COVID-19 has disrupted educational institutes across the world. Teachers and students are now forced to teach and study online for an unidentified period, which severely hampers the learning capacity as well the student’s intention toward entrepreneurship. This study compared the impact of traditional teaching and teaching through online management simulation games on student learning performance and further leads to entrepreneurial intention. To further understand the desirability of business simulation games, we used the technology acceptance model (TAM) and extended it by employing knowledge sharing, knowledge application, learnability, perceived pleasure, and self-efficacy as exogenous variables. For this purpose, time-lagged data were collected from 277 students enrolled in entrepreneurship courses in public sector universities. To deal with homogeneity and generalizability issues, students from different collaborative universities were asked to participate in the study. Structural equation modeling was employed for analysis, where the results depict that the students learning performance was enhanced after using simulation games compared to regular theoretical online lectures, which further encouraged them to be entrepreneurs. We also concluded that simulation games are novel and effective online teaching methodology for students during a time of crisis. The study concludes with its theoretical, practical implications, and directions for future researchers.

Businesses need to use sentiment analysis, powered by artificial intelligence and machine learning to forecast accurately whether or not consumers are satisfied with their offerings. This paper uses a deep learning model to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company with an online presence to detect customer sentiment from their reviews automatically. This research aims to present a suitable method for analyzing the users’ reviews of Amazon Echo and categorizing them into positive or negative thoughts. A dataset containing reviews of 3150 users has been used in this research work. Initially, a word cloud of positive and negative reviews was plotted, which gave a lot of insight from the text data. After that, a deep learning model using a multinomial naive Bayesian classifier was built and trained using 80% of the dataset. Then the remaining 20% of the dataset was used to test the model. The proposed model gives 93% accuracy. The proposed model has also been compared with four models used in the same domain, outperforming three.

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively.

Healthcare occupies a central role in sustainable societies and has an undeniable impact on the well‐being of individuals. However, over the years, various diseases have adversely affected the growth and sustainability of these societies. Among them, heart disease is escalating rapidly in both economically settled and undeveloped nations and leads to fatalities around the globe. To reduce the death ratio caused by this disease, there is a need for a framework to continuously monitor a patient’s heart status, essentially doing early detection and prediction of heart disease. This paper proposes a scalable Machine Learning (ML) and Internet of Things‐(IoT‐) based three‐layer architecture to store and process a large amount of clinical data continuously, which is needed for the early detection and monitoring of heart disease. Layer 1 of the proposed framework is used to collect data from IoT wearable/implanted smart sensor nodes, which includes various physiological measures that have significant impact on the deterioration of heart status. Layer 2 stores and processes the patient data on a local web server using various ML classification algorithms. Finally, Layer 3 is used to store the critical data of patients on the cloud. The doctor and other caregivers can access the patient health conditions via an android application, provide services to the patient, and inhibit him/her from further damage. Various performance evaluation measures such as accuracy, sensitivity, specificity, F1‐measure, MCC‐score, and ROC curve are used to check the efficiency of our proposed IoT‐based heart disease prediction framework. It is anticipated that this system will assist the healthcare sector and the doctors in diagnosing heart patients in the initial phases.

The desire of students towards adopting the mobile wallet technology can be affected by various factors. The exploration of these factors and the discussion of the future uses of this technology among university students are the targets of this study. The conceptual model employs the unified theory of acceptance and use of technology besides the self-efficacy construct in this research. Primary data was collected online by surveying 389 university students. The model was subsequently validated by employing the structural equation model. From the results obtained, the intention of students in the utilization of mobile wallet technology are largely affected by trust, self-efficacy, facilitating conditions (FC), hedonic motivation (HM), effort expectation (EE), performance expectation (PE), social influence (SI) and price value (PV). This research's findings highlight the most significant factors in determining the intention behind mobile wallet technology utilization. The developed model is an appropriate research model to explore mobile wallet technology among students. This model focuses on students from private universities, which may limit its universality. The results of this analysis can benefit multiple institutions that use mobile wallet technology as a part of their services, such as banks, financial organizations and mobile payment system suppliers. Factors influencing customers' intention behind adopting the latest technology-related services in developing countries are not sufficiently reported in the literature. This research paper can be considered the first to examine the intention behind the utilization of mobile wallets in developing countries.