International Journal of Intelligent Enterprise (4 papers in press)

Regular Issues

• A study on perceptions of students on intelligent virtual learning environment during and post COVID-19  
  by S. Santha Kumari, M. Nagalakshmi, Srinivasa Rao Angati 
  Abstract: Most nations implemented an emergency lockdown during the COVID-19 epidemic in 2020, which dramatically impacted all aspects of employment and social contact for everyone. During the pandemic, almost all physical classes are cancelled and started through virtual mode in all educational institutions. Along with other fields, education faced many difficulties due to this COVID-19. This paper’s key objective is to know various college students’ perceptions about studying through virtual mode. The learning and teaching communities valued students’ viewpoints, and the questions in the online survey were meant to aid in understanding students’ learning experiences and perceptions of the online delivery form. To know students’ opinions, the researcher conducted a small survey through a semi-structured questionnaire made with Google Forms. Students and most faculty were new to this virtual mode and needed some training. In this survey, the opinions are collected from 230 students and used statistical tools for analysis. Analytical papers investigate all questions and develop inferences from the outcomes. The literature has tried to understand students’ views of virtual learning and identify success factors.
  Keywords: post COVID-19; e-learning; electronic learning; internet; online platforms; perceptions; students; virtual learning; intelligent virtual learning environment.
  DOI: 10.1504/IJIE.2024.10061833
   
  
• Optimised hybrid CNN bi-LSTM model for stock price forecasting  
  by Deepti Patnaik, N. V. Jagannadha Rao, Brajabandhu Padhiari, Srikanta Patnaik 
  Abstract: Financial markets are considered as back bone of the country’s economy. This article focuses on the stock price forecasting using deep learning models. Here, a hybrid model, i.e., convolutional neural network, bidirectional long short-term memory network has been proposed and its parameters are optimised by self-adaptive multi-population elitist JAYA algorithm. Stock prices of more than 13 years of various challenging stock exchanges of the globe such as: Standard & Poor 500, NIFTY 50, Nikkei 225, Dow Jones are used here for analysis purposes. The performance parameters such as root mean square error, mean absolute percentage error and mean absolute error are used for analysing the model. The proposed hybrid model is also compared with state-of-art models and it is found that this proposed model out performs the existing models.
  Keywords: forecasting; convolutional neural network; bidirectional long short-term memory; LSTM; hybrid model; evolutionary computation; SAMPE Jaya algorithm; RMSE; MAE.
  DOI: 10.1504/IJIE.2024.10061912
   
  
• Comparison: Taguchi method and full factorial design in WACC  
  by Amir Ahmad Dar, Zameer Gulzar, Ramesh A. Babu, Layak Ali, G. Shreedevi 
  Abstract: To examine the effectiveness of two different experimental design techniques, Taguchi and full factorial design (FFD), in optimising the weighted average cost of capital (WACC). Several input variables, including total equity, total debt, tax rates, cost of debt, and cost of equity, have an impact on WACC. To set up trials and determine the optimal combination of these input elements for WACC, the Taguchi methodology was used. The Taguchi method is being applied for the first time to find the optimal combination for the cost of capital. The study uses the Taguchi L16 DOE, regression analysis, and the analysis of means (ANOM), all of which are carried out using the MINITAB 18 Software, to examine the effects of various input components. The experiment’s results show that the Taguchi method and the FFD produce comparable performance results.
  Keywords: Taguchi method; full factorial design; weighted average cost of capital; WACC; analysis of means; ANOM; regression.
  
  

Special Issue on: Digital Technologies for Enterprise Transformation

• Advancing sustainable e-waste management systems integrated with artificial intelligence in developing countries  
  by Varun Kumar, Om Ji Shukla 
  Abstract: Industry 5.0 represents a paradigm shift in manufacturing and industrial processes, fostering collaboration between humans, robots, and smart devices. This evolution leverages advanced technologies such as artificial intelligence to boost productivity. In the context of e-waste management in developing countries, this study addresses the various influencing factors as drivers for enhancing the efficiency. These drivers were refined through an inter-consistency test and categorised using Fuzzy-DEMATEL. Notably, public awareness and technological advancement emerged as the most influential factors in integrating AI for sustainable e-waste management. Among these drivers, training and empowerment was found to be the most effective in driving positive change, while government policy and regulation played a pivotal role as the primary causal driver. This research offers valuable insights that can empower policymakers and environmental agencies to facilitate the adoption of AI and promote cleaner and more efficient e-waste management in developing countries.
  Keywords: artificial intelligence; Cronbach’s alpha; DEMATEL; e-waste management; influencing factors; sensitivity analysis.
  DOI: 10.1504/IJIE.2024.10063448