Artificial neural networks - Strategic Insights Report (Page 4)

Technical institutes

Researchers Detail New Data in Artificial Intelligence

Researchers at Kielce University of Technology have made a significant breakthrough in understanding the applications of machine learning (ML) and neural network (NN) techniques in computational mechanics. Their comprehensive review paper, published in Applied Sciences, provides an in-depth analysis of the recent advancements in the field. The study highlights the

Artificial intelligence

Predicting 3D Bone Mineral Density Distribution Using Machine Learning

Researchers at Ewha Womans University have developed a novel method for predicting the 3D distribution of bone mineral density (BMD) using artificial intelligence. By employing machine learning algorithms, the researchers aimed to bridge the gap between dual-energy X-ray absorptiometry (DXA) scans and computed tomography (CT) in terms of volumetric bone

Femtosecond lasers

Breakthrough in Inverse Design for Femtosecond-Laser Photonic Surfaces

Scientists at the Qatar Environment and Energy Research Institute have made a significant discovery in the field of physics, particularly in the area of photonic surfaces. The research, published in the EPJ Web of Conferences, presents a new approach called Direct Gradient Optimization (DGO) to tackle the challenge of inverse

Artificial neural networks

Breakthrough in Free-Space Optical Communication: Computer Vision-Assisted Tracking System

Researchers in China have made significant strides in the field of free-space optical communication, developing a computer vision-assisted tracking system that maintains robust optical alignment in real-time. This innovative system, which combines a lightweight convolutional neural network (CNN) with a Kalman filter, achieves high tracking accuracy and reliable data transmission

Artificial neural networks

Researchers Develop Convolutional Neural Network Algorithm for Vegetable Classification

Researchers at Universitas Islam Negeri have developed a Convolutional Neural Network (CNN) algorithm to classify vegetable types using a dataset of 31,000 images. The study aimed to explore the potential of the CNN algorithm for image classification and its application in the agricultural sector. The researchers achieved a highest

Artificial neural networks

Breakthrough in Monitoring Zika Virus Production Using Synchronous Fluorescence Spectroscopy

Researchers from the University of Sao Paulo (USP) have made a significant discovery in the field of Zika virus monitoring, utilizing synchronous fluorescence spectroscopy with chemometric modeling techniques to track key biochemical parameters during particle production. This innovative approach enables accurate prediction of concentrations of essential biochemicals, such as lactate,

Artificial neural networks

Efficient Fully Parallel Convolutional Neural Network Architecture Reduces Power Consumption and Chip Area

A groundbreaking study from Babol Noshirvani University of Technology in Iran proposes a novel fully parallel convolutional neural network (FP-CNN) architecture that leverages single-memristor crossbar arrays to optimize area and power efficiency. This innovative design enables the computation of multiple feature maps in one processing cycle, leading to significant reductions

Artificial neural networks

Dual Neural Network Method for Elliptic Partial Differential Equations and Systems

Researchers at Purdue University have developed a new method for solving elliptic partial differential equations and systems, using a deep neural network function and a dual formulation. The Dual Neural Network (DuNN) method is designed to provide accurate solutions to complex problems in a wide range of fields, including physics

Artificial neural networks

Adaptive Beamforming Using Neural Networks for Conformal Antenna Arrays

Researchers at the University of Tunis El Manar have developed a novel approach to adaptive beamforming for conformal antenna arrays using neural networks. This breakthrough offers significant improvements in size, 3D scanning capability, and performance compared to existing configurations. By electronically steering the main radiating beam in both azimuth and

Artificial neural networks

Advances in Civil Structural Health Monitoring: A Novel Methodology for Buried Water Pipelines

A new study from researchers at Dalian University of Technology has proposed a novel methodology for the joint identification of foundation voids and structural deformations in buried water pipelines. The method uses physics-informed neural networks (PINNs) to analyze distributed strain data and accurately identify void characteristics, including location and length.

Artificial neural networks

Development of Machine Learning and Deep Learning Models for Predicting Groundwater Levels in South Korea

Research conducted at Duy Tan University in Da Nang, Vietnam, has led to the development of machine learning and deep learning models to predict groundwater levels in South Korea. The study utilized four machine learning and two deep learning algorithms to forecast groundwater levels in Bongseong well on Jeju Island,

Artificial neural networks

Advances in Networks: Robust Shrimp Disease Detection Using Multi-model Convolutional Neural Networks

Researchers from the Isparta University of Applied Sciences have made significant strides in the development of early and accurate detection methods for viral shrimp diseases. According to a new report, convolutional neural networks (CNNs) have emerged as a promising solution for nondestructive identification of shrimp diseases. However, individual CNN models

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