Forthcoming articles

International Journal of Spatio-Temporal Data Science

International Journal of Spatio-Temporal Data Science (IJSTDS)

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International Journal of Spatio-Temporal Data Science (2 papers in press)

Regular Issues

    by S.B. Goyal, Pradeep Bedi 
    Abstract: In the last decade, major complications of big-data-to-IoT are data-management, security and time-management and thus the objective of the research is recognizing with the problems in big data to extend IoT. In order to accord with the data-management problem an ontology-based matching using H matching algorithm is implementing subsequent to Pillar k-means clustering algorithm that segregates meaningful and meaningless information. The time management problem is recognizing with the aid of Hadoop with mapreduce that specifies the location hitherto. Consequently, Triple DES algorithm with WPA is utilized and that is accountable for providing security by the usage of three keys for both the encryption-decryption. On the other hand, WPA is employed to the data integrity to substantiate whether the data is altered. We have applied ontology based on H-Matching algorithm proposed in the sequence of steps: the stop word removal, stemming process, grouping process and the ontolog making for matching. We have proposed the methodology to handle three parameters: Confidentiality, Integrity and Authenticity in the context of Big-data.
    Keywords: Big data and IoT; ontology-based H-matching algorithm; Pillar k-means clustering algorithm; Hadoop framework; Triple-DES (Data Encryption Standard) algorithm; Wireless-Protected-Access (WPA).

  • Post Classification Change Detection based on Feature based Ensemble Classifiers   Order a copy of this article
    by Sowmya D R 
    Abstract: Change detection is a challenging task in the field of remote sensing. Mainly, the change map is used for disaster assessment, monitoring deforestation and urban studies. In this paper, we present a novel method for post classification change detection. Google earth images of 2011 and 2016 of Bangalore East are used for the study. Multiple features such as texture features, morphological features are extracted using Gray Level Co-occurrence Matrix (GLCM) and morphological operations respectively. Linear Discriminant Analysis (LDA) is used to reduce the dimension of the selected features for the training set. Proposed Ensemble Classifier System (ECS) exploits K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC). The proposed method adopts the subsample kernel based subtraction technique to find the difference image, this method greatly reduces data complexity compared to per-pixel based image subtraction method.
    Keywords: Change Detection; Dimensionality Reduction Technique; Ensemble Classifier; Feature Extraction; Image Difference.