International Journal of System of Systems Engineering
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International Journal of System of Systems Engineering (4 papers in press)
AN EMPIRICAL HYBRID DBN-EL SYSTEM MODEL FOR STOCK MARKET PREDICTION WITH BIG DATA by Ishwarappa K, Anuradha Jagadeesan Abstract: Recent years, big data analytics have become the prominent aspect for different sectors for the prediction of large datasets. The stock market is also the important exertions in the field of business in which big data play an important role for the prediction of stocks. For this reason, the big data and hybrid Deep Belief Network-Ensemble Learning (DBN-EL) model is proposed for analysis of large stock market data in order to improve the performance of its prediction. Here, the Hive based distributed database with MapReduce technique for storing and mapping of data into other for fast processing. Furthermore, the hybrid DBN-EL system model will be used as classifier for stock market prediction. The stock futures dataset is used for analysis. The simulation results shows that the proposed model outperforms by predicting the stock futures trend upto 98% when compared with other existing techniques in terms of precision, recall, and F-Measure. Keywords: Stock Market Prediction; Deep Belief Network (DBN); Ensemble Learning (EL); National Stock Exchange (NSE); Big Data; MapReduce. DOI: 10.1504/IJSSE.2021.10033298
A Comparison of the System Engineering Standard and INCOSE Complexity Primer by Samuel Kovacic, Andres Sousa-Poza Abstract: Complexity is frequently used to describe the environment in which a Systems Engineering must work as well as the nature of the systems that they must design. Relevant publications establish a relationship between complexity and understanding, however, it is questionable whether system engineering methodologies are well suited to deal with the nuances of complexity and potential lack of understanding. This paper provides an analysis between the System Engineering standard and the INCOSE Complexity Primer to assess the state of the standard against the nature of complexity in systems. The analysis conducted shows that present descriptions of how complexity affects Systems Engineering present a problems space that is distinctly different to the problem space that can be addressed by our present practices. Furthermore, it is found that System Engineering methodology will encounter significant challenges in dealing with the attributes of complexity documented in the INCOSE Complexity Primer. Keywords: System Engineering; Complexity; Complexity Primer; System; Methodology; Complex Systems; SEBOK; Observer; System Perspective; Analysis; Understanding; IEEE; ISO; INCOSE. DOI: 10.1504/IJSSE.2021.10033797
Ten Views: Towards a Process Meta-Model in a System of Systems Context by Ikram Akkiyat, Nissrine Souissi Abstract: Improvement and risk management plans are essentials and required in any process lifecycle, particularly when the process is linked to a System of Systems (SoS). The first step in the process lifecycle is process modeling. The ISO 9001 Standard recommends the design of improvement and risk plans in the process modeling step. Thus, this paper proposes a process meta-model which takes into account the SoS context and integrates the improvement and risk management processes, based on ISO standards. It also introduces knowledge bases related to improvement and risks in order to ensure continuous improvement of the process and anticipate the malfunctions. The proposed meta-model is an extension of Seven Views, which is a process modeling approach, by three additional views, Risk view, Improvement view, and SoS view. Keywords: System of Systems; SoS; UML; Seven Views; Process modeling; Process improvement; Process meta-model; Risk management; ISO 9001:2015; ISO 31000:2018. DOI: 10.1504/IJSSE.2021.10034348
PERFORMANCE ANALYSIS OF HYBRID CLASSIFICATION SYSTEM MODEL FOR BIG DATA STREAM USING INTERNET OF THINGS by Gayathri Devi N, Manikandan K Abstract: Big data and IoT have become the worlds prominent technology and reached a high impact on millions of peoples daily life that helps in managing environmental and physical systems processed in real-time. In this research, we proposed a hybrid classification system model named Multi-Output Regression with Deep Belief Networks (MOR-DBN) to improve the performance of the classifier when huge amount of streaming data is transferred from IoT devices. Moreover, the improved Privacy-Preserving Rotation based Condensation Algorithm (P2RoCAl) with Geometric Transformation is also used for obtaining high utility for data streaming to protect from various kinds of attacks during data reconstruction. The simulation results show that proposed framework obtains a high accuracy when compared with other existing algorithms in terms of precision, recall, and F-measure. Overall the proposed MOR_DBN model outperforms by obtaining a 96.21% for SSDS dataset, 97.89% for FRDS dataset, 95.7% for HPDS dataset, and 99.23% for ESDS dataset. Keywords: Multi-Output Regression (MOR); Deep Belief Network (DBN); Privacy Preserving Rotation based Condensation Algorithm (P2RoCAl); Geometric Transformation; Data Streaming; Big Data; Internet of Things. DOI: 10.1504/IJSSE.2021.10034877