AIMC Topic: Handwriting

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Design of a deep fusion model for early Parkinson's disease prediction using handwritten image analysis.

Scientific reports
Parkinson's Disease (PD) is a deteriorating condition that mostly affects older people. The lack of conclusive treatment for PD makes diagnosis very challenging. However, using patterns like tremors for early diagnosis, handwriting analysis has becom...

Lightweight hybrid transformers-based dyslexia detection using cross-modality data.

Scientific reports
Early and precise diagnosis of dyslexia is crucial for implementing timely intervention to reduce its effects. Timely identification can improve the individual's academic and cognitive performance. Traditional dyslexia detection (DD) relies on length...

Handwriting strokes as biomarkers for Alzheimer's disease prediction: A novel machine learning approach.

Computers in biology and medicine
In recent years, machine learning-based handwriting analysis has emerged as a valuable tool for supporting the early diagnosis of Alzheimer's disease and predicting its progression. Traditional approaches represent handwriting tasks using a single fe...

A deep learning approach to remotely assessing essential tremor with handwritten images.

Scientific reports
Essential tremor (ET) is the most prevalent movement disorder, with its incidence increasing with age, significantly impacting motor functions and quality of life. Traditional methods for assessing ET severity are often time-consuming, subjective, an...

Deep Learning-Based Diagnostic Model for Parkinson's Disease Using Handwritten Spiral and Wave Images.

Current medical science
OBJECTIVE: To develop and validate a deep neural network (DNN) model for diagnosing Parkinson's Disease (PD) using handwritten spiral and wave images, and to compare its performance with various machine learning (ML) and deep learning (DL) models.

What is the impact of discrete memristor on the performance of neural network: A research on discrete memristor-based BP neural network.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks are receiving increasing attention from researchers. However, with the advent of big data era, artificial neural networks are limited by the Von Neumann architecture, making it difficult to make new breakthroughs in hardwar...

Highly sensitive, breathable, and superhydrophobic dome structure nonwoven-based flexible pressure sensor utilizing machine learning for handwriting recognition.

International journal of biological macromolecules
Wearable devices that incorporate flexible pressure sensors have shown great potential for human-machine interaction, speech recognition, health monitoring, and handwriting recognition. However, achieving high sensitivity, durability, wide detection ...

Late feature fusion using neural network with voting classifier for Parkinson's disease detection.

BMC medical informatics and decision making
Parkinson's disease (PD) is classified as a neurological, progressive illness brought on by cell death in the posterior midbrain. Early PD detection will assist doctors in reducing the disease's consequences. A collection of skilled models that may b...

Explainability of CNN-based Alzheimer's disease detection from online handwriting.

Scientific reports
With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer's disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning technique...

Air-Writing Recognition Enabled by a Flexible Dual-Network Hydrogel-Based Sensor and Machine Learning.

ACS applied materials & interfaces
Accurate air-writing recognition is pivotal for advancing state-of-the-art text recognizers, encryption tools, and biometric technologies. However, most existing air-writing recognition systems rely on image-based sensors to track hand and finger mot...