Latest AI and machine learning research in adhd/add for healthcare professionals.
The data extraction stages of reviews are resource-intensive, and researchers may seek to expediate data extraction using online (large language models) LLMs and review protocols. Claude 3.5 Sonnet was used to trial two approaches that used a review protocol to prompt data extraction from 10 evidence sources included in a case study scoping review. A protocol-based approach was also used to revi...
Recently, research into chatbots (also known as conversational agents, AI agents, voice assistants), which are computer applications using artificial intelligence to mimic human-like conversation, has grown sharply. Despite this growth, sociology lags other disciplines (including computer science, medicine, psychology, and communication) in publishing about chatbots. We suggest sociology can adv...
Document shadow removal is a crucial task in the field of document image enhancement. However, existing methods tend to remove shadows with constant...
In the past, the chest X-ray (CXR) was a traditional age and amount requirement used to assess potential mortality risk in life insurance applicants. ...
Depth map enhancement using paired high-resolution RGB images offers a cost-effective solution for improving low-resolution depth data from lightwei...
Small object detection in UAV imagery is crucial for applications such as search-and-rescue, traffic monitoring, and environmental surveillance, but...
Optical computing and spiking neural networks (SNNs) have garnered significant attention as next-generation technologies due to their high parallelism...
Knowledge distillation is a model compression technique in which a compact "student" network is trained to replicate the predictive behavior of a la...
This paper addresses two main objectives. Firstly, we demonstrate the impressive performance of the LLaVA-NeXT-interleave on 22 datasets across thre...
Dual encoder Vision-Language Models (VLM) such as CLIP are widely used for image-text retrieval tasks. However, those models struggle with compositi...
Data in the form of images or higher-order tensors is ubiquitous in modern deep learning applications. Owing to their inherent high dimensionality, ...
The importance of clinical variables in the prognosis of the disease is explained using statistical correlation or machine learning (ML). However, t...
Automated respiratory sound classification faces practical challenges from background noise and insufficient denoising in existing systems. We pro...
Multimodal artificial intelligence (AI) is a powerful new technological advance, capable of simultaneously learning from diverse data types, such as t...
Artificial intelligence in radiology critically depends on vast amounts of quality data, and there are controversies surrounding the topic of data own...
Rapid spread of false images and videos on online platforms is an emerging problem. Anyone may add, delete, clone or modify people and entities from...
Despite advances in general video understanding, Video Large Language Models (Video-LLMs) face challenges in precise temporal localization due to di...
Recent advancements in Text-to-Speech (TTS) models, particularly in voice cloning, have intensified the demand for adaptable and efficient deepfake ...
Prompt learning is a crucial technique for adapting pre-trained multimodal language models (MLLMs) to user tasks. Federated prompt personalization (...
Software systems have grown as an indispensable commodity used across various industries, and almost all essential services depend on them for effec...