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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Yenireddy, Ankireddy
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Computer Science & IT Electrical & Electronics Engineering

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Stock market index prediction based on market trend using LSTM Yenireddy, Ankireddy; Narayana, Marimganti Srinivasa; Bangaru Ganesh, Kalla Venkata; Kumar, Guvvaladinne Prasanna; Venkateswarlu, Madduri
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1601-1609

Abstract

The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learning techniques are developing quickly, and because of the machine’s improved computational power, complex stock price prediction algorithms can be developed. This paper presents stock market index prediction based on market trend using long short-term memory (LSTM). Using built-in application programmable interface (API), Yahoo Finance offers a simple method to programmatically retrieve any historical stock prices of an organization using the ticker name. The standard and poor’s 500 index (S&P 500 index) include the firms that have been taken into consideration here. Utilizing the selected input variable, single-layer and multi-layer LSTM models are implemented, and the measurement parameters of mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) are used to compare each performance. Nearly all of the real closing price’s curve and the prediction curve’s closing price for test data overlap. A potential stock investor may benefit significantly from such a prediction by using it to make well-informed choices that would increase his earnings.
An intelligent approach to design big data on e-commerce in cloud computing environment Syed, salma; Sundari, Nadimpalli Usha Deepa; Dogiparti, Satish Babu; Rani, Duggimpudi Mary Sharmila; Yenireddy, Ankireddy; Kumar, Narayana Srinivas; Sunkara, Rajeev; Narasimha Raju, Buddaraju Naga Venkata
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3439-3448

Abstract

Web resources extract useful knowledge by the process of web mining. Web server maintains the log files for analyzing them from behavior of customer and improves business as the challenging task for E-commerce companies. The processing and computing of big data was increased day by day by the demand of computer system’s ability. The emphasis on data was increased gradually by the rapid development of information technology. Various businesses are exploring effective data analysis methods, and this system proposes an intelligent approach to designing big data for e-commerce in a cloud computing environment. This paper aims to develop and implement the relevancy vector (RV) algorithm, an innovative page ranking algorithm based on Hadoop distributed file system (HDFS) map reduce. The research provides customers with a robust meta search tool that makes it easy for them to understand personalized search requirements and make purchases based on their preferences. The intelligent meta search system adverse events (IMSS-AE) tool and the RV page ranking algorithm were shown to be efficient and effective by a thorough experimental evaluation in terms of reduced response time, enhanced page freshness, high personalized relevance, and high hit rates.
Co-Authors Bangaru Ganesh, Kalla Venkata Dogiparti, Satish Babu Kumar, Guvvaladinne Prasanna Kumar, Narayana Srinivas Narasimha Raju, Buddaraju Naga Venkata Narayana, Marimganti Srinivasa Rani, Duggimpudi Mary Sharmila Sundari, Nadimpalli Usha Deepa Sunkara, Rajeev Syed, salma Venkateswarlu, Madduri