IEEE

Early Radar Work Mode Recognition Using Hybrid CNN-Transformer and Reinforcement Learning

In this paper, a novel approach is proposed for early recognition of Radar Work Mode, which integrates a hybrid CNN-Transformer architecture and a Reinforcement Learning strategy. The model processes ...
IEEE

Leveraging CNN-RF Models for Enhanced Serve Shot Strategies in Table Tennis

Abstract: The definition of which serve strokes to use depends on the technique practiced and the specific tactical conditions during the game because of the strategic complexity inherent in serve ...
IEEE

Predictive Autism Analysis: Integrating CNN and RNN with Ensemble Learning

Abstract: Timely identification of Autism Spectrum Disorder (ASD) is essential for successful intervention, but current diagnostic methods often depend on subjective observations, potentially missing ...
IEEE

Deep Learning for Sentiment Analysis: Combining CNN and LSTM for Enhanced Performance on Facebook Data

Abstract: This research evaluates the effectiveness of different sentiment analysis techniques, especially the hybrid CNN-LSTM model, on a large dataset of 100,000 Facebook posts and comments. For ...
IEEE

Carbon Emission Quantification of Machine Learning: A Review

Abstract: The rapid growth of machine learning (ML) technologies has raised significant concerns about their environmental impact, particularly regarding energy consumption and carbon emissions. This ...
IEEE

A Comparative Analysis of CNN-based Deep Learning Models for Landslide Detection

Abstract: Landslides inflict substantial societal and economic damage, underscoring their global significance as recurrent and destructive natural disasters. Recent landslides in northern parts of ...
IEEE

Deep Learning with 3D CNN for Knee Osteoarthritis Detection and Classification

Abstract: Of all joints, the knee is the most commonly afflicted with osteoarthritis (OA), which is the most common form of arthritis. Even though CNNs are seriously being utilized in medical imaging, ...
IEEE

Deep Learning-Based Fault Diagnosis in Quadcopter Actuators: A CNN-LSTM Network with Bayesian Optimization

Abstract: In recent years, deep neural networks have shown promising results in modern fault diagnosis. This paper focuses on diagnosing actuator faults in quadcopters using a deep learning strategy.
IEEE

Quantum Machine Learning: Recent Advances, Challenges, and Perspectives

Abstract: This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning ...
IEEE

An Explainable Deep Learning Network With Transformer and Custom CNN for Bean Leaf Disease Classification

Abstract: Bean rust and angular leaf spot pose significant challenges to bean cultivation, impacting yields. Prompt disease identification maximizes productivity, but traditional methods need ...
IEEE

An Ensemble Deep Learning Model Using CNN, LSTM and Autoencoder for EEG-Based Parkinson’s Disease Classification

Abstract: Parkinson's disease is a neurological disorder hat effects the movements including shaking, stiffness, difficulty while walking and speaking. This condition will occur when the nerve cells ...
IEEE

Enhanced Grasshopper Detection in Smart Crops Using Faster R-CNN Deep Learning Model

Abstract: Grasshoppers inflict a consequential risk to agricultural outcome that require an effective identification technique for prompt management. Traditional methods of identifying pests often ...