International Journal of System of Systems Engineering (12 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.
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.
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.
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.
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.
Modelling higher education environments Based on Knowledge system Transfer Between Instructor and Learners Using Genetic Algorithm
by Sowmiya J., Kalaiselvi K.
Abstract: Performance of a teacher in classroom evaluates via feedback from students, as-signment and exam. Traditionally, the performance of teachers in classrooms eval-uate by administrators. However, the administrators never evaluate teacher per-formance by considering factors such as subject, experience, and students per-formance, which influence student learning and performance in assignment and exams. In this paper, we evaluate the performance of teachers based on students assignment and term exam results via Genetic algorithm. The Genetic algorithm adjusts the weightage of students performance in the assignment and terms exam with respect to various factors such as syllabus difficulty level and preparation time for term exams. The study was conducted for BCA course second-year stu-dents of total 50 in number. From the proposed teacher evaluation, the relation be-tween teacher knowledge and student performance at the various level are analyzed and suggests certain parameter must be considered in the evaluation for exact teachers performance more accurately.
Keywords: Genetic algorithm; Teacher performance assessment; teacher knowledge transfer; student performance; knowledge transfer; Knowledge system Transfer,.
Challenges in implementing Video deduplication in cloud storage system
by Sujatha G, Jeberson Retna Raj
Abstract: Cloud storage is one of the common services provided by the cloud application system to its users It helps the users to store their digital data at remote storage disks and provide them permission to access the data from anywhere through the internet The public cloud storage service model provides the resources available to public users via the internet Any organization or individual user who owns or shares the public cloud storage can store their digital data in cloud storage When many users are sharing the cloud storage space, there is a possibility of duplicate data copies get stored in the cloud storage space Among various digital data, Video content will take a huge storage space The duplicate copies of Video stored in the cloud lead to huge wastage of storage space because the memory consumption of Video data is high This kind of problem can be solved with the
Keywords: Video deduplication; Digital data; Cloud services; Storage as a service; Big data; Frames Per Second; Compression; Volume; Cloud models.
Graph Classification System using Normalized Graph Convolutional Networks
by Naga Chandrika Gogulamudi, E. SREENIVASA REDDY
Abstract: Recent years have authenticated a dramatic increase in graph applications due to their advancements in their informative and social connectivity. In large scale networks, graph data contains huge information and it exhibits distinct characteristics. Usually, graph data may come with the nodes or edges that show the property of an entity, which can represent complex relationships between nodes. The complexity of the graph network data has imposed significant issues and challenges. Traditional graph convolutional networks cannot handle the problem of covariate shift in the neural networks during analyzing the patterns. This research presents an intelligence-based graph classification model for citation networks by using normalized graph convolution networks. This can handle the problem of covariate shift in the networks and provides greater regularization with a decrement in loss to a greater extent. This problem is vanquished for graph convolution network by utilizing the normalization constraint for the individual batch constructed as
Keywords: Citation Network System; Classification; Document Classification; Entity Classification; Graph Convolutional Networks.
Design of Energy and EDP Efficient 1-bit Full Subtractor Based Divider Circuits for Computing Systems
by Srinivasulu Gundala, M. Mahaboob Basha, Sibghatullah Khan
Abstract: In todays Very Large Scale Integration (VLSI) systems energy efficiency, power consumption per clock frequency is major concern in SoC designs. The two performance parameters namely, power and delay, which generally make a decision the energy metric of the system. In this paper, a 10T (Transistor) Full Subtractor is proposed and compared between various One Bit Full Subtractor (OBFS) circuits with fewer transistor count. From the results it is observed that, the proposed 10T OBFS circuit achieves savings of more than 48% in delay, 49% in energy consumption and 74% in Energy Delay Product (EDP) when compared with CMOS transistor scheme and 20T OBFS. Similarly, the 14T OBFS circuit achieves savings of more than 40% in power, energy consumption and EDP when compared with the 10T OBFS scheme and other designs. The proposed 10 transistors full subtractor based divider circuit is power, energy and EDP efficient.
Keywords: VLSI; OBFS; Divider; Power; Power Delay Product (PDP) and Energy Delay Product (EDP).
VLSI System Architecture Optimization for DLMS Adaptive Filter using PPG based Multiplier
by Premson Yohannan, Ramachandran Sakthivel
Abstract: A partial product generator based multiplier is used to reduce the complexity of multiplication operation. This multiplier, designed using decoders and and-or cells in earlier work are modified with a multiplexer to improve the speed and area of the architecture. When approximate multipliers are employed, the complexity can be further reduced and they can be used for highly data intensive processing applications like image restoration, pattern classification etc., where an approximate operation is enough in most of the cases . A Delayed LMS adaptive filter is designed, which itself contains an FIR filter, and is modified using multiplexer and approximate compressor type adder combination. For the FIR filter part alone, the percentage decrease in maximum path delay is 40 % and that in total area is 37%. For the DLMS adaptive filter these values are 17 and 47 respectively.
Keywords: Adaptive DLMS filter; AND-OR cell; approximate compressor adder; error computation; partial product generator; VLSI system architecture; weight updating.
Implementation and system analysis of load balancing algorithms for public cloud systems
by Resma K. S, Akash Hegde, G.S. Sharvani, Vijaya Shetty S.
Abstract: Cloud computing paradigm is on its peak in recent times. With the advancement of datacenter infrastructures organizations are incrementally depending on cloud computing for most of their operations. As this dependency on cloud computing is increasing load balancing in cloud environment is gaining significance. This paper is focusing on the load balancing in a real time public cloud environment. Load balancing being an important aspect of traffic management, detailed implementation and analysis of three load balancing algorithms, namely Round Robin, Least Connections and IP Hash, is carried out with respect to different scenarios and conducted stress tests. An active health check module is also implemented to identify the status of active and dormant servers. The observations are made and the matrices are tabulated with respect to different performance parameters of a datacenter. The parameters such as network throughput, bandwidth and CPU utilization are observed to have upgraded values.
Keywords: cloud computing; load balancing; traffic management; health check; traffic analysis; security groups.
Analysis of Text Classification Methods with Large Volume of Tweets using Deep Learning
by Hemavathi D, Simbarashe Herbert Chaputsira, Sujatha G, Sindhu S, Sornalakshmi K
Abstract: In day today life, large amount of data is generated by social media Analyzing the data and providing the opinions about data also increased in modern era Sentiment Analysis performs vibrant role for developing opinion mining systems Many research work has been carried out in finding the important features and provides the better text classification Compared to traditional feature extraction methods, deep learning based text feature extraction methods provides the accurate text classification This paper focused on an analysis of text classification using Radial Basis Function (RBF), Multilayer perceptron and Support Vector Machine and obtained the improved classification accuracy In our proposed work, an analysis has been done with the model contains best features that are extracted using Recurrent Neural Networks, Long Short Term Memory (LSTM) and auto encoder methods In total 50000 Tweets are collected from Twitter for extracting the best features related to social and political information The
Keywords: Text classification; Recurrent Neural Networks; Long Short Term Memory; Radial Basis Function (RBF); Multilayer perceptron; Support Vector Machine. Deep Learning System.