International Journal of System of Systems Engineering
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International Journal of System of Systems Engineering (5 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
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
An Efficient Vote Casting System with Aadhar Verification through Blockchain by Kuppani Sathish, Basetty Mallikarjuna, J. Gitanjali, Venkata Krishna Abstract: In the recent past security for vote casting system plays a vital role in the autonomous constitutional authority of election. A democratic country like India highly depends on the fail proof voting system to elect the government. The many security systems have evolved to ensure the electronic voting system to avoid malpractice during election. This paper describes an electoral system for the Indian election on blockchain technology and Aadhaar verification. The proposed system provides optimal security for the voting system and preserves votes stored in the Electronic Voting Machine (EVM). The technology behind blockchain and Aadhaar verification are used to simplify the process of the voting system. The Aadhaar number has to be fetched and verified with the database after entering every vote on the machine. The information has to be verified for the vote of an individual to be accepted by EVM and stored in block chain ledger. Keywords: Aadhar verification; Electronic Voting Machine (EVM); Cyber-attack; Blockchain; IoT. DOI: 10.1504/IJSSE.2021.10034938
Fog and Edge Computing Simulators Systems: Research Challenges and an Overview by Sathish Kumar, IYAPPARAJA M Abstract: To support the requirement of geographically data scale, low latency, heterogeneous applications two new computing paradigms have been introduced namely, fog and edge computing. Discrete Event Simulation (DES) and Discrete Time Simulation (DTS) are the two types of simulation, where DES approach is applied to the problem where the application requires more scalability and DTS is the simulation model it is applied to the system when there is a higher possibility of inaccuracy. There is a wide number of simulators available for cloud, however the simulators for fog and edge computing are limited. An overview of fog and edge computing simulators like iFogSim, CloudSimSDN, and YAFS has be analyzed in this paper. Finally resource consumption metrics and fault tolerance metrics are analyzed and results has shown that that resource consumption metrics increases when the complexity of the scenario increases. Keywords: Fog Computing; Simulation; CDN; iFogSim; CloudSimSDN; YAFS; EMUFOG. DOI: 10.1504/IJSSE.2021.10034939
Enriching Module Dependency Graphs for Improved Software Clustering by Harleen Kaur, Geeta Sikka Abstract: The requirements for systems change over time and as the software systems evolve their designs degenerate, making it necessary to restructure the system to recoup with the apprehension that was lost. Without complete cognizance to a software system, a software maintainer may find it difficult to modify the system. The process of reverse engineering starts with an analysis phase where a system is analyzed by extracting its structure using automated tools. It is crucial for the developers to understand the system structure before making an attempt to modify it. The discovered structure can be viewed as a directed Module dependency graph. In this paper we enrich the MDG for a better understanding of the system structure by assigning different weights to different kind of code dependencies. To each kind of coupling relation distinctive weights are assigned. A naive function has been defined to generate weighted MDG's. Keywords: MDG; Dependency; Clustering. DOI: 10.1504/IJSSE.2022.10038005