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.
Impact of big data congestion in IT: An adaptive knowledgebased Bayesian networkIJECEIAES
Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
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.
Evaluation of a Multiple Regression Model for Noisy and Missing Data IJECEIAES
The standard data collection problems may involve noiseless data while on the other hand large organizations commonly experience noisy and missing data, probably concerning data collected from individuals. As noisy and missing data will be significantly worrisome for occasions of the vast data collection then the investigation of different filtering techniques for big data environment would be remarkable. A multiple regression model where big data is employed for experimenting will be presented. Approximation for datasets with noisy and missing data is also proposed. The statistical root mean squared error (RMSE) associated with correlation coefficient (COEF) will be analyzed to prove the accuracy of estimators. Finally, results predicted by massive online analysis (MOA) will be compared to those real data collected from the following different time. These theoretical predictions with noisy and missing data estimation by simulation, revealing consistency with the real data are illustrated. Deletion mechanism (DEL) outperforms with the lowest average percentage of error.
Content an Insight to Security Paradigm for BigData on Cloud: Current Trend a...IJECEIAES
The sucesssive growth of collabrative applications producing Bigdata on timeline leads new opprutinity to setup commodities on cloud infrastructure. Mnay organizations will have demand of an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presented
A gigantic archive of terabytes of information is created every day from current data frameworks and computerized advances, for example, Internet of Things and distributed computing. Examination of these gigantic information requires a ton of endeavors at various levels to extricate information for dynamic. Hence, huge information examination is an ebb and flow region of innovative work. The essential goal of this paper is to investigate the likely effect of huge information challenges, and different instruments related with it. Accordingly, this article gives a stage to investigate enormous information at various stages. Moreover, it opens another skyline for analysts to build up the arrangement, in light of the difficulties and open exploration issues.
Anonymization of data using mapreduce on cloudeSAT Journals
Abstract In computer world cloud services are provided by the service providers. The user wants to share the private data which are stored in cloud server for different reasons like data mining, data analysis etc. These can bring the privacy concern. Privacy preservation can be satisfied by Anonymizing data sets through generalization to satisfy privacy requirements by using k-anonymity technique which is a widely used type of privacy preserving techniques. At present days the data of cloud applications are increasing their scale day by day concern with Big Data trend. So it is very difficult thing to accept, manage, maintain and process the large scaled data with-in the required time stamps. Thus for privacy preserving on privacy sensitive , large scaled data is very difficult task for existing anonymization techniques because they will not manage the scaled data sets. This approach addresses the anonymization problem on large scale cloud data sets using two phase top down specialization approach and MapReduce framework. Innovative MapReduce jobs are carefully designed in both phases of this technique to achieve specialization computation on scalable data sets. Scalability and efficiency of Top Down Specialization (TDS) is significantly increased over the existing approach. Keywords: Top Down Specialization, MapReduce, Data Anonymization, Cloud Computing, Privacy Preservation
Impact of big data congestion in IT: An adaptive knowledgebased Bayesian networkIJECEIAES
Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
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.
Evaluation of a Multiple Regression Model for Noisy and Missing Data IJECEIAES
The standard data collection problems may involve noiseless data while on the other hand large organizations commonly experience noisy and missing data, probably concerning data collected from individuals. As noisy and missing data will be significantly worrisome for occasions of the vast data collection then the investigation of different filtering techniques for big data environment would be remarkable. A multiple regression model where big data is employed for experimenting will be presented. Approximation for datasets with noisy and missing data is also proposed. The statistical root mean squared error (RMSE) associated with correlation coefficient (COEF) will be analyzed to prove the accuracy of estimators. Finally, results predicted by massive online analysis (MOA) will be compared to those real data collected from the following different time. These theoretical predictions with noisy and missing data estimation by simulation, revealing consistency with the real data are illustrated. Deletion mechanism (DEL) outperforms with the lowest average percentage of error.
Content an Insight to Security Paradigm for BigData on Cloud: Current Trend a...IJECEIAES
The sucesssive growth of collabrative applications producing Bigdata on timeline leads new opprutinity to setup commodities on cloud infrastructure. Mnay organizations will have demand of an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presented
A gigantic archive of terabytes of information is created every day from current data frameworks and computerized advances, for example, Internet of Things and distributed computing. Examination of these gigantic information requires a ton of endeavors at various levels to extricate information for dynamic. Hence, huge information examination is an ebb and flow region of innovative work. The essential goal of this paper is to investigate the likely effect of huge information challenges, and different instruments related with it. Accordingly, this article gives a stage to investigate enormous information at various stages. Moreover, it opens another skyline for analysts to build up the arrangement, in light of the difficulties and open exploration issues.
Anonymization of data using mapreduce on cloudeSAT Journals
Abstract In computer world cloud services are provided by the service providers. The user wants to share the private data which are stored in cloud server for different reasons like data mining, data analysis etc. These can bring the privacy concern. Privacy preservation can be satisfied by Anonymizing data sets through generalization to satisfy privacy requirements by using k-anonymity technique which is a widely used type of privacy preserving techniques. At present days the data of cloud applications are increasing their scale day by day concern with Big Data trend. So it is very difficult thing to accept, manage, maintain and process the large scaled data with-in the required time stamps. Thus for privacy preserving on privacy sensitive , large scaled data is very difficult task for existing anonymization techniques because they will not manage the scaled data sets. This approach addresses the anonymization problem on large scale cloud data sets using two phase top down specialization approach and MapReduce framework. Innovative MapReduce jobs are carefully designed in both phases of this technique to achieve specialization computation on scalable data sets. Scalability and efficiency of Top Down Specialization (TDS) is significantly increased over the existing approach. Keywords: Top Down Specialization, MapReduce, Data Anonymization, Cloud Computing, Privacy Preservation
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
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.
Insights on critical energy efficiency approaches in internet-ofthings applic...IJECEIAES
Internet-of-things (IoT) is one of the proliferated technologies that result in a larger scale of connection among different computational devices. However, establishing such a connection requires a fault-tolerant routing scheme. The existing routing scheme results in communication but does not address various problems directly linked with energy consumption. Cross layer-based scheme and optimization schemes are frequently used scheme for improving the energy efficiency performance in IoT. Therefore, this paper investigates the approaches where cross-layer-based schemes are used to retain energy efficiencies among resource-constrained devices. The paper discusses the effectivity of the approaches used to optimize network performance in IoT applications. The study outcome of this paper showcase that there are various open-end issues, which is required to be addressed effectively in order to improve the performance of application associated with the IoT system.
Privacy preserving and delegated access control for cloud applicationsredpel dot com
Privacy preserving and delegated access control for cloud applications
for more ieee paper / full abstract / implementation , just visit www.redpel.com
The influence of information security onIJCNCJournal
Cloud computing is the current IT buzzword synonymous with outsourced data center management and agile solution architecture. It has the potential to improve scalability of large enterprise network delivery of services and the capability to revolutionize how data is delivered as a service. At its core, cloud computing is not a new technology but rather a new approach to distributed shared pooling of IT infrastructure linked together to offer centralized IT services on demand. The study results determined that management’s perception of security, cost-effectiveness and IT compliance factors significantly influence their decisions to adopt cloud computing. The results of multiple linear regression analysis testing in this study showed that managements’ perception of cost-effectiveness is more significantly correlated to their
decision to adopt cloud computing than it is to security.
Providing healthcare as-a-service using fuzzy rule-based big data analytics i...nexgentechnology
GET IEEE BIG DATA, JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
VOLUME-7 ISSUE-8, AUGUST 2019 , International Journal of Research in Advent Technology (IJRAT) , ISSN: 2321-9637 (Online) Published By: MG Aricent Pvt Ltd
Clinical Decision Support Systems (CDSS) were explicitly introduced in the 90’s with the aim of providing knowledge to clinicians in order to influence its decisions and, therefore, improve patients’ health care. There are different architectural approaches for implementing CDSS. Some of these approaches are based on cloud computing, which provides on-demand computing resources over the internet. The goal of this paper is to determine and discuss key issues and approaches involving architectural designs in implementing a CDSS using cloud computing. To this end, we performed a standard Systematic Literature Review (SLR) of primary studies showing the intervention of cloud computing on CDSS implementations. Twenty-one primary studies were reviewed. We found that CDSS architectural components are similar in most of the studies. Cloud-based CDSS are most used in Home Healthcare and Emergency Medical Systems. Alerts/Reminders and Knowledge Service are the most common implementations. Major challenges are around security, performance, and compatibility. We concluded on the benefits of implementing a cloud-based CDSS since it allows cost-efficient, ubiquitous and elastic computing resources. We highlight that some studies show weaknesses regarding the conceptualization of a cloud-based computing approach and lack of a formal methodology in the architectural design process.
A tutorial on secure outsourcing of large scalecomputation for big dataredpel dot com
A tutorial on secure outsourcing of large scalecomputation for big data
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Big data Mining Using Very-Large-Scale Data Processing PlatformsIJERA Editor
Big Data consists of large-volume, complex, growing data sets with multiple, heterogenous sources. With the
tremendous development of networking, data storage, and the data collection capacity, Big Data are now rapidly
expanding in all science and engineering domains, including physical, biological and biomedical sciences. The
MapReduce programming mode which has parallel processing ability to analyze the large-scale network.
MapReduce is a programming model that allows easy development of scalable parallel applications to process
big data on large clusters of commodity machines. Google’s MapReduce or its open-source equivalent Hadoop
is a powerful tool for building such applications.
A STUDY OF TRADITIONAL DATA ANALYSIS AND SENSOR DATA ANALYTICSijistjournal
The growth of smart and intelligent devices known as sensors generate large amount of data. These generated data over a time span takes such a large volume which is designated as big data. The data structure of repository holds unstructured data. The traditional data analytics methods well developed and used widely to analyze structured data and to limit extend the semi-structured data which involves additional processing over heads. The similar methods used to analyze unstructured data are different because of distributed computing approach where as there is a possibility of centralized processing in case of structured and semi-structured data. The under taken work is confined to analysis of both verities of methods. The result of this study is targeted to introduce methods available to analyze big data.
Big data is a prominent term which characterizes the improvement and availability of data in all three
formats like structure, unstructured and semi formats. Structure data is located in a fixed field of a record
or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file
includes text and multimedia contents. The primary objective of this big data concept is to describe the
extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V”
dimensions namely Volume, Velocity and Variety, and two more “V” also added i.e. Value and Veracity.
Volume denotes the size of data, Velocity depends upon the speed of the data processing, Variety is
described with the types of the data, Value which derives the business value and Veracity describes about
the quality of the data and data understandability. Nowadays, big data has become unique and preferred
research areas in the field of computer science. Many open research problems are available in big data
and good solutions also been proposed by the researchers even though there is a need for development of
many new techniques and algorithms for big data analysis in order to get optimal solutions. In this paper,
a detailed study about big data, its basic concepts, history, applications, technique, research issues and
tools are discussed.
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.
Insights on critical energy efficiency approaches in internet-ofthings applic...IJECEIAES
Internet-of-things (IoT) is one of the proliferated technologies that result in a larger scale of connection among different computational devices. However, establishing such a connection requires a fault-tolerant routing scheme. The existing routing scheme results in communication but does not address various problems directly linked with energy consumption. Cross layer-based scheme and optimization schemes are frequently used scheme for improving the energy efficiency performance in IoT. Therefore, this paper investigates the approaches where cross-layer-based schemes are used to retain energy efficiencies among resource-constrained devices. The paper discusses the effectivity of the approaches used to optimize network performance in IoT applications. The study outcome of this paper showcase that there are various open-end issues, which is required to be addressed effectively in order to improve the performance of application associated with the IoT system.
Privacy preserving and delegated access control for cloud applicationsredpel dot com
Privacy preserving and delegated access control for cloud applications
for more ieee paper / full abstract / implementation , just visit www.redpel.com
The influence of information security onIJCNCJournal
Cloud computing is the current IT buzzword synonymous with outsourced data center management and agile solution architecture. It has the potential to improve scalability of large enterprise network delivery of services and the capability to revolutionize how data is delivered as a service. At its core, cloud computing is not a new technology but rather a new approach to distributed shared pooling of IT infrastructure linked together to offer centralized IT services on demand. The study results determined that management’s perception of security, cost-effectiveness and IT compliance factors significantly influence their decisions to adopt cloud computing. The results of multiple linear regression analysis testing in this study showed that managements’ perception of cost-effectiveness is more significantly correlated to their
decision to adopt cloud computing than it is to security.
Providing healthcare as-a-service using fuzzy rule-based big data analytics i...nexgentechnology
GET IEEE BIG DATA, JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
VOLUME-7 ISSUE-8, AUGUST 2019 , International Journal of Research in Advent Technology (IJRAT) , ISSN: 2321-9637 (Online) Published By: MG Aricent Pvt Ltd
Clinical Decision Support Systems (CDSS) were explicitly introduced in the 90’s with the aim of providing knowledge to clinicians in order to influence its decisions and, therefore, improve patients’ health care. There are different architectural approaches for implementing CDSS. Some of these approaches are based on cloud computing, which provides on-demand computing resources over the internet. The goal of this paper is to determine and discuss key issues and approaches involving architectural designs in implementing a CDSS using cloud computing. To this end, we performed a standard Systematic Literature Review (SLR) of primary studies showing the intervention of cloud computing on CDSS implementations. Twenty-one primary studies were reviewed. We found that CDSS architectural components are similar in most of the studies. Cloud-based CDSS are most used in Home Healthcare and Emergency Medical Systems. Alerts/Reminders and Knowledge Service are the most common implementations. Major challenges are around security, performance, and compatibility. We concluded on the benefits of implementing a cloud-based CDSS since it allows cost-efficient, ubiquitous and elastic computing resources. We highlight that some studies show weaknesses regarding the conceptualization of a cloud-based computing approach and lack of a formal methodology in the architectural design process.
A tutorial on secure outsourcing of large scalecomputation for big dataredpel dot com
A tutorial on secure outsourcing of large scalecomputation for big data
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Big data Mining Using Very-Large-Scale Data Processing PlatformsIJERA Editor
Big Data consists of large-volume, complex, growing data sets with multiple, heterogenous sources. With the
tremendous development of networking, data storage, and the data collection capacity, Big Data are now rapidly
expanding in all science and engineering domains, including physical, biological and biomedical sciences. The
MapReduce programming mode which has parallel processing ability to analyze the large-scale network.
MapReduce is a programming model that allows easy development of scalable parallel applications to process
big data on large clusters of commodity machines. Google’s MapReduce or its open-source equivalent Hadoop
is a powerful tool for building such applications.
A STUDY OF TRADITIONAL DATA ANALYSIS AND SENSOR DATA ANALYTICSijistjournal
The growth of smart and intelligent devices known as sensors generate large amount of data. These generated data over a time span takes such a large volume which is designated as big data. The data structure of repository holds unstructured data. The traditional data analytics methods well developed and used widely to analyze structured data and to limit extend the semi-structured data which involves additional processing over heads. The similar methods used to analyze unstructured data are different because of distributed computing approach where as there is a possibility of centralized processing in case of structured and semi-structured data. The under taken work is confined to analysis of both verities of methods. The result of this study is targeted to introduce methods available to analyze big data.
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.
Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.
Stacked Generalization of Random Forest and Decision Tree Techniques for Libr...IJEACS
The huge amount of library data stored in our modern research and statistic centers of organizations is springing up on daily bases. These databases grow exponentially in size with respect to time, it becomes exceptionally difficult to easily understand the behavior and interpret data with the relationships that exist between attributes. This exponential growth of data poses new organizational challenges like the conventional record management system infrastructure could no longer cope to give precise and detailed information about the behavior data over time. There is confusion and novel concern in selecting tools that can support and handle big data visualization that deals with multi-dimension. Viewing all related data at once in a database is a problem that has attracted the interest of data professionals with machine learning skills. This is a lingering issue in the data industry because the existing techniques cannot be used to remove or filter noise from relevant data and pad up missing values in order to get the required information. The aim is to develop a stacked generalization model that combines the functionality of random forest and decision tree to visualization library database visualization. In this paper, the random forest and decision tree techniques were employed to effectively visualize large amounts of school library data. The proposed system was implemented with a few lines of Python code to create visualizations that can help users at a glance understand and interpret the behavior of data and its relationships. The model was trained and tested to learn and extract hidden patterns of data with a cross-validation test. It combined the functionalities of both models to form a stacked generalization model that performed better than the individual techniques. The stacked model produced 95% followed by the RF which produced a 95% accuracy rate and 0.223600 RMSE error value in comparison with the DT which recorded an 80.00% success rate and 0.15990 RMSE value.
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 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.
Similar to A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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.
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just for one patient is so massive that it is really difficult to perform analysis. The problem of data analysis
worsens when complete hospital data is considered. Basically, the prime reason of this problem is obviously
the size of the data which could be in terms of petabytes, however, storage is still not a bigger challenge in
the era of cloud computing. The bigger challenges related to processing such data are i) data variety, ii) data
uncertainty, and iii) data velocity. Data variety is related to heterogeneity in the forms of the data which
makes the data highly non-applicable to be subjected to any forms of data analysis. Data uncertainty speaks
about the possibility of missing data as well as presence of ambiguous data due to which the complete
volume of data becomes unreliable. This problem is quite difficult to handle and is mainly caused due to
defective data storage and retrieval processing. The final problem i.e. data velocity is something which is
quite difficult to be solved as the data arrival rate is unknown. At present, there is no such model or
parameter that has been reportedly used to gauge the rate of data arrival from a particular source in a highly
distributed network system. Although, there are existing software frameworks like Hadoop, MapReduce,
Cassandra, neo4j and many more, all of them are mainly open source, which is never said to be secured as
the adversaries mainly uses open source to initiate attacks over cloud. Apart from this, there are many cases
wherein existing frameworks have reported problems. Therefore, we review some of the existing techniques
of data analytical application which points towards medical data processing. From this we learn that this is
still in its nascent stage and hence there is scope for evolving up with a solution. A significant research gap of
integrated framework towards addressing majority of problems in data analytics in cloud in found. We,
therefore, present a novel framework that has the capability to address multiple problems in one platform in
highly cost effective manner using big data approach. Section 1.1 discusses about the existing literatures
where different techniques are discussed for energy harvesting followed by discussion of problem
identification in Section 1.2. Section 1.3 briefs about the proposed contribution to address research problems.
Section 2 elaborates about the algorithm implementation followed by result discussion in Section 3. Finally
summary of the paper is discussed in Section 4.
1.1. Background
This section discusses about the existing technqiues towards big data analytics. Our prior work [6-9]
have discussed about the existing technqiues pertaining to significance of classification approach, tools of big
data analytics, and applicability of big data analytics over healthcare sector. Chen et al. [10] have presented
architecture for supportability of agile methodologies over big data. Dabek and Caban [11] have introduced a
technique where a visualization mechanism is designed for modeling the user interaction. Fiadino et al. [12]
have presented a discussion towards using cellular network in the viewpoint of big data analytics. Ordonez et
al. [13] have introduced a technique that makes use of matrix multiplication in order to perform
summarization of big data using statistical modeling. The technique uses array-based operators exclusively
for sparse as well as for dense dataset to show memory consumption and scalability accomplishment. Paul et
al. [14] have used the big data analytics in order to solve the problem associated with human behaviour. The
author discusses about introducing a system that could act as a communication bridge between the internet-
of-things application and data mining technqiues over larger data scale.Sheng et al. [15] have emphasized on
the applicability of big data analytics with respect to information theory and cyber-physical system. This
paper presents a superior form of mathematical modeling connected with information theory followed by
significant channel models that could be potentially helpful for extracting knowledge from bigger scale of
data.Tawalbeh et al. [16] have discussed the generation of massive loads of data from healthcare sector and
how such forms of data can be analyzed using big data analytics. The problems pertaining to data uncertainty
is being recently discussed by Wang and He [17]. The authors point out 6 potential problems to be overcome
for future application of big data analytics e.g. i) highly complex representation of data, ii) pervasive
uncertainty, iii) extremely weaker relationship among data, iv) computation-scalability problems, v)
extraordinary massive size of complex data, and vi) larger number of classes involved in mining process.
Similar direction of study considering Electronic Health Records-based data and its applicability over mining
approach is discussed by Wu et al. [18]. The discussion highlights that dimensionality reduction is one of the
prominent problems along with processing capability. Castellano et al. [19] have presented a classification-
based approach for discriminating critical condition of arrhythmia. The authors have presented a unique
technique for involuntary clustering of electrograms in presence of cloud environment. The technique is also
claimed to offer lower computational load. The study outcome witness around 90% accuracy with 2.5% of
error in classification performance. Cavallaro et al. [20] have used learning algorithm for performing an
effective classification of images. Lu et al. [21] have presented a modeling of a concept that allows
performing big data analytics over the cloud environment. The next section discusses about the problem
being identified from the existing literature.
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1.2. Problem Identification
The existing technqiues towards big data analytics has associated advantages witth respect to
various individual applications; however, it is also associated with limitations too. This section briefs about
the open research issues in the line of various potential problems of data processing particularly relating to
the big data analytics. The identified problems in the existing system are as follows:
Majority of existing literature have not considered many cases from healthcare sector. The data arriving
from heathcare sector is highly complex in comparison to other forms of bigger data. Such problems are
less addressed in existing literature.
Existing system introduces big data analytics with more focus on applying mining operation and very
less focus on performing processing operation on the top of it. Majority of the processing is left of
existing software frameworks, which already has reported pitfalls.
There are no research attempts where the data before storing over cloud undergoes processing in order to
eliminate problems. Moreover, there is integrated system which offers mitigation procedure for data
variety, data uncertainty, and data speed in existing literature.
1.3. Proposed Solution
The proposed system is a continuation of our prior study [22-23], where we have presented an
individual algorithm for solving the problems related to big data analytics e.g. data variety, data uncertainty,
and data speed. Figure 1 highlights the architecture of proposed system.
Data Variety
Data Veracity
Data Velocity
Algorithm
d
odata
udata
fdata
Healthcare Facility
d
d
Figure 1 Proposed architecture
It is already known that there are different levels of complexities associated in addressing the
problem related to medical big data analytics e.g. data variety, data uncertainty, and data speed. The proposed
system offers an integrated framework where it is feasible to address all the three significant problems
without using any sophisticated tool or expensive process. We assume that a healthcare facility already uses
cloud server in order to stack out the generated information from the facility. However, we consider that our
algorithm runs over such cloud server where before storing the data, the system implements a chain of
algorithm in order to eliminate the problems associated in processing big data. We also assume that the data
generated by the healthcare facility is highly unstructured and it should not be stored in this state that may
further invite challenges during analytical operation. The unstructured data after arriving to cloud server it
has to undergo processing from its first algorithm that removes the unstructuredness charecteristic of the data
and make it more organized data. This data than further undergo processing with next chain of the algorithm
to explore better substitution of the missing values or eliminiate the ambiguities. The obtained data from this
part of the algorithm is then subjected to last part of the algorithm that makes the data modeling structure in
such a way that the storage system will be able to store the arrived data irrespective of its time of arrival i.e.
velocity. The next section further elaborated about such algorithms descriptively.
2. ALGORITHM IMPLEMENTATION
The framework discussed in the prior section mainly incorporates three types of algorithm targeting
to countermeasures the problems associated with the medical data analysis of larger size. The algorithm takes
the input of medical data which has the corresponding information about the patient e.g. patient id, hostpital
name, Referral doctor, Date, Patient Name, Gender, Age, Attending Doctor, and Clinical History and
perform a series of operation in order to overcome the problems associated with big data analysis.
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2.1. Algorithm for Addressing the Variety Problem
The rationale of this algorithm is based on the fact that data with high score of variety are highly
unorganized and hence it is required to provide a robust structure to this data so that further operation can be
carried out. This algorithm is responsible for minimizing the problem associated with variety of medical data.
The algorithm takes the input of unstructured data d which has different forms of variables of patient
information residing under category C (Line-1). The algorithm read the text file and extracts textual contents
on the basis of cardinality (card) of different entries as well as positions and finally stores it in a matrix
(Line-2). The algorithm first finds the list of categories in the form of index and then takes one by one entry
for creating new categories (Line-3). Finally, it is capable of finding the feasible number of array. The
possibility of empty value in the data is also addressed in this system where it checks the size of all the
matrix elements matelem. In case, a matrix element is found null than it is replaced by an integer value
corresponding with the single matrix (Line-5-6). Finally, all the total entries are accumulated by exploring
the size of the matrix. This process of data transformation finally generates organized data odata free for data
variety problem.
Algorithm for Addressing the Variety Problem
Input: pid, hn, Rd, D, pname, G, A, Adoc, Chis, C, d, Epos
Output: odata
Start
1. init d, read C [C={pid, hn, Rd, D, pname, G, A, Adoc, Chis, C}]
2. Read & Storecard (entries) & Epos
3. Get Cindex & content
4. If matelem=0
5. Substitutematelem(SingleMat)
6. odatamatelem=[matelem ii]
End
2.2. Algorithm for Addressing Uncertainty Problem
The design principle of this algorithm is based on the fact that uncertainty score in the medical big
data may occur due to incompleteness of data or ambiguities in data sourcing model. Therefore, uncertainty
problem in medical big data can be countermeasured if incomplete or data ambiguity is addressed in the
micro-scale. The input to the study is the output from prior algorithm i.e. organized data, which is initially
read (Line-1). In this case, all the field of the data sources are read and checked if all of them have undergone
the process of removal of data variety problems. This process is then further followed by an iterative
operation for checking the matched identification of the data corresponding to all the case studies of the
hospital database. The main objective of this operation is to chek for scores of matched elements along with
identity and respective categories. Hence, if any one of the field information is missing, the other key
attributes e.g. atched elements along with identity and respective categories will assist to find the missing
elements or even ambiguous elements in the matrix. Finally, based on the selected identity of the newly
explored data, it is then stored. The mechanism also obtains the initial word from the columnar element and
converts it to character followed by extraction of mean value. A new matrix is then formulated using unique
element in order to generate a random number within a range of size of the matrix. This phenomenon will
significantly avoid any kind of ambiguity in the data and only the matched data will be retrieved. Hence, if
the category element do exists (Line-2) than it will use the same category identity (Line-3) or else it will
generate a new category identity that corresponds with the size of category matrix (Line-5). The matrix with
newly positioned data is then updated followed by calculation of data purity as the ranking mechanism of
addressing uncertainty problems in medical big data (Line-7). We also compute error that could possibly
occur during the substitution process to fill up missing or ambiguous data (Line-8); however, it is just a
performance paramerer.
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Algorithm for Addressing Uncertainty Problem
Input: odata (Organized Data), Celem (Category Element)
Output: udata (updated data), dpur (data purity), E (Error)
Start
1. Read odata
2. If (Celem≠0)
3. use Cid
4. Else
5. use newCid[Cid, size(C)]
6. Update matelem
7. dpursize(unique(C), matelem) / card(Celem)
8 E=E+size(newEntry)
9. Update matelemudata
End
2.3. Algorithm for Addressing Speed Problem
The prime rationale of this algorithm is that data speed cannot be controlled by any means but we
consider that if the speed of data is known to some extent than a proper data management could be done.
Hence, we offer a very simple model where irrespective of any speed of arrival of the incoming data, the data
could be efficiently stored in out database framework. The algorithm takes the output of prior algorithm
where uncertainty problem is addressed. The algorithm initially read the column value of the incoming data
(Line-1) and finds its numerical value. A classification of different matrix are generated depending on the last
column of the incoming data (Line-2) followed by extraction of different labels too (Line-3). Basically,
Labels corresponds to each individual cell in the matrix. We formulate a temporary matrix match that is
responsible for identifying similar elements in incoming data and storage area. If the incoming data is found
to have match than the data is directly discarded and only the column index is updated (Line-4). By this
process, the algorithm ensures cost effective usage of data storage model by only considering unique
incoming data to be stored. However, if the incoming data is found to be unique i.e. it has no matched
elements between itself and data storage structure (Line-4), than it instantly update its label with respect to
size of the label (Line-5). It will mean that a single label is sufficient enough to store the different frequencies
of the incoming data, so that other part of the cells offers enough buffers for new arrival of massive data.
Finally, all the explicit columnar information col is accomplished (Line-6) and cells are updated with respect
to labels and output cells (Line-7). Hence, irrespective of any flow of the incoming data, the proposed system
can offer significant room for data storage system in order to store the incoming data arriving from certain
healthcare facilities.
Algorithm for Addressing Speed Problem
Input: udata, colval
Output: fdata
Start
1. findcolval
2. classify based on col
3. get Label
4. If match=0
5. update Label[1:size(label)]
6. Get col
7. fdataupdate cells (Label, Output Cells)
End
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2803
Table 1 Notation used in Algorithm Design
Notation Meaning
pid patient id
hn hostpital name
Rd Referral Doctor
D Date
pname Patient Name
G Gender
A Age
Adoc Attending Doctor
Chis Clinical History
C Category
d unstructured data
Epos Entry Position
odata Organized Data
Celem Category Element
udata updated data
dpur data purity
E Error
colval numeric value of column
fdata final data
3. RESULT ANALYSIS
This part of the study discusses about the results being accomplished from the proposed study. The
implementation of the proposed system is carried out in Matlab. The constructed framework offer a highly
comprehensive single environment for testifying the three significant problems associated with medical big
data i.e. data variety, data uncertainty, and data speed. The reason for selecting Matlab for design and
development is its easiness, convenient, and scalable. The implementation of the study was carried out using
sythentic medical data of larger size. As the proposed system is nearly similar to optimize the performance of
a framework for big data analytics, hence, it is wise enough to be compared with similar frequently used
techniquesof optimization. We find that adoption of Support Vector Machine has been carried out by
Cavallaro et al. [20] as well as by Singh et al. [24]. One of the optimization feature of SVM is its excellent
classification approach that has capability to be applied over high-dimensioanl data and it is independent of
any form of conventional feature selection procedures in over to resists the problem associated with larger
dimensionality of big data. Different forms of regularization elements as well as extracted features are
concatenated during the process of learning. In order to make the SVM works efficiently for big data, it is
required for the class boundary to be very close to the outcome of the anticipated samples of training. Apart
from SVM, we also find the adoption of neural network in optimizing big data analytics. The most recent
work carried out by Chung et al. [25] has discussed the usage of neural network for high performance
computation of big data. One of the significant advantages of applying neural network over big data analysis
is its capability of approximation of any forms of function. This operation can be significantly helpful while
exploring for the sub-space clustering in high dimensional data. A significant sigmoid function can be further
fine tuned in order to arrive into ellite outcome using neural network in big data analysis. For simplicity, we
implement support vector machine as the training algorithm to address the problem discussed in our paper.
We take the feed-forward algorithm as the learning approach for neural network too. Hence, our existing
system is a combined result arrived from implementing support vector machine and neural network. The
comparative assessment of the study was carried out considering accuracy parameter and computational
response time.
Figure 2. Comparative performances on accuracy
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The comparative assessment of the accuracy is carried out using True Positive, False Positive, and
False Negative for both proposed system as well as existing system. The study outcome shows that proposed
system accomplishes better true positive from statistical score (probability) viewpoint, whereas the existing
system offer slightly reduced accuracy performance as compared to proposed system. The prime reason
behind this that the proposed system performs a sequential operation where output of first operation becomes
input of second process. By this process, the proposed system offers better optimization without using any
recursive function as well as with every increasing steps the problems get reduced. This procedure of filtering
the problems is more iterative and less filtered and hence eixtsing system couldn’t offer better accuracy.
Figure 3. Comparative performances on response time
We also compute the amount of computational complexities associated with both proposed and
existing system with respect to compuatational response time. Figure 3 clearly shows that proposed system
offers better response time in comparison to existing system. However, because of too many dependencies on
iteration, the existing system encounters the problem of faster convergence. Therefore, such iterative
operations cannot be suitably applied over high-dimensional data thereby causing time lags to yield the
outcome. On the other hand, the proposed system offers the results owing to faster convergence while
making transition from one algorithm to other. Therefore, the proposed system offer better computational
response time as well as lower memory consumption over normal machine to prove its cost effectiveness.
The storage complexity is highly controlled as the complete framework offers results only over runtime
processing of the algorithm and hence enough resource consumption is saved.
4. CONCLUSION
This paper emphasizes on the inherent problems associated with the big data analytics over cloud
i.e. data variety, data uncertainty, and data speed. We have reviewed some of the recent literatures to find that
there is a big tradeoff in the existing approaches between problems being addressed as claimed and real-time
problems. The problems being addressed as claimed in literature only emphasizes on part of one problem
where in real-sense there could be endless number of occurance of all the reported problems while
performing real-time analysis over cloud environment. This paper describes one novel research model which
is experimented over synthetic bigdata of healthcares sector. The technique introduces a novel framework
where all the reported problems e.g. .data variety, data uncertainty, and data speed is addressed. At present,
we find that it offer better accuracy and response time in comparison to existing optimization system. Our
future work direction will be to further optimize the outcomes.
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