IEEE

Design of a Low-Power Analog Integrated Deep Convolutional Neural Network

Abstract: In this article, a framework for the analog implementation of a deep convolutional neural network (CNN) is introduced and used to derive a new circuit architecture which is composed of an ...
IEEE

Gas Volume Fraction Measurement for Gas-Liquid Two-Phase Flow Based on Dual CNN-Transformer Mixture Neural Network

Abstract: Accurate gas volume fraction (GVF) measurement in gas-liquid two-phase flow remains a key challenge in industrial process monitoring and control. In order to address this, a deep ...
IEEE

Real-Time Detection of Unusual Activities Using CNN and Deep Learning

Abstract: With the evolution of technologies like artificialintelligence and machine learning and deep learning, video surveillance is increasing in today's globalised society. Using these ...
IEEE

A fusion approach of handcrafted and deep features for recognition of handwritten characters

Abstract: A fused feature set for recognition of Meitei Mayek handwritten characters is presented in this paper. The approach combines traditional hand-crafted feature and deep feature descriptors ...
IEEE

Research on Image Classification Based on CNN-Transformer and ResNet Neural Network

Abstract: With the rapid development of deep learning, Convolutional Neural Network (CNN), Vision Transformer, and Residual Network (ResNet) have become commonly used and efficient technologies in ...
IEEE

Enhancing CNN-Based Blind Image Quality Assessment via Deep Cross-Layer Pattern Encoding

Abstract: Evaluating image quality without reference images, known as blind image quality assessment (BIQA), is crucial for image communication. Recently, convolutional neural networks (CNNs) have ...
IEEE

Deep Neural Network Based Channel Estimation Method for OTFS Systems

Abstract: Orthogonal time frequency space (OTFS) modulation has emerged as a promising paradigm for 6G communications due to its inherent adaptability to rapidly time-varying multipath channels.
IEEE

Intelligent Fault Diagnosis of Rolling Bearings Using Adaptive WBKA-Optimized Hybrid CNN-SE-BiLSTM Network

Abstract: To address the limitations of traditional deep learning models, which rely on empirical hyperparameter tuning and suffer from limited diagnostic accuracy due to insufficient feature ...
IEEE

Spatio-Temporal Deep Neural Modelling for Climate Anomaly Detection using CNN-LSTM Networks

Abstract: When it comes to studying environmental problems, it is growing increasingly vital to discover climatic anomalies and measure temperature changes, particularly in areas like Ethiopia, where ...
Frontiers

A lightweight deep convolutional neural network development for soybean leaf disease recognition

Soybean is one of the world’s major oil-bearing crops and occupies an important role in the daily diet of human beings. However, the frequent occurrence of soybean leaf diseases caused serious threats ...