Hybrid ConvLSTM Model for Evaluating the Performance of SMEs in The Software Sector

Year 2024, Volume: 5 Issue: 1, 24 - 30, 29.06.2024

Anıl Utku

https://doi.org/10.46572/naturengs.1475626

Abstract

SME is a term used for businesses based on the number of employees, size, and turnover. SMEs form the basis of the economy and are indispensable organizations of business life worldwide. In this study, the ConvLSTM model was created to evaluate the financial performance of SMEs operating in the software sector in Turkey. The motivation of the study is to analyze the performance of SMEs operating in the software sector in Turkey. The study used data from the Small and Medium Enterprises Development of Türkiye for 2018-2022. ConvLSTM was compared with LR, LSTM, SVM, CNN, RF, and MLP. Experiments showed that ConvLSTM outperformed other models, performing above 0.8 R2 for all parameters.

Keywords

SMEs, CNN, Deep learning, Machine learning, LSTM, Software sector

References

• Utku, A., Sevinç, A., and Akcayol, M. A. (2023). KOBİ’lerin Ekonomiye Sağladıkları Katkının Tahmini İçin Derin Öğrenme Tabanlı Model. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 35(2), 865-874.
• Hoang, T. G., and Bui, M. L. (2023). Business intelligence and analytic (BIA) stage-of-practice in micro-, small-and medium-sized enterprises (MSMEs). Journal of Enterprise Information Management, 36(4), 1080-1104.
• Naab, R., and Bans-Akutey, A. (2021). Assessing the use of e-business strategies by SMEs in Ghana during the Covid-19 pandemic. Annals of Management and Organization Research, 2(3), 145-160.
• Surya, B., Menne, F., Sabhan, H., Suriani, S., Abubakar, H., and Idris, M. (2021). Economic growth, increasing productivity of SMEs, and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 20.
• Demestichas, K., & Daskalakis, E. (2020). Information and communication technology solutions for the circular economy. Sustainability, 12(18), 7272.
• Stray, V., and Moe, N. B. (2020). Understanding coordination in global software engineering: A mixed-methods study on the use of meetings and Slack. Journal of Systems and Software, 170, 110717.
• Haque, A. B., Bhushan, B., and Dhiman, G. (2022). Conceptualizing smart city applications: Requirements, architecture, security issues, and emerging trends. Expert Systems, 39(5), e12753.
• Golrizgashti, S., Hosseini, S., Zhu, Q., and Sarkis, J. (2023). Evaluating supply chain dynamics in the presence of product deletion. International Journal of Production Economics, 255, 108722.
• Mourtzis, D., Angelopoulos, J., and Panopoulos, N. (2022). A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0. Energies, 15(17), 6276.
• Alheet, A. F., and Hamdan, Y. (2020). Evaluating innovation-driven economic growth: a case of Jordan. Entrepreneurship and Sustainability Issues, 7(3), 1790.
• Zhu, Y., Zhou, L., Xie, C., Wang, G. J., and Nguyen, T. V. (2019). Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics, 211, 22-33.
• Belhadi, A., Kamble, S. S., Mani, V., Benkhati, I., and Touriki, F. E. (2021). An ensemble machine learning approach for forecasting credit risk of agricultural SMEs’ investments in agriculture 4.0 through supply chain finance. Annals of Operations Research, 1-29.
• Malakauskas, A., and Lakštutienė, A. (2021). Financial distress prediction for small and medium enterprises using machine learning techniques. Engineering Economics, 32(1), 4-14.
• Schalck, C., and Yankol-Schalck, M. (2021). Predicting French SME failures: new evidence from machine learning techniques. Applied Economics, 53(51), 5948-5963.
• Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K., & Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two-stage multi-objective feature selection. Decision Support Systems, 140, 113429.
• Hamal, S., and Senvar, Ö. (2021). Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs. Int. J. Comput. Intell. Syst., 14(1), 769-782.
• Hajduk, S. (2021). Multi-criteria analysis in the decision-making approach for the linear ordering of urban transport based on TOPSIS technique. Energies, 15(1), 274.
• Sharma, D., Sridhar, S., and Claudio, D. (2020). Comparison of AHP-TOPSIS and AHP-AHP methods in multi-criteria decision-making problems. International journal of industrial and systems engineering, 34(2), 203-223.
• Chen, C. H. (2020). A novel multi-criteria decision-making model for building material supplier selection based on entropy-AHP weighted TOPSIS. Entropy, 22(2), 259.
• Zhang, W., Cui, G., Wang, Y., Zheng, C., and Zhu, Q. (2023, July). A human comfort prediction method for indoor personnel based on time-series analysis. Building Simulation, 16(7), 1187-1201.
• Belete, D. M., and Huchaiah, M. D. (2022). Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results. International Journal of Computers and Applications, 44(9), 875-886.
• Rath, S., Tripathy, A., and Tripathy, A. R. (2020). Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes & metabolic syndrome: clinical research & reviews, 14(5), 1467-1474.
• Jebli, I., Belouadha, F. Z., Kabbaj, M. I., and Tilioua, A. (2021). Prediction of solar energy guided by pearson correlation using machine learning. Energy, 224, 120109.
• Dagnew, G., and Shekar, B. H. (2021). Ensemble learning‐based classification of microarray cancer data on tree‐based features. Cognitive Computation and Systems, 3(1), 48-60.
• Han, S., Kim, H., and Lee, Y. S. (2020). Double random forest. Machine Learning, 109, 1569-1586.
• Şenol, A., Canbay, Y., and Kaya, M. (2021). Trends in Outbreak Detection in Early Stage by Using Machine Learning Approaches. Bilişim Teknolojileri Dergisi, 14(4), 355-366.
• Yoshida, R., Takamori, M., Matsumoto, H., and Miura, K. (2023). Tropical support vector machines: Evaluations and extension to function spaces. Neural Networks, 157, 77-89.
• Dudzik, W., Nalepa, J., and Kawulok, M. (2021). Evolving data-adaptive support vector machines for binary classification. Knowledge-Based Systems, 227, 107221.
• Utku, A. (2023). Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries. Expert Systems with Applications, 231, 120769.
• Naskath, J., Sivakamasundari, G., and Begum, A. A. S. (2023). A study on different deep learning algorithms used in deep neural nets: MLP SOM and DBN. Wireless personal communications, 128(4), 2913-2936.
• Mohammed, A. J., Arif, M. H., and Ali, A. A. (2020). A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems. IAES International Journal of Artificial Intelligence, 9(4), 609.
• Utku, A. (2023). Deep Learning Based an Efficient Hybrid Model for Urban Traffic Prediction. Bilişim Teknolojileri Dergisi, 16(2), 107-117.
• Canbay, Y., Ismetoğlu, A., and Canbay, P. (2021). Covid-19 Hastaliğinin Teşhisinde Derin Öğrenme Ve Veri Mahremiyeti. Mühendislik Bilimleri ve Tasarım Dergisi, 9(2), 701-715.
• Behera, R. K., Jena, M., Rath, S. K., and Misra, S. (2021). Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Information Processing & Management, 58(1), 102435.
• Cho, M., Kim, C., Jung, K., and Jung, H. (2022). Water level prediction model applying a long short-term memory (lstm)–gated recurrent unit (gru) method for flood prediction. Water, 14(14), 2221.
• Kuncan, F., Kaya, Y., Yiner, Z., and Kaya, M. (2022). A new approach for physical human activity recognition from sensor signals based on motif patterns and long-short term memory. Biomedical Signal Processing and Control, 78, 103963.