The COVID-19 surge has mostly affected people and wreaked havoc on multiple sectors of the global economy. This study uses artificial neural networks (ANN) to develop COVID-19 prediction models to minimize the perilous situation. With positive infection data, these hybrid artificial neural network models looked at COVID-19 cases in Andhra Pradesh, India. Then, COVID-19 data were divided into training and testing for simulation. The developed model that takes the previous 14 days into account outperforms the others, depending on the results. According to the developed ANN models for Andhra Pradesh districts, the prediction model that works well and yields positive results is the one that receives lower values of error metrics like MSE, RMSE, MAE, and MAPE and higher values of R2. The hybrid neural network model that considers the previous 14 days for prophecy has suggested anticipating daily positive suffering, notably in areas of Andhra Pradesh, as a result of the collected data. Linear regression, ARIMA, and LSTM have been used for model assessment. The proposed 14-day model statistically surpasses all metrics in RMSE, MAE, MAPE, and R2. This study showed that an ANN-based model can predict the COVID-19 outbreak as well as other epidemics.

This paper addresses the conceptual exploration of various issues that form an integral part of the ethical dimensions surrounding the use of artificial intelligence (AI) in the field of marketing. We critically review some of the main ethical challenges AI poses to humanity: privacy, data security, bias, transparency, and accountability. Therefore, we will discuss the delicate balance between exploiting the transformation potential of AI for personally designed marketing strategies and ethical imperatives to safeguard the rights of consumers to retain trust. Then, the present regulatory landscape responding to these ethical challenges will be assessed by building on effective regulation, like the GDPR, existing proposed legislative frameworks, and industry guidelines. To underscore the role of an ongoing dialogue between marketers, technologists, ethicists, and regulators for developing a responsible AI ecosystem in marketing. Further, strategic recommendations for implementing company-based ethical AI marketing practices. The further strong guidelines provide enhancement transparency to consumers and investment in research to remove biases from algorithms. It also underscores principal scopes of further research and development, focusing on the need for new-age solutions to guide the ethical quagmires that AI-led marketing throws up. We suggest that integration should be done proactively and collaboratively. Thus, if attention is paid more to ethics and multidisciplinary dialogue, only such hyperbolic promises can be realized by AI-driven marketing. There are promises through which AI-driven marketing will meet business goals and be huge at societies' values.

The current study aims to determine the role of artificial intelligence (AI) in developing the accounting system (AS) in Jordanian Islamic banks. Currently, Islamic banks in Jordan are included in the research population. Using a quantitative research approach, 128 workers of Islamic banks in Jordan were chosen as a sample for this study. The study used a survey questionnaire instrument that was created based on past relevant literature and studies to collect the required data. The results indicated that there is an influence of AI (big data, intelligent agents, expert systems and automation processes) on the development of the AS in Jordanian Islamic banks. Accordingly, the study recommends that to improve AS tasks and reduce associated costs, accountants and accounting companies should always increase their understanding of artificial intelligence.

Technology has significantly transformed knowledge, education, and access to information by introducing online learning platforms, interactive games, and virtual reality simulations in traditional classrooms, creating a dynamic, engaging, and inclusive learning environment. The ChatGBT project (a pre-developed transformer for training) is a remarkable achievement in artificial intelligence technology. It allows students tailored and efficient learning experiences by providing individual feedback and explanations. ChatGPT e-learning platform has been extensively studied for its adoption and acceptance, but there is a significant gap in research on its acceptability and use, highlighting the need for further exploration. The goal of this work is to bridge this disparity by introducing a comprehensive model that includes three basic elements: performance expectation, expected effort, and social impact. A total of 241 graduate students were surveyed and their data were analyzed using structural equation modeling techniques. The results indicate that “expectation of performance and expected effort” have the greatest impact and importance in determining students’ intentions to use learning platforms via ChatGPT, while social influence does not play an important role. This study enhances the current body of knowledge related to artificial intelligence and environmental sustainability, and provides important insights for professionals, policymakers, and producers of artificial intelligence products. These observations may provide guidance for creating and implementing artificial intelligence technologies to match consumers’ needs and preferences more effectively, while also taking into account broader environmental conditions.

The advent of technology has dramatically reshaped the ways in which we assimilate knowledge, teach, and access information. From online learning platforms to interactive educational games and virtual reality simulations, technology has transformed the traditional classroom into a vibrant, engaging, and inclusive educational landscape. A notable advancement in artificial intelligence technology is ChatGPT (Generative Pre-trained Transformer), which provides personalized and effective learning experiences by delivering customized feedback and explanations to students. Despite the considerable research on the adoption or acceptance of e-learning, there is a paucity of research on the acceptance and utilization of ChatGPT, highlighting the need for further investigation. This study aims to bridge this gap by proposing an integrated model that incorporates three key constructs: perceived learning value, perceived satisfaction, and personal innovativeness. A questionnaire survey was administered to 289 university students in the United Arab Emirates (UAE), and the data collected were analyzed using the partial least squares-structural equation modeling (PLS-SEM) approach. The results revealed that "perceived learning value, perceived satisfaction, and personal innovativeness" are the most influential and critical determinants of students' intentions to use learning platforms through ChatGPT. This research contributes to the existing body of literature on AI and environmental sustainability, providing invaluable insights for practitioners, policymakers, and AI product developers. These insights can guide the development and implementation of AI technologies to better align with users' needs and preferences, while also considering the broader environmental context.

Machine learning (ML) is implementing artificial intelligence (AI) research within medicine that has made dramatic progress in recent years. In addition to standard treatments, the role of complementary and alternative medicine should be mentioned. Traditional Thai medicine has received growing acceptance as a complementary approach to modern medicine by using local herbs. A vast amount of Thai herbal knowledge and information is freely available on the Internet. The reader must evaluate each website and decide to use trustworthy and appropriate information. This study aimed to acquire Thai herbal knowledge recorded in the Thai language system on the Internet by scraping websites using programming techniques. The knowledge was extracted with programming, and the types of Thai herbs were classified corresponding to target symptoms by the machine learning algorithm. The ML method organized the process when sufficient achievement was reached in order to give reliable and high accuracy results from the training data set. The validation of extracted knowledge was achieved by using the part-of-speech tag patterns analysis. This study showed that the programming and machine learning system was appropriate for obtaining and classifying Thai herbal medicines knowledge.