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
In recent times, classification of human age has gained more attention. In this paper we propose to use gait identification points mainly to classify human's age based on their way of walking style. In this paper, we propose a Gait energy image Projection Model (GPM) for gait representation, which represents both Gait energy image Longitudinal Projection (GLP) and Gait energy image Transverse Projection (GTP) during a gait cycle. The proposed method mainly focuses on four parameters, namely head movement, body size, arm movement, and stride length. Regarding classification of age, OU-ISIR dataset is considered and the random forest is selected as the classifier. Moreover, the obtained experimental results are compared with the existing ones like FED, GEI, and SM. Further, descriptors are fused to check whether they get better results or not.
The paper describes about brain waves and its uses for various applications based on their frequencies and different parameters, which can be implemented as a real time application using a smart brain wave sensor system for paralyzed patients. Brain wave sensing detects a person's mental status. The purpose of brain wave sensing is to give exact treatment to paralyzed patients. The data or signal is obtained from the brain waves’ sensing band. This data are converted as object files using Visual Basics. The processed data is further sent to Arduino, which has the human's behavioral aspects like emotions, sensations, feelings, and desires. The proposed device can sense human brainwaves and detect the percentage of paralysis that the person is suffering. The advantage of this paper is to give a real-time smart sensor device for paralyzed patients with paralysis percentage for their exact treatment.
The paper proposes an Improved Gesture Precision Virtual Personal Assistant (IGP-VPA) system for speech impaired people that would act as a personal assistant for them. The proposed system first recognizes the sign language performed by the impeded individuals and converts it to text and speech format. The converted speech format will be given as input to the available assistant system such as Amazon Alexa. Then the VPA system responds to the speech, which would act as a personal assistant for speech impaired people. The previous method, Static and Dynamic Hand Gesture Recognition in Depth Data using Dynamic Time Warping also helps the speech impaired persons with 71.9% of gesture identification precision. To increase the gesture identification precision, IGP-VPA has been proposed. IGP-VPA uses iterative optimization process using Convolutional Neural Network (CNN), which overcomes drawbacks, such as memory intensive storage problem, poor generalization, and performance drops when training with voluminous samples. IGP-VPA is trained with 2262 gestures, each with approximately 1500 image. Indoctrinating with the huge dataset makes a difference to realize the precision of the recognition. The experimental results concluded the average precision in gesture identification as 82.9%, which is 11.78% higher compared to the Static and Dynamic Hand Gesture Recognition in Depth Data using Dynamic Time Warping.
In today’s advanced world, skin cancer is the most common cause of death amongst humans. Skin cancer is the abnormal development of skin cells. It develops on the body always exposed to daylight, however it can happen anywhere on the body too. The greater part of the skin cancers is they are curable at early stages. So an early and fast detection of skin cancer can save a patient's life. Skin cancer (Melanoma) is one among the most deadly cancers. Using the improvement of image preprocessing in noise removal, the images of skin disease are obtained. The images are then segmented using K-means clustering technique. Segmented images are input to the feature extraction phase and the extracted images are classified by using classification techniques like Naive Bayes and PNN.
Fusion image is obtained by processing information from various sources of images. Raw images are attained from different source or same source images. In the medical field, images are collected from several modalities like CT, MRI, etc., and merged into a single fused image. This fused image has significant features and information which can be used to diagnose easily. The fundamental or major task for medical image fusion is image registration. The image registration is done as a raw or distorted image. Distorted image registration methods are overviewed and empowering advancements in this field are done through this paper. The techniques and components for registering images are identified and given in an efficient way. The main impact of this paper is to present various techniques used for image registration in a systematic approach.
