Analysis of Stock Price Fluctuations Accuracy using a Cloud-Based Recurrent Neural Network’s Long Short-Term Memory Model

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 5

Year of Publication : 2023

Authors : P. Deivendran, C. Geetha, P. Suresh Babu, G. Malathi

10.14445/23488379/IJEEE-V10I5P103

How to Cite?

P. Deivendran, C. Geetha, P. Suresh Babu, G. Malathi, "Analysis of Stock Price Fluctuations Accuracy using a Cloud-Based Recurrent Neural Network’s Long Short-Term Memory Model," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 5, pp. 23-35, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I5P103

Abstract:

In the field of vision, applying accurate- time tracking technology in sequence images is of excellent use value to many fields, for example. It has broad prospects for development in alcohol and tobacco, passenger flow statistics, traffic surveillance, etc. In this paper, a Visual tracking simulation is carried out on corresponding detection, extraction, recognition and tracking of the moving target in an image sequence to obtain the moving parameters and trajectory. The tracked moving object performs further processing and analysis accordingly and provides a reliable database for the next more advanced task. Therefore, visual tracking plays an important role in the localization analysis of moving objects in the military, work, scientific research, intelligent transportation and other fields. The computer has undergone four development stages and made a qualitative leap from hardware to software. Stock price fluctuation represents current market trends and corporate development that can be used to determine whether to sell or acquire stocks. A stock market estimate is one of the most complex and vital duties because of the nonlinear or dynamic nature of the market. Therefore, according to the requirements of target tracking in video, a particle filter algorithm is selected to optimize the technology and is addressed for efficient outcomes through simulation. The news, blogs, sentiment analysis, and other media are connected to this relationship between supply and demand. Sentiment analysis stock market forecast focused on the stock market’s autonomous performance.

Keywords:

Video analysis, Video annotation, Feature vector, Hidden markov model, Simulation training, Computer vision.

References:

[1] Alice Zheng, and Jack Jin, “Using AI to Make Predictions on Stock Market,” 2017. [Google Scholar][Publisher Link]
[2] Ayush Jain et al., “Detection of Sarcasm through Tone Analysis on video and Audio files: A Comparative Study on AI Models Performance,” SSRG International Journal of Computer Science and Engineering, vol. 8, no. 12, pp. 1-5, 2021.
[CrossRef][Google Scholar][Publisher Link]
[3] P. Deivendran, and E. R. Naganathan, “Scalability Services in Cloud Computing using Eyeos,” Journal of computer Science, vol. 11, no. 1, pp. 254-261, 2015.
[CrossRef][Google Scholar][Publisher Link]
[4] P. Deivendran, and E. R. Naganathan, “Scalability in Dynamic Performance and Utilization Techniques in Scalable Cloud Computing,” Journal of Advanced Research in Dynamical and Control Systems, vol. 9, pp. 2975-2996, 2020.
[5] D. Dhinakaran, and P. M. Joe Prathap, “Protection of Data Privacy from Vulnerability using Two-Fish Technique with Apriori Algorithm in Data Mining,” The Journal of Supercomputing, vol. 78, pp. 17559-17593, 2022.
[CrossRef][Google Scholar][Publisher Link]
[6] P. Deivendran et al., “Scalability and Security Requirements for the Internet of Things Architecture,” Artificial Intelligence for Internet of Things, pp. 109-147, 2022.
[Google Scholar][Publisher Link]
[7] R. Surendiran et al., “A Systematic Review using Machine Learning Algorithms for Predicting Preterm Birth,” International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 46-59, 2022.
[CrossRef][Publisher Link]
[8] Mashael M. Alsulami, “Sentiment Analysis Model to Predict People’s Opinion of the Trimester System in Saudi Arabia,” International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 450-456, 2023.
[CrossRef][Publisher Link]
[9] Fransiscus, and Abba Suganda Girsang, “Sentiment Analysis of COVID-19 Public Activity Restriction (PPKM) Impact using BERT Method,” International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 281-288, 2022.
[CrossRef][Google Scholar][Publisher Link]
[10] Fu Rongrong, Wang Hong, and Zhao Wenbo, “Dynamic Driver Fatigue Detection using Hidden Markov Model in Real Driving Condition,” Expert Systems with Applications, vol. 63, pp. 397-411, 2016.
[CrossRef][Google Scholar][Publisher Link]
[11] C. Geetha et al., “An Efficient Technique for Scene Text Extraction from Videos,” International Journal of Engineering and Advanced Technology, vol. 9, no. 1, pp. 6510-6514, 2019.
[12] Yu Huang, Joan Llach, and Sitaram Bhagavathy, “Players and Ball Detection in Soccer Videos Based on Colour Segmentation and Shape Analysis,” Multimedia Content Analysis and Mining, pp. 416-425, 2007.
[CrossRef][Google Scholar][Publisher Link]
[13] Ishwar S. Jadhav, V. T. Gaikwad, and Gajanan U. Patil, “Human Identification using Face and Voice Recognition,” International Journal of Computer Science and Information Technologies, vol. 2, no. 3, pp. 1248-1252, 2011.
[Google Scholar][Publisher Link]
[14] S. Kaliappan et al., “Mechanical, DMA, and Fatigue Behavior of Vitis Vinifera Stalk Cellulose Bambusa Vulgaris Fiber Epoxy Composites,” Polymer Composites, vol. 44, no. 4, pp. 2115-2121, 2023.
[CrossRef][Google Scholar][Publisher Link]
[15] G. Srinivasan, R. Senthil Kumar, and S. Muthukaruppasamy, “Evaluation of Additional Power Loss Reduction in DG Integrated Optimal Distribution Network,” Control Engineering and Applied Informatics, vol. 24, no. 1, pp. 68-76, 2022.
[Google Scholar][Publisher Link]
[16] Jothiperumal Nandha Gopal, Natarajan Balasubramanian Muthuselvan, and Subburaj Muthukaruppasamy, “Model Predictive Controller–Based Quadratic Boost Converter for WECS applications,” International Transactions on Electrical Energy Systems, vol. 31, no. 12,  2021.
[CrossRef][Google Scholar][Publisher Link]
[17] Laxmi M Ramdas, M. Helen Santhi, and G. Malathi, “A Study on High-Rise RC Structure with Fluid Viscous Damper using Python,” Research on Engineering Structures and Materials, vol. 8, no. 2, pp. 359-370, 2022.
[CrossRef][Google Scholar][Publisher Link]
[18] Luigi Troiano, Elena Mejuto Villa, and Vincenzo Loia, “Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications,” IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3226-3234, 2018.
[CrossRef][Google Scholar][Publisher Link]
[19] Zhen Li, Tao Tang, and Chunhai Gao, “Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction,” Algorithms, vol. 12, no. 8, pp. 173, 2019.
[CrossRef][Google Scholar][Publisher Link]
[20] Wu Liu et al., “Deep Learning based Basketball Video Analysis for Intelligent Arena Application,” Multimedia Tools and Applications, vol. 76, no. 23, pp. 24983-25001, 2017.
[CrossRef][Google Scholar][Publisher Link]
[21] Negin Mirriahi et al., “Uncovering Student Learning Profiles with a Video Annotation Tool: Reflective Learning with and without Instructional Norms,” Educational Technology Research and Development, vol. 64, no. 6, pp. 1083-1106, 2016.
[CrossRef][Google Scholar][Publisher Link]
[22] M. Murphy, K. Curtis, and A. Mc Cloughen, “What is the Impact of Multidisciplinary Team Simulation Training on Team Performance and Efficiency of Patient Care? An Integrative Review,” Australasian Emergency Nursing Journal, vol. 19, no. 1, pp. 44-53, 2016.
[CrossRef][Google Scholar][Publisher Link]
[23] Paramita Ray, “Document Level Sentiment Analysis for Product Review using Dictionary Based Approach,” SSRG International Journal of Computer Science and Engineering, vol. 4, no. 6, pp. 24-29, 2017.
[CrossRef][Publisher Link]
[24] Inyoung Park et al., “Temperature Prediction using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network,” Atmosphere, vol. 10, no. 11, p. 718, 2019.
[CrossRef][Google Scholar][Publisher Link]
[25] Xi Zhang et al., “Improving Stock Market Prediction via Heterogeneous Information Fusion,” Knowledge-Based Systems, vol. 143, pp. 236-247, 2018.
[CrossRef][Google Scholar][Publisher Link]
[26] Qixiang Ye et al., “Exciting Event Detection in Broadcast Soccer Video with Mid-Level Description and Incremental Learning,” Proceedings of the 13th annual ACM international conference on Multimedia, pp. 455-458, 2015.
[CrossRef][Google Scholar][Publisher Link]
[27] Qing Li et al., “A Tensor-Based Information Framework for Predicting the Stock Market,” ACM Transactions on Information Systems, vol. 34, no. 2, 1-30, 2019.
[CrossRef][Google Scholar][Publisher Link]
[28] Yong Yeol Ahn, James P. Bagrow, and Sune Lehmann, “Link Communities Reveal Multiscale Complexity in Networks,” Nature, vol. 466, pp. 761-764, 2010.
[CrossRef][Google Scholar][Publisher Link]
[29] A. Rhemimet, S. Raghay, and O. Bencharef, “Short-Term Long Memory Recurrent Neural Network Architectures for Prediction of HIV-1 Protease Cleavage Sites,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 1, pp. 194-200, 2020.
[30] Qiang Song, Anqi Liu, and Steve Y. Yang, “Stock Portfolio Selection using Learning-to-Rank Algorithms with News Sentiment,” Neurocomputing, vol. 264, pp. 20-28, 2017.
[CrossRef][Google Scholar][Publisher Link]
[31] Xiaoqing Yu, and Shuying Sun, “HMM-DM: Identifying Differentially Methylated Regions using a Hidden Markov Model,” Statistical Applications in Genetics and Molecular Biology, vol. 15, no. 1, pp. 69-81, 2016.
[CrossRef][Google Scholar][Publisher Link]
[32] S. Selvakanmani et al., “Deep Learning Approach to Solve Image Retrieval Issues Associated with IOT Sensors,” Measurement: Sensors, vol. 24,  2022.
[CrossRef][Google Scholar][Publisher Link]
[33] Fuming Sun et al., “Social Video Annotation Combining Features with a Tri-Adaptation Approach,” Multimedia systems, vol. 22, pp. 413-422, 2016.
[CrossRef][Google Scholar][Publisher Link]