Latest AI and machine learning research in adhd/add for healthcare professionals.
Serving LLMs requires substantial memory due to the storage requirements of Key-Value (KV) embeddings in the KV cache, which grows with sequence length. An effective approach to compress KV cache is quantization. However, traditional quantization methods face significant memory overhead due to the need to store quantization constants (at least a zero point and a scale) in full precision per data...
The recently proposed facial cloaking attacks add invisible perturbation (cloaks) to facial images to protect users from being recognized by unauthorized facial recognition models. However, we show that the "cloaks" are not robust enough and can be removed from images. This paper introduces PuFace, an image purification system leveraging the generalization ability of neural networks to diminis...
Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models ...
This chapter proposes a prototype-based classification approach for analyzing DNA barcodes that uses a spectral representation of DNA sequences and a ...
To investigate the hepatitis B surface antigen (HBsAg) clearance condition and its predictive factors after treatment with nucleos(t)ide analogues to...
Amyotrophic lateral sclerosis (ALS) is a devastating and progressive neurodegenerative disease with limited treatment options available. Cerebrolysin ...
The explosive growth of Machine Learning provided scientists with insights into the data in the ways unattainable using established research technique...
Epilepsy is one of the most common neurological diseases, and video EEG is the most commonly used examination method for epilepsy diagnosis. However, ...
Automatic signal analysis using artificial intelligence is getting popular in digital healthcare, such as ECG rhythm analysis, where ECG signals are c...
We introduce an explainable deep neural architecture that combines brain structure with genetic influence to improve disease severity prediction in Al...
Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for gapped quantum phases of matter. Here, we propose a model-indepe...
Psychiatrists and psychotherapists specialising in the fields of addiction, personality disorders, ADHD and suicidal crisis, we questioned the ChatGPT...
Artificial intelligence technology is trending in nearly every medical area. It offers the possibility for improving analytics, therapy outcome, and u...
Convolutional neural network (CNN)-based models are widely used in human movement decoding based on surface electromyography. However, they capture on...
Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage ...
The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such ...
Missing data is a very common challenge in health monitoring systems and one reason for that is that they are largely dependent on different types of ...
Squatting is a dynamic task that is often done for strengthening and improving balance. Most squat training systems partially support body weight. How...
Evolution and development operate at different timescales; generations for the one, a lifetime for the other. These two processes, the basis of much o...
Ribonucleic acid (RNA) is a pivotal nucleic acid that plays a crucial role in regulating many biological activities. Recently, one study utilized a ma...