Improve the Bi-LSTM Model of University Financial Information Management Platform Construction
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Abstract
Information Management Systems are vital tools for businesses and organizations to manage their data effectively, make informed decisions, ensure data security and compliance, and enhance operational efficiency. This paper presents a robust approach to information management through the utilization of the Optimized Probabilistic Bidirectional Long Short-Term Memory (OP_Bi-LSTM) model. The study developed into the architecture and application of Simulated Annealing (SA) for hyperparameter optimization, emphasizing the impact of fine-tuning on model performance. OP_Bi-LSTM, rooted in Bidirectional Long Short-Term Memory, exhibits superior sequential data processing capabilities, making it well-suited for a variety of information management tasks. The proposed OP_BI-LSTM hierarchical architecture further enhances its pattern recognition capabilities in the information management system. Results from extensive experimentation demonstrate the model's versatility and adaptability in various applications, such as sentiment analysis, fraud detection, and time-series forecasting. The proposed OP_BI-LSTM model performance analysis is evaluated for the consideration of the different customers and product data. The propsoed OP_Bi-LSTM model achieves the classification accuracy of 0.98. It is stated that OP_Bi-LSTM emerges as a powerful tool for information management, offering exceptional performance and adaptability. As the field of deep learning and information management continues to evolve, this model holds great promise for addressing complex data challenges and facilitating data-driven decision-making in a variety of industries.
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References
J.Kaur, T.S.Kochhar, S.Ganguli, S.S.Rajest: Evolution of management system certification: an overview. Innovations in Information and Communication Technology Series, (2021) 082-092.
A.Di Vaio, R.Palladino, A.Pezzi, D.E.Kalisz: The role of digital innovation in knowledge management systems: A systematic literature review. Journal of business research, 123(2021), 220-231.
L. F.Rahman, L.Alam, M.Marufuzzaman, U. R. Sumaila: Traceability of sustainability and safety in fishery supply chain management systems using radio frequency identification technology. Foods, 10(2021), 2265.
Q. K.Jahanger, J.Louis, C.Pestana, D.Trejo: Potential positive impacts of digitalization of construction-phase information management for project owners. Journal of Information Technology in Construction, 26(2021).
N. T. Nguyen: A study on satisfaction of users towards learning management system at International University–Vietnam National University HCMC. Asia Pacific Management Review, 26(2021), 186-196.
N. T. Nguyen: A study on satisfaction of users towards learning management system at International University–Vietnam National University HCMC. Asia Pacific Management Review, 26(2021), 186-196.
N.Yuvaraj, K.Praghash, R.A.Raja, T.Karthikeyan, An investigation of garbage disposal electric vehicles (GDEVs) integrated with deep neural networking (DNN) and intelligent transportation system (ITS) in smart city management system (SCMS). Wireless personal communications, 123(2022), 1733-1752.
C.Janiesch, P.Zschech, K.Heinrich: Machine learning and deep learning. Electronic Markets, 31(2021), 685-695.
M.Abbasi, A.Shahraki, A.Taherkordi: Deep learning for network traffic monitoring and analysis (NTMA): A survey. Computer Communications, 170(2021), 19-41.
L.Von Rueden, S.Mayer, K.Beckh, B.Georgiev, S.Giesselbach, R.Heese, ... & J.Schuecker: Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Transactions on Knowledge and Data Engineering, 35(2021), 614-633.
C.Shorten, T.M.Khoshgoftaar, B.Furht: Deep Learning applications for COVID-19. Journal of big Data, 8(2021), 1-54.
M.Zekić-Sušac, S.Mitrović, A.Has: Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities. International journal of information management, 58(2021) 102074.
M.Alshurideh, B.Al Kurdi, S.A.Salloum, I.Arpaci, M.Emran: Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments, 31(2023), 1214-1228.
H.Xie, Z.Qin, G.Y.Li, B.H.Juang: Deep learning enabled semantic communication systems. IEEE Transactions on Signal Processing, 69(2021) 2663-2675.
P.Rohini, S.Tripathi, C. M.Preeti, A.Renuka, J. L. A.Gonzales, D.Gangodkar: A study on the adoption of Wireless Communication in Big Data Analytics Using Neural Networks and Deep Learning. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 1071-1076). IEEE, 2022.
S.Zhong, K.Zhang, M.Bagheri, J. G.Burken, A.Gu, B.Li, ... & H.Zhang: Machine learning: new ideas and tools in environmental science and engineering. Environmental Science & Technology, 55(2021), 12741-12754.
J.Liu, J.Huang, Y.Zhou, X.Li, S.Ji, H.Xiong, D.Dou: From distributed machine learning to federated learning: A survey. Knowledge and Information Systems, 64(2022), 885-917.
Y.Luo, Y.Xiao, L.Cheng, G.Peng, D.Yao: Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities. ACM Computing Surveys (CSUR), 54(2021), 1-36.
I. H. Sarker: Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(2021) 160.
J.Willard, X.Jia, S.Xu, M.Steinbach, V.Kumar: Integrating scientific knowledge with machine learning for engineering and environmental systems. ACM Computing Surveys, 55(2022), 1-37.
D.Chen, P.Wawrzynski, Z. Lv: Cyber security in smart cities: a review of deep learning-based applications and case studies. Sustainable Cities and Society, 66(2021) 102655.