IEEE

A Hybrid CNN-LSTM Model with Signal Decomposition for Multi-Step Electrical Load Forecasting

Abstract: Accurate power load forecasting is a cornerstone for the reliable operation and economic dispatch of modern power grids, particularly as the integration of Variable Renewable Energy ...
IEEE

Adversial Learning Through GAN and Feature Extraction Through CNN for Deep Fake Detection

Abstract: Deepfake technology, using deep learning, creates highly realistic yet artificial media, creating challenges for security and privacy. Convolutional Neural Networks (CNNs) play a crucial ...
IEEE

Feature Extraction and Classification of ECG Signals Using 2-D CNN

Abstract: This work investigates ECG arrhythmia classification using two-dimensional convolutional neural networks (2D CNNs) applied to wavelet-based time–frequency representations. Three CNN ...
IEEE

A Hybrid Approach Using CNN Feature Extraction and SVM/XGBoost for Lung Nodule Classification

Abstract: Early detection of lung cancer is highly beneficial for patient survival. This paper proposes a hybrid deep learning diagnostic pipeline for pulmonary nodules in chest CT. We constructed a ...
IEEE

Low-Power High-Precision Approximate Multiplier in Convolutional Neural Network (CNN) Feature Extractor

Abstract: Convolutional Neural Networks (CNNs) are deep learning algorithms that have a wide scope in various applications such as image processing, healthcare and agriculture. However, their high ...
IEEE

A Reconstructed Phase Space and Multi-Scale Feature Fusion Approach for RF Signal Modulation Identification

Abstract: Modulation identification of radio frequency (RF) signals is a fundamental task in contemporary wireless communications, electronic warfare, and spectrum surveillance. While deep ...
IEEE

CNN-Based EOG Signal Classification for Human Computer Interfaces

Abstract: Eye-movement-based communication is a new alternative for individuals who are unable to communicate through traditional means. This paper proposes a deep learning approach for ...
IEEE

Mixed Intrusion Vibration Signal Recognition Based on Fiber Bragg Grating and RL-KAN-CNN

Abstract: Fiber Bragg grating (FBG) sensing systems have demonstrated strong potential for distributed vibration monitoring, yet recognizing mixed intrusion events remains challenging due to the ...
IEEE

Recognition of Stroke Level Based on EEG Signal Using Multi-networks of CNN-Transformer

Abstract: Stroke is a major cause of long-term neurological impairment, and continuous monitoring of post-stroke patients is essential for rehabilitation and relapse prevention. Electroencephalogram ...
CNBC

AI startup Replit launches feature to vibe code mobile apps

Replit's new feature allows users to create publishable and monetizable mobile apps using only natural language prompts. As more vibe-coding products come online, some software companies could see one ...