Our project seek to create an Al-powered tarot card reader that bridges the gap between traditional fortune-telling and modern technology. By leveraging a combination of 3D modeling, natural language processing, text-to-speech (TTS), and speech-to-text (STT) systems, the service will deliver an interactive and culturally sensitive experience in Thai and English. Users will input their queries through voice, which will be processed via STT, and receive engaging Al-generated tarot readings through TTS. Additionally, a 3D animated avatar will mimic a real-life fortune teller, adding a visual dimension to the experience. Hosted on a user-friendly website, this platform will redefine fortune-telling by blending tradition with innovation, making it both accessible and engaging for modern users.
ในปัจจุบัน AI Chatbots และ LLM ถูกนำมาใช้ในหลายธุรกิจ รวมถึงการให้คำแนะนำและการพยากรณ์ อย่างไรก็ตาม ในตลาดการพยากรณ์ออนไลน์ยังไม่มีระบบ AI ที่สามารถให้บริการพยากรณ์ไพ่ทาโรต์ได้อย่างมีประสิทธิภาพและให้ประสบการณ์ที่ใกล้เคียงกับนักพยากรณ์จริง จากการสังเกตพฤติกรรมของผู้ใช้สื่อโซเชียลในไทย พบว่ามีผู้ใช้จำนวนมากที่นิยมใช้ ChatGPT และ AI อื่น ๆ เพื่อขอคำพยากรณ์เกี่ยวกับอนาคตของตนเอง ซึ่งบ่งบอกถึงความสนใจในบริการลักษณะนี้ อย่างไรก็ตาม ยังไม่มีแพลตฟอร์มที่พัฒนาเพื่อจุดประสงค์ในการดูดวงโดยเฉพาะ ดังนั้น โครงการนี้จึงมีเป้าหมายในการพัฒนา AI ที่สามารถโต้ตอบกับผู้ใช้ได้อย่างสมจริง โดยใช้โมเดลภาษาและเทคโนโลยีที่ทันสมัย
คณะเทคโนโลยีสารสนเทศ
Cancer is one of the major health issues in Thailand, particularly as the country enters an aging society. The risk of chronic diseases among the elderly often results in limitations in treatment, making it difficult for most patients to achieve a complete recovery. This necessitates continuous care and the provision of accurate information and guidance about cancer. However, current health record systems for patients lack effective interconnectivity, which hinders data analysis and the development of patient care models. Additionally, incorrect information about cancer spread across social media can lead to misunderstandings among elderly patients. To address these issues, researchers have developed a chatbot system that utilizes Natural Language Processing (NLP) technology to understand human language and accurately respond to questions about elderly cancer patient care. The chatbot provides reliable and up-to-date information based on medical knowledge sourced from a database reviewed by healthcare professionals. Furthermore, a web application has been developed to record and analyze patient assessments according to medical standards, enabling healthcare providers to plan and develop appropriate treatment approaches in a better way. This system also facilitates data sharing and connectivity across hospital systems, allowing information to be used to enhance the precision and modernity of treatment approaches. In addition, the chatbot acts as an assistant, providing information and guidance to patients, reducing the workload of healthcare staff in answering questions and encouraging patients to take a more active role in managing their own health.
คณะวิทยาศาสตร์
Cancer remains a major global health challenge as the second-leading cause of human death worldwide. The traditional treatments for cancer beyond surgical resection include radiation and chemotherapy; however, these therapies can cause serious adverse side effects due to their high killing potency but low tumor selectivity. The FDA approved monoclonal antibodies (mAbs) that target TIGIT/PVR (T-cell immunoglobulin and ITIM domain/poliovirus receptor) which is an emerging immune checkpoint molecules has been developed; however, the clinical translation of immune checkpoint inhibitors based on antibodies is hampered due to immunogenicity, immunological-related side effects, and high costs, even though these mAbs show promising therapeutic efficacy in clinical trials. To overcome these bottlenecks, small-molecule inhibitors may offer advantages such as better oral bioavailability and tumor penetration compared to mAbs due to their smaller size. Here, we performed structure-based virtual screening of FDA-approved drug repertoires. The 100 screened candidates were further narrowed down to 10 compounds using molecular docking, with binding affinities ranging from -9.152 to -7.643 kcal/mol. These compounds were subsequently evaluated for their pharmacokinetic properties using ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis, which demonstrated favorable drug-like characteristics. The lead compounds will be further analyzed for conformational changes and binding stability against TIGIT through molecular dynamics (MD) simulations to ensure that no significant conformational changes occur in the protein structure. Collectively, this study represents the potential of computational methods and drug repurposing as effective strategies for drug discovery, facilitating the accelerated development of novel cancer treatments.
คณะศิลปศาสตร์
Layla, the hotel robot, is responsible for carrying guests’ luggage and guiding them to their accommodations. It is equipped with an internal map of the hotel, allowing it to navigate various locations efficiently. Additionally, it features an AI-powered system that enables interactive conversations in three major languages: Thai, English, and Chinese.