Security Issues in Biomedical Wireless Sensor Networks Applications: A SurveyIJARTES
Abstract The use of wireless sensor networks in healthcare
applications is growing in a fast pace. Numerous applications
such as heart rate monitor, blood pressure monitor and
endoscopic capsule are already in use. To address the growing
use of sensor technology in this area, a new field known as
wireless body area networks has emerged. As most devices
and their applications are wireless in nature, security and
privacy concerns are among major areas of concern. Body
area networks can collect information about an individual’s
health, fitness and energy expenditure. Comprising body
sensors that communicate wirelessly with the patients
control device for monitoring and external communication.
This paper provides the challenges of using the wireless
sensor network in biomedical field and how to solve most of
these issues. To analyze the different security strategies in
Wireless Sensor Networks and propose this system to give
highest quality medical care with full security in their
reliability
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Hybrid deep learning model using recurrent neural network and gated recurrent...IJECEIAES
This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.
Data Analytics Project proposal: Smart home based ambient assisted living - D...Tarun Swarup
In Ambient Assisted Living environments, monitoring the elderly population can detect a wide range of environmental and user-specific parameters such as daily activities, a regular period of inactivity, usual behavioural patterns and other basic routines. The prime goal of this proposal is to experiment the anomaly detection methods and clustering techniques such as K-means, local outlier factor, K-nearest, DBSCAN and CURE on data and determine the most efficient and accurate method among all.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data analysis comprising data collected from real patients highlighting the challenges in deploying such an application. The results show strong promise to derive actionable information using a combination of physiological indicators from active and passive sensors that can help doctors determine more precisely the cause, severity, and control level of asthma. Information synthesized from kHealth can be used to alert patients and caregivers for seeking timely clinical assistance to better manage asthma and improve their quality of life.
Paper: http://www.knoesis.org/library/resource.php?id=2153
Citation:
Pramod Anantharam, Tanvi Banerjee, Amit Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan, Shalini G. Forbis, Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children , IEEE 4th International Conference on Mobile Services, June 27 - July 2, 2015, New York, USA.
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Security Issues in Biomedical Wireless Sensor Networks Applications: A SurveyIJARTES
Abstract The use of wireless sensor networks in healthcare
applications is growing in a fast pace. Numerous applications
such as heart rate monitor, blood pressure monitor and
endoscopic capsule are already in use. To address the growing
use of sensor technology in this area, a new field known as
wireless body area networks has emerged. As most devices
and their applications are wireless in nature, security and
privacy concerns are among major areas of concern. Body
area networks can collect information about an individual’s
health, fitness and energy expenditure. Comprising body
sensors that communicate wirelessly with the patients
control device for monitoring and external communication.
This paper provides the challenges of using the wireless
sensor network in biomedical field and how to solve most of
these issues. To analyze the different security strategies in
Wireless Sensor Networks and propose this system to give
highest quality medical care with full security in their
reliability
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
Hybrid deep learning model using recurrent neural network and gated recurrent...IJECEIAES
This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.
Data Analytics Project proposal: Smart home based ambient assisted living - D...Tarun Swarup
In Ambient Assisted Living environments, monitoring the elderly population can detect a wide range of environmental and user-specific parameters such as daily activities, a regular period of inactivity, usual behavioural patterns and other basic routines. The prime goal of this proposal is to experiment the anomaly detection methods and clustering techniques such as K-means, local outlier factor, K-nearest, DBSCAN and CURE on data and determine the most efficient and accurate method among all.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data analysis comprising data collected from real patients highlighting the challenges in deploying such an application. The results show strong promise to derive actionable information using a combination of physiological indicators from active and passive sensors that can help doctors determine more precisely the cause, severity, and control level of asthma. Information synthesized from kHealth can be used to alert patients and caregivers for seeking timely clinical assistance to better manage asthma and improve their quality of life.
Paper: http://www.knoesis.org/library/resource.php?id=2153
Citation:
Pramod Anantharam, Tanvi Banerjee, Amit Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan, Shalini G. Forbis, Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children , IEEE 4th International Conference on Mobile Services, June 27 - July 2, 2015, New York, USA.
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Will healthcare be delivered by george jetson in the futureNick van Terheyden
Gartner ranked Dell the #1 worldwide IT services provider in healthcare in 2014. Dell sees global disruptions in healthcare delivery and continues to invest in strategies to address these rapid changes. They are actively enhancing development, implementation and adoption of novel technologies, services, and applications that will revolutionize information-driven care, resulting in improved patient outcomes and overall cost savings worldwide. Dr. Nick is responsible for providing strategic insight and will discuss some Dell’s strategies to achieve an IT environment that is interconnected, efficient and patient-focused.
Predictive Data Mining for Converged Internet ofJames Kang
Kang, J. J., Adibi, S., Larkin, H., & Luan, T. (2016). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International (pp. 5-10). IEEE Xplore: IEEE. doi: http://dx.doi.org/10.1109/ATNAC.2015.7366781
Internet of things-based photovoltaics parameter monitoring system using Node...IJECEIAES
The use of the internet of things (IoT) in solar photovoltaic (PV) systems is a critical feature for remote monitoring, supervising, and performance evaluation. Furthermore, it improves the long-term viability, consistency, efficiency, and system maintenance of energy production. However, previous researchers' proposed PV monitoring systems are relatively complex and expensive. Furthermore, the existing systems do not have any backup data, which means that the acquired data could be lost if the network connection fails. This paper presents a simple and low-cost IoT-based PV parameter monitoring system, with additional backup data stored on a microSD card. A NodeMCU ESP8266 development board is chosen as the main controller because it is a system-on-chip (SOC) microcontroller with integrated Wi-Fi and low-power support, all in one chip to reduce the cost of the proposed system. The solar irradiance, ambient temperature, PV output voltage and PV output current, are measured with photo-diodes, DHT22, impedance dividers and ACS712. While, the PV output power is a product of the PV voltage and PV current. ThingSpeak, an opensource software, is used as a cloud database and data monitoring tool in the form of interactive graphics. The results showed that the system was designed to be highly accurate, reliable, simple to use, and low-cost.
The SENSACTION-AAL project addressed one of the main problems for older people: motor disabilities.
By Lorenzo Chiari, Carlo Tacconi. DEIS - Università di Bologna
Charith Perera, Arkady Zaslavsky, Peter Christen, Ali Salehi, Dimitrios Georgakopoulos, Capturing Sensor Data from Mobile Phones using Global Sensor Network Middleware, Proceedings of the IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Sydney, Australia, September, 2012
Security Requirements, Counterattacks and Projects in Healthcare Applications...arpublication
Healthcare applications are well thought-out as interesting fields for WSN where patients can be examine using wireless medical sensor networks. Inside the hospital or extensive care surroundings there is a tempting need for steady monitoring of essential body functions and support for patient mobility. Recent research cantered on patient reliable communication, mobility, and energy-efficient routing. Yet deploying new expertise in healthcare applications presents some understandable security concerns which are the important concern in the inclusive deployment of wireless patient monitoring systems. This manuscript presents a survey of the security features, its counter attacks in healthcare applications including some proposed projects which have been done recently.
Efficient radio resource allocation scheme for 5G networks with device-to-devi...IJECEIAES
A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.
Sensor Networks and its Application in Electronic MedicineIJEACS
In recent times, there has been a tectonic shift in the manner through which medical services are being rendered and the organization of the practice of Medicine as a whole. This tremendous diversification in the techniques employed for medical service delivery has noticeably been achieved through the integration of Engineering with medical sciences and the efficient latch of Medicine on constant improvements across the field of Computer and Electronic Engineering. Treatment of patients, medical research, education, disease tracking and monitoring of public health have been efficiently optimized through innovations in Engineering. To this effect, medical practices in advanced countries have now transitioned from being largely one-to-one/human-to-human interactivity to a characteristic distributed healthcare delivery system whereby patients can receive both remote health advice as well as remote medical treatments usually through electronic gadgets operating within a standardized Sensor Network (SN) architecture. This paper seeks to explore the concept behind Sensor Networks, the technology framework, its application in the field of Electronic Medicine, prospects, challenges, ethical issues and a thorough analysis of the socio-economic impact of this new application of Electronic and Computer Engineering in Medicine.
Caso de Estudo: O Inventário do Património da Cidade do PortoJoaquim Flores
Comunicação apresentada no Seminário «Inventariação e Classificação Patrimonial: Conceitos e Métodos», promovido pela URBE – Núcleos Urbanos de Pesquisa e Intervenção e Secção Regional Sul da Ordem dos Arquitectos, no âmbito do 4º Fórum Internacional de Urbanismo - dia 8 de Fevereiro de 2002, Auditório da Ordem dos Arquitectos, Lisboa.
Will healthcare be delivered by george jetson in the futureNick van Terheyden
Gartner ranked Dell the #1 worldwide IT services provider in healthcare in 2014. Dell sees global disruptions in healthcare delivery and continues to invest in strategies to address these rapid changes. They are actively enhancing development, implementation and adoption of novel technologies, services, and applications that will revolutionize information-driven care, resulting in improved patient outcomes and overall cost savings worldwide. Dr. Nick is responsible for providing strategic insight and will discuss some Dell’s strategies to achieve an IT environment that is interconnected, efficient and patient-focused.
Predictive Data Mining for Converged Internet ofJames Kang
Kang, J. J., Adibi, S., Larkin, H., & Luan, T. (2016). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International (pp. 5-10). IEEE Xplore: IEEE. doi: http://dx.doi.org/10.1109/ATNAC.2015.7366781
Internet of things-based photovoltaics parameter monitoring system using Node...IJECEIAES
The use of the internet of things (IoT) in solar photovoltaic (PV) systems is a critical feature for remote monitoring, supervising, and performance evaluation. Furthermore, it improves the long-term viability, consistency, efficiency, and system maintenance of energy production. However, previous researchers' proposed PV monitoring systems are relatively complex and expensive. Furthermore, the existing systems do not have any backup data, which means that the acquired data could be lost if the network connection fails. This paper presents a simple and low-cost IoT-based PV parameter monitoring system, with additional backup data stored on a microSD card. A NodeMCU ESP8266 development board is chosen as the main controller because it is a system-on-chip (SOC) microcontroller with integrated Wi-Fi and low-power support, all in one chip to reduce the cost of the proposed system. The solar irradiance, ambient temperature, PV output voltage and PV output current, are measured with photo-diodes, DHT22, impedance dividers and ACS712. While, the PV output power is a product of the PV voltage and PV current. ThingSpeak, an opensource software, is used as a cloud database and data monitoring tool in the form of interactive graphics. The results showed that the system was designed to be highly accurate, reliable, simple to use, and low-cost.
The SENSACTION-AAL project addressed one of the main problems for older people: motor disabilities.
By Lorenzo Chiari, Carlo Tacconi. DEIS - Università di Bologna
Charith Perera, Arkady Zaslavsky, Peter Christen, Ali Salehi, Dimitrios Georgakopoulos, Capturing Sensor Data from Mobile Phones using Global Sensor Network Middleware, Proceedings of the IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Sydney, Australia, September, 2012
Security Requirements, Counterattacks and Projects in Healthcare Applications...arpublication
Healthcare applications are well thought-out as interesting fields for WSN where patients can be examine using wireless medical sensor networks. Inside the hospital or extensive care surroundings there is a tempting need for steady monitoring of essential body functions and support for patient mobility. Recent research cantered on patient reliable communication, mobility, and energy-efficient routing. Yet deploying new expertise in healthcare applications presents some understandable security concerns which are the important concern in the inclusive deployment of wireless patient monitoring systems. This manuscript presents a survey of the security features, its counter attacks in healthcare applications including some proposed projects which have been done recently.
Efficient radio resource allocation scheme for 5G networks with device-to-devi...IJECEIAES
A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.
Sensor Networks and its Application in Electronic MedicineIJEACS
In recent times, there has been a tectonic shift in the manner through which medical services are being rendered and the organization of the practice of Medicine as a whole. This tremendous diversification in the techniques employed for medical service delivery has noticeably been achieved through the integration of Engineering with medical sciences and the efficient latch of Medicine on constant improvements across the field of Computer and Electronic Engineering. Treatment of patients, medical research, education, disease tracking and monitoring of public health have been efficiently optimized through innovations in Engineering. To this effect, medical practices in advanced countries have now transitioned from being largely one-to-one/human-to-human interactivity to a characteristic distributed healthcare delivery system whereby patients can receive both remote health advice as well as remote medical treatments usually through electronic gadgets operating within a standardized Sensor Network (SN) architecture. This paper seeks to explore the concept behind Sensor Networks, the technology framework, its application in the field of Electronic Medicine, prospects, challenges, ethical issues and a thorough analysis of the socio-economic impact of this new application of Electronic and Computer Engineering in Medicine.
Caso de Estudo: O Inventário do Património da Cidade do PortoJoaquim Flores
Comunicação apresentada no Seminário «Inventariação e Classificação Patrimonial: Conceitos e Métodos», promovido pela URBE – Núcleos Urbanos de Pesquisa e Intervenção e Secção Regional Sul da Ordem dos Arquitectos, no âmbito do 4º Fórum Internacional de Urbanismo - dia 8 de Fevereiro de 2002, Auditório da Ordem dos Arquitectos, Lisboa.
Ficha del Grand Bahia Principe Bávaro ResortBahia Principe
Situado en una de las playas más bonitas del Caribe, Playa Bávaro, en la costa este de Republica Dominicana. Esta zona la llaman “ la costa del coco” y es la más bonita y llena de cocoteros.
Presentation that gives an overview of the impact of IT on radiology, including the growing role of biomarkers and artificial intelligence and deep learning on the (future) radiology profession. The shift to precision medicine and personalized care are explained, the reasons for a re-definition of radiology are addressed.
Rapid detection of diabetic retinopathy in retinal images: a new approach usi...IJECEIAES
The challenge of early detection of diabetic retinopathy (DR), a leading cause of vision loss in working-age individuals in developed nations, was addressed in this study. Current manual analysis of digital color fundus photographs by clinicians, although thorough, suffers from slow result turnaround, delaying necessary treatment. To expedite detection and improve treatment timeliness, a novel automated detection system for DR was developed. This system utilized convolutional neural networks. Visual geometry group 16-layer network (VGG16), a pre-trained deep learning model, for feature extraction from retinal images and the synthetic minority over-sampling technique (SMOTE) to handle class imbalance in the dataset. The system was designed to classify images into five categories: normal, mild DR, moderate DR, severe DR, and proliferative DR (PDR). Assessment of the system using the Kaggle diabetic retinopathy dataset resulted in a promising 93.94% accuracy during the training phase and 88.19% during validation. These results highlight the system's potential to enhance DR diagnosis speed and efficiency, leading to improved patient outcomes. The study concluded that automation and artificial intelligence (AI) could play a significant role in timely and efficient disease detection and management.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
Diabetic retinopathy (DR) is one of the most common causes of blindness. The necessity for a robust and automated DR screening system for regular examination has long been recognized in order to identify DR at an early stage. In this paper, an embedded DR diagnosis system based on convolutional neural networks (CNNs) has been proposed to assess the proper stage of DR. We coupled the power of CNN with transfer learning to design our model based on state-of-the-art architecture. We preprocessed the input data, which is color fundus photography, to reduce undesirable noise in the image. After training many models on the dataset, we chose the adopted ResNet50 because it produced the best results, with a 92.90% accuracy. Extensive experiments and comparisons with other research work show that the proposed method is effective. Furthermore, the CNN model has been implemented on an embedded target to be a part of a medical instrument diagnostic system. We have accelerated our model inference on a field programmable gate array (FPGA) using Xilinx tools. Results have confirmed that a customized FPGA system on chip (SoC) with hardware accelerators is a promising target for our DR detection model with high performance and low power consumption.
An innovative IoT service for medical diagnosis IJECEIAES
Due to the misdiagnose of diseases that increased recently in a scarily manner, many researchers devoted their efforts and deployed technologies to improve the medical diagnosis process and reducing the resulted risk. Accordingly, this paper proposed architecture of a cyber-medicine service for medical diagnosis, based internet of things (IoT) and cloud infrastructure (IaaS). This service offers a shared environment for medical data, and extracted knowledge and findings between patients and doctors in an interactive, secured, elastic and reliable way. It predicts the medical diagnosis and provides an appropriate treatment for the given symptoms and medical conditions based on multiple classifiers to assure high accuracy. Moreover, it entails different functionalities such as on-demand searching for scientific papers and diseases description for unrecognized combination of symptoms using web crawler to enrich the results. Where such searching results from crawler, are processed, analyzed and added to the resident knowledge base (KB) to achieve adaptability and subsidize the service predictive ability.
Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...cscpconf
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.
Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to
screen abnormalities through retinal fundus images by clinicians. However, limited number of
well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a
big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy
severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images
in the training set, we also automatically segment the abnormal patches with an occlusion test,
and model the transformations and deterioration process of DR. Our results can be widely used
for fast diagnosis of DR, medical education and public-level healthcare propagation.
DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...csandit
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test,and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.
Carestream’s Vue Motion provides on-demand access to patient images throughout and beyond the healthcare enterprise and it is easily adapted to work with healthcare IT systems already installed. http://www.carestream.com/motion.
For more information, please visit us at: http://www.carestream.com/vue
Telemedicine, the remote delivery of healthcare services using telecommunications technology, has gained significant prominence in recent years, including its application in clinical research. Telemedicine in clinical research refers to the use of virtual visits, remote monitoring, and telecommunication tools to facilitate participant enrollment, data collection, study assessments, and monitoring in research studies. Here are some key aspects and benefits of telemedicine in clinical research:
Participant Recruitment and Access:
Telemedicine enables researchers to expand their participant pool beyond geographical limitations. By leveraging telecommunication tools, researchers can reach individuals who may face challenges with physical access to study sites, such as those living in remote areas or individuals with mobility issues. This broader reach enhances participant recruitment and diversity, contributing to the generalizability and representativeness of research findings.
Remote Study Visits and Data Collection:
Telemedicine allows for remote study visits, eliminating the need for participants to travel to study sites. Through video consultations and remote assessments, researchers can conduct interviews, collect data, perform examinations, and administer questionnaires remotely. This approach reduces participant burden, increases convenience, and enhances compliance by minimizing the time and effort required for in-person visits.
Real-Time Monitoring and Data Capture:
Telemedicine facilitates the collection of real-time data by remotely monitoring participants' health status and adherence to study protocols. Wearable devices, mobile health applications, and sensors can be used to capture vital signs, medication adherence, activity levels, and other relevant data. Researchers can access and analyze this data in real-time, enabling early detection of adverse events, improving safety monitoring, and enhancing the accuracy and completeness of data collection.
Patient Engagement and Retention:
Telemedicine enhances patient engagement by providing more frequent interactions with study staff and personalized support. Regular virtual check-ins, educational sessions, and feedback can strengthen the participant-researcher relationship, leading to increased participant satisfaction and retention. Telemedicine also offers flexibility in scheduling visits, reducing missed appointments, and improving overall study adherence.
Cost and Time Efficiency:
Telemedicine can reduce the overall cost and time associated with conducting clinical research. It eliminates or reduces travel expenses for participants and reduces the need for physical study site infrastructure.
Medical imaging is part of a changing medical environment, a changing
patient environment and consequently a new medical world. In the
recent decennium one of the most important changes in radiology is the
conversion from analogue to digital. In no time medical images have
become interchangeable through the digital highway and could be postprocessed
in a different location. Teleradiology has become a reality
since then. We have seen the maturation of commercial international
teleradiology companies offering a wide portfolio of services. Another
aspect is the availability of image data for all medical specialties beyond
radiology and beyond the regular medical disciplines. An increasing
number of surgical or oncological specialties and even pharmaceutical
companies increasingly use image data to prepare a strategy for
operative procedures, to choose the right therapy, to decide which
prosthesis to the best to use, for follow-up or for post-processing
purposes. They are supported by many new techniques and software.
An increasing number of medical computer applications such as complex
navigation and visualisation tools based upon digital images is already
in clinical use or under development. Another trend is the increasing
interest in E-health and telemedicine in Europe, also among European
policy makers. Now we see mobile health that brings care directly into
the patient environment. The purpose of this presentation is to give a
comprehensive overview of and insight into these new developments and
to create awareness among radiologists of the increasing importance of
integration of medical imaging in a multidisciplinary environment.
Design AI platform using fuzzy logic technique to diagnose kidney diseasesTELKOMNIKA JOURNAL
Artificial intelligence (AI) is an advanced scientific technology that can provide strong ability to assist in analysis and diagnosis of almost every type of data, therefore; AI widely used in medical fields, which is applied in the diagnosis and early detection of diseases. Kidney disease is one of the common diseases that are diagnosed and the necessary treatments are suggested by artificial intelligence. In this research, a logic system was used. The fuzzy logic system (FLS) is one of the artificial intelligence systems for diagnosing kidney diseases, where the fuzzy logic system divided into five variable inputs, namely urea, creatinine, glucose, bun, and uric acid, and they represented laboratory tests of the patients, this variables and also three outputs were identified, which are chronic inflammation and kidney failure, stones and salts, acute inflammation of the kidneys and bladder, which is the result of the medical diagnosis of the disease. Five memberships for inputs and three memberships for outputs are used in FLS. Diseases are concluded based on the values of the inputs, and thus the system proved its effectiveness and accuracy in diagnosis and this system is considered an aid to the specialized doctors in the field of kidney diseases.
Updated Policy Brief: Cooperatives Bring Fiber Internet Access to Rural AmericaEd Dodds
Originally published in 2017, our report, Cooperatives Fiberize Rural America: A Trusted Model for the Internet Era, focuses on cooperatives as a proven model for deploying fiber optic Internet access across the country. An update in the spring of 2019 included additional information about the rate co-ops are expanding Internet service, and now we’ve updated it again, with a new map and personal stories from areas where co-ops have drastically impacted local life.
Digital Inclusion and Meaningful Broadband Adoption Initiatives Colin Rhinesm...Ed Dodds
This report presents findings from a national study of digital inclusion organizations that help low-income individuals and families adopt high-speed Internet service. The study looked at eight digital inclusion organizations across the United States that are working at the important intersection between making high-speed Internet available and strengthening digital skills—two essential and interrelated components of digital inclusion, which is focused on increasing digital access, skills, and relevant content.
Innovation Accelerators:
Defining Characteristics Among Startup Assistance Organizations by C. Scott Dempwolf, Jennifer Auer, and
Michelle D’Ippolito
Optimal Solutions Group, LLC
College Park, MD 20740
contract number SBAHQ -13-M-0197
Release Date: October 2014
This report was developed under a contract with the Small Business Administration, Office of Advocacy, and contains information and analysis that were reviewed by officials of the Office of Advocacy. However, the final conclusions of the report do not necessarily reflect the views of the Office of Advocacy.
Executive Summary. Thriving in a Turbulent, Technological and Transformed Global Economy | Council on Competitiveness 900 17th Street, NW, Suite 700 Washington, D.C. 20006 T 202 682 4292 Compete.org
America has long been a nation of innovators. The United States is the birthplace of the Internet, which today connects three billion people around the world. American scientists and engineers sequenced the human genome, invented the semiconductor, and sent humankind to the moon. And America is not done yet. For an advanced economy such as the United States, innovation is a wellspring of economic growth. While many countries can grow by adopting existing technologies and business practices, America must continually innovate because our workers and firms are often operating at the technological frontier. Innovation is also a powerful tool for addressing our most pressing challenges as a nation, such as enabling more Americans to lead longer, healthier lives, and accelerating the transition to a low-carbon economy.
Report to the President and Congress Ensuring Leadership in Federally Funded ...Ed Dodds
In the report, PCAST focuses on eight R&D areas: cybersecurity, IT and health, Big Data and data-intensive computing, IT and the physical world, privacy protection, cyber-human systems, high capability computing, and foundational computing research. All of these areas help to achieve the Nation’s priorities. For example, Big Data, IT and the physical world, and high-capability computing are essential contributors to addressing issues within energy and the environment.
Data Act Federal Register Notice Public Summary of ResponsesEd Dodds
Summary of Responses to the Treasury Bureau of the Fiscal Service Notice in the Federal Register on 9/26/2014 for “Public Input on the Establishment of Financial Data Standards (Data Exchange)
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Best Ayurvedic medicine for Gas and IndigestionSwastikAyurveda
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Géant and DECIDE - improving quality of life for sufferers from Alzheimer's Disease
1. CASE STUDY
connect • communicate • collaborate
GÉANT and DECIDE: Improving quality of life
for sufferers from Alzheimer’s Disease
Using advanced networks and grid computing technology to
enable earlier diagnosis and faster treatment for patients
Dementia – the growing challenge for healthcare
Over 35 million people around the world suffer from a form of
dementia, with 7.3 million in Europe alone. This number is predicted
to double over the next 20 years to 65.7 million sufferers by 2030.
With no current cure for dementia conditions such as Alzheimer’s
Disease and schizophrenia, the medical focus is on earlier diagnosis,
which dramatically improves the quality of life for both patients and
their carers, as well as research into the causes and prevention of
these crippling diseases.
However early diagnosis is often difficult, as many of the symptoms
of Alzheimer’s can be confused with common signs of aging. A user-
friendly way to help make an informed diagnosis is therefore vital.
To achieve this clinicians need to analyse the rapidly increasing
amount of patient data, such as medical images from Positron
Emission Tomography (PET), Magnetic Resonance Imaging (MRI), The Challenge
Computerised Tomography (CT) and Electroencephalography (EEG) Worldwide, dementia is one of the fastest growing medical
scans, and compare it with large reference databases. This will allow conditions, with 65.7 million global sufferers predicted by 2030.
doctors to spot diagnostic markers for the disease, providing them With no current cure for conditions such as Alzheimer’s Disease
with the tools to help make informed, early diagnoses.
earlier diagnosis is critical to improving patient care. However
DECIDE – making early diagnosis a reality spotting diagnostic markers for the disease requires high speed
These increasingly detailed medical scans help provide better access to medical databases and intense processing power in
information on patient conditions, but consequently create ever order to compare patient imaging data, which are often beyond
growing volumes of data. Every PET scan is at least 1 gigabyte of the computing resources of individual hospitals.
data, and has many different parameters to analyse in order to
complete a diagnosis.
The Solution
This means that the computing power needed to compare scans to A unique infrastructure of powerful computing resources, high
help diagnosis has traditionally been costly and complex. The ground speed networks and international databases that enables
breaking Diagnostic Enhancement of Confidence by an International
Distributed Environment (DECIDE) project aims to solve this issue and clinicians to quickly upload, analyse and compare medical imaging
make early Alzheimer’s diagnosis straightforward, secure and data, enabling informed diagnoses.
available to doctors irrespective of location. DECIDE brings together
the power of research networks, distributed databases, powerful
diagnostic algorithms and grid computing to provide a secure, user-
Key Benefits
By making advanced, simple to use diagnostic tools available to
friendly service to clinicians across Europe.
clinicians across Europe, DECIDE enables faster diagnosis of
At the heart of DECIDE is a grid-based e-infrastructure which links Alzheimer’s and consequently improved patient care.
powerful computing resources across Europe through high speed,
Diagnostic Enhancement of Confidence by
an International Distributed Environment
connect • communicate • collaborate
2. reliable research networks. By using the capabilities of the pan-
European GÉANT network and National Research and Education “The neurological data that researchers and clinicians
Networks (NRENs), alongside their own networks, the whole process collect is dramatically increasing, meaning that
will be straightforward for clinicians. They will simply turn on their
managing and analysing this information is becoming
PC, access the DECIDE portal through their web browser, upload the
biomedical images of the patient and, by a simple click, let DECIDE more and more difficult and expensive,” said Laura
handle the processing, extracting any markers from the data that Leone, DECIDE project co-ordinator. “DECIDE will
demonstrate the onset of Alzheimer’s. If specific markers match, ensure that the diagnostic process is much more
faster, more confident and crucially earlier diagnoses can be made, straightforward and simple, using distributed
helping deliver major improvements in patients’ lives. Prior to DECIDE computing resources and the power of the GÉANT
doctors would not have had the tools or resources to quickly carry network to create an advanced e-infrastructure that
out this level of analysis themselves as it required a level of
spans the entire European medical community. Once it
infrastructure that was too complex or costly for individual hospitals
to provide. is rolled out it will help clinicians make better
informed decisions, delivering a positive impact on
patient care when it comes to conditions such as
Alzheimer’s Disease.”
“A new case of dementia develops every 24 seconds in
Europe, showing its steady increase and in 2008
alone, the total cost of caring for dementia patients
was estimated to be ⇔160 billion,” said Jean Georges, There are a wide variety of local networks involved in healthcare –
executive director of Alzheimer Europe. “Because of including commercial providers that provide connectivity to hospitals
and national research networks that link universities and research
Europe’s ageing population and increasing pressures
centres. GÉANT works with all of these and provides support that is
on public finances, dementia will be one of the major customised to their particular needs, meaning they are all able to use
challenges for national health systems moving the resources they have to best advantage. To make it seamless and
forward. Projects such as DECIDE that improve and transparent to the end user, GÉANT’s perfSONAR MDM monitoring
facilitate early diagnosis are therefore welcome in tool is currently being used to monitor service across the multiple
improving the lives of sufferers and carers.” network domains that make up the DECIDE infrastructure.
Rolling out the service
While the project only began in November 2010, progress has been
neuGRID – the Google for brain imaging
swift. The first version of the service, based on Statistical Parametric
DECIDE, which is funded by the European Union under the FP7
Modelling (SPM) techniques, was validated and tested in the first
programme, is an international project that brings together partners
half of 2011 prior to wider release. The researchers are also
from across Europe. It builds on the pioneering neuGRID e-
developing other diagnostic algorithms to search for new markers
infrastructure, which was designed specifically for scientific research
within the DECIDE infrastructure, such as the GriSPM algorithm
into Alzheimer's and other neurodegenerative brain diseases. In
evaluating brain metabolism and perfusion as well as co-operating
neuGRID, the collection/archiving of large amounts of imaging data
with projects outside Europe. Looking longer term the infrastructure
is paired with facilities and services to provide a virtual imaging
could be extended to cover algorithms relating to other diseases of
laboratory that can be accessed by any scientist with a PC and a web
the brain and other organs, involving the whole life science research
browser. For example, by comparing over 6,000 medical imaging
community.
scans from 715 patients, neuGRID successfully found and extracted a
promising biomarker that tracks Alzheimer’s disease progression in a
record time of two weeks.
Making diagnosis simple for all connect • communicate • collaborate
Essentially DECIDE extends neuGRID, enabling doctors and
researchers to access its databases and computing power through an GÉANT is the pan-European data network dedicated to the
easy to use, secure service tailored to their needs. As it is a grid- research and education community, built and operated by
based infrastructure they do not need to invest in additional DANTE. Together with Europe's national research networks
computing resources – it can be used through any PC with a (NRENs), GÉANT connects 40 million users in over 8,000
network connection within a hospital or surgery. institutions across 40 countries and supports research types as
diverse as medicine, climate change and performing arts.
Grid computing relies on high speed networks that can link large
amounts of processing power together, seamlessly and reliably - GÉANT and national research networks provide DECIDE with the
currently DECIDE has over 1,000 CPU computing processors with 70 high quality, reliable and high speed connections to allow easy
access to distributed computing resources and information
terabytes of storage. Consequently DECIDE has worked in partnership
databases. This helps clinicians across Europe make more
with the pan-European GÉANT network, as well as Italian national informed diagnostic decisions, improving patient’s lives.
network GARR to design a network that matches its needs.
For more information:
DECIDE: http://www.eu-decide.eu/
neuGRID: http://www.neugrid.eu
GARR: http://www.garr.it
GÉANT: www.geant.net
This document has been produced with the financial assistance of the European Union. The contents of this document are the sole responsibility of
DANTE and can under no circumstances be regarded as reflecting the position of the European Union.