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LOW-RESOURCE LANGUAGE MODELS FOR MULTILINGUAL AI

Low-resource language models aim to extend the power of artificial intelligence to languages with limited digital data. While mainstream AI models like GPT and BERT perform exceptionally well in English and other high-resource languages, they struggle with underrepresented ones. This research focuses on developing multilingual AI systems that can understand, translate, and generate text across diverse languages with minimal training data. Techniques such as transfer learning, data augmentation, and cross-lingual embeddings are explored to overcome data scarcity. The study also emphasizes preserving linguistic diversity and promoting inclusivity in global AI applications. Building efficient low-resource models can empower communities, enhance accessibility, and reduce digital inequality. The work aims to create scalable models adaptable to new languages with minimal retraining. Ultimately, this research contributes to a more linguistically balanced and culturally aware AI ecosystem.

ARTIFICIAL INTELLIGENCE IN HEALTHCARE AND MEDICINE : ENHANCING THE EXPERT

Medical professionals will be able to perform more accurate diagnoses, develop more personalized treatment plans, and provide more efficient patient care with the integration of Artificial Intelligence (AI) into healthcare and medicine. This study explores how artificial intelligence technologies could complement rather than replace practitioners' expertise in healthcare by using machine learning, natural language processing, and predictive analytics.

A study will assess both the benefits and challenges of applying AI to medical imaging, disease prediction, robotic surgery, and clinical decision support. Additionally, data privacy concerns as well as collaboration between humans and artificial intelligence will be examined. In the research, case studies, surveys, and expert interviews are used to establish a framework for integrating AI into healthcare practices that enhance clinical expertise, improve patient outcomes, and promote patient safety and effectiveness.

E-COMMERCE SECURITY AND TRUST: BUILDING CUSTOMER CONFIDENCE IN ONLINE TRANSACTION

In spite of the rapid expansion of e-commerce, concerns about security and trust remain major barriers to its adoption by customers. The research explores the factors that contribute to customer confidence in online transactions, such as technological safeguards, regulatory frameworks, and trust-building mechanisms. Security aspects of e-commerce, including data privacy, authentication, fraud prevention, and secure payment systems, as well as psychological factors such as perceived risk, reputation, and transparency, are examined. The study aims to enhance online trustworthiness by integrating perspectives from information security, consumer behavior, and digital trust models.

As a methodological approach, the research will use surveys and case studies to examine consumer attitudes, behavioral patterns, and responses to security practices on leading e-commerce platforms. The results of this research are expected to offer insight for policymakers, businesses, and technology developers making a positive impact on e-commerce ecosystem security and fostering long-term customer loyalty.

EXPLORING THE INFLUENCE OF E-LEARNING AND TECHNOLOGY-BASED TRAINING METHODS ON EMPLOYEE LEARNING AND DEVELOPMENT

A proposed research project titled "Exploring the Influence of E-Learning and Technology-Based Training Methods on Employee Learning and Development" will investigate how digital learning platforms, virtual classrooms, mobile learning, and other technologies impact employee skill acquisition, knowledge retention, and professional development. Because of e-learning's scalability, flexibility, and cost-effectiveness, workplaces are rapidly transforming digitally.

In this study, employee engagement, learning outcomes, and career advancement will be compared between technology-based and traditional methods of training. Learning experiences will also be enhanced through factors such as personalization, interaction, and accessibility. This study integrates perspectives from organizational learning theory and adult learning principles to provide insight into best practices for implementing technology-based training. As a result of these findings, HR and training professionals will be able to design strategies that optimize employee development in the digital era.