Recently proposed health applications are able to enforce essential advancements in the
healthcare sector. The design of these innovative solutions is often enabled through the cloud
computing model. With regards to this technology, high concerns about information security
and privacy are common in practice. These concerns with respect to sensitive medical
information could be a hurdle to successful adoption and consumption of cloud-based health
services, despite high expectations and interest in these services. This research attempts to
understand behavioural intentions of healthcare professionals to adopt health clouds in their
clinical practice. Based on different established theories on IT adoption and further related
theoretical insights, we develop a research model and a corresponding instrument to test the
proposed research model using the partial least squares (PLS) approach. We suppose that
healthcare professionals’ adoption intentions with regards to health clouds will be formed by
their outweighing two conflicting beliefs which are performance expectancy and medical
information security and privacy concerns associated with the usage of health clouds. We
further suppose that security and privacy concerns can be explained through perceived risks.
Framework for efficient transformation for complex medical data for improving...IJECEIAES
The adoption of various technological advancement has been already adopted in the area of healthcare sector. This adoption facilitates involuntary generation of medical data that can be autonomously programmed to be forwarded to a destined hub in the form of cloud storage units. However, owing to such technologies there is massive formation of complex medical data that significantly acts as an overhead towards performing analytical operation as well as unwanted storage utilization. Therefore, the proposed system implements a novel transformation technique that is capable of using a template based stucture over cloud for generating structured data from highly unstructured data in a non-conventional manner. The contribution of the propsoed methodology is that it offers faster processing and storage optimization. The study outcome also proves this fact to show propsoed scheme excels better in performance in contrast to existing data transformation scheme.
Framework for propagating stress control message using heartbeat based iot re...IJECEIAES
Abnormal level of stress is the root indicator factor to have significant impact over the health of heart and there is a close relationship between the stress levels with heart rate. Review of the existing literature showcase that there has been various work that has been carried out towards investigation of considering heart rate with an internet-of-things (IoT) system. Apart from this, existing system doesnt offer any instantaneous solution where certain intimation is offered in real-time to the user with wearables as a solution to control the stress condition. Therefore, the current paper introduces a novel framework where the sampled heart rates of the patients are captured by IoT deivices. The aggregated data are further forwarded to the cloud analytic system that uses correlation to extract the appropriate message. The system after being applied with teh machine learning approach could further extract the elite outcome followed by forwarding the contextual data to teh user. Using an analytical modelliig, the proposed system shows that it offers better accuracy and reduced processing time when compared with other machine learning approach and thereby it proves to be cost effective solution in IoT system over medical case study.
Framework for efficient transformation for complex medical data for improving...IJECEIAES
The adoption of various technological advancement has been already adopted in the area of healthcare sector. This adoption facilitates involuntary generation of medical data that can be autonomously programmed to be forwarded to a destined hub in the form of cloud storage units. However, owing to such technologies there is massive formation of complex medical data that significantly acts as an overhead towards performing analytical operation as well as unwanted storage utilization. Therefore, the proposed system implements a novel transformation technique that is capable of using a template based stucture over cloud for generating structured data from highly unstructured data in a non-conventional manner. The contribution of the propsoed methodology is that it offers faster processing and storage optimization. The study outcome also proves this fact to show propsoed scheme excels better in performance in contrast to existing data transformation scheme.
Framework for propagating stress control message using heartbeat based iot re...IJECEIAES
Abnormal level of stress is the root indicator factor to have significant impact over the health of heart and there is a close relationship between the stress levels with heart rate. Review of the existing literature showcase that there has been various work that has been carried out towards investigation of considering heart rate with an internet-of-things (IoT) system. Apart from this, existing system doesnt offer any instantaneous solution where certain intimation is offered in real-time to the user with wearables as a solution to control the stress condition. Therefore, the current paper introduces a novel framework where the sampled heart rates of the patients are captured by IoT deivices. The aggregated data are further forwarded to the cloud analytic system that uses correlation to extract the appropriate message. The system after being applied with teh machine learning approach could further extract the elite outcome followed by forwarding the contextual data to teh user. Using an analytical modelliig, the proposed system shows that it offers better accuracy and reduced processing time when compared with other machine learning approach and thereby it proves to be cost effective solution in IoT system over medical case study.
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.
Selection of Articles using Data Analytics for Behavioral Dissertation Resear...PhD Assistance
Outcomes in health-related issues including psychological, educational, Behavioral, environmental, and social are intended to sustain positive change by digital interferences. These changes may be delivered using any digital device like a phone or computer, and make them gainful for the provider. Complex and large-scale datasets that contain usage data can be yielded by testing a digital intervention. This data provides invaluable detail about how the users interact with these interventions and notify their knowledge of engagement, if they are analyzed properly. This paper recommends an innovative framework for the process of analyzing usage associated with a digital intervention .
PhD Assistance is an Academic The Best Dissertation Writing Service & Consulting Support Company established in 2001. specialiWeze in providing PhD Assignments, PhD Dissertation Writing Help , Statistical Analyses, and Programming Services to students in the USA, UK, Canada, UAE, Australia, New Zealand, Singapore and many more.
Website Visit: https://bit.ly/3dANXUD
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
Email: info@phdassistance.com
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Propose a Enhanced Framework for Prediction of Heart DiseaseIJERA Editor
Heart disease diagnosis requires more experience and it is a complex task. The Heart MRI, ECG and Stress Test etc are the numbers of medical tests are prescribed by the doctor for examining the heart disease and it is the way of tradition in the prediction of heart disease. Today world, the hidden information of the huge amount of health care data is contained by the health care industry. The effective decisions are made by means of this hidden information. For appropriate results, the advanced data mining techniques with the information which is based on the computer are used. In any empirical sciences, for the inference and categorisation, the new mathematical techniques to be used called Artificial neural networks (ANNs) it also be used to the modelling of the real neural networks. Acting, Wanting, knowing, remembering, perceiving, thinking and inferring are the nature of mental phenomena and these can be understand by using the theory of ANN. The problem of probability and induction can be arised for the inference and classification because these are the powerful instruments of ANN. In this paper, the classification techniques like Naive Bayes Classification algorithm and Artificial Neural Networks are used to classify the attributes in the given data set. The attribute filtering techniques like PCA (Principle Component Analysis) filtering and Information Gain Attribute Subset Evaluation technique for feature selection in the given data set to predict the heart disease symptoms. A new framework is proposed which is based on the above techniques, the framework will take the input dataset and fed into the feature selection techniques block, which selects any one techniques that gives the least number of attributes and then classification task is done using two algorithms, the same attributes that are selected by two classification task is taken for the prediction of heart disease. This framework consumes the time for predicting the symptoms of heart disease which make the user to know the important attributes based on the proposed framework.
Along with improvement of clouds, cloudlet and wearable devices, it becomes necessary to provide better medical information sharing over the internet. As we know, sharing medical information is very critical and challenging issue because medical information contains patient’s delicate information. The medical information sharing mainly includes collection, storage and sharing of medical information over the internet. In existing healthcare framework, patient asks health query/question which is being answered by multiple doctors. The user is provided with correct answer with the help of truth discovery method. The medical information and history of patient is directly delivered to the remote cloud. The paper proposes system to provide confidentiality of medical information and protect healthcare system from intrusion. In this proposed system, during the medical information collection stage, the Number Theory Research Unit (NTRU) algorithm is used to encrypt user’s vital signs collected various sensors. Then to protect medical information stored at remote cloud against the malicious attacks, a Collaborative Intrusion Detection System (CIDS) built on cloudlet mesh is proposed. Medical Knowledge Extraction (MKE) System is proposed to provide most reliable answer to the user’s health related query. MKE extract the medical knowledge from noisy query-answers pairs and estimate the trustworthiness degree and doctor expertise using truth discovery method.
Cloud computing in eHealthis an emerging area for only few years. There needs to identify the state of the
art and pinpoint challenges and possible directions for researchers and applications developers. Based on
this need, we have conducted a systematic review of cloud computing in eHealth. We searched ACM
Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as well as relevant open-access
journals for relevant articles. A total of 237 studies were first searched, of which 44 papers met the Include
Criteria. The studies identified three types of studied areas about cloud computing in eHealth, namely (1)
cloud-based eHealth framework design (n=13);(2) applications of cloud computing (n=17); and (3)
security or privacy control mechanisms of healthcare data in the cloud (n=14). Most of the studies in the
review were about designs and concept-proof. Only very few studies have evaluated their research in the
real world, which may indicate that the application of cloud computing in eHealth is still very immature.
However, our presented review could pinpoint that a hybrid cloud platform with mixed access control and
security protection mechanisms will be a main research area for developing citizen centred home-based
healthcare applications.
Big data is to be implemented in as full way in real-time; it is still in a research. People
need to know what to do with enormous data. Insurance agencies are actively participating for the
analysis of patient's data which could be used to extract some useful information. Analysis is done in
term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
allergies & insurance policies which are made by the application of map reduce and useful data is
extracted. We are analysing more number of factors like disease Types with its agreeing reasons,
insurance policy details along with sanctioned amount, family grade wise segregation.
Keywords: Big data, Stemming, Map reduce Policy and Hadoop.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision making for an organization. Combining multiple operational databases and external data create data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.
Novel holistic architecture for analytical operation on sensory data relayed...IJECEIAES
With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost-effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.
DIGITAL HEALTH: DATA PRIVACY AND SECURITY WITH CLOUD COMPUTING Akshay Mittal
Emerging Threats and Countermeasures - Digital health is the convergence of digital technology in healthcare. The emerging technology and the use of innovations are needed in healthcare for advancements and better outcomes. With the use of innovations, new threats and challenges are emerging in the industry which needs to be managed for efficient operations.
A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analyt...IJECEIAES
With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an errorfree data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.
Bacterial virulence proteins, which have been classified on structure of virulence, causes
several diseases. For instance, Adhesins play an important role in the host cells. They are
inserted DNA sequences for a variety of virulence properties. Several important methods
conducted for the prediction of bacterial virulence proteins for finding new drugs or vaccines.
In this study, we propose a method for feature selection about classification of bacterial
virulence protein. The features are constituted directly from the amino acid sequence of a given
protein. Amino acids form proteins, which are critical to life, and have many important
functions in living cells. They occurring with different physicochemical properties by a vector of
20 numerical values, and collected in AAIndex databases of known 544 indices.
For all that, this approach have two steps. Firstly, the amino acid sequence of a given protein
analysed with Lyapunov Exponents that they have a chaotic structure in accordance with the
chaos theory. After that, if the results show characterization over the complete distribution in
the phase space from the point of deterministic system, it means related protein will show a
chaotic structure.
Empirical results revealed that generated feature vectors give the best performance with chaotic
structure of physicochemical features of amino acids with Adhesins and non-Adhesins data sets.
This paper provides a compressive sensing (CS) method of denoising images using Bayesian framework. Some images, for example like magnetic resonance images (MRI) are usually very
weak due to the presence of noise and due to the weak nature of the signal itself. So denoising
boosts the true signal strength. Under Bayesian framework, we have used two different priors:
sparsity and clusterdness in an image data as prior information to remove noise. Therefore, it is
named as clustered compressive sensing based denoising (CCSD). After developing the
Bayesian framework, we applied our method on synthetic data, Shepp-logan phantom and
sequences of fMRI images. The results show that applying the CCSD give better results than
using only the conventional compressive sensing (CS) methods in terms of Peak Signal to Noise
Ratio (PSNR) and Mean Square Error (MSE). In addition, we showed that this algorithm could
have some advantages over the state-of-the-art methods like Block-Matching and 3D
Filtering (BM3D).
The objective of this paper is to propose a combinatorial encoding method based on VLAD to
facilitate the promotion of accuracy for large scale image retrieval. Unlike using a single
feature in VLAD, the proposed method applies multiple heterogeneous types of features, such as
SIFT, SURF, DAISY, and HOG, to form an integrated encoding vector for an image
representation. The experimental results show that combining complementary types of features
and increasing codebook size yield high precision for retrieval.
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.
Selection of Articles using Data Analytics for Behavioral Dissertation Resear...PhD Assistance
Outcomes in health-related issues including psychological, educational, Behavioral, environmental, and social are intended to sustain positive change by digital interferences. These changes may be delivered using any digital device like a phone or computer, and make them gainful for the provider. Complex and large-scale datasets that contain usage data can be yielded by testing a digital intervention. This data provides invaluable detail about how the users interact with these interventions and notify their knowledge of engagement, if they are analyzed properly. This paper recommends an innovative framework for the process of analyzing usage associated with a digital intervention .
PhD Assistance is an Academic The Best Dissertation Writing Service & Consulting Support Company established in 2001. specialiWeze in providing PhD Assignments, PhD Dissertation Writing Help , Statistical Analyses, and Programming Services to students in the USA, UK, Canada, UAE, Australia, New Zealand, Singapore and many more.
Website Visit: https://bit.ly/3dANXUD
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
Email: info@phdassistance.com
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Propose a Enhanced Framework for Prediction of Heart DiseaseIJERA Editor
Heart disease diagnosis requires more experience and it is a complex task. The Heart MRI, ECG and Stress Test etc are the numbers of medical tests are prescribed by the doctor for examining the heart disease and it is the way of tradition in the prediction of heart disease. Today world, the hidden information of the huge amount of health care data is contained by the health care industry. The effective decisions are made by means of this hidden information. For appropriate results, the advanced data mining techniques with the information which is based on the computer are used. In any empirical sciences, for the inference and categorisation, the new mathematical techniques to be used called Artificial neural networks (ANNs) it also be used to the modelling of the real neural networks. Acting, Wanting, knowing, remembering, perceiving, thinking and inferring are the nature of mental phenomena and these can be understand by using the theory of ANN. The problem of probability and induction can be arised for the inference and classification because these are the powerful instruments of ANN. In this paper, the classification techniques like Naive Bayes Classification algorithm and Artificial Neural Networks are used to classify the attributes in the given data set. The attribute filtering techniques like PCA (Principle Component Analysis) filtering and Information Gain Attribute Subset Evaluation technique for feature selection in the given data set to predict the heart disease symptoms. A new framework is proposed which is based on the above techniques, the framework will take the input dataset and fed into the feature selection techniques block, which selects any one techniques that gives the least number of attributes and then classification task is done using two algorithms, the same attributes that are selected by two classification task is taken for the prediction of heart disease. This framework consumes the time for predicting the symptoms of heart disease which make the user to know the important attributes based on the proposed framework.
Along with improvement of clouds, cloudlet and wearable devices, it becomes necessary to provide better medical information sharing over the internet. As we know, sharing medical information is very critical and challenging issue because medical information contains patient’s delicate information. The medical information sharing mainly includes collection, storage and sharing of medical information over the internet. In existing healthcare framework, patient asks health query/question which is being answered by multiple doctors. The user is provided with correct answer with the help of truth discovery method. The medical information and history of patient is directly delivered to the remote cloud. The paper proposes system to provide confidentiality of medical information and protect healthcare system from intrusion. In this proposed system, during the medical information collection stage, the Number Theory Research Unit (NTRU) algorithm is used to encrypt user’s vital signs collected various sensors. Then to protect medical information stored at remote cloud against the malicious attacks, a Collaborative Intrusion Detection System (CIDS) built on cloudlet mesh is proposed. Medical Knowledge Extraction (MKE) System is proposed to provide most reliable answer to the user’s health related query. MKE extract the medical knowledge from noisy query-answers pairs and estimate the trustworthiness degree and doctor expertise using truth discovery method.
Cloud computing in eHealthis an emerging area for only few years. There needs to identify the state of the
art and pinpoint challenges and possible directions for researchers and applications developers. Based on
this need, we have conducted a systematic review of cloud computing in eHealth. We searched ACM
Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as well as relevant open-access
journals for relevant articles. A total of 237 studies were first searched, of which 44 papers met the Include
Criteria. The studies identified three types of studied areas about cloud computing in eHealth, namely (1)
cloud-based eHealth framework design (n=13);(2) applications of cloud computing (n=17); and (3)
security or privacy control mechanisms of healthcare data in the cloud (n=14). Most of the studies in the
review were about designs and concept-proof. Only very few studies have evaluated their research in the
real world, which may indicate that the application of cloud computing in eHealth is still very immature.
However, our presented review could pinpoint that a hybrid cloud platform with mixed access control and
security protection mechanisms will be a main research area for developing citizen centred home-based
healthcare applications.
Big data is to be implemented in as full way in real-time; it is still in a research. People
need to know what to do with enormous data. Insurance agencies are actively participating for the
analysis of patient's data which could be used to extract some useful information. Analysis is done in
term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
allergies & insurance policies which are made by the application of map reduce and useful data is
extracted. We are analysing more number of factors like disease Types with its agreeing reasons,
insurance policy details along with sanctioned amount, family grade wise segregation.
Keywords: Big data, Stemming, Map reduce Policy and Hadoop.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision making for an organization. Combining multiple operational databases and external data create data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.
Novel holistic architecture for analytical operation on sensory data relayed...IJECEIAES
With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost-effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.
DIGITAL HEALTH: DATA PRIVACY AND SECURITY WITH CLOUD COMPUTING Akshay Mittal
Emerging Threats and Countermeasures - Digital health is the convergence of digital technology in healthcare. The emerging technology and the use of innovations are needed in healthcare for advancements and better outcomes. With the use of innovations, new threats and challenges are emerging in the industry which needs to be managed for efficient operations.
A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analyt...IJECEIAES
With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an errorfree data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.
Bacterial virulence proteins, which have been classified on structure of virulence, causes
several diseases. For instance, Adhesins play an important role in the host cells. They are
inserted DNA sequences for a variety of virulence properties. Several important methods
conducted for the prediction of bacterial virulence proteins for finding new drugs or vaccines.
In this study, we propose a method for feature selection about classification of bacterial
virulence protein. The features are constituted directly from the amino acid sequence of a given
protein. Amino acids form proteins, which are critical to life, and have many important
functions in living cells. They occurring with different physicochemical properties by a vector of
20 numerical values, and collected in AAIndex databases of known 544 indices.
For all that, this approach have two steps. Firstly, the amino acid sequence of a given protein
analysed with Lyapunov Exponents that they have a chaotic structure in accordance with the
chaos theory. After that, if the results show characterization over the complete distribution in
the phase space from the point of deterministic system, it means related protein will show a
chaotic structure.
Empirical results revealed that generated feature vectors give the best performance with chaotic
structure of physicochemical features of amino acids with Adhesins and non-Adhesins data sets.
This paper provides a compressive sensing (CS) method of denoising images using Bayesian framework. Some images, for example like magnetic resonance images (MRI) are usually very
weak due to the presence of noise and due to the weak nature of the signal itself. So denoising
boosts the true signal strength. Under Bayesian framework, we have used two different priors:
sparsity and clusterdness in an image data as prior information to remove noise. Therefore, it is
named as clustered compressive sensing based denoising (CCSD). After developing the
Bayesian framework, we applied our method on synthetic data, Shepp-logan phantom and
sequences of fMRI images. The results show that applying the CCSD give better results than
using only the conventional compressive sensing (CS) methods in terms of Peak Signal to Noise
Ratio (PSNR) and Mean Square Error (MSE). In addition, we showed that this algorithm could
have some advantages over the state-of-the-art methods like Block-Matching and 3D
Filtering (BM3D).
The objective of this paper is to propose a combinatorial encoding method based on VLAD to
facilitate the promotion of accuracy for large scale image retrieval. Unlike using a single
feature in VLAD, the proposed method applies multiple heterogeneous types of features, such as
SIFT, SURF, DAISY, and HOG, to form an integrated encoding vector for an image
representation. The experimental results show that combining complementary types of features
and increasing codebook size yield high precision for retrieval.
Weights Stagnation in Dynamic Local Search for SATcsandit
Since 1991, tries were made to enhance the stochast
ic local search techniques (SLS). Some
researchers turned their focus on studying the stru
cture of the propositional satisfiability
problems (SAT) to better understand their complexit
y in order to come up with better
algorithms. Other researchers focused in investigat
ing new ways to develop heuristics that alter
the search space based on some information gathered
prior to or during the search process.
Thus, many heuristics, enhancements and development
s were introduced to improve SLS
techniques performance during the last three decade
s. As a result a group of heuristics were
introduced namely Dynamic Local Search (DLS) that c
ould outperform the systematic search
techniques. Interestingly, a common characteristic
of DLS heuristics is that they all depend on
the use of weights during searching for satisfiable
formulas.
In our study we experimentally investigated the wei
ghts behaviors and movements during
searching for satisfiability using DLS techniques,
for simplicity, DDFW DLS heuristic is chosen.
As a results of our studies we discovered that whil
e solving hard SAT problems such as blocks
world and graph coloring problems, weights stagnati
on occur in many areas within the search
space. We conclude that weights stagnation occurren
ce is highly related to the level of the
problem density, complexity and connectivity.
This paper analyzes vulnerabilities of the SSL/TLS
Handshake
protocol
, which
is
responsible
for
authentication of
the parties in the
communication
and
negotiation of
security parameters
that
will be used
to protect
confidentiality and
integrity of the
data
. It
will
be
analyzed the
attacks
against the implementation of Handshake
protocol, as well as the
attacks against the other
elements
necessary to SSL/TLS protocol to discover security
flaws that were exploited, modes of
attack, the potential consequences, but also studyi
ng methods of defense
.
All versions of the
protocol are going to be the subject of the researc
h but
emphasis will be placed
on the critical
attack that
the most endanger the safety of data.
The goal of
the research
is
to point out the
danger of
existence
of at least
vulnerability
in the SSL/TLS protocol
, which
can be exploited
and
endanger the safety of
the data
that should be protected.
A Text Mining Research Based on LDA Topic Modellingcsandit
A Large number of digital text information is gener
ated every day. Effectively searching,
managing and exploring the text data has become a m
ain task. In this paper, we first represent
an introduction to text mining and a probabilistic
topic model Latent Dirichlet allocation. Then
two experiments are proposed - Wikipedia articles a
nd users’ tweets topic modelling. The
former one builds up a document topic model, aiming
to a topic perspective solution on
searching, exploring and recommending articles. The
latter one sets up a user topic model,
providing a full research and analysis over Twitter
users’ interest. The experiment process
including data collecting, data pre-processing and
model training is fully documented and
commented. Further more, the conclusion and applica
tion of this paper could be a useful
computation tool for social and business research.
Majority Voting Approach for the Identification of Differentially Expressed G...csandit
Understanding gene function (GF) is still a signifi
cant challenge in system biology. Previously,
several machine learning and computational techniqu
es have been used to understand GF.
However, these previous attempts have not produced
a comprehensive interpretation of the
relationship between genes and differences in both
age and gender. Although there are several
thousand of genes, very few differentially expresse
d genes play an active role in understanding
the age and gender differences. The core aim of thi
s study is to uncover new biomarkers that
can contribute towards distinguishing between male
and female according to the gene
expression levels of skeletal muscle (SM) tissues.
In our proposed multi-filter system (MFS),
genes are first sorted using three different rankin
g techniques (t-test, Wilcoxon and ROC).
Later, important genes are acquired using majority
voting based on the principle that
combining multiple models can improve the generaliz
ation of the system. Experiments were
conducted on Micro Array gene expression dataset an
d results have indicated a significant
increase in classification accuracy when compared w
ith existing system
Virtualized Infrastructures are increasingly deployed in many data centers. One of the key
components of this virtualized infrastructure is the virtual network – a software-defined
communication fabric that links together the various Virtual Machines (VMs) to each other and
to the physical host on which the VMs reside. Because of its key role in providing connectivity
among VMs and the applications hosted on them, Virtual Networks have to be securely
configured to provide the foundation for the overall security of the virtualized infrastructure in
any deployment scenario. The objective of this paper is to illustrate a deployment-driven
methodology for deriving a security configuration for Virtual Networks. The methodology
outlines two typical deployment scenarios, identifies use cases and their associated security
requirements, the security solutions to meet those requirements, the virtual network security
configuration to implement each security solution and then analyzes the pros and cons of each
security solution.
Semantic Analysis Over Lessons Learned Contained in Social Networks for Gener...csandit
This paper shows the construction of an organizatio
nal memory metamodel focused on R&D
centers. The metamodel uses lessons learned extract
ed from corporative social networks; the
metamodel aims to promote learning and management o
f organizational knowledge at these
types of organizations. The analysis is applied ini
tially from lessons learned on topics of R&D
in Spanish language. The metamodel use natural lang
uages processing together with ontologies
for analyze the semantic and lexical the each lesso
n learned. The final result involves a
knowledge base integrated by RDF files interrogated
by SPARQL queries.
Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...csandit
Data Aggregation is a vital aspect in WSNs (Wireless Sensor Networks) and this is because it
reduces the quantity of data to be transmitted over the complex network. In earlier studies
authors used homomorphic encryption properties for concealing statement during aggregation
such that encrypted data can be aggregated algebraically without decrypting them. These
schemes are not applicable for multi applications which lead to proposal of Concealed Data
Aggregation for Multi Applications (CDAMA). It is designed for multi applications, as it
provides secure counting ability. In wireless sensor networks SN are unarmed and are
susceptible to attacks. Considering the defence aspect of wireless environment we have used
DYDOG (Dynamic Intrusion Detection Protocol Model) and a customized key generation
procedure that uses Digital Signatures and also Two Fish Algorithms along with CDAMA for
augmentation of security and throughput. To prove our proposed scheme’s robustness and
effectiveness, we conducted the simulations, inclusive analysis and comparisons at the ending.
Ant colony algorithms have become recently popular in solving many optimization problems
because of their collaborative decentralized behavior that mimics the behavior of real ants
when foraging for food. Recommender systems present an optimization problem in which they
aim to accurately predict a user’s rating for an unseen item by trying to find similar users in the
network. Trust-based recommender systems make use of trust between users. T-BAR was the
first successful application of an ant colony algorithm to trust-based recommender systems but
it lacked the ability to deal with cold start users. In this paper we propose a dynamic trust-based
ant recommender (DT-BAR) that solves the problem of cold start users by locally initializing the
pheromone level on edges using the dynamically changing information within each
neighborhood as ants pass by. DT-BAR increases the communication between ants and
emphasizes the importance of trust in the pheromone initialization process.
Beim 3. Deutschen Designerkongress der iDD sprach Nils Holger Moormann über die Entwicklungen und Erfahrungen als Designmöbel-Produzent, von Leidenschaft und dem Ursprung neuer Ideen, von Einzigartigkeit - aber keinesfalls von Artigkeiten.
CLOUD COMPUTING71Dissertation Factors affecting the adoptWilheminaRossi174
CLOUD COMPUTING
71
Dissertation: Factors affecting the adoption of cloud computing in healthcare
Shiva Kumar Pagadala
University of the Cumberlands
Advanced Research Methods
DSRT 839
Dr. Bryian Ramsey
03/04/2022
Abstract
In medical care, cloud technology allows hospital treatment. This research intends to evaluate variables impacting cloud-based diagnostic medical alternatives by clinical staff. Regression analysis tests were employed to assess the conceptual framework and outcome findings. Based on multivariate regression tests, the results demonstrated that all control variations perceived beneficial, the relative advantage of usages, perceived risk, productivity, and availability have a numerically substantial effect apart from organizational commitment and interoperability with the reliant involved in the decision making. Findings reflect the influence and relevance of the response variable and illustrate the crucial role such parameters play in consumers' inclination to employ central data centers in the healthcare industry. These results also corroborate results from earlier relevant investigations. Findings from this study in clinical technology would give greater emphasis to these aspects.
Table of Contents
Chapter One: Introduction
Overview……………………………………………………………………...
Background and Problem Statement………………………………………….
Chapter Two: Literature Review
Introduction
Cloud computing has intensely grown to be one of the most deployed services because of its relative benefits and advantages to firms, organizations, and enterprises. There are four main service deployment models of cloud computing, whereby the models differ according to physical and foundational infrastructure layers (Amron et al., 2017). The models include hybrid cloud, community cloud, private and public cloud. The central service model is a platform as a service, software as a service, and infrastructure as a service.
The complexity of healthcare information systems has been the leading cause of the shift from traditional to modern mobile-based technology systems, as cloud computing helps incorporate solutions to the technologies while adopting new information technology outsourcing (Amron et al., 2017). Apart from improving service quality and meeting various healthcare needs, cloud computing also aids in storing and sharing information such as electric health records and opening new horizons for patients (Amron et al., 2017).
On a theoretical framework, all cloud computing models applicable shall be examined in review on technological aspect, organization-environmental framework aspect, and technological innovation. All the currently and internally ...
Clinical Simulation as an Evaluation Method in Health Inf.docxbartholomeocoombs
Clinical Simulation as an Evaluation Method
in Health Informatics
Sanne JENSEN
a,1
a
The Capital Region of Denmark, Copenhagen, Denmark
Abstract. Safe work processes and information systems are vital in health care.
Methods for design of health IT focusing on patient safety are one of many
initiatives trying to prevent adverse events. Possible patient safety hazards need to
be investigated before health IT is integrated with local clinical work practice
including other technology and organizational structure. Clinical simulation is
ideal for proactive evaluation of new technology for clinical work practice.
Clinical simulations involve real end-users as they simulate the use of technology
in realistic environments performing realistic tasks. Clinical simulation study
assesses effects on clinical workflow and enables identification and evaluation of
patient safety hazards before implementation at a hospital. Clinical simulation also
offers an opportunity to create a space in which healthcare professionals working
in different locations or sectors can meet and exchange knowledge about work
practices and requirement needs. This contribution will discuss benefits and
challenges of using clinical simulation, and will describe how clinical simulation
fits into classical usability studies, how patient safety may benefit by use of
clinical simulation, and it will describe the different steps of how to conduct
clinical simulation. Furthermore a case study is presented.
Keywords. Ergonomics, eHealth, qualitative evaluation, clinical simulation, risk,
safety.
1. Introduction
Implementation of health IT in relation to improvement of patient safety and
optimization of work flow is a paradox [1]. Even though health IT is intended and
anticipated to have a positive impact on quality and efficiency of health care [2], the
application of new technology in healthcare may also increase patient safety hazards [3,
4]. Studies show that adverse events are indeed often related to the use of technology
[5-7].
Design of health IT focusing on protecting patient safety is one of many initiatives
trying to prevent adverse events [8, 9].
2
Patient safety does not entirely rely on
technology but is highly influenced by the interaction between users and technology in
a specific context [10], and sociotechnical issues and human factors are related to many
unintended consequences and patient safety hazards [7, 8, 11]. Possible patient safety
hazards such as design of the IT system itself; embedding of IT system into local work
1
Corresponding author: Sanne Jensen, The Capital Region of Denmark, Borgervanget 7, 2100
Copenhagen O, Denmark, [email protected]
2
See also: F. Magrabi et al., Health IT for patient safety and improving the safety of health IT, in: E.
Ammenwerth, M. Rigby (eds.), Evidence-Based Health Informatics, Stud Health .
Framework for efficient transformation for complex medical data for improving...IJECEIAES
The adoption of various technological advancement has been already adopted in the area of healthcare sector. This adoption facilitates involuntary generation of medical data that can be autonomously programmed to be forwarded to a destined hub in the form of cloud storage units. However, owing to such technologies there is massive formation of complex medical data that significantly acts as an overhead towards performing analytical operation as well as unwanted storage utilization. Therefore, the proposed system implements a novel transformation technique that is capable of using a template based stucture over cloud for generating structured data from highly unstructured data in a non-conventional manner. The contribution of the propsoed methodology is that it offers faster processing and storage optimization. The study outcome also proves this fact to show propsoed scheme excels better in performance in contrast to existing data transformation scheme.
Enhancing healthcare services through cloud service: a systematic reviewIJECEIAES
Although cloud-based healthcare services are booming, in-depth research has not yet been conducted in this field. This study aims to address the shortcomings of previous research by analyzing all journal articles from the last five years using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) systematic literature review methodology. The findings of this study highlight the benefits of cloud-based healthcare services for healthcare providers and patients, including enhanced healthcare services, data security, privacy issues, and innovative information technology (IT) service delivery models. However, this study also identifies challenges associated with using cloud services in healthcare, such as security and privacy concerns, and proposes solutions to address these issues. This study concludes by discussing future research directions and the need for a complete solution that addresses the conflicting requirements of the security, privacy, efficiency, and scalability of cloud technologies in healthcare.
Big data analytics and internet of things for personalised healthcare: opport...IJECEIAES
With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (AI) can provide useful methods and solutions in monitoring, diagnosis, and self-management of health issues for a better personalized healthcare. In this paper, we identify the current personalized healthcare trends and challenges. Then, propose an architecture to support big data analytics using POC test results of an individual. The proposed architecture can facilitate an integrated and self-managed healthcare as well as remote patient care by adapting three popular machine learning algorithms to leverage the current trends in IoT, big data infrastructures and data analytics for advancing personalized healthcare of the future.
RANKING THE MICRO LEVEL CRITICAL FACTORS OF ELECTRONIC MEDICAL RECORDS ADOPTI...hiij
In many countries, the health care sector is entering into a time of unprecedented change. Electronic
Medical Record (EMR) has been introduced into healthcare organizations in order to incorporate better
use of technology, to aid decision making, and to facilitate the search for medical solution. This needs
those professionals in healthcare organizations to be in the process of changing from the use of paper to
maintain medical records into computerized medical recordkeeping opportunities. However, the adoption
of these electronic medical records systems has been slow throughout the healthcare field. The critical
users are physicians which play an important role to success of health information technology including
Electronic Medical Record systems. As a result user adoption is necessary in order to understand the
benefits of an EMR. Therefore, in the current paper, a model of ranking factors of micro-level in EMRs
adoption was developed. Surveys distributed to physicians as this study’s respondent in two private
hospitals in Malaysia. The findings indicate that physicians have a high perception means for the
technology and showed that EMR would increase physician’s performance regarding to decision making.
They have been and continue to be positively motivated to adopt and use the system. The relevant factors
according to micro-level perspective prioritized and ranked by using the Technique for Order of
Preference by Similarity to Ideal Solution (TOPSIS). The aim of ranking and using this approach is to
investigate which factors are more important in EMRs adoption from the micro-level perspectives. The
results of performing TOPSIS is as a novelty which assist health information systems (HIS) success and
also healthcare organizations to motivate their users in accepting of new technology.
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Abstract Data-drivenhealthcareistrulyvaluableandpromising.Aslongasrele- vant data are gathered, probed, used, and managed in a good fashion, significant improvements in the dependability of healthcare practices are achievable. Neverthe- less, unless privacy facets of relevant sensitive data are addressed, there are notable concerns regarding data-driven healthcare policies and applications. In general, tech- nical and engineering facets of such interventions are concentered on to a greater extent, but privacy facets are not adequately addressed. This chapter highlights and discusses privacy issues in data-driven health care. A comprehensive review and distillation of pertinent literature and works yielded relevant results and interpreta- tions. Purposefully, generic privacy issues are elaborated in the beginning. Addition- ally, areas for improvement regarding privacy issues in data-driven health care are underlined and discussed. People, policy, and technology aspects are also explained and deliberated. Moreover, how privacy is related to people and policy to ensure the success in data-driven healthcare practices is discussed in this chapter. Besides, people’s perceptions about privacy are distilled and reported. The focal impact of this chapter is to deliver a contemporary interpretation and discussion regarding privacy issues in data-driven health care. Product developers and managers, policy-makers, and pertinent researchers might benefit from this chapter in order to improve related knowledge and implementations.
Because putting patients’ needs first is essential in the healthcare industries, many healthcare systems
face health information technology (HIT) related challenges and a patient service dilemma.We will firstpresent
the patient service dilemma and provide a high-leveloverview of technologies that have increased the productivity,
efficiency in providing care, and clinical collaboration across their various healthcare campuses. Then, we will
suggest changesto current HIT practice that will enableHealth Systems to be Health Insurance Portability and
Accountability Act (HIPAA) compliant, while meeting the needs of patients, their expectations of care, and the
changing healthcare industry.
Role of Cloud Computing in Healthcare Systemsijtsrd
The healthcare industry is complex because it is so vast in terms of the processes involved and the amount of private and sensitive information it needs to deal with. The industry’s complexity often leads to two major challenges - increased operational cost including data storage cost and difficulty in building a self sufficient health ecosystem. Technology has always been the savior that workaround for overcoming major healthcare industry challenges. One such technology is cloud computing. It has been in use in the healthcare industry for several years and continuously evolving with industry changes. Cloud computing is transforming the healthcare industry at different levels with features like collaboration, scalability, reach ability, efficiency, and security.The on demand computing feature of the cloud adds value, especially when healthcare institutes and care providers need to deploy, access and handle network information at the drop of a hat. With the rise in demand for data based security, there needs to be a shift in the creation, usage, better storage, collaboration, and sharing of healthcare data techniques. It is where cloud computing leaves no stone unturned Healthcare is one such sector that has been at the forefront of adopting cloud technology. Healthcare providers are coming to realize the true potential of cloud solutions across the globe.According to the BBC research report, estimated global spending by stakeholders in the industry on cloud computing is expected to be around 35 billion dollars by 2022. It is anticipated that the CAGR of cloud services and solutions will maintain a trajectory of 15 rise and the size of the Cloud powered healthcare market is to be around 55 billion dollars by the year 2025. Nidhi Prasad | Mahima Chaurasia "Role of Cloud Computing in Healthcare Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49488.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/49488/role-of-cloud-computing-in-healthcare-systems/nidhi-prasad
Ranking the micro level critical factors of electronic medical records adopti...hiij
In many countries, the health care sector is entering into a time of unprecedented change. Electronic
Medical Record (EMR) has been introduced into healthcare organizations in order to incorporate better
use of technology, to aid decision making, and to facilitate the search for medical solution. This needs
those professionals in healthcare organizations to be in the process of changing from the use of paper to
maintain medical records into computerized medical recordkeeping opportunities. However, the adoption
of these electronic medical records systems has been slow throughout the healthcare field. The critical
users are physicians which play an important role to success of health information technology including
Electronic Medical Record systems. As a result user adoption is necessary in order to understand the
benefits of an EMR. Therefore, in the current paper, a model of ranking factors of micro-level in EMRs
adoption was developed. Surveys distributed to physicians as this study’s respondent in two private
hospitals in Malaysia. The findings indicate that physicians have a high perception means for the
technology and showed that EMR would increase physician’s performance regarding to decision making.
They have been and continue to be positively motivated to adopt and use the system. The relevant factors
according to micro-level perspective prioritized and ranked by using the Technique for Order of
Preference by Similarity to Ideal Solution (TOPSIS). The aim of ranking and using this approach is to
investigate which factors are more important in EMRs adoption from the micro-level perspectives. The
results of performing TOPSIS is as a novelty which assist health information systems (HIS) success and
also healthcare organizations to motivate their users in accepting of new technology.
Accessing Information of Emergency Medical Services through Internet of ThingsIJARIIT
IoT is the advanced technology which is use in daily life. IoT make easy to connect different smart devices with
each other by using the internet. IOT is given the ability to computer system to run application program from different
vendors. So in this paper we are accessing the data based on IoT technology for emergency medical services. The fast
development of Internet of Thing.
Clustering algorithms for analysing electronic medical record: A mapping studyIAESIJAI
Electronic Medical Records (EMRs) contain patients’ history related to their medication, vaccine, test results and insurance information. EMRs need to be stored to facilitate the application of clinical treatment and prevention protocols. Clustering algorithms automate the process of information extraction and support health data management. Hence, in this mapping study, we systematically examine the literature on clustering algorithms used for analysing EMRs. We focus on studies published in 2016-2021 to present an overview of clustering techniques used in these studies to analyse medical data. We found 27 studies on clustering techniques, clustering technique problems and the evaluation parameters for analysing EMRs. However, although several studies have focused on this topic, only a few have taken the significant step of examining the clustering techniques used for analysing medical data particularly electronic medical record. Our results highlight that three clustering techniques have been used to analyse medical data, namely, the partitioning, the hierarchical and the density-based algorithms. We identified several clustering technique problems and 10 different evaluation parameters. The results suggest that researchers should focus on analysing medical data that will drive data-driven decision-making by management and promote a data-driven culture to ensure health care quality.
Virtual data integration for a clinical decision support systems IJECEIAES
Clinical decision support (CDS) supplies clinicians and their patients, and relevant staff with meaningful and timely information intelligently integrated or visualized to enhance health and the health sector. Data is the backbone of decision support systems, especially (clinical) ones. Data integration (either virtual or physical manner) is a powerful technique to manipulate a vast amount of heterogeneous data and prepare it as input for the decision-making process. The difficulties in manipulating data that have a physical data integration technique motivated the decision support developers to tend to data virtualization as a data integration technique. In this paper, a clinical decision support system was developed using the virtual data integration technique. The developed system was evaluated in terms of usability and its capability of providing clinical decision support. The evaluation findings indicate that the proposed system is highly usable and has a positive impact on supporting the clinical decision-making process.
The Case Study of an Early Warning Models for the Telecare Patients in TaiwanIJERA Editor
To propose a practical early warning analysis model for the telecare patients, this study applied data mining
technology as a basis to investigate the classification of patient groups by disease severity and incidence using
data contained in a telecare database regarding the number of a clinic. The ultimate purpose of this study was to
provide a new direction for telecare system planning and developing strategies.
The subject of this case study was a private clinic which is providing telecare system to patients in Taiwan, and
we used three data mining techniques including discriminant analysis, logistic regression and artificial neural
network to construct an early warning analysis model based on several factors such as: Demographic variables,
pathological signals, health management index, diagnosis and treatment records, emergency notification signal.
According the results, the telecare system can build stronger physician-patient relationship in advance through
previously paying attention to patients’ physiological conditions, reminding them to do self-management, even
taking them to the hospital for observation. A comparison of discriminative rates showed that the artificial neural
network model had the highest overall correct classification rate, 85.52%, and thus is a tool worthy of
recommendation
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
2. 18 Computer Science & Information Technology (CS & IT)
([3][3],[27]). The exposure of security and privacy concerns related to sensitive medical
information could be a serious hurdle to successful adoption and consumption of cloud-based
health services, as repeatedly demonstrated by prior empirical evidence in other healthcare
settings ([1][1], [2], [3], [18], [15], [29], [6], [35], and [5]).
This research examines which determinants can explain the extent to which medical workers will
be willing to adopt health clouds in their daily work. To conduct the research, we follow the
guidelines proposed by [44][44], [38], [31] and [19]. We build on well-established theories and
works on adoption of information technologies ([47], [48]) and existing theoretical insights into
the factors influencing healthcare IT and cloud computing adoption. We further draw on utility
maximization theory ([3], [12]) arguing that one tries to maximize his or her total utility. We
suppose the utility function to be given by the trade-off between expected positive and negative
outcomes in a healthcare professional’s decision-making process with regards to the usage of
health clouds.
The paper is divided into four sections. In Section 2, we introduce the background of our
research, highlight main theoretical foundations and formulate research hypothesis. Section 3
proceeds with presenting the research model where we illustrate the hypothesized relations. It
further deals with the instrument developed to test the proposed research model using the partial
least squares (PLS) approach ([19][19], [20], [44]). We conclude by recapitulating the results of
this work, extensively discussing its limitations and thus giving recommendations for further
research.
2. BACKGROUND AND THEORETICAL FOUNDATIONS
The availability of medical data is of utmost importance to physicians during the medical service
delivery [42][42]. The healthcare sector can further profit from modern data analysis techniques.
Their application fields in the healthcare area range from disease detection, disease outbreak
prediction, and choice of a therapy to useful information extraction from doctors’ free-note
clinical notes, and medical data gathering and organizing [21]. These techniques can also be
applied to assessment of plausibility and performance of medical services and medical therapies
development [7]. Recently, [32] introduced an interactive three-dimensional e-learning portal for
novice surgeons. Under real time conditions, their surgical procedures are to be compared to the
practice of experienced surgeons. [30] presented a decision support system aimed to assist
physicians in finding a successful treatment for some certain illness based on the currently
available best practices and the characteristics of a given patient.
By provisioning adequate capacities to store and process huge amounts of data, cloud computing
facilitates the design of these innovative applications in the healthcare area. However, this
technology is also known for users’ concerns about their information security and privacy ([24],
[36]). While the providers of healthcare-related websites are interested in collecting medical
information ([22][22], [25]), the misuse of medical information might result in different
harassment and discrimination scenarios for patients ([3],[27]). In the recent past, there were
cases where, based on disclosed medical information, marketers of medical products and services
sent their promotional offers; employers refused to hire applicants and even fired employees;
insurance firms denied life insurances. The exposure of the concerns surrounding information
security and privacy could therefore negatively affect adoption and consumption of cloud-based
health services, as multiple empirical studies demonstrated this in the healthcare context ([1], [2],
[3][3], [18], [15], [29], [6], [35] and [5]) and other settings ([13], [14] and [40]).
In the present work, we try to understand the predictors of behavioural intention of healthcare
professionals to adopt health clouds in their work. In the research related to management of
3. Computer Science & Information Technology (CS & IT) 19
information systems (MIS), a variety of theories have been applied to explain an individual’s
adoption of information technologies. Among others, these include theories of reasoned action
(TRA), planned behaviour (TPB), technology acceptance model (TAM), and unified theory of
acceptance and use of technology (UTAUT) ([47], [48]). In line with these theories, we suppose
that healthcare professionals’ adoption of health clouds is a product of beliefs surrounding the
system. We additionally assume that medical workers’ intentions are consistent with utility
maximization theory ([3], [12]) which posits that an individual attempts to maximize his or her
total utility. As usage of health clouds is associated with numerous risks for a healthcare
professional, we suppose that his or her utility function in the presented context is given by the
calculus of conflicting beliefs which involve performance expectancy of the services, on one side,
and associated security and privacy concerns about medical information, on the other side. We
further postulate that information security and privacy concerns result from perceived risks.
2.1 Performance Expectancy
In one of the recent works on information technology acceptance, Venkatesh et al. [47] defines
performance expectancy as the extent to which individuals believe that using the information
technology is helpful in attaining certain gains in their job performance. Performance expectancy
and other factors that pertain to performance expectancy such as perceived usefulness are
generally shown to be the strongest predictors of behavioural intention [47]. Previous work
suggests that healthcare professionals tend to be higher willing to adopt technological advances in
their practice the higher they perceive their usefulness ([17], [8], [46], [35]). Similarly, cloud
computing is more likely to be adopted the more beneficial it appears to the decision maker ([23],
[28], [29], [36]). Therefore, we hypothesize that:
Hypothesis 1. Performance expectancy will be positively associated with behavioural intention to
accept health clouds.
2.2 Security and Privacy Concerns
Online companies rely on use of their customers’ personal information to select their marketing
strategies ([36], [25]). As a result of this, Internet users view their privacy as being invaded. A
recent survey revealed that 90% of Americans and Britons felt concerned about their online
privacy and over 70% of Americans and 60% of Britons were even higher concerned than in the
previous year [43].
Healthcare professionals appear to be ones of the most anxious Internet users in terms of
information privacy. Dinev and Hart [13] argue that Internet “users with high social awareness
and low Internet literacy tend to be the ones with the highest privacy concerns”. Although this
group of users constitute the intellectual core of society, they are not able or willing to keep up
with protecting technologies while using the Internet. Simon et al. [39] further state that
physicians are worried about patient privacy even more than the patients themselves.
In this study, privacy concerns are related to healthcare professionals’ beliefs regarding cloud
computing companies’ ability and willingness to protect medical information ([40], [4], [36]).
The dimensions of privacy concerns involve errors, improper access, collection, and unauthorized
secondary usage.
Due to the open Internet infrastructure vulnerable to multiple security threats [36], we further
consider security concerns. They refer to healthcare professionals’ beliefs regarding cloud
computing companies’ ability and willingness to safeguard medical information from security
breaches ([4], [36]). The dimensions of security concerns include information confidentiality and
4. 20 Computer Science & Information Technology (CS & IT)
integrity, authentication (verification) of the parties involved and non-repudiation of transactions
completed.
Similarly to [4], we distinguish six dimensions of the combined security and privacy concerns,
where we consider the dimensions of errors and improper access to be equivalent to the
expectancy are to be measured with items adapted from Venkatesh et al. ([47], [48]). Security and
privacy concerns are to be explored at a more detailed level, as recommended by [1]. With
regards to the concerns, we draw on the multi-dimensional view proposed by Bansal [4]. The
dimensions of privacy-related concerns, i.e., collection, errors, unauthorized secondary use, and
improper access, originate from the work by Smith et al. [40] and were validated in healthcare
privacy studies ([15], [18]). To measure the factors associated with collection, integrity/errors,
and confidentiality/improper access, the questions from [18] were adapted. For the secondary use
construct, we took items from [12]. Measures for the remaining underlying factors, i.e.,
authentication and non-repudiation, were developed based on [4]. To measure perceived risks, we
rely on the items by [16].
Table 1. Research Model Constructs and Related Questionnaire Items
Construct Items
Behavioural Intention to
Adopt Health Clouds
(based on [47], [48])
Given I get the system offered in the future and the patient consent for
medical information transmission over the system is given, …
… I intend to use it whenever possible.
… I plan to use it to the extent possible.
… I expect that I have to use it.
Performance Expectancy
(based on [47], [48])
Using the system would make it easier to do my job.
I would find the system useful in my job.
If I use the system, I will spend less time on routine job tasks.
Security and Privacy
Concerns – Integrity / Errors
(based on [4], [40], [18])
I would be concerned that in the system…
… medical information can be modified (altered, corrupted).
… medical information is not enough protected against modifications.
… accurate medical information can hardly be guaranteed.
Security and Privacy
Concerns – Confidentiality /
Improper Access
(based on [4], [40], [18])
I would be concerned that in the system…
… medical information can be accessed by unauthorized people.
… medical information is not enough protected against unauthorized
access.
… authorized access to medical information can hardly be guaranteed.
Security and Privacy
Concerns – Authentication
(based on [4])
I would be concerned that in the system…
… transactions with a wrong user can take place in the system.
… verifying the truth of a user in the system is not enough ensured.
… transacting with the right user in the system can hardly be
guaranteed.
Security and Privacy
Concerns – Nonrepudiation
(based on [4])
I would be concerned that transactions in the system …
…. could be declared untrue.
… are disputable.
… are deniable.
Security and Privacy
Concerns – Collection
(based on [4], [40], [18])
I would be concerned that medical information transmitted over the
system …
… does not get deleted from the cloud.
… is kept as a copy.
… is collected by the cloud provider.
Security and Privacy
Concerns – Unauthorized
Secondary Use
(based on [4], [40], [12])
I would be concerned that medical information transmitted over the
system can be …
… used in a way I did not foresee.
… misused by someone unintended.
… made available/sold to companies or unknown parties without your
knowledge.
5. Computer Science & Information Technology (CS & IT) 21
Perceived Risks
(based on [16])
In general, it would be risky to transmit medical information over the
system.
Transmitting medical information over the system would involve many
unexpected problems.
I would not have a good feeling when transmitting medical information
over the system.
The respondents are supposed to be presented one of the above mentioned scenarios. They further
will be asked to provide their answers to the questions on a 7 Likert scale (e.g., 1: Not likely at
all, 2: Highly unlikely, 3: Rather unlikely, 4: Neither likely nor unlikely, 5: Rather likely, 6:
Highly likely, 7: Fully likely). Additionally, they will be asked about practice period [9], their
workplace location (e.g. rural or urban) ([35], [9]), gender, and age, etc. [35]. These questions
will mainly allow describing the sample.
3. CONCLUSION, LIMITATIONS AND SUGGESTIONS FOR FUTURE
RESEARCH
In this work, we defined a theoretical model aimed to explain behavioural intention of healthcare
professionals to adopt health clouds in their clinical practice. We operationalized the research
model and transferred it into a structural equation model to further analyse with the PLS
approach.
Drawing on utility maximization theory and further related research, we suppose that healthcare
professionals’ adoption intentions with regards to health clouds will be formed by outweighing
two conflicting beliefs. They involve expected performance expectancy and security and privacy
concerns associated with the usage of health clouds. We further postulate that security and
privacy concerns can be explained through perceived risks.
Our work implies some limitations. First, there might be some other possible casual relationships
between the constructs proposed in the research model. For example, [46] hypothesize that EMR
security/confidentiality influences its perceived usefulness, while [17] find a positive relationship
between perceived importance of data security and perceived usefulness of electronic health
services. As identified by [26], perceived privacy risk directly influences personal information
disclosure in the context of online social networks. In our future research, we are going to verify
all possible paths, as recommended by [20].
Second, we left some other factors out of consideration such as effort expectancy, social
influence, and facilitating conditions which are often investigated and can extend the study in the
future.
Venkatesh et al. [47] define effort expectancy as referring to the extent to which an individual
finds the system easy to use. The factor is also captured by perceived ease of use specified in
TAM. Perceived ease of use is important for potential cloud computing users ([28], [36]).
Physicians view easy-to-use services as more useful and stronger intend to use them ([17], [6],
[35], [6]). Contrary to these findings and other previous research assertions (e.g., [47], [48]),
perceived ease of use did not exert any significant effects on perceived usefulness or attitude,
when tested in the telemedicine context [8]. The authors suggest that physicians comprehend new
information technologies more easily and quickly than other user groups do. Alternatively, the
importance of perceived ease of use may be weakened by increases in general competence or staff
assistance [8]. These aspects are implied in the concept of facilitating conditions which relates to
the extent to which individuals believe in the existence of an organizational and technical
6. 22 Computer Science & Information Technology (CS & IT)
infrastructure to support their system use [47]. They were found to play a role in formation of
behavioural intention to use cloud computing in hospital [29] and perceived usefulness of
healthcare information technologies ([8], [34], [6], [35]).
Social influence refers to the degree to which individuals perceive that others’ beliefs about their
system use are important [47]. Being differently labelled across studies, social influence was
found to have contradictory results when tested with regards to behavioural intention. Cloud
computing users were significantly guided by the way they believe they are viewed by others as
having used the cloud computing technology [28]. However, practicing physicians’ intentions to
use telemedicine technology were not significantly influenced by social norms [8]. Dinev and Hu
[11] observe subjective norm influencing behavioural intention rather for IT aware groups. Dinev
and Hu believe that the more IT knowledgeable the group are, the more they communicate about
IT related issues and are willing to learn IT solutions their peers already use.
Finally, some variables which are to be used to describe the sample (e.g., workplace location) can
further be controlled for their role. As observed by [35], urban hospitals could be expected to
adopt innovative solutions rather than rural ones. Hospitals located outside cities and towns are
the only alternative for people living nearby. So they do not have to compete with others in
adopting new technologies. Furthermore, they are typically under-occupied and have little
financial support.
ACKNOWLEDGEMENTS
The work presented in this paper was performed in the context of the TRESOR research project
[42]. TRESOR is funded by the German Federal Ministry of Economic Affairs and Energy
(BMWi).
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AUTHOR
Tatiana Ermakova is a research assistant at the Department of Information and
Communication Management of the Technical University of Berlin. She holds a
bachelor degree in Economics and a diploma degree in Applied Informatics in
Economics from the Russian Academy of Economics, a M.Sc. in Information Systems
from Humboldt University of Berlin.