Showing all 9 results

Accessible Melanoma Detection using Smart phones and Mobile Image Analysis

2,000.00
  Abstract:            MOBILE devices, including smart phones, are being used by billions of people all around the world. This

An Energy- Efficient User Centric Approach High Capacity SG Heterogeneous Cellular Networks

2,000.00
Abstract:                While the concept of ultra-dense small cell networks (SCNs) has brought a number of significant opportunities for the

Arbitrary-Oriented Ship Detection Framework in Optical Remote-Sensing Images

2,000.00
  Abstract                 SHIP detection in remote-sensing images is important in both civilian and military applications, such as traffic

AREA DETERMINATION OF DIABETIC FOOT ULCER IMAGES USING A CASCADED TWO-STAGE SVM BASED CLASSIFICATION

2,000.00
Aim:              The mainstay of the project is to determine the wound boundary on a foot ulcer image.  Introduction:             

ATHYNOS: Helping Children with Dyspraxia Through an Augmented Reality Serious Game

2,000.00
Abstract            Emerging technologies and ICT have changed the lifestyle of society, all scientific areas are taking advantage of technology

Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean

2,000.00
Abstract:           Brain tumor identification is really challenging task in early stages of life. But now it became advanced with

Emerging From Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer

2,000.00
ABSTRACT:             Underwater images usually suffer from degeneration, such as low contrast, color casts, and noise, due to wavelength-dependent light

Emotion Analysis for Personality Inference from EEG Signals

2,000.00
Aim:              To design a MATLAB Model for Individual’s Emotion detection through computed EEG using DEAP dataset and making the

Interactive Medical Image Segmentation Using Deep Learning with Image-Specific Fine Tuning

2,000.00
Abstract:            DEEP learning with convolution neural networks (CNNs) has achieved state-of-the-art performance for automated medical image segmentation. However, automatic