346,407 research outputs found
An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
The data mining along with emerging computing techniques have astonishingly
influenced the healthcare industry. Researchers have used different Data Mining
and Internet of Things (IoT) for enrooting a programmed solution for diabetes
and heart patients. However, still, more advanced and united solution is needed
that can offer a therapeutic opinion to individual diabetic and cardio
patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced
healthcare system for proficient diabetes and cardiovascular diseases have been
proposed. The hybridization of data mining and IoT with other emerging
computing techniques is supposed to give an effective and economical solution
to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining,
Internet of Things, chatbots, contextual entity search (CES), bio-sensors,
semantic analysis and granular computing (GC). The bio-sensors of the proposed
system assist in getting the current and precise status of the concerned
patients so that in case of an emergency, the needful medical assistance can be
provided. The novelty lies in the hybrid framework and the adequate support of
chatbots, granular computing, context entity search and semantic analysis. The
practical implementation of this system is very challenging and costly.
However, it appears to be more operative and economical solution for diabetes
and cardio patients.Comment: 11 PAGE
Modeling of macro fiber composite actuated laminate plates and aerofoils
Ā© 2019 Sage Publications . The final, definitive version of this paper has been published in the Journal of Intelligent Material Systems and Structures by Sage Publications Ltd. All rights reserved. It is available at: https://doi.org/10.1177/1045389X19888728This article investigates the modeling of macro fiber composite-actuated laminate plates with distributed actuator patches. The investigation details an analytical and finite element modeling, with experimental validation of the bending strain and deflection of an epoxy E-glass fiber composite laminate. An analytical approach is also developed to estimate the plate deflection from the experimental strain measurements. The analytical method uses direct integration of single dimensional plate bending moments obtained by strain-induced shear moments from the macro fiber composite actuators. Finite element analysis software was used with the composite laminate modeled in ANSYS ACP. The results from both analytical and numerical models show good agreement with the experimental results, with strain values agreeing within 20 ppm and the maximum difference in deflection not exceeding 0.1 mm between models. Finally, an application of the analytical model for developing morphing aerofoil designs is demonstrated.Peer reviewe
Intelligent Packaging Systems: Sensors and Nanosensors to Monitor Food Quality and Safety
IndexaciĆ³n: Web of Science y Scopus.The application of nanotechnology in different areas of food packaging is an emerging field that will grow rapidly in the coming years. Advances in food safety have yielded promising results leading to the development of intelligent packaging (IP). By these containers, it is possible to monitor and provide information of the condition of food, packaging, or the environment. This article describes the role of the different concepts of intelligent packaging. It is possible that this new technology could reach enhancing food safety, improving pathogen detection time, and controlling the quality of food and packaging throughout the supply chain.https://www.hindawi.com/journals/js/2016/4046061/cta
Modeling Stroke Diagnosis with the Use of Intelligent Techniques
The purpose of this work is to test the efficiency of specific intelligent classification algorithms when dealing with the domain of stroke medical diagnosis. The dataset consists of patient records of the āAcute Stroke Unitā, Alexandra Hospital, Athens, Greece, describing patients suffering one of 5 different stroke types diagnosed by 127 diagnostic attributes / symptoms collected during the first hours of the emergency stroke situation as well as during the hospitalization and recovery phase of the patients. Prior to the application of the intelligent classifier the dimensionality of the dataset is further reduced using a variety of classic and state of the art dimensionality reductions techniques so as to capture the intrinsic dimensionality of the data. The results obtained indicate that the proposed methodology achieves prediction accuracy levels that are comparable to those obtained by intelligent classifiers trained on the original feature space
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
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