Forthcoming articles


International Journal of Spatio-Temporal Data Science


These articles have been peer-reviewed and accepted for publication in IJSTDS, 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 Articles will not be published until the final proofs are validated by their authors.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 (4 papers in press)


Regular Issues


  • Adaptive Background Modeling Technique for Moving Object Detection in Video under Dynamic Environment   Order a copy of this article
    by Dileep Yadav, Karan Singh 
    Abstract: This work proposes a novel method for detection of motion based object having dynamic scenario in the background. The suggested scheme has a strong potential for real-time applications especially for rafting, river, sea-beach, swimming pools, ponds, etc. Apart from these, this work is very beneficial for surveillance of border, tunnel, traffic in the sea, forest, restricted zones, deep zones, etc. This work develops a statistical p based background subtraction method and implemented in three stages. In the first stage, a background model is developed using few initial frames. In the second stage, this work classifies the foreground using the difference frame and the appropriate threshold value. An automatic threshold value is generated at run-time and updated iteratively. It also reduces the problem of using a constant threshold. In the third stage, morphological filters and connected component based region filtering technique is applied to enhance the detection quality. The extensive experimental result shows more accurate results of proposed method. It also demonstrates better performance against considered state-of-the-art methods.
    Keywords: Cluttered Background; Adaptive Modeling; Background Subtraction; Outliers; Moving Object Segmentation; Visual Surveillance.

Special Issue on: Remote Sensing Big Data Theory, Methods and Applications

  • Application of AHP-VIKOR and GMDH Framework to develop an indicator to identify utilization potential of Wave energy converter with respect to location   Order a copy of this article
    by Tilottama Chakraborty, Mrinmoy Majumder, Ankit Khare 
    Abstract: The potential of Analytical Hierarchy Process (AHP)- rough number based compromise ranking method (also known as VIKOR) Multi Criteria Decision Making (MCDM) and Group Method of Data Handling (GMDH) Multimodal predictive method in development of an indicator for smart representation of "utilization potential" of wave energy converters with respect to specific locations. The significant parameters were identified by their consideration in different case studies and their influence on converter efficiency. The soft-computation methods like AHP-VIKOR and GMDH are used to find the relative priority values of the parameters and to develop an automatic framework for estimation of the indicator which is made directly proportional to the ability of the converter to utilize existing potential of wave energy in a specific location. The results from the multi-method estimation model were validated with the help of Multi Linear Regression Equation and some real time case analysis. With an accuracy of above, 99% the ensemble MCDM-ANN model depicts a reliability which ensures the author of its wide application for the real benefits like cost reduction and efficiency maximization of converters in the utilization of the potential energy of the locations.
    Keywords: Analytical Hierarchy Process (AHP); VIKOR; Group Method of Data Handling (GMDH); Ensemble Modeling; Wave Energy Converter.

  • State of Art on Efficient Document Co-editing in Cloud Collaboration   Order a copy of this article
    by Tanuja Kumari Sharma, Hemraj Saini 
    Abstract: Cloud collaboration is an important technique which helps distant place authorize users to share their work, information and files over the cloud at the same instant of time. This technique reduces the communication cost because the mutual sharing work office can set up easily over the network at distant locations. The focus of this study is to remove the problem of the deadlock or long time waiting zone condition for requests edit threads, by the users in collaborative structure. The co-authoring or co-editing process generates a major problem in form of writing confliction in concurrent and single user editor system in cloud collaboration process, hence multi-version approach is implementing as a solution here on conflicting common event, so that simultaneous access to object get maintain for a long time in cloud collaboration co-editing environment. In this study cloud collaboration architecture is discussing in detail which describes the efficient working of central cloud, remote cloud and local cloud in collaborating environment. All the services in this cloud architecture communicate through important interfaces. This overall study is helpful due to involvement of central cloud which helps to minimize the storage space of data in the cloud and this also resolve a redundancy factor.
    Keywords: Single user editor system; Co-authoring; Co-editing; Multi-version; Cloud collaboration; Quality of service; User domain; Cloud domain; Central cloud.

  • Topic Based Hierarchical Summarization of Twitter   Order a copy of this article
    by BUSHRA SIDDIQUE, Nadeem Akhtar 
    Abstract: Twitter has become a rich source of information now days. The data generated however is so large in volume that it is not possible to manually go through each and every tweet to understand the context of data. One of the ways to get insight into the bulk of data at hand is to know the topics contained in it. As in the context of Twitter, we define topics to be long-lasting subjects around which the conversations of people revolve, such as sports, music and politics amongst others. However, the topics identified may be large in number and might be cumbersome for human interpretation. Considering these views, in this paper we address the information overload problem of Twitter data and propose a topic based hierarchical summarization framework for the same. In contrast to imposing restrictions on topic models to depict the hierarchical structure, we propose an algorithm which constructs a topic hierarchy out of any given number of topics. We showcase the effectiveness of the proposed algorithm for the Twitter dataset prepared for Egyptair MS181 flight incident.
    Keywords: Twitter; topic based summarization; topic hierarchy.