AI-Enhanced Methods in Autonomous Systems: Large Language Models, DL Techniques, and Optimization Algorithms by I. de Zarza (17378452)

“…<p dir="ltr">Presentation for PhD thesis:</p><p dir="ltr">AI-Enhanced Methods in Autonomous Systems: Large Language Models, DL Techniques, and Optimization Algorithms https://doi.org/10.4995/Thesis/10251/202201</p><p dir="ltr">Abstract:</p><p dir="ltr">The proliferation of autonomous systems, and their increasing integration with day-to-day human life, have opened new frontiers of research and development. …”

<b>The Large Language Model GPT-4 Compared to Endocrinologist Responses on Initial Choice of Antidiabetic Medication Under Conditions of Clinical Uncertainty</b> by James H. Flory (236115)

<b>Enhancing Human-AI Interactions: The Impact of Pseudo-code Engineering on Improving Predictability and Stability in Large Language Models</b> - Appendix A by Gian Michaelsen (18824614)

“…<p dir="ltr">This research focuses on an innovative approach to enhancing the predictability and stability of interactions between humans and Large Language Models (LLMs), such as ChatGPT, through the use of pseudo-code. …”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

“…<p><b>Objective:</b> To assess the feasibility, accuracy, and reliability of using ChatGPT-4.5 (early-access), a large language model (LLM), for automated scoring of Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) protocols. …”

Brief summary highlighting the study methology. a) AI Tool Development and Pediatric Repurposing: The AI tool was originally trained and validated using a large dataset of adult ch... by Prerana Agarwal (21781515)

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Benchmark of the mean difference in FKS for pre-processing to post-processing for each large language model approach, Programmatic+AI, T5, GPT-J-6b. by Karl Swanson (3580583)

Bibliometric methods for identifying AI-assisted papers by Andrew Gray (10234100)

Example of reference standard and AI output. by Prerana Agarwal (21781515)

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Flow charts showing the performance for all the relevant pathologies (a) in all age groups, (b) 7–14 years, and (c) 2–6 years. by Prerana Agarwal (21781515)

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Performance on increasingly large data sets. by Kieran Elmes (11127506)

Original large DBs. by Steven L. Yu (18150306)

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Image examples representing strengths and limitations of the AI tool. by Prerana Agarwal (21781515)

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Data Sheet 1_Knowledge, use and perceptions of artificial intelligence Chatbots among Italian physiotherapists: an online cross-sectional survey.pdf by Manuela Deodato (11135016)

Data Sheet 2_Knowledge, use and perceptions of artificial intelligence Chatbots among Italian physiotherapists: an online cross-sectional survey.pdf by Manuela Deodato (11135016)

Large species studied herein. by Steven L. Yu (18150306)

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The percentages of genes with largely changed expression after AdNKX2-1 infection. by Yuko Ito (1976692)

Deletion of murine <i>Rhoh</i> leads to de-repression of <i>Bcl-6</i> via decreased KAISO levels and accelerates a malignancy phenotype in a murine model of lymphoma by Hiroto Horiguchi (3215001)

“…RHOH was initially identified as a translocation partner with BCL-6 in non-Hodgkin lymphoma (NHL), and aberrant somatic hypermutation (SHM) in the 5ʹ untranslated region of the RHOH gene has also been detected in Diffuse Large B-Cell Lymphoma (DLBCL). …”

For a random opinion vector y(0), on ER models with <i>n</i> = 100 and <i>ρ</i> ∈ (0,1]. by Xi Chen (35903)

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For 1,000 random y(0). by Xi Chen (35903)

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For a random opinion vector y(0) with mean −0.0395, on 1,000 ER models with <i>n</i> = 100 and <i>ρ</i> = 0.4. by Xi Chen (35903)

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