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International Journal of Data Science and Analytics OnlineFirst articles

14-06-2022 | Regular Paper

Data-driven analytics of COVID-19 ‘infodemic’

The rampant of COVID-19 infodemic has almost been simultaneous with the outbreak of the pandemic. Many concerted efforts are made to mitigate its negative effect to information credibility and data legitimacy. Existing work mainly focuses on …

Minyu Wan, Qi Su, Rong Xiang, Chu-Ren Huang

06-06-2022 | Regular Paper

Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection

Epidemics like Covid-19 and Ebola have impacted people’s lives significantly. The impact of mobility of people across the countries or states in the spread of epidemics has been significant. The spread of disease due to factors local to the …

Nirmal Kumar Sivaraman, Manas Gaur, Shivansh Baijal, Sakthi Balan Muthiah, Amit Sheth

Open Access 27-05-2022 | Original Paper

COVID-19 and 5G conspiracy theories: long term observation of a digital wildfire

The COVID-19 pandemic has severely affected the lives of people worldwide, and consequently, it has dominated world news since March 2020. Thus, it is no surprise that it has also been the topic of a massive amount of misinformation, which was …

Johannes Langguth, Petra Filkuková, Stefan Brenner, Daniel Thilo Schroeder, Konstantin Pogorelov

26-05-2022 | Regular Paper

Using big data and federated learning for generating energy efficiency recommendations

Internet of Things (IoT) devices are becoming popular solutions for smart home and office environments and contribute the most to energy efficiency. The most common implementation of such solutions relies on smart home systems that are hosted on …

Iraklis Varlamis, Christos Sardianos, Christos Chronis, George Dimitrakopoulos, Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali, Abbes Amira

14-05-2022 | Regular Paper

Urban fire station location planning using predicted demand and service quality index

In this article, we propose a systematic approach for fire station location planning. We develop machine learning models, based on Random Forest and Extreme Gradient Boosting, for demand prediction and utilize the models further to define a …

Arnab Dey, Andrew Heger, Darin England

Open Access 06-05-2022 | Regular Paper

An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter

Conspiracy theories have seen a rise in popularity in recent years. Spreading quickly through social media, their disruptive effect can lead to a biased public view on policy decisions and events. We present a novel approach for LDA-pre-processing …

Gillian Kant, Levin Wiebelt, Christoph Weisser, Krisztina Kis-Katos, Mattias Luber, Benjamin Säfken

Open Access 30-04-2022 | Regular Paper

Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization

The world is witnessing the devastating effects of the COVID-19 pandemic. Each country responded to contain the spread of the virus in the early stages through diverse response measures. Interpreting these responses and their patterns globally is …

Thirunavukarasu Balasubramaniam, David J. Warne, Richi Nayak, Kerrie Mengersen

04-04-2022 | Regular Paper

Eigenvalue analysis of SARS-CoV-2 viral load data: illustration for eight COVID-19 patients

Eigenvalue analysis is an important tool in economics and nonlinear physics to analyze industrial processes and instability phenomena, respectively. A model-based eigenvalue analysis of viral load data from eight symptomatic COVID-19 patients was …

Till D. Frank

11-02-2022 | Regular Paper

Performance prediction in major league baseball by long short-term memory networks

Player performance prediction is a serious problem in every sport since it brings valuable future information for managers to make important decisions. In baseball industries, there already existed variable prediction systems and many types of …

Hsuan-Cheng Sun, Tse-Yu Lin, Yen-Lung Tsai

29-01-2022 | Regular Paper

Sampling and sparsification for approximating the packedness of trajectories and detecting gatherings

Packedness is a measure defined for curves as the ratio of maximum curve length inside any disk divided by its radius. Sparsification allows us to reduce the number of candidate disks for maximum packedness to a polynomial amount in terms of the …

Sepideh Aghamolaei, Vahideh Keikha, Mohammad Ghodsi, Ali Mohades

15-01-2022 | Regular Paper

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA

With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental models …

Benjamin Lucas, Behzad Vahedi, Morteza Karimzadeh

11-11-2021 | Regular Paper

Exploring unsupervised multivariate time series representation learning for chronic disease diagnosis

The application of various sensors in hospitals has enabled the widespread utilization of multivariate time series signals for chronic disease diagnosis in the data-driven world. The key challenge is how to model the complex temporal (linear and …

Xu Zhang, Yaming Wang, Liang Zhang, Bo Jin, Hongzhe Zhang

Open Access 03-09-2021 | Regular Paper

Identification of token contracts on Ethereum: standard compliance and beyond

Next to cryptocurrencies, tokens are a widespread application area of blockchains. Tokens are digital assets implemented as small programs on a blockchain. Being programmable makes them versatile and an innovative means for various purposes.

Monika Di Angelo, Gernot Salzer

24-07-2021 | Regular Paper

Deep multi-task learning with flexible and compact architecture search

Multi-task learning has been applied successfully in various applications. Recent research shows that the performance of multi-task learning methods could be improved by appropriately sharing model architectures. However, the existing work either …

Jiejie Zhao, Weifeng Lv, Bowen Du, Junchen Ye, Leilei Sun, Guixi Xiong

31-03-2021 | Regular Paper

Modeling uncertainty to improve personalized recommendations via Bayesian deep learning

Modeling uncertainty has been a major challenge in developing Machine Learning solutions to solve real world problems in various domains. In Recommender Systems, a typical usage of uncertainty is to balance exploration and exploitation, where the …

Xin Wang, Serdar Kadıoğlu

Open Access 09-10-2020 | Regular Paper

When algorithm selection meets Bi-linear Learning to Rank: accuracy and inference time trade off with candidates expansion

Algorithm selection (AS) tasks are dedicated to find the optimal algorithm for an unseen problem instance. With the knowledge of problem instances’ meta-features and algorithms’ landmark performances, Machine Learning (ML) approaches are applied …

Jing Yuan, Christian Geissler, Weijia Shao, Andreas Lommatzsch, Brijnesh Jain

08-10-2020 | Regular Paper

Combination of individual and group patterns for time-sensitive purchase recommendation

Due to the availability of large amounts of data, recommender systems have quickly gained popularity in the banking sphere. However, time-sensitive recommender systems, which take into account the temporal behavior and the recurrent activities of …

Anton Lysenko, Egor Shikov, Klavdiya Bochenina

01-04-2019 | Applications

Compressing unstructured mesh data from simulations using machine learning

The amount of data output from a computer simulation has grown to terabytes and petabytes as increasingly complex simulations are being run on massively parallel systems. As we approach exaflop computing in the next decade, it is expected that the …

Chandrika Kamath

22-02-2019 | Regular Paper

Entity-level stream classification: exploiting entity similarity to label the future observations referring to an entity

Stream classification algorithms traditionally treat arriving instances as independent. However, in many applications, the arriving examples may depend on the “entity” that generated them, e.g. in product reviews or in the interactions of users …

Vishnu Unnikrishnan, Christian Beyer, Pawel Matuszyk, Uli Niemann, Rüdiger Pryss, Winfried Schlee, Eirini Ntoutsi, Myra Spiliopoulou

17-01-2019 | Regular Paper

A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks

We investigate the problem of analysing the train movements in large-scale railway networks for the purpose of understanding and predicting their behaviour. We focus on different important aspects: the Running Time of a train between two stations …

Luca Oneto, Irene Buselli, Alessandro Lulli, Renzo Canepa, Simone Petralli, Davide Anguita