IEEE

Enhancing Soybean Disease Diagnosis using CNN-based Feature Extraction and Random Forest Classification

Abstract: Soybean plants are highly susceptible to various diseases, impacting crop yield and quality. Accurate and early detection of these diseases is essential for effective management and ...
IEEE

SCFNet:A Swin-CNN Synergistic Fusion Network For Urban and Rural Water Extraction In Remote Sensing Images

Accurate monitoring of water resources is essential for disaster risk reduction and sustainable development amid global climate change. At present, various methods based on convolutional neural ...
IEEE

Investigating SAR Data Denoising: A Comparative Analysis of CNN Models with Multi-Channel Signal Processing Features

Abstract: This paper investigates the necessity for denoising in Synthetic Aperture Radar (SAR) dataset, emphasizing the comparison of three distinct neural networks: U-Net, ResNet, and DeepDeblur.

How AI coding agents work—and what to remember if you use them

At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network ...
IEEE

FCTFANet: A Fused CNN-Transformer Feature Aggregator Network for Image Restoration

Abstract: Convolutional neural networks (CNNs) have been foundational in deep learning architectures for image processing, and recently, Transformer networks have emerged, bringing further ...
IEEE

A Hybrid Framework for Acute Lymphoblastic Leukemia Identification Utilizing VIT-CNN Fusion and Immunologically Inspired Deep Feature Selection

Abstract: Acute Lymphoblastic Leukemia (ALL) is a serious blood cancer characterized by the abnormal growth of progenitor white blood cells, which interferes with normal blood cell production. Early ...
IEEE

A Novel De-Mirroring Approach for Capacitance and Resistance Extraction in Complementary FET: A Quasi-Static Small Signal Model

Abstract: A quasi-static small signal model is vitally important to bridge the gap in device circuit co-design. To the best of our knowledge, for the first time in this paper, we proposed the ...
IEEE

A Dual-Branch Global-Local Feature Learning Network by Integrating CNN and Transformer for Remote Sensing Image Super-Resolution

Abstract: In the task of remote sensing image super-resolution reconstruction, both CNN and Transformer have demonstrated remarkable performance. However, many existing two-branch models only fuse the ...
IEEE

CNN-LSTM for Dynamic Gesture Recognition in Hand Electrostatic Signal Processing

Abstract: As a ubiquitous interaction modality in daily life, hand gestures convey rich interactive information. Particularly under the evolving paradigm of Natural User Interface (NUI), gesture ...
IEEE

Emotion-Aware Multi-Modal Feature Extraction using CNN-Bi-LSTM with Dynamic Emotional Salience

Abstract: Emotion-aware sentiment analysis has gained significant attention in recent years due to its applications in social media monitoring, mental health assessment, and customer feedback analysis ...
IEEE

Data Analytics for Automated Feature Extraction and Anomaly Detection in a Class of Industrial Manufacturing Processes

Abstract: Industry 4.0/Smart Manufacturing is transforming the manufacturing industry through the integration of technologies such as the Internet of Things (IoT), big data, and cloud computing. These ...
IEEE

Multi-Task 3D Model for Lung Nodule Feature Extraction and Classification Using CNN and Transformer

Abstract: Lung cancer early detection and therapy rely on an accurate diagnosis of lung nodules. Traditional computeraided diagnosis systems(CAD) typically treat nodule malignancy classification as a ...