Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI’s potential by generating human-like text through prompts. ChatGPT’s adaptability holds promise for reshaping medical practices, improving patient care, and enhancing interactions among healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical consultations, aids dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8: tools, devices and administration. Balancing AI’s role with human judgment remains a challenge. A systematic literature review using the PRISMA approach explored AI’s transformative potential in healthcare, highlighting ChatGPT’s versatile applications, limitations, motivation, and challenges. In conclusion, ChatGPT’s diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare. Additionally, this study serves as a guide, assisting students, academics, and researchers in the field of medicine and healthcare alike.

Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the target population. We used preferred reporting items for systematic reviews and meta-analysis guidelines for the review. Google Scholar, PubMed, Scopus and Web of Science were used to search for relevant published articles. The articles obtained were scrutinized by reading the abstracts and titles to determine their inclusion eligibility. Accordingly, articles with insufficient or irrelevant information were excluded from the screening. The search included studies published between 2013 and 2023 (including additional criteria). A total of 40 articles were eligible for review. The selected articles were further categorized in terms of the types of MoCap used, their application and the domain of the experiments. This review will serve as a guide for researchers and organizational management.

Flood disasters are a natural occurrence around the world, resulting in numerous casualties. It is vital to develop an accurate flood forecasting and prediction model in order to curb damages and limit the number of victims. Water resource allocation, management, planning, flood warning and forecasting, and flood damage mitigation all benefit from rain forecasting. Prior to recent decades’ worth of research, this domain demonstrated to be promising prospects in time series prediction tasks. Therefore, the main aim of this study is to build a forecasting model based on the exponential smoothing-long-short term memory (ES-LSTM) structure and recurrent neural networks (RNNs) for predicting hourly precipitation seasons; and classify the precipitation using an artificial neural network (ANN) model and decision tree (DT) algorithm. We employ the dataset from the Australian commonwealth office of meteorology named Historical Daily Weather dataset to test the effectiveness of the proposed model. The findings showed that the ES-LSTM and RNN had achieved 3.17 and 6.42 in terms of mean absolute percentage error (MAPE), respectively. Meanwhile, the ANN and DT models obtained a prediction accuracy rate of 96.65% and 84.0%, respectively. Finally, the outcomes revealed that ES-LSTM and ANN had achieved the best results compared to other models.

Natural language processing (NLP) is the art of investigating others’ positive and cooperative communication and rapprochement with others as well as the art of communicating and speaking with others. Furthermore, NLP techniques may substantially enhance most phases of the information-system lifecycle, facilitate access to information for users, and allow for new paradigms in the usage of information-system services. NLP also has an important role in designing the study, presenting two fields converging on one side and overlapping on the other, namely the field of the NAO-robot world and the field of education, technology, and progress. The selected articles classified the study into four categories: special needs, kindergartens, schools, and universities. Our study looked at accurate keyword research. They are artificial intelligence, learning and teaching, education, NAO robot, undergraduate students, and university. In two fields of twelve journals and citations on reliable/high-reputation scientific sites, 82 scientific articles were extracted. From the Scientific Journal Rankings (SJR) website, the study samples included twelve reliable/high-reputation scientific journals for the period from 2014 to 2023 from well-known scientific journals with a high impact factor. This study evaluated the effect of a systematic literature review of NAO educational robots on language programming. It aimed to be a platform and guide for researchers, interested persons, trainees, supervisors, students, and those interested in the fields of NAO robots and education. All studies recognized the superiority and progress of NAO robots in the educational field. They concluded by urging students to publish in highly influential journals with a high scientific impact within the two fields of study by focusing on the study-sample journals.

Recently, ChatGPT, an advanced AI language model, has transformed the tourism industry by redefining how travelers access information and experience destinations. This survey explores the multifaceted potential of ChatGPT in various sectors of the tourism domain. The study begins by addressing the ethical considerations related to AI and natural language processing, emphasizing the necessity of privacy and data security in ChatGPT interactions. It analyzes the recent research findings, demonstrating ChatGPT’s efficacy in translating languages for tourists and improving services. The technology’s impact on tourism education and research is explored, highlighting its disruptive effects, benefits, and challenges, particularly in academic contexts. The paper delves into ChatGPT’s influence on the hospitality and tourism sector, focusing on its role in customer interactions, backend operations, and research methodologies. Additionally, it examines ChatGPT’s implications for content creation, visitor motivations, cultural perceptions, and regional tourism management. These insights shed light on ChatGPT’s potential to enhance customer experiences, influence visitor behaviors, and bridge cultural gaps in diverse tourism contexts. Concluding with preliminary guidelines for ChatGPT adoption, this paper equips industry professionals with essential knowledge to leverage the technology effectively. By embracing ChatGPT, the tourism industry can provide travelers with more informed, personalized, and immersive experiences, thus enhancing overall satisfaction and engagement during their journeys.

Identifying the gender of a person and his age by way of speaking is considered a crucial task in computer vision. It is a very important and active research topic with many areas of application, such as identifying a person, trustworthiness, demographic analysis, safety and health knowledge, visual monitoring, and aging progress. Data matching is to identify the gender of the person and his age. Thus, the study touches on a review of many research papers from 2016 to 2022. At the heart of the topic, many systematic reviews of multimodal pedagogies in Age and Gender Estimation for Adaptive were undertaken. However, no current study of the theme concerns connected to multimodal pedagogies in Age and Gender Estimation for Adaptive Learning has been published. The multimodal pedagogies in four different databases within the keywords indicate the heart of the topic. A qualitative thematic analysis based on 48 articles found during the search revealed four common themes, such as multimodal engagement and speech with the Human-Robot Interaction life world. The study touches on the presentation of many major concepts, namely Age Estimation, Gender Estimation, Speaker Recognition, Speech recognition, Speaker Localization, and Speaker Gender Identification. According to specific criteria, they were presented to all studies. The essay compares these themes to the thematic findings of other review studies on the same topic such as multimodal age, gender estimation, and dataset used. The main objective of this paper is to provide a comprehensive analysis based on the surveyed region. The study provides a platform for professors, researchers, and students alike, and proposes directions for future research.