Generative AI, exemplified by large language models like the OpenAI GPT and Meta LLaMA families, can produce diverse content in response to prompts. This capability offers a promising solution to challenges in precision medicine, which seeks to tailor treatments to individual clinical profiles but often struggles with data collection, cost, and privacy concerns. By generating realistic, privacy-preserving patient data, generative AI has the potential to transform patient-centric healthcare. With such motivation, this research develops a comprehensive Generative AI pipeline emphasizing data granularity for accurate prediction of personalized treatments. The pipeline features a central Large Language Model interacting with a Machine Learning agent to determine key factors affecting a patient’s condition. This information is compiled into a query to retrieve personalized suggestions from a guideline database. We exam- in model development, experimental processes, and concerns such as data quality, response evaluation, trust, and reliability. Then, we apply our developed framework on three chronic diseases, namely diabetes, heart disease, and mental illness, on the capability of generating tailored treatment recommendations. Experiments show the proposed framework is a promising step toward explainable, personalized, and clinically aligned AI-driven treatment planning, laying the foundation for future trustworthy, patient-focused medical AI systems.

Breakthroughs in Large Language Models (LLMs) have allowed Artificial Intelligence (AI) assistant systems to provide quality information with conveniences. An issue is paralleling the advantages, however. One among the problems of LLM generated content is that they seem indistinguishable from that of human which leads to numerous issues in areas like science, education, information security, etc. Furthermore, approaches in LLM content detection are either computationally expensive or need the LLMs’ internal computations which make them more difficult to be used by the public. Addressing the research gap, we present a metric learning framework for LLM text detection that is balanced for resources, accessibility, and performance. Specifically, the detection framework relies on metric learning to evaluate the similarity between a given text to an equivalent example from LLMs and verify whether the former is from human or AI. The framework can be trained in triplets or pairs of text instances from the same contexts at either the full-text or the sentence granularity levels. For benchmarking, five corpora totalling over 95,000 contexts and responses from human and GPT-3.5 TURBO or GPT-4 TURBO are developed. In term of performance, our architectures maintain 0.87 to 0.95 F1 scores throughout multiple experiment settings. Our framework also requires much less time in training and inference compared to RoBERTa, LLaMA 3, and Ghostbuster, while having 90% to 150% performances of the best benchmark.

Tracking the concentration of students during online learning offers great benefits. For examples, distracted students can be suggested to do a brief exercise to refresh their brains; or the teacher can be notified when too many students have difficulties on concentration so the class could take a short break. Traditionally, mental states like concentration levels can be analyzed using Electroencephalogram (EEG) or Functional Near-Infrared Spectroscopy (fNIRS). However, methods that utilize these data require specialized equipment which is not feasible to deploy on a large scale. On the other hand, recent breakthroughs in deep learning provide possibilities of scalable solutions to detect concentration levels using only webcam. Leveraging this advancement, we investigate the task of tracking students’ concentration levels during online learning using facial data coupled with deep learning based computer vision technologies. More specifically, we examine the performances of different representations of facial data integrated with various deep architectures to empirically determine a solution balanced between prediction accuracy and time efficiency that is suitable for real-time application. Our experimental study shows that the proposed solution achieves over 91% accuracy while keeping execution time low enough for real-time deployment.

This paper proposes a new deep learning architecture that aligns human poses to be used in an exercise/rehabilitation assistant system. In short, the assistant system aims to provide users with visual feedback for their physical exercises. The feedback is generated by first extracting a user's poses while they are performing an exercise through the video feed of the session. The extracted poses are then overlaid with the correct poses and display for the user to observe and fix their errors. This paper focuses on the task of aligning the user's pose with the correct pose so that they can be overlaid on each other with minimal differences, including scales, locations, and perspectives. We design a new deep architecture to accomplish this task, and show that our methods can effectively reduce alignment errors by 70% on average.

Stock price movement is typically affected by a lot of hidden factors. Predicting stock price direction, especially short-term direction, is very challenging and consistently attracts researches. Deep recurrent neural networks, such as Long Short-Term Memory, typically outperform statistical time series models and traditional machine learning approaches with their mechanisms of learning to vectorize historical information. However, encoding entire history into a vector may unavoidably causes information loss regardless of memory learning and updating mechanisms, especially for those tasks where decisions need to be made on the current time point and similar historical time points are of great references to the decision making. In this paper, we propose a new deep architecture called Recurrent Embedding Kernel (REK) that can learn to make optimal decisions by referring to the entire history instead of just current memory vectors. Experimental results on multiple stock ETFs with different long-term trends show that REK outperforms RNN, LSTM, and GRU, on predicting daily price direction.

The k Nearest Neighbors (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space. Efforts have been made to map data to a better feature space either implicitly with kernel functions, or explicitly through learning linear or nonlinear transformations. However, all these methods use pre-determined distance or similarity functions, which may limit their learning capacity. In this paper, we propose a novel deep learning architecture, which is called the Deep Similarity-Enhanced K Nearest Neighbors (DSE-KNN), to learn an optimized similarity function of the data directly towards the goal of optimizing the KNN decision making. In other words, the type of similarity function that is used in our method is not pre-determined but rather learned to map data to a high-dimensional feature space where the accuracy of the KNN decision making is maximized. Experimental results show that DSE-KNN outperforms other common machine learning methods on classifying different types of disease datasets and predicting daily price direction of different stock ETFs.

Kernel function implicitly maps data from its original space to a higher dimensional feature space. Kernel based machine learning algorithms are typically applied to data that is not linearly separable in its original space. Although kernel methods are among the most elegant part of machine learning, it is challenging for users to define or select a proper kernel function with optimized parameter settings for their data. In this paper, we propose a novel method called Deep Kernel that can automatically learn a kernel function from data using deep learning. The deep kernel is currently utilized in classification, and dimension reduction and visualization. For the classification task, we evaluate the deep kernel method by comparing its performance with the optimized Gaussian kernels, both using support vector machines as the decision model, on different types of datasets. The experimental results show that the proposed deep kernel method outperforms the traditional methods with Gaussian kernels on most of the data sets. For the dimension reduction and visualization task, the deep kernel is used along with kernel PCA. The results are also compared and contrasted with using the RBF kernel with multiple parameters. The deep kernel is shown to be more powerful in dimension reduction and visualization than the RBF kernel.

This paper studies feasibility and scalable computing processes for visualizing big high dimensional data in a 3 dimensional space by using dimension reduction techniques. More specifically, we propose an unsupervised approach to compute a measure that is called visualizability in a 3 dimensional space for a high dimensional data. This measure of visualizability is computed based on the comparison of the clustering structures of the data before and after dimension reduction. The computation of visualizability requires finding an optimal clustering structure for the given data sets. Therefore, we further implement a scalable approach based on K-Means algorithm for finding an optimal clustering structure for the given big data. Then we can reduce the volume of a given big data for dimension reduction and visualization by sampling the big data based on the discovered clustering structure of the data.