The rapid evolutions in Artificial Intelligence (AI) and the Machine Learning era have significantly improved accuracy for ionospheric space weather forecasting models. The ionospheric Total Electron Content (TEC) forecasting is necessary to alert Global Navigation Satellite System (GNSS) users about ionospheric space weather influences on satellitereceiver radio communications. Precise modeling and forecasting of the ionospheric TEC are critical for reliable and accurate GNSS applications. In this paper, a deep learning-based Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm is implemented for 26 GPS (Global Positioning System) stations TEC data over the Indian region. The Bi-LSTM ionospheric TEC forecasting maps are generated and compared with ANN, and LSTM models during both geomagnetic quiet and disturbed periods. The potential of Bi-LSTM networks in time sequence processing is improved by having forward and backward connections. The Bi-LSTM results are demonstrated with and without solar and geomagnetic indices as input to the network. This work's outcome would help develop an ionospheric weather alert system for GNSS users.
This paper investigates the performance of a Fast kNearest Neighbor Classifier (FkNN) for Network Intrusion Detection System (NIDS) on Cloud Environment. For this study Variance Index based Partial Distance Search (VIPDS) kNN [7] is adopted as an FkNN classifier. A benchmark dataset CICIDS2017[16] is considered for the evaluation process because it is a 78 featured dataset with most updated cloud related attacks. To achieve this objective a frame work is proposed for implementing FkNN and compared with kNN classifier by considering two performance measures Accuracy and computational time. This study explores the gain in the computational time without compromising the Accuracy while using FkNN instead of kNN over a large featured dataset. The conclusions are drawn as per the results obtained from the experiments conducted on CICIDS2017 dataset.
The ionosphere is perceived to be a predominant source of ranging error of Global Navigation Satellite System (GNSS) signals to degrade the positional accuracy. The suitable ionospheric model is necessary to improve the positional accuracy of GNSS and Satellite-Based Augmentation System (SBAS) users. In this paper, the regional ionospheric model (RIM) over the Indian region is implemented based on the adjusted spherical harmonics function (ASHF) model from dense GPS TEC stations over the Indian region. Also, the evaluation of the different ASHF order models conducted to identify the proper order of the ASHF model for Indian low latitude ionospheric calm and adverse space weather conditions. The results confirm that the 4 th order ASHF ionospheric model can identify the northern Equatorial Ionization Anomaly (EIA) TEC crest patterns over the Indian region. The comparison of the 4 th order ASHF ionospheric model carried out with dual and single frequency ionospheric models like as Klobuchar model, Centre for Orbit Determination in Europe (CODE) Klobuchar model, NeQuick G model, BeiDou System (BDS2) model and CODE Global Ionospheric Maps (GIM) models. The outcome of the results indicates that the 4 th Order ASHF ionospheric model would be potential for single and dual-frequency ionospheric models for GNSS and SBAS systems. INDEX TERMS Ionosphere, Total Electron Content (TEC), GPS Aided GEO Augmented Navigation(GAGAN), Adjusted Spherical Harmonics Function (ASHF).
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