A5031271088- Advanced Image Fusion Techniques
- Image and Signal Denoising Methods
- Advanced Image Processing Techniques
- COVID-19 diagnosis using AI
- Remote-Sensing Image Classification
- IoT and Edge/Fog Computing
- Artificial Intelligence in Healthcare
- Brain Tumor Detection and Classification
- Video Surveillance and Tracking Methods
- AI in cancer detection
- Image Enhancement Techniques
- Blockchain Technology Applications and Security
- Cloud Computing and Resource Management
- Anomaly Detection Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Network Security and Intrusion Detection
- Advanced Malware Detection Techniques
- Image Processing Techniques and Applications
- Face recognition and analysis
- Smart Agriculture and AI
- Digital Imaging for Blood Diseases
- Photoacoustic and Ultrasonic Imaging
- IPv6, Mobility, Handover, Networks, Security
- IoT-based Smart Home Systems
- Advanced Text Analysis Techniques
Bennett University
2022-2025
Amity University
2019-2023
Graphic Era University
2023
Guru Gobind Singh Indraprastha University
2021
Amity University
2021
Babasaheb Bhimrao Ambedkar University
2016-2018
Introduction/ Background Medical diagnoses have increasingly depended on digitized images obtained through cutting-edge technology. These algorithms offer a promising avenue to transform diagnostic processes in healthcare, with their application scope continually widening due ongoing advancements. This paper explores machine learning's role clinical analysis and prediction, examining various studies that apply these techniques diagnosis, focusing use analyzing providing accurate diagnoses....
This paper introduces a new deep learning paradigm using the Denoising Convolutional-Neural Network (DnCNN) model for denoising Gaussian noise in Computed Tomography (CT) images. By nature, is inherently random and additive, potentially obscuring vital diagnostic features significantly reducing image quality, resulting difficulties medical interpretation. Initially, distorted images are sourced from addition of with different intensity levels (σ = 5,10,15,20). The process DnCNN employs...
It has become an important task to remove noise from the image and restore a high-quality in order process further for purpose like object segmentation, detection, tracking etc. This paper presents denoising of using convolutional neural network (CNN) model deep learning. analysis is done by adding 1% 10% Gaussian white then applying CNN denoise it. Further, qualitative quantitative denoised performed. Under comes quality where edge factor, texture, uniform region non-uniform region,...
The identification of abnormal behavior has several applications. There are ways, ranging from classical to deep learning based. It may be used monitor campuses, banks, transportation, and airports. In many circumstances, the context determines whether real-life events common or unusual. Recent video surveillance anomaly detection systems good enough, but they come at a significant computational cost require particular hardware resources. When it comes real-time detection, extra emphasis...
Introduction/Background This study explores the limitations of conventional encryption in real-world communications due to resource constraints. Additionally, it delves into integration Deep Reinforcement Learning (DRL) autonomous cars for trajectory management within Connected And Autonomous Vehicles (CAVs). unveils resource-constrained communications, faces challenges that hinder its feasibility. introduction sets stage exploring DRL and transformative potential Blockchain technology...
Introduction Agriculture is an intricate blend of scientific principles and practical techniques that facilitate the growth crops cultivation livestock. It involves careful land to produce essential food, fibers, various other agricultural products. Methods Effective planning fosters self-sufficiency in food production, offers a source income for farmers, contributes government revenue. This research focuses on utilizing ensemble learning K-means clustering predict optimal crop types...
The removal of granular pattern multiplicative speckle noise is the major issue in Synthetic Aperture Radar (SAR) images. Theoretically, considered as ratio standard deviation to signal value. proposed scheme works on db2 based 2D-Discrete Wavelet Transform (DWT) using wavelet thresholding and method noise. designed despeckle simulated SAR images real It uses a hybrid combination Directional Smoothing Filter (DSF), enhanced Bayesian shrinkage rule for despeckling. After DWT decomposition,...
In the field of image restoration, noise plays most prominent role. Speckle is a granular pattern, special kind that mainly found in satellite images, removing such one major challenge and least touched issue. These images are captured by radar named as Synthetic Aperture Radar. an undesirable effect. The source this type caused due to random interference between coherent returns issued from so many scatterers present on earth surface, scale wavelength incident wave. general, speckle grainy...
Abstract The unwanted data obtained through the medical image fusion is main problem in biomedical applications, guided-image surgical and radiology. Stationary Wavelet Transform (SWT) denoted various advantages over conventional representation of imaging approach. In this research article we introduced innovative multi-modality technique for based upon SWT. our approach it disintegrates source images into approximation layers (coarse layer) detail scheme, then applying Fuzzy Local...