Imparting 3D representations to artificial intelligence for a full assessment of pressure injuries.

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning methods. The main objective of this dissertation is to prove the efficiency of Deep Learning techniques in tackling one of the important health issues we are facing in our society, through medical imaging. Pressure injuries are a dermatology related health issue associated with increased morbidity and health care costs. Managing pressure injuries appropriately is increasingly important for all the professionals in wound care. Using 2D photographs and 3D meshes of these wounds, collected from collaborating hospitals, our mission is to create intelligent systems for a full non-intrusive assessment of these wounds. Five main tasks have been achieved in this study: a literature review of wound imaging methods using machine learning techniques, the classification and segmentation of the tissue types inside the pressure injury, the segmentation of these wounds and the design of an end-to-end system which measures all the necessary quantitative information from 3D meshes for an efficient assessment of PIs, and the integration of the assessment imaging techniques in a web-based application

Recognition of leishmania parasite and macrophage infection rate using image processing techniques

Dissertação de mestrado, Ciências da Computação (Processamento de Imagem), Faculdade de Ciência e Tecnologia, Universidade do Algarve, 2013Leishmaniasis is an epidemic dangerous disease in tropical and sub tropical regions of the world and if it is not treated conveniently, it may cause several health infections and could even lead to the death of patients. Currently, there is no efficient vaccine for the disease and available treatments cause serious side effects such as toxicity and parasite resistance. Therefore, ongoing research in Drug Discovery and other related biological areas concentrate on finding adequate drug candidates for the disease. Drug discovery pipeline for Leishmaniasis disease facilitates so many biological techniques till now to understand level of effectiveness of different drug candidates for treating the disease. The accuracy and reliability of such techniques, however, highly depends on manual process of detecting, extracting, counting and analyzing components of interest such as cells, parasites and cytoplasm regions by expert biologists. Such kind of activities is subjected to so many human-made errors and is often considered to be too time and energy consuming. The existing computational based solutions, which are reduced, also suffer from limited level of analysis and in some cases inaccuracy of the results are evident. For instance, there is no package dedicated to Intracellular Parasites Counting, which is a central operation when researching drug candidates’ effects. The current research aims at addressing the urgent need to find a solution to the problem of investigating Infection Ratio of cells more accurately, which is in fact still lacking. A computational framework which fulfills all those mentioned tasks automatically, in an acceptable time range with maximum possible accuracy of the results is suggested. The proposed solution mainly uses Image Processing approaches to process clinical images and analyze the results. The visual and statistical results then could be used independently or as a complementary tool for laboratories to investigate infection ratio of drug candidates and even at a higher level introduce vaccines for the disease

JTEC panel on display technologies in Japan

This report is one in a series of reports that describes research and development efforts in Japan in the area of display technologies. The following are included in this report: flat panel displays (technical findings, liquid crystal display development and production, large flat panel displays (FPD's), electroluminescent displays and plasma panels, infrastructure in Japan's FPD industry, market and projected sales, and new a-Si active matrix liquid crystal display (AMLCD) factory); materials for flat panel displays (liquid crystal materials, and light-emissive display materials); manufacturing and infrastructure of active matrix liquid crystal displays (manufacturing logistics and equipment); passive matrix liquid crystal displays (LCD basics, twisted nematics LCD's, supertwisted nematic LCD's, ferroelectric LCD's, and a comparison of passive matrix LCD technology); active matrix technology (basic active matrix technology, investment environment, amorphous silicon, polysilicon, and commercial products and prototypes); and projection displays (comparison of Japanese and U.S. display research, and technical evaluation of work)

The 1995 Science Information Management and Data Compression Workshop

This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival, and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The Workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

The Multispectral Imaging Science Working Group. Volume 3: Appendices

The status and technology requirements for using multispectral sensor imagery in geographic, hydrologic, and geologic applications are examined. Critical issues in image and information science are identified

Research and technology highlights, 1993

This report contains highlights of the major accomplishments and applications that have been made by Langley researchers and by our university and industry colleagues during the past year. The highlights illustrate both the broad range of the research and technology activities supported by NASA Langley Research Center and the contributions of this work toward maintaining United States leadership in aeronautics and space research. This report also describes some of the Center's most important research and testing facilities

XXV Congreso Argentino de Ciencias de la Computación - CACIC 2019: libro de actas

Trabajos presentados en el XXV Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de Río Cuarto los días 14 al 18 de octubre de 2019 organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y Facultad de Ciencias Exactas, Físico-Químicas y Naturales - Universidad Nacional de Río CuartoRed de Universidades con Carreras en Informátic

LANDSAT-4 Science Characterization Early Results. Volume 3, Part 2: Thematic Mapper (TM)

The calibration of the LANDSAT 4 thematic mapper is discussed as well as the atmospheric, radiometric, and geometric accuracy and correction of data obtained with this sensor. Methods are given for assessing TM band to band registration