March 28 ~ 29, 2025, Virtual Conference
Heidrich Vicci, College of Business Florida International University, USA
Artificial intelligence has taken a central role in various industries in the past decade as the importance of data has been at the forefront of all business decisions and policies. However, the increasing introduction of AI is proposed to alter entire project management enterprises as online platforms and applications have arisen, providing users with AI emotional intelligence, project management, and organizational tools. Bots are able to create reports, provide analysis, and facilitate headway by generating prioritized tasks and delegating to individuals through teamwork recommendation engines. However, the potential for AI to completely automate project management and Scrum Master tasks and remove job opportunities has yet to be comprehensively discussed. (Auth et al.2021)(Najdawi and Shaheen2021)(Josyula et al.2023).
Artificial Intelligence (AI), Scrum Master, Project Management (PM).
Chegrani Lamari, Guerti Mhania, Boudraa Bachir, Algeria
The purpose of this work is to generate units of language using in the speech synthesis of Arabic language based on concepts of schemes to generate syllables of sequence of Arabic language. The aim of this study is to develop a spoken communication aid system for the visually impaired in the Arab world. We can generate basic units; verbs, names and particles. We can also generate all speech in different levels (syllable sequence, word sequence and sentence or text sequence) depend on different generated schemes.
Text-to-speech; Arabic scheme; speech synthesis; concatenative synthesis; generated scheme; generation of Sequence.
Israa Alsiyat, School of Computing and Communications, Lancaster University, and College of Science, Northern Border University, UK
In this paper, I discuss the testing of the Arabic Metaphor Corpus (AMC) [1] using a newly designed automatic tools for sentiment classification for AMC based on semantic tags. The tool incorporates semantic emotional tags for sentiment classification. I evaluate the tool using standard methods, which are F-score, recall and precision. The method is to show the impact of Arabic online metaphors on sentiment through the newly designed tools. To the best of our knowledge, this is the first approach to conduct sentiment classification for Arabic metaphors using semantic tags to find the impact of metaphor.
Arabic metaphor, sentiment analysis, NLP , Arabic semantic tagger.
Morten Grundetjern, Per Arne Andersen, and Morten Goodwin, University of Agder, Grimstad, Norway
Understanding diverse demographic groups presents a significant challenge in market research. In this paper, we introduce a novel system that integrates large language models with genetic algorithms to create synthetic personas capable of generating feedback that approximates real-world human responses. Our experimental evaluation demonstrates that synthetic personas not only exhibit age-differentiated technology usage patterns consistent with documented trends but also benefit from genetic algorithm optimization, which improves response accuracy from 60.4% to 78.5% on training questions and from 62.6% to 68.8% on hidden questions—outperforming human estimators. Moreover, the optimized personas achieve a 51.1% better correspondence with actual income distributions compared to random profiles. This approach makes it possible to rapidly generate feedback without requiring participants, facilitates iterative follow-ups, and systematically enhances demographic representativeness.
Synthetic Personas Large Language Models Genetic Algorithms Demographic Modeling Survey Response Simulation.