Authors - Rowan Cowper, Grant Oosterwyk, Jean-Paul Van Belle Abstract - The rapid and widespread growth of AI use has brought about a number of important areas for research. This paper aims to examine the human factors that impact AI use: whether demographic attributes, trust in AI, and perceptions about AI influence whether someone will use AI or not. A survey among South African industry and academic respondents was used. The key findings of this study include that age and education have a significant impact on trust in AI. Domain knowledge and education levels were significant indicators of perception of AI, with higher levels of domain knowledge and education leading to lower perceptions of AI. Both AI trust and perception were found to have a significant positive impact on whether someone made use of AI or not. These findings may inform decision makers on targeted interventions, such as education, to increase the use of AI in industry and academic contexts. Hopefully further academic research will also validate our findings in other research contexts, such as India and/or different population segments.
Authors - Mihlali Mqoqi, Marita Turpin, Jean-Paul Van Belle Abstract - The manufacturing industry is undergoing significant changes due to the emergence of artificial intelligence (AI) technologies. This transition has implications, particularly with regards to the skills necessary to adopt and leverage these technologies effectively. This paper addresses the issue of the expanding gap between the skill set in the current workforce and those required for the future of work. A systematic literature review was conducted to investigate these skills, which resulted in the selection of 28 studies out of 216 initially identified that offered insights into the benefits and applications of AI in manufacturing. The findings show how AI has altered manufacturing processes through predictive maintenance, quality assurance, and product design. Further, there is a need for targeted upskilling and reskilling programs to bridge the current skill gap and equip the workforce to meet the changing demands of the industry. Initiatives that could be implemented for successful skills development are also discussed.
Authors - Anupama Pandey, Anubha Dwivedi, Rashmy Moray, Vivek Divekar, Shikha Jain, Sridevi Chennamsetti Abstract - The objective of the research is to identify the factors that affects the adoption of digital currencies by the Generation Z and the millennials, utilizing UTAUT model. It highlights the role of performance expectancy, effort expectancy, social influence, and facilitating conditions on the use of digital currency. A designed questionnaire was used to collect the primary data from Gen Z and millennials. Statistical techniques SmartPLS was used to analyse the correlation and impact of the various UTAUT constructs on the intent to use digital currency. Results highlight that performance expectancy and user influence have the significant influence on the adoption of innovative solutions. The paper also describes issues concerning the digital infrastructure and various regulations. Prospective strategies to increase the acceptability of the digital currency with the young people are advanced to articulate the perceived usefulness and to make the use of digital currencies easier.
Authors - G.G. Rajput, Sumitra M Mudda Abstract - This paper presents abnormality detection from segmentation techniques for leg fracture segmentation from animal x-ray images. Gaussian filtering is used to remove the noise from the x-ray images. fracture is segmented from x-ray image by performing thresholding segmentation operations. Experiments are performed on clinical data set to present the severity of the fracture in images for threshold segmentation methods studied. Extract the Features from segmented images using GLCM techniques. SVM algorithm is use for classify the given animal x-ray image is fractured or not. Using thresholding segmentation techniques, fractures are separated from x-ray images. Utilizing GLCM techniques, extract the features from segmented images. The SVM method is used to determine whether or not the provided animal x-ray image is broken.
Authors - Rakhi Bharadwaj, Nikhil Patil, Riddhi Patel, Raj Pagar, Suchita Padhye Abstract - Integrated AI within the context of mental health has continued to be an emergent area of interest as more and more mental health applications in mobile devices, include chatbots. Such chatbots are the focus of this paper, and their design based on NLP, machine learning, and sentiment analysis is illustrated in order to help people who suffer from anxiety and depression providing them with individual therapeutic support. Despite these applications equip users with means of tracking moods, sharing concerns and getting ideas about mental health, they are not a license to practice. The study also responds to fundamental questions about their usability, usefulness, privacy, security, and protection of data. Because the need for mental health services continues to increase in parallel with technological progress, one has to maintain the harmony between innovation and ethics. Discussing the current mental health platforms, their technological model, and case studies of their usage, this paper also emphasizes the related risks – the misuse of AI in mental health treatment. Leaving aside the beautiful language and the fascinating discussions with the machines, the goal of the study is to provide theoretical perspectives as well as pragmatic suggestions for way AI can be used to improve mental health care responsibly.
Authors - Anushka Ashok Pote, Laukik Nitin Marathe, Suvarna Abhijit Patil, Sneha Kanawade, Deepali Samir Hajare, Varsha Pandagre, Arti Singh, Rasika Kachore Abstract - Due to the increase in vulnerability of different types of diseases, the use of artificial intelligence is seen to be rapidly increasing in the healthcare industry for creating systems that will provide diagnosis, treatment, and patient care. One of the major challenges that is faced in recent days is that most of the traditional healthcare systems are not transparent and comprehensible. This review explores the importance of Explainable Artificial Intelligence in order to make advancements in precision medicine, focusing on personalized treatment and disease prediction. Despite being powerful, traditional AI models function as "black boxes," which do not offer any insights into how decisions are made. This limits their application in critical sectors like healthcare where trust and accountability are crucial. Explainable AI makes systems more transparent and interpretable allowing healthcare professionals to understand and trust AI-driven insights. It exhibits significant enhancements in diagnostic accuracy and treatment personalization across various areas like oncology, cardiovascular disease, neurology, etc. The review performs comparisons between explainability driven models and traditional models. It reveals that XAI-based models offer better accuracy along with precision. It provides interpretable decision-making which makes them more suitable for clinical applications. Even though these systems exhibit certain challenges like computational complexity and need for standardized evaluation metrics. This paper highlights transformative potential of XAI in healthcare industry by fostering more ethical, transparent and patient-centered solutions. It is poised to revolutionize precision medicine by improving patient outcomes and exhibiting significant contributions in the healthcare industry.
