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Enhancing Multilingual Sentence Representation Learning For The Job Recruitment Domain, Napat Laosaengpha
Enhancing Multilingual Sentence Representation Learning For The Job Recruitment Domain, Napat Laosaengpha
Chulalongkorn University Theses and Dissertations (Chula ETD)
With the advancement in natural language processing (NLP), there has been significant development in multilingual pretraining sentence encoder. Typically, these pretraining models are trained on large-scale datasets that consist of general text data from various sources such as Wikipedia. However, the general proposed models aren't enough to understand contexts in such a domain-specific, especially in the job recruitment domain. It is due to its niche nature and the lack of readily available related information. To enhance the existing multilingual pretraining sentence encoder and mitigate the aforementioned problems, we first propose multi-task dual-encoder framework to improve the sentence encoder for general-purpose …
Benchmarking And Enhancing Generalization In Multilingual Speech Emotion Recognition, Mohamed Osman Ismael
Benchmarking And Enhancing Generalization In Multilingual Speech Emotion Recognition, Mohamed Osman Ismael
Theses and Dissertations
Speech Emotion Recognition (SER) is pivotal in advancing human-computer interaction by enabling machines to understand and respond to human emotions. Despite significant progress with self-supervised learning models, SER systems often struggle with generalization across diverse languages and unseen data distributions, limiting their real-world applicability. This thesis addresses these challenges by first introducing a large-scale benchmark to evaluate the robustness and adaptability of state-of-the-art SER models in both in-domain and out-of-domain settings. The benchmark includes a diverse set of multilingual datasets, emphasizing cross-lingual and out-of-domain evaluations to assess model generalization. Surprisingly, we find that the Whisper model, originally designed for automatic …
Inclusive Integration Of Refugees And Migrants In Djibouti's National Healthcare System: Current Situation And Prospects For Improvement, Farhan Houssein Ali, Njah Mansour, Kenza Hassouni
Inclusive Integration Of Refugees And Migrants In Djibouti's National Healthcare System: Current Situation And Prospects For Improvement, Farhan Houssein Ali, Njah Mansour, Kenza Hassouni
Health Sciences
Cet examen narratif vise à décrire et synthétiser les données actuelles sur la situation des migrants et des réfugiés à Djibouti, ainsi que les progrès de la stratégie d'intégration inclusive mise en œuvre par le gouvernement djiboutien. Il s'appuie sur diverses sources, notamment la littérature grise et les publications scientifiques. Les résultats indiquent que le nombre de migrants et de réfugiés est en augmentation constante depuis 2019, avec un état de santé vulnérable et des risques sanitaires alarmants. Il est urgent d'accélérer la mise en œuvre de la stratégie tout en l'adaptant aux données actuelles et d'établir un système fiable …
Formula 1 Commentary Generator Using Generative Artificial Intelligence, Hiral Moliya
Formula 1 Commentary Generator Using Generative Artificial Intelligence, Hiral Moliya
Master's Projects
This project aims to create a high-quality commentary generation system utilizing cutting-edge Generative AI technologies, with a particular focus on the T5 transformer-based text-to-text transfer transformer (T5). The primary goal is to create a fully autonomous and contextually aware commentary system that will be able to provide consistent and insightful commentary on dynamic events that reflect the level of detail normally associated with human commentary. Upon giving the input as a text input of the race events the model using large language models that are trained on a large range of datasets creates text-based commentary. The system in order to …
Advancing Phishing Protection: Employing Sophisticated Methods For Precise Url Evaluation, Abbhinav Reddie Nomuiia
Advancing Phishing Protection: Employing Sophisticated Methods For Precise Url Evaluation, Abbhinav Reddie Nomuiia
Master's Projects
In short, the incidence of phishing - the illegal act of people pretending to be well-known companies to secure personal information - has skyrocketed in the past few years. In 2022 alone, 300,000 consumers in the United States were captured by scammers using phishing techniques, losing in all over $50 million. In the span of two weeks, over 510 million attempts occurred in a variety of sectors, particularly instant messaging platforms, package delivery businesses, and digital currency trading. Since most businesses have recognized that they are prone to these exposure cases, there has been a sixty percent increase in businesses …
Using Ontological Methods To Compare Cybersecurity Maturity Model Certification 2.0 And Cobit 19, Aaron Marshall Ramey
Using Ontological Methods To Compare Cybersecurity Maturity Model Certification 2.0 And Cobit 19, Aaron Marshall Ramey
CCAC Theses and Dissertations
Cybersecurity frameworks developed by a variety of organizations and implemented by a much larger collection of organizations differ in their focus and application. Whether designed by a private or government organization, the primary goal is to provide a framework to assess and reduce risk. The Department of Defense (DoD) has recently implemented the second version of the Cybersecurity Maturity Model Certification (CMMC 2.0). In some situations, compliance with CMMC 2.0 has already become mandatory for the Defense Industrial Base (DIB). Compliance will soon be required for all Large Businesses (LB) and Small Businesses (SB) within the DIB. While COBIT 19 …
Word Prediction Using Dynamic Skip Connections Along With Arabert And Lstm In Arabic Language, Ahad Almalki, Faris Kateb, Rayan Mosli
Word Prediction Using Dynamic Skip Connections Along With Arabert And Lstm In Arabic Language, Ahad Almalki, Faris Kateb, Rayan Mosli
ASEAN Journal on Science and Technology for Development
Natural Language Generation (NLG) plays a crucial role in modern digital tools, including chatbots, virtual support, content suggestions, and tailored marketing, making bots more responsive and reducing the need for human staff. While there's much research on NLG for languages like English, languages like Arabic, Urdu, and Chinese still face challenges. This study examines Arabic NLG's unique aspects, dialects, and word variations. With around 420 million Arabic speakers globally, it's crucial to advance NLG for this language. We compared three models: Long Short-Term Memory (LSTM), a mix of Bidirectional Encoder Representations from Transformers (BERT) and LSTM, and a version that …
Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy
Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy
Publications
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …
Thai-English Supported Automatic Speech Recognition For Endoscopic Reporting, Arpanant Saeng-Xuto
Thai-English Supported Automatic Speech Recognition For Endoscopic Reporting, Arpanant Saeng-Xuto
Chulalongkorn University Theses and Dissertations (Chula ETD)
This thesis presents the automatic speech recognition (ASR) system for endoscopic reporting that supports Thai-English code-switching. During endoscopic procedures, gastroenterologists are required to use both hands to handle instruments, thereby complicating the real-time documentation of abnormal findings. While recent advances in speech recognition offer promising solutions, existing models face difficulties with Thai-English code-switching and tend to overfit when fine-tuned on limited datasets. To overcome these limitations, we propose an ASR model enhanced with the Mixture of Experts (MoE) technique to improve transcription accuracy. Furthermore, the Named Entity Recognition (NER) model extracts gastrointestinal (GI) terminology from the transcriptions and classifies its …
Enhancing Large Language Models For Thai Legal Chatbots, Supachoke Hanwiboonwat
Enhancing Large Language Models For Thai Legal Chatbots, Supachoke Hanwiboonwat
Chulalongkorn University Theses and Dissertations (Chula ETD)
Currently, developing a Thai legal question-answering system for the general public is highly challenging due to the complex, difficult-to-understand language and the extensive content of legal codes. This research proposes a Thai legal question-answering system designed for the public, aiming to establish best practices for developing effective legal QA systems. To improve performance, we created our own Thai legal QA dataset and incorporated data from various sources. We conducted comparative experiments to identify the most suitable language model for Thai legal contexts, and fine-tuned the models with diverse datasets for enhanced capabilities in legal QA and legal examinations. Additionally, we …
Evacuation Plan Assessment Using Agent-Based Simulation: A Case Of Mix-Use Building, Peerada Tangprasert
Evacuation Plan Assessment Using Agent-Based Simulation: A Case Of Mix-Use Building, Peerada Tangprasert
Chulalongkorn University Theses and Dissertations (Chula ETD)
This research investigates the effectiveness of fire evacuation plan in complex urban structures by developing an agent-based simulation (ABS) model tailored to a high-rise, mixed-use case building located in Bangkok, Thailand. As urban populations grow and multi-functional buildings become more prevalent, ensuring efficient evacuation during fire emergencies has become increasingly critical. The selected case building features a combination of commercial, retail, and public transit access points, making it representative of modern urban infrastructure with high pedestrian traffic and structural complexity. The simulation model incorporates key factors influencing evacuation performance, including walking speed, age demographics, and crowd density. Empirical data such …
Explainable Automated Inconsistency Detection In Biomedical And Health Literature, Prajwol Lamichhane
Explainable Automated Inconsistency Detection In Biomedical And Health Literature, Prajwol Lamichhane
UNF Graduate Theses and Dissertations
Given the exponential growth of scientific information online, researchers often face the daunting task of detecting contradictory statements on crucial health topics. This work develops a comprehensive pipeline for automated contradiction detection that integrates an Information Retrieval (IR) system, machine learning classifiers, and Explainable AI (XAI). The Information Retrieval system is tailored for biomedical data and comprises a datastore, syntactic, and semantic components. Users can input queries, initiating a pipeline that identifies top documents through syntactic analysis and refines results via semantic examination for relevant research claims. Employing a diverse range of Large Language Models such as pre-trained Distil-BERT, BioBERT, …
Integrating Generative Artificial Intelligence With Systems Architecting Diagram Creation: Advancement, Challenges, Opportunities And Future Perspectives, Cansu Yalim, Holly H. Handley
Integrating Generative Artificial Intelligence With Systems Architecting Diagram Creation: Advancement, Challenges, Opportunities And Future Perspectives, Cansu Yalim, Holly H. Handley
Engineering Management & Systems Engineering Faculty Publications
Generative AI (GenAI) serves as a powerful tool that can create a wide range of content, including but not limited to text, speech, images, code, videos, and 3D models. ChatGPT stands out as a particularly appealing Generative Pretrained Transformer (GPT) model that offers supplementary capabilities through GPTs and plugins. These extensions enable users to engage with the chatbot and improve its functionality, surpassing mere content generation. Our study delves into the potential of ChatGPT, specifically GPT-4, to expedite the creation of diagrams to support the system architecting process. To this end, we explored the use of ChatGPT's Diagrams Show Me …
การพัฒนาเมตริกสำหรับการประเมินการสรุปข้อความแบบแอบสแทร็กต์ทิฟโดยใช้การวิเคราะห์ทางศัพท์และทางความหมาย, ณภัทร สารวนางกูร
การพัฒนาเมตริกสำหรับการประเมินการสรุปข้อความแบบแอบสแทร็กต์ทิฟโดยใช้การวิเคราะห์ทางศัพท์และทางความหมาย, ณภัทร สารวนางกูร
Chulalongkorn University Theses and Dissertations (Chula ETD)
การประเมินผลการสรุปข้อความยังคงเป็นเรื่องท้าทาย เนื่องจากตัววัดแบบดั้งเดิม เช่น ROUGE และ BLEU มุ่งเน้นไปที่ความเหมือนกันของคำ ซึ่งมักจะไม่สามารถจับความหมายเชิงลึกและความสอดคล้องของเนื้อหาได้อย่างครบถ้วน งานวิจัยนี้นำเสนอ CorefSemScore ซึ่งเป็นตัววัดเชิงประเมินแบบผสมผสานที่รวมการวิเคราะห์ การแก้ไขการอ้างอิงร่วม เข้ากับการประเมินความคล้ายคลึงกันเชิงความหมาย โดยผสานการใช้ ROUGE, BERTScore, และ Sentence-BERT เพื่อการประเมินที่ครอบคลุมมากขึ้น CorefSemScore ใช้วิธีการเฉลี่ยแบบถ่วงน้ำหนักเพื่อผสานองค์ประกอบการประเมินในหลากหลายมิติ โดยเน้นความสมดุลระหว่างการประเมินความคล้ายคลึงกันเชิงคำศัพท์และความคล้ายคลึงกันเชิงความหมาย ส่งผลให้สามารถประเมินการสรุปข้อความแบบแอบสแทร็กต์ทิฟได้อย่างครอบคลุม ผลการทดลองบนชุดข้อมูล SummEval แสดงให้เห็นว่า CorefSemScore มีประสิทธิผลเหนือกว่าทั้งตัววัดแบบความคล้ายคลึงกันเชิงคำศัพท์ เช่น ROUGE และ BLEU รวมถึงตัววัดความคล้ายคลึงกันเชิงความหมาย เช่น BERTScore โดยเฉพาะในด้านการสะท้อนความเชื่อมโยงของเนื้อหา ซึ่งสะท้อนถึงศักยภาพของตัววัดในการยกระดับความน่าเชื่อถือของการประเมินผลการสรุปข้อความแบบแอบสแทร็กต์ทิฟ
Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons, Shashank Gupta
Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons, Shashank Gupta
Theses and Dissertations--Computer Science
End-to-end relation extraction (E2ERE) is a crucial task in natural language processing (NLP) that involves identifying and classifying semantic relationships between entities in text. This thesis compares three paradigms for end-to-end relation extraction (E2ERE) in biomedicine, focusing on rare diseases with discontinuous and nested entities. We evaluate Named Entity Recognition (NER) to Relation Extraction (RE) pipelines, sequence-to-sequence models, and generative pre-trained transformer (GPT) models using the RareDis information extraction dataset. Our findings indicate that pipeline models are the most effective, followed closely by sequence-to-sequence models. GPT models, despite having eight times as many parameters, perform worse than sequence-to-sequence models and …
Training Practitioners For Real-Time Product Development Using Generative Artificial Intelligence, Antoine J. Jetter, Ameeta Agrawal, Dahm Mongkol Hongchai, Charles Weber, Yufei Tao
Training Practitioners For Real-Time Product Development Using Generative Artificial Intelligence, Antoine J. Jetter, Ameeta Agrawal, Dahm Mongkol Hongchai, Charles Weber, Yufei Tao
Engineering and Technology Management Faculty Publications and Presentations
A workshop to train practitioners in real-time new product development (NPD) using generative artificial intelligence (AI) was conducted. Three teams of graduate students in Engineering and Technology Management (many with work experience as managers and engineers) were given minimalist instructions to develop a product concept and a preliminary marketing plan for a product of their choosing within six hours using ChatGPT, search engines, and other Internet-based tools. The product was to be novel, and the plan was to include customer identification, market analysis, generating personas, and a 15-minute presentation at the end of the workshop. The goal was to 'inspire …
Fake News Detection In Online Platforms, Elena Shushkevich
Fake News Detection In Online Platforms, Elena Shushkevich
Doctoral
This thesis presents research conducted during a Ph.D. program at Technological University Dublin from 2020 to 2024. The objective of this research is to develop and evaluate effective methods for detecting and classifying fake news in social media and press, addressing the critical issue of misinformation in the digital age. The relevance of this study is underscored by the increasing prevalence of fake news and its potential societal impact, emphasizing the importance of advanced tools for identifying and mitigating misinformation.
Towards Interpretable Propaganda Detection In News Text Through The Use Of Rhetorical Devices As Features, Kyle Hamilton
Towards Interpretable Propaganda Detection In News Text Through The Use Of Rhetorical Devices As Features, Kyle Hamilton
Doctoral
This doctoral thesis presents research undertaken towards interpretable propaganda detection in news text. Propaganda and mis/disinformation in the media have been identified in the social sciences literature as a major threat to the functioning of democratic society. While mis/disinformation is false by definition, propaganda is intended to modify the beliefs and behaviors of the information consumer but does not have to be false. Propagandistic text is often characterized by the use of rhetorical devices and linguistic style designed to exploit the reader’s cognitive and emotional biases. One way of counteracting this effect is by shifting the focus from “what” is …
Neurosymbolic Ai Approach To Attribution In Large Language Models, Deepa Tilwani, Revathy Venkataramanan, Amit P. Sheth
Neurosymbolic Ai Approach To Attribution In Large Language Models, Deepa Tilwani, Revathy Venkataramanan, Amit P. Sheth
Faculty Publications
Attribution in large language models (LLMs) remains a significant challenge, particularly in ensuring the factual accuracy and reliability of the generated outputs. Current methods for citation or attribution, such as those employed by tools like Perplexity.ai and Bing Search-integrated LLMs, attempt to ground responses by providing real-time search results and citations. However, so far, these approaches suffer from issues such as hallucinations, biases, surface-level relevance matching, and the complexity of managing vast, unfiltered knowledge sources. While tools like Perplexity.ai dynamically integrate web-based information and citations, they often rely on inconsistent sources such as blog posts or unreliable sources, which limits …
Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen
Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen
Electrical & Computer Engineering Faculty Publications
In this paper, we implement a comprehensive three-class system to categorize social media discussions about Islam and Muslims, enhancing the typical binary approach. These classes are: I) General Discourse About Islam and Muslims, II) Criticism of Islamic Teachings and Figures, and III) Comments Against Muslims. These categories are designed to balance the nuances of free speech while protecting diverse groups like Muslims, ex-Muslims, LGBTQ+ communities, and atheists. By utilizing machine learning and employing transformer-based models, we analyze the distribution and characteristics of these classes in social media content. Our findings reveal distinct patterns of user engagement with topics related to …
Embedding Software Engineering In Mixed Methods: Computationally Enhanced Risk Communication, Ann Marie Reinhold, Madison H. Munro, Elizabeth A. Shanahan, Ross J. Gore, Barry C. Ezell, Clemente I. Izurieta
Embedding Software Engineering In Mixed Methods: Computationally Enhanced Risk Communication, Ann Marie Reinhold, Madison H. Munro, Elizabeth A. Shanahan, Ross J. Gore, Barry C. Ezell, Clemente I. Izurieta
VMASC Publications
Mixed methods research ameliorates many convergent research challenges within the contemporary sociotechnical landscape. We suggest the integration of software engineering in mixed methods studies is a critical step to address some of the remaining and persistent challenges. One such research challenge where software engineering is particularly well suited is in hazard preparedness—in particular, the creation of risk communication messages to mitigate or prevent harm. Computationally enhanced risk communication is convergent research that integrates software engineering and social science research for the benefit of protecting humans and infrastructure. To this end, we developed a mixed methods framework for the efficient construction …
On Uncertainty For Ill-Posed Robot Decision Problems, Jared Joseph Beard
On Uncertainty For Ill-Posed Robot Decision Problems, Jared Joseph Beard
Graduate Theses, Dissertations, and Problem Reports (ETD)
As robots adopt more real world responsibilities, they will be expected to solve more complicated problems. In some cases limited prior knowledge will result in unmodelled environmental conditions; in others, multiple users may have competing perspectives on how to frame a decision problem. Many existing frameworks, namely Markov decision processes (MDP) presuppose users have identified a specific problem with models sufficient to solve or learn a problem. If we wish to extend MDPs to novel problems or those heavily dependent on user feedback, autonomous decision makers must be able to identify limitations in how a given problem is framed and …
Domain Expert Bot, Amrutha Dondemadahalli Ramegowda
Domain Expert Bot, Amrutha Dondemadahalli Ramegowda
Master's Projects
The fast growth of artificial intelligence in human-computer interaction has been aided significantly by the introduction of conversational AI systems. This project presents a Domain Expert Bot, a multi-domain conversational bot built with advanced NLP techniques incorporated through Sentence-BERT and MapReduce to allow the bot to analyze and comprehend challenging user queries on various topics. The bot can converse on different subjects ranging from technology topics to healthcare, environment, politics, and casual discussions. It excels in understanding deep language contexts and efficiently processes large datasets, ensuring prompt and accurate responses. Furthermore, it uses advanced ranking algorithms to perform real- time …
Hindi Image Captioning Using Indictrans2 And Encoder-Decoder Architecture, Anahita Vayalombrone Dinesh
Hindi Image Captioning Using Indictrans2 And Encoder-Decoder Architecture, Anahita Vayalombrone Dinesh
Master's Projects
One of the most prominent tasks that lie on the conjunction of Natural Language Processing (NLP) and computer vision, is image captioning. Image captioning is the generative task of achieving textual descriptions from images. Its application finds use in many real-world scenarios like aiding the visually impaired, editing applications, recommendation systems, and medical imaging. This research focus lies in Hindi image captioning, the official language of India, as it has not been explored as far as its need. Several challenges such as the lack of substantial Hindi text data for training models, the need for human annotators to verify the …
A Rule-Based Hybrid Translation Model For Context-Aware Speech To Indian Sign Language Tasks, Dania Jaison
A Rule-Based Hybrid Translation Model For Context-Aware Speech To Indian Sign Language Tasks, Dania Jaison
Master's Projects
In India, one of the most significant tools to communicate with the Deaf and Hard of Hearing (DHH) communities is Indian Sign Language(ISL). The issue of a scarcity of computational resources for ISL is even more pronounced when it comes to the translation of written or spoken English into ISL. This paper proposes a rule-based model for translation where the spoken english sentences are translated into ISL by focusing on the specific syntactic and grammatical differences between these two languages. ISL uses a simplified syntax unlike the nuanced sentence structures in English — we omit most of the auxiliary verbs …
Integrating Knowledge Graphs With Large Language Models For Natural Language Querying, Rakesh Kandula
Integrating Knowledge Graphs With Large Language Models For Natural Language Querying, Rakesh Kandula
Browse all Theses and Dissertations
This research explores the integration of knowledge graphs with large language models that have already been trained on a vast pool of unstructured text data. Large language models trained on this type of data have a tendency to hallucinate and produce factually inaccurate results. This behavior is primarily due to the data being trained is unstructured and huge text corpus, and large language model uses predictive text analysis methods to obtain a response. These issues can be addressed by applying Retrieval Augmented Generation and Fine-tuning to large language models, employing an underlying domainspecific knowledge graph. Integrating knowledge graph and large …
Autonomous Robot For Indoor Enhanced Living (Ariel), Prabhjot Kaur
Autonomous Robot For Indoor Enhanced Living (Ariel), Prabhjot Kaur
Wayne State University Dissertations
The global population is aging rapidly, with the total percentage of older adults (65 years and older) projected to increase from 10\% of the total population in 2022 to 16\% by 2050, according to the World Population Prospectus 2022 issued by the United Nations. For certain parts of the world such as Europe and North America, this translates to 1 in every 4 persons is projected to be 65 years or older by 2060. This trend raises concerns about providing quality long-term care for the older population. Moreover, according to the 2021 survey by the American Association of Retired Persons …
Zero-Shot Cross-Lingual Pos Tagging For Filipino, Jimson Paulo Layacan, Isaiah Edri W. Flores, Katrina Bernice M. Tan, Ma. Regina Justina Estuar, Jann Railey E. Montalan, Marlene M. De Leon
Zero-Shot Cross-Lingual Pos Tagging For Filipino, Jimson Paulo Layacan, Isaiah Edri W. Flores, Katrina Bernice M. Tan, Ma. Regina Justina Estuar, Jann Railey E. Montalan, Marlene M. De Leon
Department of Information Systems & Computer Science Faculty Publications
Supervised learning approaches in NLP, exemplified by POS tagging, rely heavily on the presence of large amounts of annotated data. However, acquiring such data often requires significant amount of resources and incurs high costs. In this work, we explore zero-shot cross-lingual transfer learning to address data scarcity issues in Filipino POS tagging, particularly focusing on optimizing source language selection. Our zero-shot approach demonstrates superior performance compared to previous studies, with top-performing fine-tuned PLMs achieving F1 scores as high as 79.10%. The analysis reveals moderate correlations between cross-lingual transfer performance and specific linguistic distances–featural, inventory, and syntactic–suggesting that source languages with …
Leveraging Large Language Models For Enhancing Well-Being In The Digital Age, Xiaobo Guo
Leveraging Large Language Models For Enhancing Well-Being In The Digital Age, Xiaobo Guo
Dartmouth College Ph.D Dissertations
The 21st century has seen dramatic shifts in human interactions with information, peers, and the environment, primarily driven by the proliferation of online platforms and social media. These advancements offer more access to information and global connectivity, but also present challenges such as information overload, misinformation, online harms, and biased reporting that can negatively impact user well-being. This thesis examines the role of Large Language Models (LLMs) — advanced forms of artificial intelligence that understand and generate human-like text — in enhancing well-being in the digital age. The study begins by exploring the potential of LLMs to detect early signs …
Enhancing Medical Chatbots With Image Diagnosis, Swatisri Chavali
Enhancing Medical Chatbots With Image Diagnosis, Swatisri Chavali
Master's Projects
Medical chatbots, at the conjunction of artificial intelligence and healthcare, are the very cornerstone of a transformative force in diagnostic capabilities and communication channels for healthcare professionals. The history of this journey, from early chatbot models to sophisticated systems, is born out of a relentless pursuit of accuracy and contextual understanding. This proposal acknowledges the critical role played by NLTK in raising the interpretability and communicative capabilities of intelligent systems, meeting challenges that arise from varying writing styles and accommodating the standards of the medical field. The integration of NLTK is a linchpin, bridging the gap between sophisticated technological architectures …