicator of the quality of a health. Ethiopia is considered to be one of the top six sub-Saharan countries with severe ...
This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the D...
Over the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is...
Optimizing quality trade-offs in an end-to-end big data science process is challenging, as not only do we need to deal with different types of software components, but also the domain knowledge has to be incor...
As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodula...
RNA-Seq data are utilized for biological applications and decision making for the classification of genes. A lot of works in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality re...
Multi-dimensional arrays (also known as raster data or gridded data) play a key role in many, if not all science and engineering domains where they typically represent spatio-temporal sensor, image, simulation...
Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not a...
Stemming has long been used in data pre-processing to retrieve information by tracking affixed words back into their root. In an Indonesian setting, existing stemming methods have been observed, and the existi...
The aim of converting relational database into Ontology is to provide applications that are based on the semantic representation of the data. Whereas, representing the data using ontologies has shown to be a u...
The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately, many application domains do not have access to big data because acquiring data involves a process that is expensive or tim...
Some of the variants detected by high-throughput sequencing (HTS) are often not reproducible. To minimize the technical-induced artifacts, secondary experimental validation is required but this step is unneces...
Weighted finite-state transducers have been shown to be a general and efficient representation in many applications such as text and speech processing, computational biology, and machine learning. The composit...
The MapReduce programming paradigm is frequently used in order to process and analyse a huge amount of data. This paradigm relies on the ability to apply the same operation in parallel on independent chunks of...
2022 Citation Impact
8.1 - 2-year Impact Factor
5.095 - SNIP (Source Normalized Impact per Paper)
2.714 - SJR (SCImago Journal Rank)
2023 Speed
56 days submission to first editorial decision for all manuscripts (Median)
205 days submission to accept (Median)
2023 Usage
2,559,548 downloads
280 Altmetric mentions
