Academic Articles & Research Papers

Framework for Implementing Quantum Neural Networks in Wireless Communications

While quantum computing is already being employed in different domains to solve large scale and highly complex optimization problems, the wireless communications community has only recently begun exploring the potential of Quantum Machine L...

ℤ₂ × ℤ₂ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks

This paper presents a comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENNs) and Deep Ne...

Canonical quantization of a memristive leaky integrate-and-fire neuron circuit

We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF circuit, we apply canon...

Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches

Abstract This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in the terahertz (THz) frequency range. Leveraging a meta learner-based sta...

Magnetic field reconstruction from proton radiography via physics-guided out-of-distribution filtering

Reconstructing multidimensional vector fields from path-integrated projection data is a fundamental challenge in high-energy-density physics, particularly when experimental sources exhibit spectral broadening and shot-to-shot jitter. We pre...

ZX-Diagram T-Count Reduction Based on Proximal Policy Optimization

In fault-tolerant quantum computing systems, the T gates consume more fault-tolerant resources. In this paper, we propose an automated optimization method based on the Proximal Policy Optimization (PPO) algorithm. We first translate quantum...

Pattern Recognition with Artificial Intelligence in Space Experiments

The application of advanced Artificial Intelligence (AI) techniques in astroparticle experiments represents a major advancement in both data analysis and experimental design. As space missions become increasingly complex, integrating AI too...

Neural Network-Based Discriminators for Single-Shot Readout of Superconducting Qubits in Non-Gaussian Conditions

Single-shot dispersive readout of superconducting qubits is a very important part of real-time quantum feedback and quantum error correction (QEC). Conventional discriminators such as thresholding and matched filtering are optimal under Gau...

A Review of Power System Fault Diagnosis with Spiking Neural P Systems

With the advancement of technologies it is becoming imperative to have a stable, secure and uninterrupted supply of power to electronic systems as well as to ensure the identification of faults occurring in these systems quickly and efficie...

Adaptive Diagnosis in Distributed Systems

Real-time problem diagnosis in large distributed computer systems and networks is a challenging task that requires fast and accurate inferences from potentially huge data volumes. In this paper, we propose a cost-efficient, adaptive diagnos...

A Model to Predict Heart Disease Treatment Using Data Mining

Background and Aim: Nowadays heart disease is very common and is a major cause of mortality. Proper and early diagnosis of this disease is very important. Diagnostic methods and treatments of the disease are so expensive and have many side ...

An Explainable AI-Driven Zero-Day Attack Detection Framework for Securing Edge Devices in Smart Cities

The rapid proliferation of edge computing in smart cities has enhanced real-time data processing capabilities, but it has also exposed critical vulnerabilities to sophisticated cyber threats such as zero-day attacks. Traditional signature-b...

Text Mining in Cybersecurity

The growth of data volume has changed cybersecurity activities, demanding a higher level of automation. In this new cybersecurity landscape, text mining emerged as an alternative to improve the efficiency of the activities involving unstruc...

Summer seasonal precipitation prediction in the middle and lower reaches of the Yangtze River using neural network approaches.

Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review

Applications of deep learning (DL) in autonomous vehicle (AV) projects have gained increasing interest from both researchers and companies. This has caused a rapid expansion of scientific production on DL-AV in recent years, encouraging res...

Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system

The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic ...

Graph neural networks for materials science and chemistry

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials...

Metamodel of the plane strain compression test as a replacement of fe model in the inverse analysis

The paper presents the application of plane strain compression (PSC) test to the identification of material model parameters. Moreover, an attempt to substitute the finite element (FE) model with the metamodel in the inverse analysis was ma...

Forecasting Crude Oil Price and Stock Price by Jump Stochastic Time Effective Neural Network Model

The interacting impact between the crude oil prices and the stock market indices in China is investigated in the present paper, and the corresponding statistical behaviors are also analyzed. The database is based on the crude oil prices of ...

Application of Convolutional Neural Network for Fault Diagnosis of Bearing Scratch of an Induction Motor

The demand for the condition monitoring of induction motors is increasing in various fields, such as industry, transportation, and daily life. Bearing faults are the most common faults, and many fault diagnosis methods have been proposed us...