IEEE

Cross-Domain Hyperspectral Image Classification via Mamba–CNN and Knowledge Distillation

Abstract: Domain adaptation (DA)-based cross-domain hyperspectral image (HSI) classification methods have garnered significant attention. The majority of DA techniques utilize models based on ...
IEEE

Novel Mask R-CNN Based Mushroom Cluster Tracking in Time-Lapse Images From a Farm Environment

Abstract: This study aims to develop a novel deep learningbased approach to support the automated mushroom growth monitoring using an object tracking algorithm in conjunction with instance ...
IEEE

A Novel CNN-Based Approach for Accurate Binary Classification of Diabetes Using Clinical Data

Abstract: Millions of individuals worldwide suffer from a chronic metabolic disease called diabetes. Conventional diagnos-tic techniques frequently depend on expert interpretation and clinical testing ...
IEEE

Enhancing Solar Panel Maintenance: A CNN-GAN Approach for Dust Detection and Classification

Abstract: The problem of dust settlement on solar panels is vital, likely decreasing their performance by 30%, which negatively affects energy yield and economic viability. Conventional dust detection ...
IEEE

CNN-Based Plant Disease Classification: An Image Analysis Approach

Abstract: The agriculture industry faces significant challenges in maintaining sustainable plant growth while combating diseases that threaten crops. Traditional disease prevention methods rely on ...
IEEE

Secure Spread Spectrum Image Steganography Using a CNN-Based Learned Detector

Abstract: Spread Spectrum Image Steganography (SSIS) represents a promising approach for embedding secret data into a cover image. In conventional methods, a pseudo-noise (PN) sequence functions as a ...
IEEE

Progressive Cluster-Guided Knowledge Distillation for Remote Sensing Image Scene Classification

Abstract: Knowledge distillation (KD) has recently demonstrated remarkable potential in developing lightweight convolutional neural networks for remote sensing image (RSI) scene classification tasks.
IEEE

SwinCNN+OE: A Swin Transformer and CNN Architecture for Breast Histopathology Classification with OOD and Grad-CAM Integration

Abstract: This study aimed to design and evaluate a fusion deep learning architecture (SwinCNN + OE) for robust and interpretable breast cancer classification using histopathological images. The ...
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

Memristor based Crossbar Array using CNN for Image Processing Applications

Abstract: Image processing has become one of the popular neuromorphic computing applications in recent years. Most of the application areas require an efficient and real time image processing system.
IEEE

CNN with Efficient Channel Attention for Multi-Class Respiratory Sound Prediction

Abstract: Computerized listening relies on machine learning tools for the analysis of breathing sounds and can be reliably used for the detection of lung diseases. This paper presents the design of a ...
IEEE

Application of Quantitative Interpretability to Evaluate CNN-Based Models for Medical Image Classification

Abstract: Convolutional Neural Networks (CNNs) dominate medical image classification, yet their “black box” nature limits understanding of their decision-making process. This study applies ...