Forthcoming and Online First Articles

International Journal of Spatio-Temporal Data Science

International Journal of Spatio-Temporal Data Science (IJSTDS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Spatio-Temporal Data Science (3 papers in press)

Regular Issues

  • Business Knowledge Database Structure and Inferencing: An Oracle Prototype   Order a copy of this article
    by Rajeev Kaula 
    Abstract: Business knowledge is a repository of insights and methods that can guide business operations, enable businesses to grasp customer needs and preferences, and provide guidance for growth in the marketplace. Often such knowledge may not be expressed in one format and repository, but instead may be spread in multiple repositories. This paper outlines an approach to structure business knowledge rules in relational database and facilitate their subsequent retrieval through backward chaining and forward chaining using traditional database techniques. The approach is implemented through a prototype that illustrates the creation of knowledge structures through a sample set of business knowledge rules, along with their retrieval through inferencing from an Oracle database.
    Keywords: Business Knowledge; Database; Knowledge Base; Backward Chain; Forward Chain; Oracle; PL/SQL.

  • Station-level bike rental prediction in bike sharing systems   Order a copy of this article
    by Rouzbeh Forouzandeh Jonaghani, Monica Wachowicz 
    Abstract: Predicting ridership is critical for the efficient operation of Bike Sharing Systems (BSSs). This paper proposes a framework for station-level Origin-Destination (OD) flow and bike rental (check-in/out) prediction in a BSS using a two-step Adaptive k-Nearest Neighbor (AkNN) model. While previous works mostly aimed to predict a particular variable in a BSS in isolation (e.g. number of check-ins), in this study, we predict all possible station-to-station flows (i.e., bike flow network) in a BSS. We illustrate how the discontinuities and anomalies in bike ridership are related to the structure of the bike flow network at origin and destination stations and meteorological factors. We also consider the spatial and temporal closeness of the bike ridership in the prediction model. The proposed method is evaluated on the Divvy Trips dataset from April to September 2019 in the City of Chicago. The results show an improvement in the prediction accuracy from baseline methods.
    Keywords: Bike-Sharing System; Evolutionary Network Analysis; Adaptive k-Nearest Neighbor.

Special Issue on: Continual and Incremental Data-Oriented Deep Lifelong Learning for Self-Modelling

  • A COMPREHENSIVE SURVEY OF VARIOUS MACHINE LEARNING TECHNIQUES TO COUNTER SECURITY ISSUES RELATED TO MOBILE MALWARES   Order a copy of this article
    by Marwan Alshar’e, Mohd Naved, K. Arumugam, F. Sammy, Sanjeev Gour, Abhishek Raghuvanshi 
    Abstract: Since its introduction, malware has been used to attack mobile devices. Fraudulent mobile applications and inserted harmful apps are the two main forms of standalone mobile malware assaults. A thorough grasp of the permissions stated in apps and API calls is essential if one wants to successfully defend against mobile malwares cyber risks. Permission requests and API calls are used in this study to create an effective categorization model. There are many APIs that Android applications utilize, thus to make it easier to detect malicious apps, weve come up with three alternative categorizing strategies: ambiguous, hazardous, and disruptive. An in-depth examination of the literature on several ways for dealing with Android malware and related security concerns is presented in this article. This article offers an in-depth examination of several approaches for combating malware in the Android operating system. According to the findings of this study, machine learning techniques such as the Support Vector Machine and the Convolution Neural Network are the most accurate in terms of malware classification and prediction on the Android operating system.
    Keywords: Security; Privacy; Android; Malware; Counter Measures; Machine Learning techniques; Blockchain.

 

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