IEEE

Deep Learning for Multi-Class DDoS Detection in SDNs: A CNN-LSTM Based Approach

Abstract: Recently Distributed Denial of Service (DDoS) attacks have increased extensively, taking about 35% of all cyber threats among which the attack characteristic rises around 300% within the ...
IEEE

Arcface-Integrated Max Feature Map CNN for Robust Face Recognition

Abstract: Face recognition remains challenging, especially with small and imbalanced datasets that reduce feature discriminability and limit generalization. This study investigates Light Convolutional ...
IEEE

Cyberbull-Net: A CNN based Deep Learning Model for the Detection of Cyberbullying

Abstract: In the recent years, cyberbullying has marked its existence and severe consequences especially among the adolescents of age between 12–20 years. Posts promoting cyberbullying spread promptly ...
IEEE

CNN-Transformer-Bi-LSTM: A Hybrid Deep Learning Framework for Wearable Sensor-Based Human Activity Recognition

Abstract: Human activity recognition (HAR) focuses on identifying and classifying human activities based on data collected from various sources. Its importance lies in its wide range of applications, ...
IEEE

BLTOO-MFFL: Automated and Adversarially Robust Deep Learning for SAR Image Classification by Bi-Level Three-Objective Optimization and Multi-Feature Fusion Loss

Abstract: The deep learning-based synthetic aperture radar (SAR) image classification model is vulnerable to adversarial attacks. Although automated deep learning (ADL) including neural architecture ...
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

A Novel Approach for Facial Emotion Recognition: Deep CNN Feature Extraction Combined with ML Models

Abstract: Facial expression recognition (FER) serves as a fundamental component of human-computer interaction, showing its influence on computing, and behavioral analysis. The proposed work here ...
IEEE

Hybrid Deep Learning Models for Plant Leaf Disease Detection: A Comparison of Classification and Feature Extraction Techniques

Abstract: Prompt diagnosis of plant disease at an early stage is crucial for sustainable agriculture and food security. Deep learning approaches, more specifically convolutional neural networks (CNNs) ...
IEEE

Hybrid Feature Extraction and Deep Learning Framework for Power Transformer Fault Classification: A Real-World Case Study

Abstract: Reliable fault diagnosis in power transformers is paramount for ensuring grid stability and safeguarding critical assets. This paper proposes a novel deep learning-based diagnostic framework ...
IEEE

Hand-crafted Vs. deep CNN features to distinguish benign from malignant lesions in breast ultrasound images

Abstract: Breast ultrasound imaging (BUS) plays a major role in the diagnosis of breast cancer. Consequently, computerised analysis of BUS images is being intensively investigated as a means to assist ...
IEEE

An Explainable Hybrid Deep Learning Framework for Network Intrusion Detection Using Feature-Guided CNN Models

Abstract: Cybersecurity risks have evolved in the linked digital terrain of today into more complex, frequent, and varied forms. Conventional intrusion detection systems sometimes find it difficult to ...