IEEE

Optimizing Hybrid Classification Using 1D-CNN and LSTM for EEG-Based Emotion Recognition

Abstract: This research aims to enhance the ability of computers to classify emotional states from brain signals using EEG data. Emotions are complex mental states that can significantly affect a ...
IEEE

Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification

Abstract: Hyperspectral image (HSI) classification presents inherent challenges due to high spectral dimensionality, significant domain shifts, and limited availability of labeled data. To address ...
IEEE

BAN: A Boundary-Aware Network Based on KAN for Robust Hyperspectral Image Classification Against Adversarial Attacks

Abstract: Deep neural networks (DNNs) have achieved significant advancements in hyperspectral image (HSI) classification, enabling critical applications in environmental monitoring, medical imaging, ...
IEEE

A Comparative Study for Yemeni Poets Detection Using TEXT-CNN and RNN-LSTM Text Classification

Abstract: The growing prevalence of internet usage has led to a substantial capacity in textual data. Text classification is an essential field in natural language processing (NLP). It differs in ...
IEEE

Histopathological Image Processing for Lung Carcinoma Classification: A Comparative Study of Stain Normalization Methods for CNN-Based Analysis

Abstract: In the present era, Cancer-related deaths are predominantly driven by lung cancer globally, causing significant deaths across all demographics. Precise prediction and evaluation of treatment ...
IEEE

Hybrid CNN-ViT Model for Intracranial Hemorrhage Detection and Classification in DICOM Images

Abstract: Intracranial hemorrhage (ICH) refers to bleeding within the brain, a global concern that underscores the im-portance of early detection. ICH is typically detected using computed tomography ...
IEEE

Spine Image Reconstruction and Lesion Classification Based on Transfer Learning and Quantum Convolutional Neural Network

Abstract: Spine CT image reconstruction and lesion classification are crucial in diagnosing spine disorders, supporting treatment through automated lesion detection. Leveraging advancements in machine ...
IEEE

Fully Perturbed Self-Ensemble Framework using Cascaded Parallel CNN-Transformer for Semi-Supervised Medical Image Segmentation

Abstract: Semi-supervised learning (SSL) has achieved remarkable progress in the field of medical image segmentation (MIS), but it still faces two main challenges. First, the consistency learning ...
IEEE

Combine Meta-Learning With Feature Alignment for Cross-Domain Heterogeneous Hyperspectral Image Classification

Abstract: The scarcity of labeled samples results in the challenge of small sample size in hyperspectral image (HSI) classification. Transfer learning offers hope for solving this problem. In ...
IEEE

FRFSL: Feature Reconstruction-Based Cross-Domain Few-Shot Learning for Coastal Wetland Hyperspectral Image Classification

Abstract: Hyperspectral image classification (HSIC) is a valuable method for identifying coastal wetland vegetation, but challenges like environmental complexity and difficulty in distinguishing land ...
IEEE

Hyperparameter Optimization of CNN Models for Plant Leaf Disease Classification Using Optuna

Abstract: Leaf diseases are a major challenge for agricultural productivity, requiring accurate and efficient detection methods. This research presents an effective method for multi-class ...
IEEE

BreNet: Attention-Enhanced Multi-Scale CNN Framework for Breast Cancer Classification in Histopathological Images

Abstract: Breast cancer remains a leading cause of mortality among women worldwide, emphasizing the critical importance of early and accurate detection in improving patient outcomes and treatment ...