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In this age, information technology has grown significantly. Computer networks, which connect a device to others, have made it easier for people to transfer data than before. Moreover, smart devices have the c...
During the last decade, the use of online search traffic data is becoming popular in examining, analyzing, and predicting human behavior, with Google Trends being a popular tool in monitoring and analyzing the...
Nowadays large data volumes are daily generated at a high rate. Data from health system, social network, financial, government, marketing, bank transactions as well as the censors and smart devices are increas...
The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with ...
Big graphs are part of the movement of “Not Only SQL” databases (also called NoSQL) focusing on the relationships between data, rather than the values themselves. The data is stored in vertices while the edges...
In the case of real-world data streams, the underlying data distribution will not be static; it is subject to variation over time, which is known as the primary reason for concept drift. Concept drift poses se...
Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and sev...
The increasing reliance on electronic health record (EHR) in areas such as medical research should be addressed by using ample safeguards for patient privacy. These records often tend to be big data, and given th...
This research proposes a system based on a combination of various components for parallel modelling and forecasting the processes in networks with data assimilation from the real network. The main novelty of t...
Urban transport investments have contributed to the exponential increase of value from land and properties around the built infrastructure. Although literature had shown evidence of value uplift from the resid...
Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples ef...
Traditional food knowledge (TFK) is an essential aspect of human life. In terms of sociocultural aspects, TFK is necessary to protect ancestral culture. In terms of health, traditional foods contain better and...
Anomaly-based Intrusion Detection System (IDS) has been a hot research topic because of its ability to detect new threats rather than only memorized signatures threats of signature-based IDS. Especially after ...
Prediction of bone age from an x-ray is one of the methods in the medical field to support predicting endocrine gland disease, growth abnormalities, and genetic disorders. A decision support system to predict ...
In the era of big data, deep learning for predicting stock market prices and trends has become even more popular than before. We collected 2 years of data from Chinese stock market and proposed a comprehensive...
Prediction using machine learning algorithms is not well adapted in many parts of the business decision processes due to the lack of clarity and flexibility. The erroneous data as inputs in the prediction proc...
Fiber optics cable has been adopted by telecommunication companies worldwide as the primary medium of transmission. The cable is steadily replacing long-haul microwave, copper cable, and satellite transmission...
The under-five mortality rate is one of the most important indicators of the socio-economic wellbeing and public health conditions of a country. Under-five death in Ethiopia has reduced, but the rate is still ...
Contraction Clustering (RASTER) is a single-pass algorithm for density-based clustering of 2D data. It can process arbitrary amounts of data in linear time and in constant memory, quickly identifying approxima...
Exploiting available condition monitoring data of industrial machines for intelligent maintenance purposes has been attracting attention in various application fields. Machine learning algorithms for fault det...
In this work we develop a series of techniques and tools to determine and quantify the presence of bias and censorship in newspapers. These algorithms are tested analyzing the occurrence of keywords ‘killed’ a...
Transfer learning allows us to train deep architectures requiring a large number of learned parameters, even if the amount of available data is limited, by leveraging existing models previously trained for ano...
The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their ...
At the moment, there are increasing trends of using deep learning for plant diseases detection. However, their implementations may be difficult in developing countries due to several reasons. First, existing d...
Group-based emotion recognition (GER) is an interesting topic in both security and social area. In this paper, a GER with hybrid optimization based recurrent fuzzy neural network is proposed which is from vide...
Forests are a global environmental priority that need to be monitored frequently and at large scales. Satellite images are a proven useful, free data source for regular global forest monitoring but these image...
This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine. An approach using ge...
Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend...
Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification....
Nowadays, machine learning classification techniques have been successfully used while building data-driven intelligent predictive systems in various application areas including smartphone apps. For an effecti...
In today's world, due to the increase of medical data there is an interest in data preprocessing, classification and prediction of disease risks. Machine learning and Artificial Intelligence indicates that the...
Big Data constructed based on the advancement of distributed computing and virtualization is considered as the current emerging trends in Data Analytics. It is used for supporting potential utilization of comp...
The existence of some differences in the results obtained from varying clustering k-means algorithms necessitated the need for a simplified approach in validation of cluster quality obtained. This is partly be...
Drawing from tenets of the resource-based theory, we propose and test a model that examines the relationship between the application of big data analytics (ABDA) and organizational performance (OP) in small an...
The article discusses the issues of improving the collection of traffic information using video cameras and the statistical processing of collected data. The aim of the article was to identify the main pattern...
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 7:34
Hadoop MapReduce is a framework to process vast amounts of data in the cluster of machines in a reliable and fault-tolerant manner. Since being aware of the runtime of a job is crucial to subsequent decisions ...
Emotion classification remains a challenging problem in affective computing. The large majority of emotion classification studies rely on electroencephalography (EEG) and/or electrocardiography (ECG) signals a...
Anomaly detection in high dimensional data is becoming a fundamental research problem that has various applications in the real world. However, many existing anomaly detection techniques fail to retain suffici...
In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from c...
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 7:2
In fact, most of information retrieval systems retrieve documents based on keywords matching, which are certainly fail at retrieving documents that have similar meaning with syntactical different keywords (for...
Data-driven healthcare policy discussions are gaining traction after the Covid-19 outbreak and ahead of the 2020 US presidential elections. The US has a hybrid healthcare structure; it is a system that does no...
In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generat...
A majority of predictive models should be updated regularly, since the most recent data associated with the model may have a different distribution from that of the original training data. This difference may ...
Glioblastoma (GBM) is a stage 4 malignant tumor in which a large portion of tumor cells are reproducing and dividing at any moment. These tumors are life threatening and may result in partial or complete menta...
Recommender systems have been an efficient strategy to deal with information overload by producing personalized predictions. Recommendation systems based on deep learning have accomplished magnificent results,...
The Correction to this article has been published in Journal of Big Data 2020 7:45
A major task that the NLP (Natural Language Processing) has to follow is Sentiments analysis (SA) or opinions mining (OM). For finding whether the user’s attitude is positive, neutral or negative, it captures ...
In this paper, a novel method for binary image comparison is presented. We suppose that the image is a set of transactions and items. The proposed method applies along rows and columns of an image; this image ...
In Distributed Database Systems (DDBS), communication costs and response time have long been open-ended challenges. Nevertheless, when DDBS is carefully designed, the desired reduction in communication costs w...
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)
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