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Annals of Data Science

18-11-2024 | Original Article

Deep Enhancement in Supplychain Management with Adaptive Serial Cascaded Autoencoder with Long Short Term Memory and Multi-layered Perceptron Framework

Authors: Ashok Kumar Sarkar, Anupam Das

Published in: Annals of Data Science

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Abstract

Recognizing and reducing risk is a major part of Supply Chain Management (SCM). Several companies are invested in Supply Chain Risk Management (SCRM) and they have the knowledge about the procurement occupancies within their companies and take steps to ensure this potent source of strategic value. Additionally, these types of companies yield the highest returns with the lowest amount of financial risk. Moreover, reducing financial risk in the SCM network requires thoughtful analysis and a proactive strategy. Hence, this task aims to make a financial risk assessment in SCM with deep learning techniques based on big data. Financial risk-related big data is collected from the Kaggle database and utilized in the data transformation phase. The transformed data is employed for evaluating the financial risk with the support of an Adaptive Serial Cascaded Autoencoder with Long Short-Term Memory and Multi-Layered Perceptron (ASCALSMLP). Here, the parameters for the deep learning techniques like LSTM and MLP were tuned by the hybrid Sandpiper Galactic Swarm Optimization (SGSO) algorithm to enhance the efficacy of the offered approach. From the results analysis, the accuracy of the developed model is 91.12% better than DHOA, 92.5% more than COA, 93.75% improved than GSO, and 94.62% superior to SOA models. Therefore, the results from the developed approach demonstrate effective prediction of financial risks.

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Zhang Q, Li F (2022) Financial resilience and financial reliability for systemic risk assessment of electricity markets with high-penetration renewables. IEEE Trans Power Syst 37(3):2312–2321CrossRef

Shahbazi Z, Byun Y-C (2022) Machine learning-based analysis of cryptocurrency market financial risk management. IEEE Access 10:37848–37856CrossRef

Zhou H, Sun G, Fu S, Liu J, Zhou X, Zhou J (2019) A Big data mining approach of PSO-based BP neural network for financial risk management with IoT. IEEE Access 7:154035–154043CrossRef

Chan JY, Milanović JV, Delahunty A (2011) Risk-based assessment of financial losses due to voltage sag. IEEE Trans Power Deliv 26(2):492–500CrossRef

Lyu X, Zhao J (2019) Compressed sensing and its applications in risk assessment for internet supply chain finance under big data. IEEE Access 7:53182–53187CrossRef

Maia W et al (2021) Evaluation of operational risk in power substations and its rational reduction on the basis of multicriteria allocating resources. IEEE Access 9:149383–149397CrossRef

Kalapodas E, Thomson ME (2006) Credit risk assessment: a challenge for financial institutions. IMA J Manag Math 17(1):25–46

Wu DD, Olson DL, Luo C (2014) A Decision support approach for accounts receivable risk management. IEEE Trans Syst Man Cybern Syst 44(12):1624–1632CrossRef

Diao Y et al (2021) A novel environment-adaptive timed up and go test system for fall risk assessment with wearable inertial sensors. IEEE Sens J 21(16):18287–18297CrossRef

10.

Assef F, Steiner MT, Steiner Neto PJ, Franco DGB (2019) Classification algorithms in financial application: credit risk analysis on legal entities. IEEE Latin Am Trans 17(10):1733–1740CrossRef

11.

Dahlgren R, Liu C-C, Lawarree J (2003) Risk assessment in energy trading. IEEE Trans Power Syst 18(2):503–511CrossRef

12.

Zhang M, Zhang J, Ma R, Chen X (2019) Quantifying credit risk of supply chain finance: a Chinese automobile supply chain perspective. IEEE Access 7:144264–144279CrossRef

13.

Choi T-M (2018) A system of systems approach for global supply chain management in the big data era. IEEE Eng Manag Rev 46(1):91–97CrossRef

14.

Tao Q, Gu C, Wang Z, Rocchio J, Hu W, Yu X (2018) Big data driven agricultural products supply chain management: a trustworthy scheduling optimization approach. IEEE Access 6:49990–50002CrossRef

15.

Jiang W (2019) An intelligent supply chain information collaboration model based on internet of things and big data. IEEE Access 7:58324–58335CrossRef

16.

Olson DL, Shi Y, Shi Y (2007) Introduction to business data mining, vol 10. McGraw-Hill, New York

17.

Shi Y (2022) Advances in big data analytics. Adv Big Data Anal

18.

Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4:149–178CrossRef

19.

Shi Y, Tian Y, Kou G, Peng Y, Li J (2011) Optimization based data mining: theory and applications. Springer, BerlinCrossRef

20.

Cai X, Qian Y, Bai Q, Liu W (2020) Exploration on the financing risks of enterprise supply chain using Back Propagation neural network. J Comput Appl Math 367:112457CrossRef

21.

Bassiouni MM, Chakrabortty RK, Hussain OK, Rahman HF (2023) Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions. Expert Syst Appl 211:118604CrossRef

22.

Feng Q, Chen H, Jiang R (2021) Analysis of early warning of corporate financial risk via deep learning artificial neural network. Microprocessors Microsyst 87:104387CrossRef

23.

Zhang H, Shi Y, Yang X, Zhou R (2021) A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance. Res Int Bus Finance 58:101482CrossRef

24.

Yao G, Hu X, Wang G (2022) A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain. Expert Syst Appl 200:117002CrossRef

25.

Dang C, Wang F, Yang Z, Zhang H, Qian Y (2022) Evaluating and forecasting the risks of small to medium-sized enterprises in the supply chain finance market using blockchain technology and deep learning model. Oper Manag Res. https://doi.org/10.1007/s12063-021-00252-6CrossRef

26.

Zhang H, Shi Y, Tong J (2021) Online supply chain financial risk assessment based on improved random forest. J Data Inf Manag 3:41–48CrossRef

27.

Wu Y, Li X, Liu Q, Tong G (2021) The analysis of credit risks in agricultural supply chain finance assessment model based on genetic algorithm and backpropagation neural network. Comput Econ 60:1269–1292CrossRef

28.

Sajid S, Haleem A, Bahl S, Javaid M, Goyal T, Mittal M (2021) Data science applications for predictive maintenance and materials science in context to Industry 4.0. Mater Today Proc 45:4898–4905CrossRef

29.

Ramalli E, Dinelli T, Nobili A, Stagni A, Pernici B, Faravelli T (2023) Automatic validation and analysis of predictive models by means of big data and data science. Chem Eng J 454:140149CrossRef

30.

Saura JR, Ribeiro-Soriano D, Palacios-Marqués D (2022) Assessing behavioral data science privacy issues in government artificial intelligence deployment. Gov Inf Q 39(4):101679CrossRef

31.

Zeng C, Ma C, Wang K, Cui Z (2022) Parking occupancy prediction method based on multi factors and stacked GRU-LSTM. IEEE Access 10:47361–47370CrossRef

32.

Cheng Z, Zhang Y, Tang C (2022) Solving monocular sensors depth prediction using MLP-based architecture and multi-scale inverse attention. IEEE Sens J 22(16):16178–16189CrossRef

33.

Kaur A, Jain S, Goel S (2020) Sandpiper optimization algorithm: a novel approach for solving real-life engineering problems. Appl Intell 50:582–619CrossRef

34.

Muthiah-Nakarajan V, Noel MM (2016) Galactic swarm optimization: a new global optimization metaheuristic inspired by galactic motion. Appl Soft Comput 38:771–787CrossRef

35.

Yuan Z, Wang W, Wang H, Ashourian M (2020) Parameter identification of PEMFC based on convolutional neural network optimized by balanced deer hunting optimization algorithm. Energy Rep 6:1572–1580CrossRef

36.

Martha TR, Kerle N, van Westen CJ, Jetten V, Kumar KV (2011) Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Trans Geosci Remote Sens 49(12):4928–4943CrossRef

37.

Liu Y, Zhao C, Huang Y (2022) A combined model for multivariate time series forecasting based on MLP-feedforward attention-LSTM. IEEE Access 10:88644–88654CrossRef

Title: Deep Enhancement in Supplychain Management with Adaptive Serial Cascaded Autoencoder with Long Short Term Memory and Multi-layered Perceptron Framework
Authors: Ashok Kumar Sarkar
Anupam Das
Publication date: 18-11-2024
Publisher: Springer Berlin Heidelberg
Published in: Annals of Data Science
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-024-00576-7

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