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.

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

Optimal Electrodermal Activity Segment for Enhanced Emotion Recognition Using Spectrogram-Based Feature Extraction and Machine Learning
Int J Neural Syst. 2024 Mar 21:2450027. doi: 10.1142/S0129065724500278. Online ahead of print.ABSTRACTIn clinical and scientific research on emotion recognition using physiological signals, selecting the appropriate segment is of utmost importance for enhanced results. In our study, we optimized the electrodermal activity (EDA) segment for an emotion recognition system. Initially, we obtained EDA signals from two publicly available datasets: the Continuously annotated signals of emotion (CASE) and Wearable stress and affect detection (WESAD) for 4-class dimensional and three-class categorical emotional classification, resp...
Source: International Journal of Neural Systems - March 21, 2024 Category: Neurology Authors: Sriram Kumar P Jac Fredo Agastinose Ronickom Source Type: research

A Parallel Convolutional Network Based on Spiking Neural Systems
Int J Neural Syst. 2024 Mar 15:2450022. doi: 10.1142/S0129065724500229. Online ahead of print.ABSTRACTDeep convolutional neural networks have shown advanced performance in accurately segmenting images. In this paper, an SNP-like convolutional neuron structure is introduced, abstracted from the nonlinear mechanism in nonlinear spiking neural P (NSNP) systems. Then, a U-shaped convolutional neural network named SNP-like parallel-convolutional network, or SPC-Net, is constructed for segmentation tasks. The dual-convolution concatenate (DCC) and dual-convolution addition (DCA) network blocks are designed, respectively, in the ...
Source: International Journal of Neural Systems - March 15, 2024 Category: Neurology Authors: Chi Zhou Lulin Ye Hong Peng Zhicai Liu Jun Wang Antonio Ram írez-De-Arellano Source Type: research

An Asynchronous Spiking Neural Membrane System for Edge Detection
Int J Neural Syst. 2024 Mar 16:2450023. doi: 10.1142/S0129065724500230. Online ahead of print.ABSTRACTSpiking neural membrane systems (SN P systems) are a class of bio-inspired models inspired by the activities and connectivity of neurons. Extensive studies have been made on SN P systems with synchronization-based communication, while further efforts are needed for the systems with rhythm-based communication. In this work, we design an asynchronous SN P system with resonant connections where all the enabled neurons in the same group connected by resonant connections should instantly produce spikes with the same rhythm. In ...
Source: International Journal of Neural Systems - March 15, 2024 Category: Neurology Authors: Luping Zhang Fei Xu Ferrante Neri Source Type: research

Edge Computing Transformers for Fall Detection in Older Adults
Int J Neural Syst. 2024 Mar 16:2450026. doi: 10.1142/S0129065724500266. Online ahead of print.ABSTRACTThe global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and the quality of life, and placing an additional burden on healthcare systems. Existing fall detection systems often have limitations, including delays due to continuous server communication, high false-positive rates, low adoption rates due to wearability and comfort issues, and high costs. In response to these ...
Source: International Journal of Neural Systems - March 15, 2024 Category: Neurology Authors: Jes ús Fernandez-Bermejo Jes ús Martinez-Del-Rincon Javier Dorado Xavier Del Toro Mar ía J Santofimia Juan C Lopez Source Type: research