Thyroid hormones control crucial physiological activities, such as metabolism, oxidative stress, erythropoiesis, thermoregulation, and organ development. Hormonal imbalances may cause serious conditions like cognitive impairment, depression, and nervous system damage. Traditional diagnostic techniques, based on hormone level measurements (TSH, T3, FT4, T4, and FTI), are usually lengthy and laborious. This study uses machine learning (ML) algorithms and feature selection based on GA to improve the accuracy and efficiency of diagnosing thyroid disorders using the UCI thyroid dataset. Five ML algorithms-LR, RF, SVM, AB, and DT- were tested using two paradigms: (1) default classifiers and (2) hybrid GA-ML models- GA-RF, GA-LR, GA-SVM, GA-DT, and GA-AB. The data pre-processed included handling missing values, feature scaling, and correlation analysis. In this case, the performance metrics used for model evaluation are accuracy, F1 Score, sensitivity, specificity, precision, and Cohen’s Kappa with 80% of the dataset to train the model and the rest 20% used to test it. Among the non-hybrid models, RF achieved the highest accuracy, which was 93.93%. The hybrid GA-RF model outperformed all others, achieving a remarkable accuracy of 97.21%, along with superior metrics across all the evaluated parameters. These findings highlight the diagnostic potential of the GA-RF model in providing faster, more accurate, and reliable thyroid disorder detection. The research illustrated the potential of the hybrid GA-ML approaches to improving the clinical diagnostic process while proposing a strong and scalable approach towards thyroid disorder identification.

Artificial intelligence (AI) is transforming the landscape of education by revolutionizing student engagement. Through personalized learning algorithms, AI analyzes individual student data to tailor educational content, keeping students engaged with material that suits their learning preferences and abilities. AI-driven gamification elements inject fun and motivation into the learning process, incentivizing students to progress. Analyzing engagement metrics with AI enables educators to identify struggling students early on and intervene effectively to keep them engaged. So, this research will investigate the impact of artificial intelligence on student engagement through bibliometric analysis. For the literature database, the Scopus database was examined, and the researcher got 300 research papers in total. After refining and limiting the years from 2014 to 2024, researchers selected 80 papers for the final examination. The selected papers are open-access and in the final stage. A few of the drivers of artificial intelligence on student engagement are also elaborated in this chapter.

This chapter explores financial mechanisms available for green tourism project development, focusing on innovative approaches that align with environmental sustainability and economic viability. This study also aims to identify effective strategies to bridge the financial gap, facilitating investments in sustainable tourism infrastructure and practices. The research utilizes qualitative analysis of case studies on successful green tourism projects with quantitative data on financing trends in the tourism sector. The findings are expected to highlight the most effective financial mechanisms that promote green tourism projects. The chapter concludes that innovative financing mechanisms are essential to the future of sustainable tourism. This study offers a unique contribution to the existing literature by focusing on financial strategies specifically designed for green tourism projects.

In an era where environmental consciousness is paramount, building sustainable brands has become a critical goal for businesses worldwide. This study explores the synergistic effects of influencer marketing, brand evangelism, and data-driven strategies on sustainable brand development. By leveraging the authenticity of influencers and the passion of brand evangelists, companies can drive deeper consumer engagement and loyalty. Advanced data analytics provide actionable insights, optimizing marketing efforts to align with sustainability goals. This comprehensive approach uniquely examines the interconnectedness of these elements, offering new insights and practical guidance for businesses. The findings reveal significant practical implications, demonstrating how integrated strategies can enhance brand reputation and drive long-term success. Preliminary data suggests that genuine influencers and dedicated brand evangelists significantly boost consumer trust and engagement, while data analytics fine-tune sustainability messages.