Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model
An Efficient Foot Ulcer Determination System for Diabetic Patients
Statistical Wavelet based Adaptive Noise Filtering Technique for MRI Modality
Real Time Sign Language: A Review
Remote Sensing Schemes Mingled with Information and Communication Technologies (ICTS) for Flood Disaster Management
FPGA Implementation of Shearlet Transform Based Invisible Image Watermarking Algorithm
A Comprehensive Study on Different Pattern Recognition Techniques
User Authentication and Identification Using NeuralNetwork
Flexible Generalized Mixture Model Cluster Analysis withElliptically-Contoured Distributions
Efficient Detection of Suspected areas in Mammographic Breast Cancer Images
Blur classification is important for blind image restoration. It is a challenging task to detect blur in a single image without any information. In this paper, we used edge detection techniques and deep learning convolutional neural network named ResNet 50 for the classification of blur-type images. ResNet 50 model effectively reduces gradient disappearance problem and uses skip connection to train the dataset. Generally, images are subjected to defocus and motion blur, which is caused by the improper depth of focus and movement of objects at the time of capture. Kaggle's blur dataset is used in this paper, which consists of sharp, defocus and motion blur images. Edge detection techniques are applied on images using Laplacian, Sobel, Prewitt, and Roberts filters to obtain features like mean, variance, maximum signal-to-noise ratio (SNR), which are used to train the system and classify the images using classification algorithm.
Nowadays breast cancer is the frequent type of cancer in women which leads to death. Mammography and ultrasound are the common ways to detect breast cancer. This paper employs Machine Learning for identification of breast cancer using mammography images. Ultrasound and Elastography are the combined imaging techniques used to separate benign and malignant breast lesions. Support vector machine is a classifier which is used for the classification of combined B-mode and Elastography image. This paper helps the physician to detect breast cancer earlier.
Sign language is used as a primary form of communication by many people who are deaf, deafened and non-verbal. Communication barriers exist for members of these populations during daily interactions with those who are unable to understand or use sign language. Advancements in technology and machine learning techniques have enabled development of innovative approaches to translate these sign languages to spoken languages. This paper proposes an intelligent system for translating sign language into text. This approach consists of hardware as well as software. The hardware consists of flex, contact, inertial sensors and SD card module mounted on a synthetic glove, additionally If-Else (Rule based learning) based learning is performed by Arduino nano (Atmega328p) to represent the proposed system. This system is able to recognize static letters, numbers and translating 26 letters from A to Z and 10 numbers from 0 to 9 from the American sign language. The database with the use of alphabet and numbers is prepared, and tested by kNN and CN2 rule inducer, where kNN has shown promising result. This experiment is done by Orange software. Experimental results demonstrate that our system is effective, cheaper and has high classification accuracy as compared to other technology available in market.
COVID-19 is a worldwide epidemic in recent times, as announced by the World Health Organization in early 2020. The disease is extremely contagious, affecting the respiratory system and affecting oxygen saturation in the circulatory system. In the current scenario, there are many testing procedures and processes to diagnose COVID-19. These procedures majorly face the challenges of accuracy and delay response because of their testing mechanisms. Machine learning techniques make it easy to support effective diagnostics with images and text data, but it requires a huge amount of test and training data, which requires a huge amount of memory and processing time. In support of efficient quality inference and minimal latency as the main challenge of COVID-19 detection, there are several methods that can provide a better solution to overcome these challenges through the use of cutting-edge trending technologies such as digital imaging techniques. In this study, as a first step to demonstrate machine learning algorithms to achieve high performance, digital imaging techniques were tested through the analysis, classification, and segmentation of chest x-ray (CXR) images. The proposed model starts with feature extraction from input chest x-ray images to determine if the image is COVID-19 or non-COVID-19. This diagnostic model delivers the most interesting results with 96.5% accuracy, minimal latency, and cost-effective standardized COVID-19 tests.
In recent years, machine learning and Deep Learning have increased and gathered epic success in traditional application domains and new areas of Artificial Intelligence. The performance using Deep Learning has dominated experimental results compared to conventional machine learning algorithms. This paper presents an overview of the progress that has occurred in Deep Learning (DL) concerning some application domains like Autonomous Driving, Healthcare, Voice Recognition, Image Recognition, Advertising, Predicting Natural Calamities, National Stock Exchange and many more. Additionally, deeper insights into several Deep Learning techniques, their working principles, and experimental results are scrutinized. The survey covers Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).
