A5003400590- Mobile Health and mHealth Applications
- Artificial Intelligence in Healthcare
- Peer-to-Peer Network Technologies
- Caching and Content Delivery
- Heart Rate Variability and Autonomic Control
- Mental Health Research Topics
- Cloud Computing and Resource Management
- Artificial Intelligence in Healthcare and Education
- Machine Learning in Healthcare
- Digital Mental Health Interventions
- AI in Service Interactions
- Nutrition, Genetics, and Disease
- Water Quality Monitoring and Analysis
- Data Quality and Management
- Healthcare Technology and Patient Monitoring
- Electrochemical sensors and biosensors
- Advanced ceramic materials synthesis
- Context-Aware Activity Recognition Systems
- Distributed and Parallel Computing Systems
- Carbon and Quantum Dots Applications
- Recommender Systems and Techniques
- Sleep and Work-Related Fatigue
- Physical Activity and Health
- Recycling and utilization of industrial and municipal waste in materials production
- Emotion and Mood Recognition
University of California, Irvine
2023-2024
Iran University of Science and Technology
2008-2020
Effective diabetes management is crucial for maintaining health in diabetic patients. Large Language Models (LLMs) have opened new avenues management, facilitating their efficacy. However, current LLM-based approaches are limited by dependence on general sources and lack of integration with domain-specific knowledge, leading to inaccurate responses. In this paper, we propose a knowledge-infused LLM-powered conversational agent (CHA) We customize leverage the open-source openCHA framework,...
To perform effective causal inference in high-dimensional datasets, initiating the process with discovery is imperative, wherein a graph generated based on observational data. However, obtaining complete and accurate poses formidable challenge, recognized as an NP-hard problem. Recently, advent of Large Language Models (LLMs) has ushered new era, indicating their emergent capabilities widespread applicability facilitating reasoning across diverse domains, such medicine, finance, science. The...
Effective diabetes management is crucial for maintaining health in diabetic patients. Large Language Models (LLMs) have opened new avenues management, facilitating their efficacy. However, current LLM-based approaches are limited by dependence on general sources and lack of integration with domain-specific knowledge, leading to inaccurate responses. In this paper, we propose a knowledge-infused LLM-powered conversational agent (CHA) We customize leverage the open-source openCHA framework,...
Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial. Current studies primarily centered on immediate short-term affect detection using data from wearable and mobile devices. These typically focus objective sensory measures, often neglecting other forms self-reported information like diaries notes. In this paper, we propose a multimodal deep learning model for status forecasting. This combines transformer encoder with...
Sleep quality is crucial to both mental and physical well-being. The COVID-19 pandemic, which has notably affected the population's health worldwide, been shown deteriorate people's sleep quality. Numerous studies have conducted evaluate impact of pandemic on efficiency, investigating their relationships using correlation-based methods. These methods merely rely learning spurious correlation rather than causal relations among variables. Furthermore, they fail pinpoint potential sources bias...
One of the main challenges unstructured peer-to-peer (P2P) systems that greatly affects performance is resource searching. The early proposed mechanisms use blind searching, but they have a lot shortcomings. Informed search strategies better in comparison with ones, still suffer from low success-rates and long response times due to their inadaptability dynamic P2P environments where nodes can frequently join leave system. To address this problem we propose new strategy called Dynamic...
In today's binary world, digital health is of paramount importance, primarily due to the prevalence IoT devices, upsurging costs, growing elderly population, and shortage clinical providers, name a few. light these, providing efficient smarter full-stack healthcare data analytics manage process crucial topic from both academic professional perspectives. The key goals this are detect issues promote human-being proactively. existing stacks generally classified into commercial or open-source...
Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial. Current studies primarily centered on immediate short-term affect detection using data from wearable and mobile devices. These typically focus objective sensory measures, often neglecting other forms self-reported information like diaries notes. In this paper, we propose a multimodal deep learning model for status forecasting. This combines transformer encoder with...
Abstract Sleep quality is crucial to both mental and physical well-being. The COVID-19 pandemic, which has notably affected the population’s health worldwide, been shown deteriorate people’s sleep quality. Numerous studies have conducted evaluate impact of pandemic on efficiency, investigating their relationships using correlation-based methods. These methods merely rely learning spurious correlation rather than causal relations among variables. Furthermore, they fail pinpoint potential...
The profound impact of food on health necessitates advanced nutrition-oriented recommendation services. Conventional methods often lack the crucial elements personalization, explainability, and interactivity. While Large Language Models (LLMs) bring interpretability their standalone use falls short achieving true personalization. In this paper, we introduce ChatDiet, a novel LLM-powered framework designed specifically for personalized chatbots. ChatDiet integrates personal population models,...
Background: The health benefits of regular physical activity (PA) are well-established and widely acknowledged. Through the integration wearable trackers, Internet Things (IoT)—a network interconnected devices capable collecting exchanging data—coupled with mobile (mHealth), which refers to use support medical public practices, it is now feasible systematically gather present individual exercise behaviors. This advanced approach enables precise correlation users' physiological data daily...
Study of the quality carbon black dispersion by using UV-Visible Spectroscopy was carried out. Carbon materials have a main absorption around 200 nm (UV region) due to π→π* excitation. Based on UV and parameter affects absorption, Visible spectroscopy used study CB dispersion. Result showed that Triton X-100 proper kinds dispersant which resulted in higher compared other dispersants. Also weight ratio initial solid calculated 50 wt% no significant change observed for sample containing amount...
Abstract Background There are indisputable health benefits to physical activity (PA). By collecting and displaying individual exercise behaviors via wearable trackers, the Internet of Things (IoT) mobile (mHealth) have made it possible correlate users’ physiological data daily information with their fitness requirements. Objective This study aimed recommend personalized non-pregnant subjects increase level. Methods We developed smartphone smartwatch applications collect, monitor, exercises...
Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, mental health support, objective substantially augment outcomes, all while...
The effect of kaolin on pressability, sintering, mechanical properties, and machinability a fluor–phlogopite glass ceramic was investigated. Crystallinity microstructure specimens were determined by X-ray diffraction scanning electron microscopy methods. Machinability sintered specimen investigated visual assessment chipping the drilled rims measurement particle size distribution chips obtained during drilling. results indicated that while adding improves pressability hardness samples,...