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Socio-economic status measurement is an ongoing problem where different suggested measurements are given by researchers. This work investigates a socio-economic status measurement derived from natural correlat...
This paper presents a stochastic imputation approach for large datasets using a correlation selection methodology when preferred commercial packages struggle to iterate due to numerical problems. A variable ra...
The number of Web APIs for accessing information and services is continuously increasing, and yet, no tools exist to automate the time-consuming and error-prone process of invoking those APIs and composing the...
Any nation's health policy aims to properly care for its citizens and the society's quality of life. Since in the healthcare system, the population's health is the essential component of national wealth, healt...
Social media data has provided various insights into the behaviour of consumers and businesses. However, extracted data may be erroneous, or could have originated from a malicious source. Thus, quality of soci...
Incredible amounts of data is being generated by various organizations like hospitals, banks, e-commerce, retail and supply chain, etc. by virtue of digital technology....
For getting up-to-date insight into online services, extracted data has to be processed in near real time. For example, major big data companies (Facebook, LinkedIn, Twitter) analyse streaming data for develop...
The detection of anomalous behavior in business process data is a crucial task for preventing failures that may jeopardize the performance of any organization. Supervised learning techniques are impracticable ...
Given the growing importance of customer behavior in the business market nowadays, telecom operators focus not only on customer profitability to increase market share but also on highly loyal customers as well...
During the coronavirus pandemic, the number of depression cases has dramatically increased. Several depression sufferers disclose their actual feeling via social media. Thus, big data analytics on social netwo...
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...
Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from ...
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data ...
New generation databases also called NoSQL (Not only SQL) databases are highly scalable, flexible, and low-latent. These types of databases emerge as a result of the rigidity shown by traditional databases to ...
Detecting botnets in a network is crucial because bots impact numerous areas such as cyber security, finance, health care, law enforcement, and more. Botnets are becoming more sophisticated and dangerous day-b...
Prevention of hospital readmissions has the potential of providing better quality of care to the patients and deliver significant cost savings. A review of existing readmission analysis frameworks based on dat...
‘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...
Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mech...
This paper presents an ontology based deep learning approach for extracting disease names from Twitter messages. The approach relies on simple features obtained via conceptual representations of messages to ob...
Big data analytics utilizes different techniques to transform large volumes of big datasets. The analytics techniques utilize various computational methods such as Machine Learning (ML) for converting raw data...
The big data phenomenon is becoming a fact. Continuous increase of digitization and connecting devices to Internet are making current solutions and services smarter, richer and more personalized. The emergence...
Many data analytics algorithms are originally designed for in-memory data. Parallel and distributed computing is a natural first remedy to scale these algorithms to “Big algorithms” for large-scale data. Advan...
Data mining techniques and extracting patterns from large datasets play a vital role in knowledge discovery. Most of the decision makers encounter a large number of decision rules resulted from association rul...
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
