The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial re...
The decision-making process for attaining Sustainable Development Goals (SDGs) can be enhanced through the use of predictive modelling. The application of predictive tools like deep neural networks (DNN) empowers stakeholders with quality information...
Recently, bio-inspired neuromorphic systems have been attracting widespread interest thanks to their energy-efficiency compared to conventional von Neumann architecture computing systems. Previously, we reported a silicon synaptic transistor with an...
This paper presents a method for modeling ReRAM in TCAD and validating its accuracy for neuromorphic systems. The data obtained from TCAD are used to analyze the accuracy of the neuromorphic system. The switching behaviors of ReRAM are implemented us...
In subtropical regions, heavy rains from cumulonimbus clouds can cause disasters such as flash floods and mudslides. The accurate prediction of cumulonimbus cloud distribution is crucial for mitigating such losses. Traditional machine learning approa...
Quantum machine learning (QML) has attracted significant research attention over the last decade. Multiple models have been developed to demonstrate the practical applications of the quantum properties. In this study, we first demonstrate that the pr...
We present a coupled variational autoencoder (VAE) method, which improves the accuracy and robustness of the model representation of handwritten numeral images. The improvement is measured in both increasing the likelihood of the reconstructed images...
A flash memory is a non-volatile memory that has a large memory window, high cell density, and reliable switching characteristics and can be used as a synaptic device in a neuromorphic system based on 3D NAND flash architecture. We fabricated a TiN/A...
In the previous article, we introduced a neural network framework based on symmetric differential equations. This novel framework exhibits complete symmetry, endowing it with perfect mathematical properties. While we have examined some of the system&...
This work showcases the physical insights of a core-shell dual-gate (CSDG) nanowire transistor as an artificial synaptic device with short/long-term potentiation and long-term depression (LTD) operation. Short-term potentiation (STP) is a temporary p...
Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of...
Convolutional neural networks (CNNs) have become a primary approach in the field of artificial intelligence (AI), with wide range of applications. The two computational phases for every neural network are; the training phase and the testing phase. Us...
Recent achievements on CNN (convolutional neural networks) and DNN (deep neural networks) researches provide a lot of practical applications on computer vision area. However, these approaches require construction of huge size of training data for lea...
Memristor-type synaptic devices that can effectively emulate synaptic plasticity open up new directions for neuromorphic hardware systems. Here, a double high-k oxide structured memristor device (TaOx/HfO2) was fabricated, and its synaptic applicatio...
Capsule Network (CapsNet) is a methodology with good prospects in visual tasks, since it can keep a stronger relationship of spatial information than Convolutional Neural Networks (CNNs). However, the current Capsule Network do not provide performanc...
A hardware-based spiking neural network (SNN) has attracted many researcher’s attention due to its energy-efficiency. When implementing the hardware-based SNN, offline training is most commonly used by which trained weights by a software-based artifi...
Image classification is typically a research area that trains an algorithm for accurately identifying subjects in images that have never been seen before. Training a model to recognize images within a dataset is significant as image classification ge...
In this work, resistive switching and synaptic behaviors of a TiO2/Al2O3 bilayer device were studied. The deposition of Pt/Ti/TiO2/Al2O3/TiN stack was confirmed by transmission electron microscopy (TEM) and energy X-ray dispersive spectroscopy (EDS)....
Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks...
Materials exhibiting memory or those capable of implementing certain learning schemes are the basic building blocks used in hardware realizations of the neuromorphic computing. One of the common goals within this paradigm assumes the integration of h...
This study advances the precision and efficiency of precipitation nowcasting, particularly under extreme weather conditions. Traditional forecasting methods struggle with precision, spatial feature generalization, and recognizing long-range spatial c...
Hierarchical Temporal Memory (HTM) has been known as a software framework to model the brain’s neocortical operation. However, mimicking the brain’s neocortical operation by not software but hardware is more desirable, because the hardwar...
Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still...
In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in differe...
In this study, a high-performance bio-organic memristor with a crossbar array structure using milk as a resistive switching layer (RSL) is proposed. To ensure compatibility with the complementary metal oxide semiconductor process of milk RSL, a high-...
The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular screening tool for cognitive functions. In spite of its qualitative capabilities in diagnosis of neurological diseases, the assessment of the CDT has depended on quantitative methods a...
This paper presents a novel approach to in situ memristive learning by training spiking neural networks (SNNs) entirely within the circuit using memristor emulators in SPICE. The circuit models neurons using Lapicque neurons and employs pulse-based s...
In this study, we propose the use of artificial synaptic transistors with coplanar-gate structures fabricated on paper substrates comprising biocompatible and low-cost potato-starch electrolyte and indium–gallium–zinc oxide (IGZO) channel...