Results 81 to 90 of about 21,396 (309)

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source

MEASUREMENT OF CLASSIFICATION PERFORMANCE WITH THE LEARNING VECTOR QUANTIZATION METHOD ON COVID-19 VACCINATION DATA AT THE PARUMPANAI HEALTH CENTER

Jurnal Matematika UNAND
In the midst of the COVID-19 pandemic, various countries are always trying their best to restore global stability. One effective way is the discovery of several vaccines to prevent transmission of the virus.
ADHIYAKSA PRANANDA, Siswanto Siswanto, Sri Astuti Thamrin, A. Muh. Amil Siddik   +3 more
doaj   +1 more source

PREDIKSI TERJANGKITNYA PENYAKIT JANTUNG DENGAN METODE LEARNING VECTOR QUANTIZATION

Media Statistika, 2010
Learning Vector Quantization (LVQ) is a method that train the competitives layer with supervised. The competitives layer will learn automatically to classify the input vector given.
Nurul Hidayati, Budi Warsito
doaj   +1 more source

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Advanced Electronic Materials, EarlyView.
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky   +4 more
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System

ABUAD Journal of Engineering Research and Development
Facial Recognition (FR) has been an active area of research and has diverse applicable environment, it continues to be a challenging research topic. With the development of image processing and pattern recognition technology, there are many challenges ...
Michael Olumuyiwa Adio, Ogunmakinde Jimoh Ogunwuyi, Mayowa Oyedepo Oyediran, Adebimpe Omolayo Esan, Olufikayo Adepoju Adedapo   +4 more
doaj   +1 more source

Gaussian mixture model based switched split vector quantization of LSF parameters

, 2007
We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is ...
Chatterjee, Saikat, Sreenivas, TV
core  

Noise Fuzzy Learning Vector Quantization

, 2010
Fuzzy learning vector quantization (FLVQ) benefits from using the membership values coming from fuzzy c-means (FCM) as learning rates and it overcomes several problems of learning vector quantization (LVQ). However, FLVQ is sensitive to noises because it
Xiao Hong Wu, Jie Wen Zhao, Bin Wu
core   +1 more source