Journal of Artificial Intelligence and Big Data https://www.scipublications.com/journal/index.php/jaibd <p><strong><em>Journal of Artificial Intelligence and Big Data</em></strong> is an international and interdisciplinary scholarly open access journal on artificial intelligence. It publishes original research articles, reviews, communications, that offer substantial new insight into any field of study that involves artificial intelligence (AI) and big data, including machine and deep learning, knowledge reasoning and discovery, automated planning and scheduling, natural language processing and recognition, computer vision, robotics, big data, and artificial general intelligence.</p> en-US Journal of Artificial Intelligence and Big Data Universal Evaluation of SAP S/4 Hana ERP Cloud System https://www.scipublications.com/journal/index.php/jaibd/article/view/882 <p>Regardless of their traditional ERP Systems, it is essential for every business to acquire a universal advantage in the contemporary international market. When everything is considered, end users in these kinds of businesses have to deal with poorly designed interfaces and unusable technologies. Despite the claims of significant benefits from using S4 Hana cloud ERP software, the possibility of achieving maximum productivity is not fully utilized. One of the causes of this reality is the underfunding of ergonomic measures and the newest technologies. Through the design of S4 Hana cloud ERP software applications, we will demonstrate how important and highly recommended ergonomic research is in order to minimize the financial and human costs that enterprises are currently facing.</p> Venkata Pavan Kumar Juturi Copyright (c) 2024 Journal of Artificial Intelligence and Big Data 2024-02-17 2024-02-17 14 18 The use of contemporary Enterprise Resource Planning (ERP) technologies for digital transformation https://www.scipublications.com/journal/index.php/jaibd/article/view/881 <p>Our lives are becoming more and more digital, and this has an impact on how we work, study, communicate, and interact. Businesses are currently digitally altering their information systems, procedures, culture, and strategy. Existing businesses and economies are severely disrupted by the digital revolution. The Internet of Things, microservices, and mobile services are examples of IT systems with numerous, dispersed, and very small structures that are made possible by digitization. Utilizing the possibilities of cloud computing, mobile systems, big data and analytics, services computing, Internet of Things, collaborative networks, and decision support, numerous new business prospects have emerged throughout the years. The logical basis for robust and self-optimizing run-time environments for intelligent business services and adaptable distributed information systems with service-oriented enterprise architectures comes from biological metaphors of living, dynamic ecosystems. This has a significant effect on how digital services and products are designed from a value- and service-oriented perspective. The evolution of enterprise architectures and the shift from a closed-world modeling environment to a more flexible open-world composition establish the dynamic framework for highly distributed and adaptive systems, which are crucial for enabling the digital transformation. This study examines how enterprise architecture has changed over time, taking into account newly established, value-based relationships between digital business models, digital strategies, and enhanced enterprise architecture.</p> Hariprasad Mandava Copyright (c) 2024 Journal of Artificial Intelligence and Big Data 2024-02-19 2024-02-19 31 35 An Appraisal of Challenges in Developing Information Literacy Skills in the Colleges of Education of Ghana https://www.scipublications.com/journal/index.php/jaibd/article/view/878 <p>The purpose of this study was to examine the challenges faced by students of Colleges of Education (CoEs) in developing their Information Literacy skills. The study adopted the post-positivism paradigm. Descriptive survey research design used in this study Survey. The population for this study comprised all Level 200 students at Wiawso CoE, Enchi CoE, and Bia Lamplighter CoE in the Western North Region. Purposive, stratified, and convenience sampling techniques were used to select colleges of education and level 200 students. The three (3) colleges of education were stratified and purposively selected while 256 level 200 students were stratified and conveniently sampled. The study employed questionnaires to collect data from the sampled students. Questionnaires (open and closed-ended questions) focused on the challenges faced by the students in developing their Information Literacy (IL) skills. The quantitative data was captured, analysed, and presented in descriptive statistics such as percentages, and frequency tables, to determine the objective of the study. It is recommended that to improve digital literacy and academic pursuits, the college management should improve access to desktop computers and the Internet in the library and computer centre. It is also recommended that Management and librarians of the Colleges of Education ensure that students have access to these devices at the library and can use them to develop their IL skills and help them manage their references more effectively.</p> Martha Baidoo William Jones Copyright (c) 2024 Journal of Artificial Intelligence and Big Data 2024-02-18 2024-02-18 19 30 Stock Closing Price and Trend Prediction with LSTM-RNN https://www.scipublications.com/journal/index.php/jaibd/article/view/877 <p>The stock market is very volatile and hard to predict accurately due to the uncertainties affecting stock prices. However, investors and stock traders can only benefit from such models by making informed decisions about buying, holding, or investing in stocks. Also, financial institutions can use such models to manage risk and optimize their customers' investment portfolios. In this paper, we use the Long Short-Term Memory (LSTM-RNN) Recurrent Neural Networks (RNN) to predict the daily closing price of the Amazon Inc. stock (ticker symbol: AMZN). We study the influence of various hyperparameters in the model to see what factors the predictive power of the model. The root mean squared error (RMSE) on the training was 2.51 with a mean absolute percentage error (MAPE) of 1.84%.</p> Nathan Smith Vivek Varadharajan Dinesh Kalla Ganesh R Kumar Fnu Samaah Copyright (c) 2024 Journal of Artificial Intelligence and Big Data 2024-02-15 2024-02-15 1 13