International Journal of Neural Systems This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer's Disease
Int J Neural Syst. 2024 Apr 5:2450029. doi: 10.1142/S0129065724500291. Online ahead of print.ABSTRACTArtificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer's disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into...
Source: International Journal of Neural Systems - April 5, 2024 Category: Neurology Authors: Noushath Shaffi Karthikeyan Subramanian Viswan Vimbi Faizal Hajamohideen Abdelhamid Abdesselam Mufti Mahmud Source Type: research

Spatio-Temporal Image-Based Encoded Atlases for EEG Emotion Recognition
Int J Neural Syst. 2024 May;34(5):2450024. doi: 10.1142/S0129065724500242.ABSTRACTEmotion recognition plays an essential role in human-human interaction since it is a key to understanding the emotional states and reactions of human beings when they are subject to events and engagements in everyday life. Moving towards human-computer interaction, the study of emotions becomes fundamental because it is at the basis of the design of advanced systems to support a broad spectrum of application areas, including forensic, rehabilitative, educational, and many others. An effective method for discriminating emotions is based on Ele...
Source: International Journal of Neural Systems - March 27, 2024 Category: Neurology Authors: Danilo Avola Luigi Cinque Angelo Di Mambro Alessio Fagioli Marco Raoul Marini Daniele Pannone Bruno Fanini Gian Luca Foresti Source Type: research

Encrypted Image Classification with Low Memory Footprint Using Fully Homomorphic Encryption
In this study, we propose a solution to this issue by exploring an intersection between Machine Learning and cryptography. In particular, Fully Homomorphic Encryption (FHE) emerges as a promising solution, as it enables computations to be performed on encrypted data. We therefore propose a Residual Network implementation based on FHE which allows the classification of encrypted images, ensuring that only the user can see the result. We suggest a circuit which reduces the memory requirements by more than [Formula: see text] compared to the most recent works, while maintaining a high level of accuracy and a short computation...
Source: International Journal of Neural Systems - March 22, 2024 Category: Neurology Authors: Lorenzo Rovida Alberto Leporati Source Type: research