IEEE

Visualizing Crop Disease Detection Exploring Deep Learning with Custom CNN Model and XAI for Enhanced Interpretability

Abstract: Accurately detecting plant diseases is essential for safeguarding food security and promoting agricultural sustainability. This study presents an advanced deep learning approach using ...
Frontiers

Artificial intelligence and deep learning for neural data analysis

Artificial intelligence and deep learning have revolutionized the field of neural data analysis in recent years. The explosion of complex, high-dimensional ...
IEEE

Indoor Positioning Deep Learning Network based on CNN-GAN for High-precision Bluetooth

Abstract: In recent years, Bluetooth Low Energy (BLE) positioning technology has garnered significant attention from researchers in the field of indoor localization. This study leverages IQ signal ...
IEEE

Design and Analysis of an Enhanced Cnn Accelerator for Deep Learning Applications

Abstract: This paper presents an optimized Convolutional Neural Network (CNN) accelerator with a focus on improving power efficiency and computational performance. Traditional CNN accelerators often ...
IEEE

Predicting Pedestrian Intent: Evaluating Deep Learning with LSTM, CNN, and Transformers

Abstract: Pedestrian Intent Prediction (PIP) plays a key role in the safety of autonomous driving systems by predicting whether a pedestrian intends to cross a street. This paper presents a ...
IEEE

Intelligent Resume Screening and Candidate Ranking Using CNN-GRU: A Deep Learning Approach for Smarter Recruitment

Abstract: Automated resume screening is a crucial task in modern recruitment, requiring efficient and accurate candidate evaluation. This research proposes a hybrid Convolutional Neural Network-Gated ...
IEEE

Development of Scalable Deep Learning Frameworks Utilizing Advanced Hybrid Models like Transformer-CNN for Distributed Computing Environments

Abstract: Over the past few years especially in the context of communication and information processing the importance of Natural language processing which demands efficient deep learning models has ...
IEEE

Automated Trash Detection and Classification Using CNN: A Deep Learning Approach

Abstract: A research project focuses on creating automated trash detection and classification through convolutional neural networks (CNNs) with an objective to improve waste management systems. The ...
IEEE

Efficiency and Adaptability of Deep Learning Architectures: A Performance Comparison of CNN, Transformer, and Hybrid Models

Abstract: Innovations in deep learning architectures are driving a paradigm shift in computer vision. While traditional convolutional neural networks (CNNs) have long dominated image tasks due to ...
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

FedCNN: Federated Learning Framework Based CNN Model for Fault Classification in IEEE 30 Bus Transmission System

Abstract: Fault classification in multi-bus transmission lines with high precision and security is crucial for ensuring a reliable and stable power supply. Data-driven approaches have demonstrated ...
IEEE

Deep Learning-based mmWave MIMO Channel Estimation using sub-6 GHz Channel Information: CNN and UNet Approaches

Abstract: Future wireless multiple-input multiple-output (MIMO) systems will integrate both sub-6 GHz and millimeter wave (mmWave) frequency bands to meet the growing demands for high data rates. MIMO ...