Business and healthcare application are tuned to automatically detect and react events generated from local are remote sources. Event detection refers to an action taken to an activity. The association rule mining techniques are used to detect activities from data sets. Events are divided into 2 types' external event and internal event. External events are generated under the remote machines and deliver data across distributed systems. Internal events are delivered and derived by the system itself. The gap between the actual event and event notification should be minimized. Event derivation should also scale for a large number of complex rules. Attacks and its severity are identified from event derivation systems. Transactional databases and external data sources are used in the event detection process. The new event discovery process is designed to support uncertain data environment. Uncertain derivation of events is performed on uncertain data values. Relevance estimation is a more challenging task under uncertain event analysis. Selectability and sampling mechanism are used to improve the derivation accuracy. Selectability filters events that are irrelevant to derivation by some rules. Selectability algorithm is applied to extract new event derivation. A Bayesian network representation is used to derive new events given the arrival of an uncertain event and to compute its probability. A sampling algorithm is used for efficient approximation of new event derivation. Medical decision support system is designed with event detection model. The system adopts the new rule mapping mechanism for the disease analysis. The rule base construction and maintenance operations are handled by the system. Rule probability estimation is carried out using the Apriori algorithm. The rule derivation process is optimized for domain specific model.
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
Developing predictive modelling solutions for risk estimation is extremely challenging in health-care
informatics. Risk estimation involves integration of heterogeneous clinical sources having different
representation from different health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel
computing tools collectively termed big data tools are in need which can synthesize and assist the physician
to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel
approach for combining the predictive ability of multiple models for better prediction accuracy. We
demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study.
Results show that the proposed multi-model predictive architecture is able to provide better accuracy than
best model approach. By modelling the error of predictive models we are able to choose sub set of models
which yields accurate results. More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
Developing predictive modelling solutions for risk estimation is extremely challenging in health-care
informatics. Risk estimation involves integration of heterogeneous clinical sources having different
representation from different health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel
computing tools collectively termed big data tools are in need which can synthesize and assist the physician
to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel
approach for combining the predictive ability of multiple models for better prediction accuracy. We
demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study.
Results show that the proposed multi-model predictive architecture is able to provide better accuracy than
best model approach. By modelling the error of predictive models we are able to choose sub set of models
which yields accurate results. More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATAcseij
Due to big data progress in biomedical and healthcare communities, accurate study of medical data
benefits early disease recognition, patient care and community services. When the quality of medical data
is incomplete the exactness of study is reduced. Moreover, different regions exhibit unique appearances of
certain regional diseases, which may results in weakening the prediction of disease outbreaks. In the
proposed system, it provides machine learning algorithms for effective prediction of various disease
occurrences in disease-frequent societies. It experiment the altered estimate models over real-life hospital
data collected. To overcome the difficulty of incomplete data, it use a latent factor model to rebuild the
missing data. It experiment on a regional chronic illness of cerebral infarction. Using structured and
unstructured data from hospital it use Machine Learning Decision Tree algorithm and Map Reduce
algorithm. To the best of our knowledge in the area of medical big data analytics none of the existing work
focused on both data types. Compared to several typical estimate algorithms, the calculation exactness of
our proposed algorithm reaches 94.8% with a convergence speed which is faster than that of the CNNbased
unimodal disease risk prediction (CNN-UDRP) algorithm.
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
Predictive Data Mining for Converged Internet ofJames Kang
Kang, J. J., Adibi, S., Larkin, H., & Luan, T. (2016). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International (pp. 5-10). IEEE Xplore: IEEE. doi: http://dx.doi.org/10.1109/ATNAC.2015.7366781
Distributed Scalable Systems Short OverviewRNeches
Closing description of work in the Distributed Scalable Systems Division just prior to reorganization as the Collaborative Systems component of the merged Computational Systems and Technology Division.
Fundamentals of Medical Device ConnectivityNuvon, Inc.
I this three part series, John R. Zaleski, PhD, CPHIMS, Vice President of Clinical Applications & CTO of Nuvon, Inc. discusses 3 areas of Medical Device Connectivity beginning with the Fundamentals, to Clinical Decision Support, and next generation Mobile Device Connectivity.
The efficient and effective monitoring of mobile networks is vital given the number of users who rely on
such networks and the importance of those networks. The purpose of this paper is to present a monitoring
scheme for mobile networks based on the use of rules and decision tree data mining classifiers to upgrade
fault detection and handling. The goal is to have optimisation rules that improve anomaly detection. In
addition, a monitoring scheme that relies on Bayesian classifiers was also implemented for the purpose of
fault isolation and localisation. The data mining techniques described in this paper are intended to allow a
system to be trained to actually learn network fault rules. The results of the tests that were conducted
allowed for the conclusion that the rules were highly effective to improve network troubleshooting.
Understanding Physicians' Adoption of Health Cloudscsandit
Recently proposed health applications are able to enforce essential advancements in the
healthcare sector. The design of these innovative solutions is often enabled through the cloud
computing model. With regards to this technology, high concerns about information security
and privacy are common in practice. These concerns with respect to sensitive medical
information could be a hurdle to successful adoption and consumption of cloud-based health
services, despite high expectations and interest in these services. This research attempts to
understand behavioural intentions of healthcare professionals to adopt health clouds in their
clinical practice. Based on different established theories on IT adoption and further related
theoretical insights, we develop a research model and a corresponding instrument to test the
proposed research model using the partial least squares (PLS) approach. We suppose that
healthcare professionals’ adoption intentions with regards to health clouds will be formed by
their outweighing two conflicting beliefs which are performance expectancy and medical
information security and privacy concerns associated with the usage of health clouds. We
further suppose that security and privacy concerns can be explained through perceived risks.
Challenges in deep learning methods for medical imaging - PubricaPubrica
1. Broad between association cooperation.
2. Need to Capitalize Big Image Data.
3. Progression in Deep Learning Methods.
4. Black-Box and Its Acceptance by Health Professional.
5. Security and moral issues.
6. Wrapping up.
Continue Reading: https://bit.ly/37zT2ur
Reference: https://pubrica.com/services/physician-writing-services/clinical-litearture-review-for-an-evidence-based-medicine/
Why Pubrica?
When you order our services, Plagiarism free|on Time|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Analysis of Catalyst Support Ring in a pressure vessel based on ASME Section ...ijsrd.com
In reactors, catalyst support rings and tray support rings that support heavy catalyst beds and catalyst support grids, are subjected to high pressure and temperature and other dead loads, so their safe design is essential as they are critical parts in a reactor and their finite element analysis is carried out using ASME Sec VIII Div.2 in the industry. Analysis of skirt support to bottom head junction is also very important as this welded joint is subjected to wind loads, seismic loads, dead loads, high thermal gradient etc. The skirt support supports the whole reactor so the welded joint must be strong enough to endure stresses due to various reasons. This safety can be determined using FEA software using ASME Sec VIII Div.2.
Colour Object Recognition using Biologically Inspired Modelijsrd.com
Human visual system can categorize objects rapidly and effortlessly despite the complexity and objective ambiguities of natural images. Despite the ease with which we see, visual categorization is an extremely difficult task for computers due to the variability of objects, such as scale, rotation, illumination, position and occlusion. Utilization of characteristics of biological systems for solving practical problems inevitably leads towards reducing the gap between manmade machines and live systems. Biologically motivated information processing has been an important area of scientific research for decades. This paper presents a Biologically Inspired Model which gives a promising solution to object categorization in colour space. The features are extracted in YCbCr colour space and classified by using SVM classifier. The framework has been applied to the image dataset taken from the Amsterdam Library of Object Images (ALOI). The proposed framework can successfully detect and classify the object categories with a good accuracy rate of about 91.3% for the Cb plane.
DISEASE PREDICTION USING MACHINE LEARNING OVER BIG DATAcseij
Due to big data progress in biomedical and healthcare communities, accurate study of medical data
benefits early disease recognition, patient care and community services. When the quality of medical data
is incomplete the exactness of study is reduced. Moreover, different regions exhibit unique appearances of
certain regional diseases, which may results in weakening the prediction of disease outbreaks. In the
proposed system, it provides machine learning algorithms for effective prediction of various disease
occurrences in disease-frequent societies. It experiment the altered estimate models over real-life hospital
data collected. To overcome the difficulty of incomplete data, it use a latent factor model to rebuild the
missing data. It experiment on a regional chronic illness of cerebral infarction. Using structured and
unstructured data from hospital it use Machine Learning Decision Tree algorithm and Map Reduce
algorithm. To the best of our knowledge in the area of medical big data analytics none of the existing work
focused on both data types. Compared to several typical estimate algorithms, the calculation exactness of
our proposed algorithm reaches 94.8% with a convergence speed which is faster than that of the CNNbased
unimodal disease risk prediction (CNN-UDRP) algorithm.
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
Predictive Data Mining for Converged Internet ofJames Kang
Kang, J. J., Adibi, S., Larkin, H., & Luan, T. (2016). Predictive data mining for Converged Internet of Things: A Mobile Health perspective. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International (pp. 5-10). IEEE Xplore: IEEE. doi: http://dx.doi.org/10.1109/ATNAC.2015.7366781
Distributed Scalable Systems Short OverviewRNeches
Closing description of work in the Distributed Scalable Systems Division just prior to reorganization as the Collaborative Systems component of the merged Computational Systems and Technology Division.
Fundamentals of Medical Device ConnectivityNuvon, Inc.
I this three part series, John R. Zaleski, PhD, CPHIMS, Vice President of Clinical Applications & CTO of Nuvon, Inc. discusses 3 areas of Medical Device Connectivity beginning with the Fundamentals, to Clinical Decision Support, and next generation Mobile Device Connectivity.
The efficient and effective monitoring of mobile networks is vital given the number of users who rely on
such networks and the importance of those networks. The purpose of this paper is to present a monitoring
scheme for mobile networks based on the use of rules and decision tree data mining classifiers to upgrade
fault detection and handling. The goal is to have optimisation rules that improve anomaly detection. In
addition, a monitoring scheme that relies on Bayesian classifiers was also implemented for the purpose of
fault isolation and localisation. The data mining techniques described in this paper are intended to allow a
system to be trained to actually learn network fault rules. The results of the tests that were conducted
allowed for the conclusion that the rules were highly effective to improve network troubleshooting.
Understanding Physicians' Adoption of Health Cloudscsandit
Recently proposed health applications are able to enforce essential advancements in the
healthcare sector. The design of these innovative solutions is often enabled through the cloud
computing model. With regards to this technology, high concerns about information security
and privacy are common in practice. These concerns with respect to sensitive medical
information could be a hurdle to successful adoption and consumption of cloud-based health
services, despite high expectations and interest in these services. This research attempts to
understand behavioural intentions of healthcare professionals to adopt health clouds in their
clinical practice. Based on different established theories on IT adoption and further related
theoretical insights, we develop a research model and a corresponding instrument to test the
proposed research model using the partial least squares (PLS) approach. We suppose that
healthcare professionals’ adoption intentions with regards to health clouds will be formed by
their outweighing two conflicting beliefs which are performance expectancy and medical
information security and privacy concerns associated with the usage of health clouds. We
further suppose that security and privacy concerns can be explained through perceived risks.
Challenges in deep learning methods for medical imaging - PubricaPubrica
1. Broad between association cooperation.
2. Need to Capitalize Big Image Data.
3. Progression in Deep Learning Methods.
4. Black-Box and Its Acceptance by Health Professional.
5. Security and moral issues.
6. Wrapping up.
Continue Reading: https://bit.ly/37zT2ur
Reference: https://pubrica.com/services/physician-writing-services/clinical-litearture-review-for-an-evidence-based-medicine/
Why Pubrica?
When you order our services, Plagiarism free|on Time|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
PREDICTIVE ANALYTICS IN HEALTHCARE SYSTEM USING DATA MINING TECHNIQUEScscpconf
The health sector has witnessed a great evolution following the development of new computer technologies, and that pushed this area to produce more medical data, which gave birth to multiple fields of research. Many efforts are done to cope with the explosion of medical data on one hand, and to obtain useful knowledge from it on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. With the promises of predictive analytics in big data, and the use of machine learning
algorithms, predicting future is no longer a difficult task, especially for medicine because predicting diseases and anticipating the cure became possible. In this paper we will present an overview on the evolution of big data in healthcare system, and we will apply a learning algorithm on a set of medical data. The objective is to predict chronic kidney diseases by using Decision Tree (C4.5) algorithm.
Analysis of Catalyst Support Ring in a pressure vessel based on ASME Section ...ijsrd.com
In reactors, catalyst support rings and tray support rings that support heavy catalyst beds and catalyst support grids, are subjected to high pressure and temperature and other dead loads, so their safe design is essential as they are critical parts in a reactor and their finite element analysis is carried out using ASME Sec VIII Div.2 in the industry. Analysis of skirt support to bottom head junction is also very important as this welded joint is subjected to wind loads, seismic loads, dead loads, high thermal gradient etc. The skirt support supports the whole reactor so the welded joint must be strong enough to endure stresses due to various reasons. This safety can be determined using FEA software using ASME Sec VIII Div.2.
Colour Object Recognition using Biologically Inspired Modelijsrd.com
Human visual system can categorize objects rapidly and effortlessly despite the complexity and objective ambiguities of natural images. Despite the ease with which we see, visual categorization is an extremely difficult task for computers due to the variability of objects, such as scale, rotation, illumination, position and occlusion. Utilization of characteristics of biological systems for solving practical problems inevitably leads towards reducing the gap between manmade machines and live systems. Biologically motivated information processing has been an important area of scientific research for decades. This paper presents a Biologically Inspired Model which gives a promising solution to object categorization in colour space. The features are extracted in YCbCr colour space and classified by using SVM classifier. The framework has been applied to the image dataset taken from the Amsterdam Library of Object Images (ALOI). The proposed framework can successfully detect and classify the object categories with a good accuracy rate of about 91.3% for the Cb plane.
Computationally Efficient ID-Based Blind Signature Scheme in E-Votingijsrd.com
Blind signatures introduced by Chaum, allow a user to obtain a signature on a message without revealing anything about the message to the signer. Blind signatures play an important role in plenty of applications such as e-voting, e-cash system where anonymity is of great concern. ID based public key cryptography can be a good alternative for certificate based public key setting, especially when efficient key management and moderate security are required. In this we propose an ID based blind signature scheme from bilinear pairings.
VHDL Implementation of Flexible Multiband Dividerijsrd.com
In this paper an efficient multiband flexible divider for Bluetooth, Zigbee and other wireless standards is proposed based on pulse swallow topology and is implemented using Xilinx ISE 13.2 and modalism 6.4c.It consist of a propose wideband multimodulus 32/33/47/48 prescaler, swallow s-counter ,p-counter. As a modification I have implemented a modified multiband flexible divider by combining p and s counters together and by using a modified 2/3 prescaler. Compared to the proposed system modified one will reduce the circuit complexity, power consumption, gate counts etc.
Design of AMBA AHB interface around OpenRISC 1200 processor and comparing the...ijsrd.com
Open source Components are gaining popularity because they not only reduce the cost significantly but also provide power to the designer due to the availability of source code. The OpenRISC 1200 processor is a widely used processor in small and medium embedded and networking application. It is open source and code of it is written in Verilog Hardware description language. It used Wishbone as an internal bus and also uses it to connect to external peripherals. Due to this it has some limitation. Today, most of the chips do not use wishbone bus but instead use AMBA AHB. So in this paper AMBA AHB interface has been added to the existing OpenRISC Architecture and Comparison after adding the interface is done with the implementation before.
GPS and GSM based Steered navigation and location tracking device for Visionlessijsrd.com
This paper gives a conceptual perception about a device which helps the visionless or partly sighted for navigation and tracks them in case of emergencies. It is a robotics centered hurdle evading device. The device consists of a Microcontroller and obstacle sensors that avoids collision and GPS which provides location information of the visionless person. This device is capable of moving in specific direction depending upon command received by sensor input and can change the direction before hitting the obstacle. The overall concept of this paper is the result of numerous present concepts and focusing on tracking of the blind individual.
Modeling Analysis& Solution of Power Quality Problems Using DVR & DSTATCOMijsrd.com
A Power quality problem is an occurrence manifested as a nonstandard voltage, current or frequency that results in a failure or a disoperation of end use equipment. Utility distribution networks, sensitive industrial loads, and critical commercial operations all suffer from various types of outages and service interruptions which can cost significant financial loss per incident based on process down-time, lost production, idle work forces, and other factors. With the restructuring of Power Systems and with shifting trend towards Distributed and Dispersed Generation, the issue of Power Quality is going to take newer dimensions. The aim therefore, in this work, is to identify the prominent concerns in the area and thereby to recommend measures that can enhance the quality of the power, keeping in mind their economic viability and technical repercussions. In this paper electromagnetic transient studies are presented for the following two custom power controllers: the distribution static compensator (DSTATCOM), and the dynamic voltage restorer (DVR). Comprehensive results are presented to assess the performance of each device as a potential custom power solution.
Simulation of Single and Multilayer of Artificial Neural Network using Verilogijsrd.com
Artificial neural network play an important role in VLSI circuit to find and diagnosis multiple fault in digital circuit. In this paper, the example of single layer and multi-layer neural network had been discussed secondly implement those structure by using verilog code and same idea must be implement in mat lab for getting number of iteration and verilog code gives us time taken to adjust the weight when error become almost equal to zero. The purposed aim at reducing resource requirement, without much compromises on the speed that neural network can be realized on single chip at lower cost.
Design Fabrication and Static Analysis of Single Composite Lap Jointijsrd.com
Mechanically fastened joints are critical parts in composite aircraft structures. The composite structural members are highly used in the following applications such as aerospace, automobiles, marine, architecture etc., In the past decades, Adhesive bonding is a practical joint method for joining composite materials which provide low shear and Tensile strength .To improve the strength material joint is to be used in the work. A Glass fibre Epoxy composite is to be fabricated by hand lay-up method. And experimentally results are to be obtained. The Experimental results are to be compared with Analytical and Numerical results. For numerical analysis ANSYS software is to be used.
Power Swing Phenomena and Comparative Study of Its Detection on Transmission ...ijsrd.com
Power systems are subject to a wide range of small or large disturbances during operating conditions. Small changes in loading conditions occur continually. The power system must adjust to these changing conditions and continue to operate satisfactorily and within the desired limits of voltage and frequency. The power system should be designed to survive larger types of disturbances, such as faults, loss of a large generator, or line switching. Certain system disturbances may cause loss of synchronism between a generator and rest of the utility system, or between interconnected power systems of neighboring utilities. If such a loss of synchronism occurs, it is imperative that the generator or system areas operating asynchronously are separated immediately to avoid widespread outages and equipment damage. Here it is described to distinguish between power swing and real fault. It is also discussed recent enhancements in the design of out of step tripping and blocking protection functions that improve the security and reliability of the power system. In addition to that the behavior of distance relay element during power swing and during fault is simulated using MATLAB and SIMULINK simulations.
Study on effect of Alccofine & Fly ash addition on the Mechanical properties ...ijsrd.com
This paper presents the results of an experimental investigation carried out for M-70 Grad Concrete and to evaluate the compressive strength and Flexural Strength of Concrete. High Performance Concrete is made by partial replacement of cement by alccofine, fly ash, silica fume. In this study the Class F fly ash used in various proportions 20 to 35%, alccofine 4 to 14% and silica fume 4% to 14% by weight of cement. The mix proportions of concrete had a water binder ratio for Alccofine mix concrete 0.30 and Silica-fume mix concrete 0.32.super plasticizer was added based on the required degree of workability. The total binder content was 600 kg/m3. The concrete specimens were cured on normal moist curing under normal atmospheric temperature. The compressive strength was determined at 7 , 28 , 56 days and flexural strength was determined at 28 and 56 days The results indicate the concrete made with these proportions generally show excellent fresh and hardened properties. The addition of Alccofine, silica fume shows early strength gaining property and that of fly ash shows a long term strength. The ternary system that is Portland cement-fly ash-Alccofine concrete was found to increase the compressive strength of concrete on all age when compared to concrete made with Portland cement-fly ash-silica fume.
Reviews of Cascade Control of Dc Motor with Advance Controllerijsrd.com
The proportional- integral-derivative (PID) control is the most used algorithm to regulate the armature current and speed of cascade Control system in motor drives. The controller uses two PID controllers. One PI controller is for speed control and second PID controller for current control in cascade structure. Inner loop is for the current control which is faster than the outer loop. Outer loop is for speed control. The output of the encoder is compared with a preset reference speed. The output of the PI controller is summed and is given as the input to the current controller.
Comparatively analysis of AODV and DSR in MAC layer for Ad Hoc Environmentijsrd.com
In Wireless Adhoc Network is a group of wireless mobile nodes is an autonomous system of mobile nodes connected by wireless links. Every node operates as an end system and as a router to forward packets. In this paper mainly focused on Mac layer because this layer is most important for the data communication using control the packet loss and we worked on the comparison based performance of wimax802.16 and wireless802.11 networks using Ad hoc on- demand Distance Vector Routing Protocol and Dynamic Source Routing Protocol. In this paper we used the different maximum speed for the network. And this comparison based on unicast On-demand routing procedure and our simulation for mobile ad hoc networks discover and maintain only needed the design and follows the idea that each node by sending routing packets whenever a communication is requested and compared various parameter packet delivery ratio, normalized routing load and e-e delay. These simulations are carried out using the Network simulator version-2. The results presented in this work illustrate the importance in carefully evaluating and implementing routing protocols in an ad hoc environment.
The World Wide Web and cache coherence, while robust in theory, have not until recently been considered intuitive. Given the current status of knowledge-based technology, researchers dubiously desire the analysis of IPv7, which embodies the intuitive principles of theory [25]. We validate not only that neural networks can be made authenticated, decentralized, and permutable, but that the same is true for SMPs.
Analysis Approach for Five Phase Two-Level Voltage Source Inverter with PWM T...ijsrd.com
this paper gives idea of comparison of five phase two-level voltage inverter (FPTLVSI) without filter circuit and control scheme and FPTLVSI with filter circuit and PWM control scheme for induction motor drive. The paper demonstrates using mat lab simulations about comparison in term of harmonics analysis for different firing angles and find best angle suitable for output with minimum harmonics for FPTLVSI without filter circuit and control scheme and harmonics analysis of FPTLVSI with filter and PWM control scheme. This paper suggests simulation of comparison of harmonics point of view five phase two-level voltage inverter (FPTLVSI) without filter circuit and control scheme and with filter circuit and PWM control scheme for induction motor drive.
Speech Recognition using HMM & GMM Models: A Review on Techniques and Approachesijsrd.com
Many ways of communications are used between human and computer, while using gesture is considered to be one of the most natural ways in a virtual reality system. Speech recognition is one of the typical methods of non-verbal communication for human beings and we naturally use various gestures to express our own intentions in everyday life. Gesture recognizers are supposed to capture and analyze the information transmitted by the hands of a person who communicates in sign language. This is a prerequisite for automatic sign-to-spoken-language translation, which has the potential to support the integration of deaf people into society. This paper present part of literature review on ongoing research and findings on different technique and approaches in gesture recognition using Hidden Markov Models for vision-based approach.
Design & Check Cyclic Redundancy Code using VERILOG HDLijsrd.com
the CRC or cyclic redundancy check is a widely used technique for error checking in many protocols used in data transmission. The aim of this project is to design the CRC RTL generator or a tool that calculates the CRC equations for the given CRC polynomials and generates the Verilog RTL code .This block deals with the calculation of equations for standard polynomials like CRC-4, CRC-8, CRC-16, CRC-32 and CRC-48, CRC-64 and also user defined proprietary polynomial. To use PERL as the platform it also aims at having a simpler user interface. To generate the RTLs for any data width and for any standard polynomial or user defined polynomial, this design aims to be complete generic. The RTLs generated by this tool are verified by System Verilog constrained random testing to make it more robust and reliable.
Automatic missing value imputation for cleaning phase of diabetic’s readmissi...IJECEIAES
Recently, the industry of healthcare started generating a large volume of datasets. If hospitals can employ the data, they could easily predict the outcomes and provide better treatments at early stages with low cost. Here, data analytics (DA) was used to make correct decisions through proper analysis and prediction. However, inappropriate data may lead to flawed analysis and thus yield unacceptable conclusions. Hence, transforming the improper data from the entire data set into useful data is essential. Machine learning (ML) technique was used to overcome the issues due to incomplete data. A new architecture, automatic missing value imputation (AMVI) was developed to predict missing values in the dataset, including data sampling and feature selection. Four prediction models (i.e., logistic regression, support vector machine (SVM), AdaBoost, and random forest algorithms) were selected from the well-known classification. The complete AMVI architecture performance was evaluated using a structured data set obtained from the UCI repository. Accuracy of around 90% was achieved. It was also confirmed from cross-validation that the trained ML model is suitable and not over-fitted. This trained model is developed based on the dataset, which is not dependent on a specific environment. It will train and obtain the outperformed model depending on the data available.
A Proposed Security Architecture for Establishing Privacy Domains in Systems ...IJERA Editor
Information and communication technology (ICT) are becoming a natural part in healthcare. Instead of keeping patient information inside a written file, you can find all information stored in an organized database as well defined files using a specific system in almost every hospital. But those files sometimes got lost or information was split up in files in different hospitals or different departments so no one could see the whole picture from this point we come up with our idea. One of this paper targets is to keep that information available on the cloud so doctors and nurses can have an access to patient record everywhere, so patient history will be clear which helps doctors in giving the right decision. We present security architecture for establishing privacy domains in e-Health bases. In this case, we will improve the availability of medical data and provide the ability for patients to moderate their medical data. Moreover, e-Health system in cloud computing has more than one component to be attacked. The other target of this paper is to distinguish between different kinds of attackers and we point out several shortcomings of current e-Health solutions and standards, particularly they do not address the client platform security, which is a crucial aspect for the overall security of systems in cloud. To fill this gap, we present security architecture for establishing privacy domains in e-Health infrastructures. Our solution provides client platform security and appropriately combines this with network security concepts.
Proactive Intelligent Home System Using Contextual Information and Neural Net...IJERA Editor
Nowadays, cities around the world intend to use information technology to improve the lives of their citizens.
Future smart cities will incorporate digital data and technology to interact differently with their human
inhabitants.
Among the key component of a smart city, we find the smart home component. It is an autonomic environment
that can provide various smart services by considering the user’s context information. Several methods are used
in context-aware system to provide such services. In this paper, we propose an approach to offer the most
relevant services to the user according to any significant change of his context environment. The proposed
approach is based on the use of context history information together with user profiling and machine learning
techniques. Experimentations show that the proposed solution can efficiently provide the most useful services to
the user in an intelligent home environment.
Advancing the cybersecurity of the healthcare system with self- optimising an...Petar Radanliev
This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing,
and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare
system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms – i.e., for optimising and securing digital healthcare systems in anticipation of Disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.
Due to availability of internet and evolution of embedded devices, Internet of things can be useful to contribute in energy domain. The Internet of Things (IoT) will deliver a smarter grid to enable more information and connectivity throughout the infrastructure and to homes. Through the IoT, consumers, manufacturers and utility providers will come across new ways to manage devices and ultimately conserve resources and save money by using smart meters, home gateways, smart plugs and connected appliances. The future smart home, various devices will be able to measure and share their energy consumption, and actively participate in house-wide or building wide energy management systems. This paper discusses the different approaches being taken worldwide to connect the smart grid. Full system solutions can be developed by combining hardware and software to address some of the challenges in building a smarter and more connected smart grid.
A Survey Report on : Security & Challenges in Internet of Thingsijsrd.com
In the era of computing technology, Internet of Things (IoT) devices are now popular in each and every domains like e-governance, e-Health, e-Home, e-Commerce, and e-Trafficking etc. Iot is spreading from small to large applications in all fields like Smart Cities, Smart Grids, Smart Transportation. As on one side IoT provide facilities and services for the society. On the other hand, IoT security is also a crucial issues.IoT security is an area which totally concerned for giving security to connected devices and networks in the IoT .As, IoT is vast area with usability, performance, security, and reliability as a major challenges in it. The growth of the IoT is exponentially increases as driven by market pressures, which proportionally increases the security threats involved in IoT The relationship between the security and billions of devices connecting to the Internet cannot be described with existing mathematical methods. In this paper, we explore the opportunities possible in the IoT with security threats and challenges associated with it.
In today’s emerging world of Internet, each and every thing is supposed to be in connected mode with the help of billions of smart devices. By connecting all the devises used in our day to day life, make our life trouble less and easy. We are incorporated in a world where we are used to have smart phones, smart cars, smart gadgets, smart homes and smart cities. Different institutes and researchers are working for creating a smart world for us but real question which we need to emphasis on is how to make dumb devises talk with uncommon hardware and communication technology. For the same what kind of mechanism to use with various protocols and less human interaction. The purpose is to provide the key area for application of IoT and a platform on which various devices having different mechanism and protocols can communicate with an integrated architecture.
Study on Issues in Managing and Protecting Data of IOTijsrd.com
This paper discusses variety of issues for preserving and managing data produced by IoT. Every second large amount of data are added or updated in the IoT databases across the heterogeneous environment. While managing the data each phase of data processing for IoT data is exigent like storing data, querying, indexing, transaction management and failure handling. We also refer to the problem of data integration and protection as data requires to be fit in single layout and travel securely as they arrive in the pool from diversified sources in different structure. Finally, we confer a standardized pathway to manage and to defend data in consistent manner.
Interactive Technologies for Improving Quality of Education to Build Collabor...ijsrd.com
Today with advancement in Information Communication Technology (ICT) the way the education is being delivered is seeing a paradigm shift from boring classroom lectures to interactive applications such as 2-D and 3-D learning content, animations, live videos, response systems, interactive panels, education games, virtual laboratories and collaborative research (data gathering and analysis) etc. Engineering is emerging with more innovative solutions in the field of education and bringing out their innovative products to improve education delivery. The academic institutes which were once hesitant to use such technology are now looking forward to such innovations. They are adopting the new ways as they are realizing the vast benefits of using such methods and technology. The benefits are better comprehensibility, improved learning efficiency of students, and access to vast knowledge resources, geographical reach, quick feedback, accountability and quality research. This paper focuses on how engineering can leverage the latest technology and build a collaborative learning environment which can then be integrated with the national e-learning grid.
Internet of Things - Paradigm Shift of Future Internet Application for Specia...ijsrd.com
In the world more than 15% people are living with disability that also include children below age of 10 years. Due to lack of independent support services specially abled (handicap) people overly rely on other people for their basic needs, that excludes them from being financially and socially active. The Internet of Things (IoT) can give support system and a better quality of life as well as participation in routine and day to day life. For this purpose, the future solutions for current problems has been introduced in this paper. Daunting challenges have been considered as future research and glimpse of the IoT for specially abled person is given in the paper.
A Study of the Adverse Effects of IoT on Student's Lifeijsrd.com
Internet of things (IoT) is the most powerful invention and if used in the positive direction, internet can prove to be very productive. But, now a days, due to the social networking sites such as Face book, WhatsApp, twitter, hike etc. internet is producing adverse effects on the student life, especially those students studying at college Level. As it is rightly said, something which has some positive effects also has some of the negative effects on the other hand. In this article, we are discussing some adverse effects of IoT on student’s life.
Pedagogy for Effective use of ICT in English Language Learningijsrd.com
The use of information and communications technology (ICT) in education is a relatively new phenomenon and it has been the educational researchers' focus of attention for more than two decades. Educators and researchers examine the challenges of using ICT and think of new ways to integrate ICT into the curriculum. However, there are some barriers for the teachers that prevent them to use ICT in the classroom and develop supporting materials through ICT. The purpose of this study is to examine the high school English teachers’ perceptions of the factors discouraging teachers to use ICT in the classroom.
In recent years usage of private vehicles create urban traffic more and more crowded. As result traffic becomes one of the important problems in big cities in all over the world. Some of the traffic concerns are traffic jam and accidents which have caused a huge waste of time, more fuel consumption and more pollution. Time is very important parameter in routine life. The main problem faced by the people is real time routing. Our solution Virtual Eye will provide the current updates as in the real time scenario of the specific route. This research paper presents smart traffic navigation system, based on Internet of Things, which is featured by low cost, high compatibility, easy to upgrade, to replace traditional traffic management system and the proposed system can improve road traffic tremendously.
Ontological Model of Educational Programs in Computer Science (Bachelor and M...ijsrd.com
In this work there is illustrated an ontological model of educational programs in computer science for bachelor and master degrees in Computer science and for master educational program “Computer science as second competence†by Tempus project PROMIS.
Understanding IoT Management for Smart Refrigeratorijsrd.com
Lately the concept of Internet of Things (IoT) is being more elaborated and devices and databases are proposed thereby to meet the need of an Internet of Things scenario. IoT is being considered to be an integral part of smart house where devices will be connected to each other and also react upon certain environmental input. This will eventually include the home refrigerator, air conditioner, lights, heater and such other home appliances. Therefore, we focus our research on the database part for such an IoT’ fridge which we called as smart Fridge. We describe the potentials achievable through a database for an IoT refrigerator to manage the refrigerator food and also aid the creation of a monthly budget of the house for a family. The paper aims at the data management issue based on a proposed design for an intelligent refrigerator leveraging the sensor technology and the wireless communication technology. The refrigerator which identifies products by reading the barcodes or RFID tags is proposed to order the required products by connecting to the Internet. Thus the goal of this paper is to minimize human interaction to maintain the daily life events.
DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...ijsrd.com
Double wishbone designs allow the engineer to carefully control the motion of the wheel throughout suspension travel. 3-D model of the Lower Wishbone Arm is prepared by using CAD software for modal and stress analysis. The forces and moments are used as the boundary conditions for finite element model of the wishbone arm. By using these boundary conditions static analysis is carried out. Then making the load as a function of time; quasi-static analysis of the wishbone arm is carried out. A finite element based optimization is used to optimize the design of lower wishbone arm. Topology optimization and material optimization techniques are used to optimize lower wishbone arm design.
A Review: Microwave Energy for materials processingijsrd.com
Microwave energy is a latest largest growing technique for material processing. This paper presents a review of microwave technologies used for material processing and its use for industrial applications. Advantages in using microwave energy for processing material include rapid heating, high heating efficiency, heating uniformity and clean energy. The microwave heating has various characteristics and due to which it has been become popular for heating low temperature applications to high temperature applications. In recent years this novel technique has been successfully utilized for the processing of metallic materials. Many researchers have reported microwave energy for sintering, joining and cladding of metallic materials. The aim of this paper is to show the use of microwave energy not only for non-metallic materials but also the metallic materials. The ability to process metals with microwave could assist in the manufacturing of high performance metal parts desired in many industries, for example in automotive and aeronautical industries.
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logsijsrd.com
With an expontial growth of World Wide Web, there are so many information overloaded and it became hard to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of user navigation pattern from web log. This paper presents an overview of web mining and also provide navigation pattern from classification and clustering algorithm for web usage mining. Web usage mining contain three important task namely data preprocessing, pattern discovery and pattern analysis based on discovered pattern. And also contain the comparative study of web mining techniques.
APPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEMijsrd.com
Application of FACTS controller called Static Synchronous Compensator STATCOM to improve the performance of power grid with Wind Farms is investigated .The essential feature of the STATCOM is that it has the ability to absorb or inject fastly the reactive power with power grid . Therefore the voltage regulation of the power grid with STATCOM FACTS device is achieved. Moreover restoring the stability of the power system having wind farm after occurring severe disturbance such as faults or wind farm mechanical power variation is obtained with STATCOM controller . The dynamic model of the power system having wind farm controlled by proposed STATCOM is developed . To validate the powerful of the STATCOM FACTS controller, the studied power system is simulated and subjected to different severe disturbances. The results prove the effectiveness of the proposed STATCOM controller in terms of fast damping the power system oscillations and restoring the power system stability.
Making model of dual axis solar tracking with Maximum Power Point Trackingijsrd.com
Now a days solar harvesting is more popular. As the popularity become higher the material quality and solar tracking methods are more improved. There are several factors affecting the solar system. Major influence on solar cell, intensity of source radiation and storage techniques The materials used in solar cell manufacturing limit the efficiency of solar cell. This makes it particularly difficult to make considerable improvements in the performance of the cell, and hence restricts the efficiency of the overall collection process. Therefore, the most attainable maximum power point tracking method of improving the performance of solar power collection is to increase the mean intensity of radiation received from the source used. The purposed of tracking system controls elevation and orientation angles of solar panels such that the panels always maintain perpendicular to the sunlight. The measured variables of our automatic system were compared with those of a fixed angle PV system. As a result of the experiment, the voltage generated by the proposed tracking system has an overall of about 28.11% more than the fixed angle PV system. There are three major approaches for maximizing power extraction in medium and large scale systems. They are sun tracking, maximum power point (MPP) tracking or both.
A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...ijsrd.com
In day today's relevance, it is mandatory to device the usage of diesel in an economic way. In present scenario, the very low combustion efficiency of CI engine leads to poor performance of engine and produces emission due to incomplete combustion. Study of research papers is focused on the improvement in efficiency of the engine and reduction in emissions by adding ethanol in a diesel with different blends like 5%, 10%, 15%, 20%, 25% and 30% by volume. The performance and emission characteristics of the engine are tested observed using blended fuels and comparative assessment is done with the performance and emission characteristics of engine using pure diesel.
Study and Review on Various Current Comparatorsijsrd.com
This paper presents study and review on various current comparators. It also describes low voltage current comparator using flipped voltage follower (FVF) to obtain the single supply voltage. This circuit has short propagation delay and occupies a small chip area as compare to other current comparators. The results of this circuit has obtained using PSpice simulator for 0.18 μm CMOS technology and a comparison has been performed with its non FVF counterpart to contrast its effectiveness, simplicity, compactness and low power consumption.
Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...ijsrd.com
Power dissipation is a challenging problem for today's system-on-chip design and test. This paper presents a novel architecture which generates the test patterns with reduced switching activities; it has the advantage of low test power and low hardware overhead. The proposed LP-TPG (test pattern generator) structure consists of modified low power linear feedback shift register (LP-LFSR), m-bit counter, gray counter, NOR-gate structure and XOR-array. The seed generated from LP-LFSR is EXCLUSIVE-OR ed with the data generated from gray code generator. The XOR result of the sequence is single input changing (SIC) sequence, in turn reduces the switching activity and so power dissipation will be very less. The proposed architecture is simulated using Modelsim and synthesized using Xilinx ISE9.2.The Xilinx chip scope tool will be used to test the logic running on FPGA.
Defending Reactive Jammers in WSN using a Trigger Identification Service.ijsrd.com
In the last decade, the greatest threat to the wireless sensor network has been Reactive Jamming Attack because it is difficult to be disclosed and defend as well as due to its mass destruction to legitimate sensor communications. As discussed above about the Reactive Jammers Nodes, a new scheme to deactivate them efficiently is by identifying all trigger nodes, where transmissions invoke the jammer nodes, which has been proposed and developed. Due to this identification mechanism, many existing reactive jamming defending schemes can be benefited. This Trigger Identification can also work as an application layer .In this paper, on one side we provide the several optimization problems to provide complete trigger identification service framework for unreliable wireless sensor networks and on the other side we also provide an improved algorithm with regard to two sophisticated jamming models, in order to enhance its robustness for various network scenarios.
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.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
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.
Dynamic Rule Base Construction and Maintenance Scheme for Disease Prediction
1. IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 5, 2013 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 1246
Abstract— Business and healthcare application are tuned to
automatically detect and react events generated from local
are remote sources. Event detection refers to an action taken
to an activity. The association rule mining techniques are
used to detect activities from data sets. Events are divided
into 2 types’ external event and internal event. External
events are generated under the remote machines and deliver
data across distributed systems. Internal events are delivered
and derived by the system itself. The gap between the actual
event and event notification should be minimized. Event
derivation should also scale for a large number of complex
rules. Attacks and its severity are identified from event
derivation systems. Transactional databases and external
data sources are used in the event detection process.
The new event discovery process is designed to support
uncertain data environment. Uncertain derivation of events
is performed on uncertain data values. Relevance estimation
is a more challenging task under uncertain event analysis.
Selectability and sampling mechanism are used to improve
the derivation accuracy. Selectability filters events that are
irrelevant to derivation by some rules. Selectability
algorithm is applied to extract new event derivation. A
Bayesian network representation is used to derive new
events given the arrival of an uncertain event and to
compute its probability. A sampling algorithm is used for
efficient approximation of new event derivation.
Medical decision support system is designed with event
detection model. The system adopts the new rule mapping
mechanism for the disease analysis. The rule base
construction and maintenance operations are handled by the
system. Rule probability estimation is carried out using the
Apriori algorithm. The rule derivation process is optimized
for domain specific model.
I. INTRODUCTION
Medical data mining has great potential for exploring the
hidden patterns in the data sets of the medical domain.
These patterns can be utilized for clinical diagnosis.
However, the available raw medical data are widely
distributed, heterogeneous in nature, and voluminous. These
data need to be collected in an organized form. This
collected data can be then integrated to form a hospital
information system. Data mining technology provides a user
oriented approach to novel and hidden patterns in the data.
Healthcare has been one of the top demands of this
generation. In the advent of technology, the provision of
healthcare continues to improve. One of the top priorities is
the provision of diagnosis, and the one responsible for this is
the physician. Physician’s diagnosis is the most relevant
factor that leads to the acquisition of proper health guides.
As technology soars, there are changing medical
requirements and solutions, and sometimes physicians are
not updated with these upgrades, that they need to surf the
internet for more information.
Medical diagnosis is regarded as an important yet
complicated task that needs to be executed accurately and
efficiently. The automation of this system would be
extremely advantageous. Regrettably all doctors do not
possess expertise in every sub specialty and moreover there
is a shortage of resource persons at certain places.
Therefore, an automatic medical diagnosis system would
probably be exceedingly beneficial by bringing all of them
together. Appropriate computer-based information and/or
decision support systems can aid in achieving clinical tests
at a reduced cost. Efficient and accurate implementation of
automated system needs a comparative study of various
techniques available.
One of the solutions that could aid in the physician’s
diagnosis is the Clinical Diagnostics Decision Support
System (CDSS). The CDSS is generally defined as any
computer program designed to help health professionals
make clinical decisions. Clinical Decision Support Systems
supports case-specific advice which addresses to the aiding
of physician’s diagnosis via computer-based facility. Since
physicians sometimes encounter complicated ailments, a
CDSS can make decision making simpler by providing
relevant pre-diagnosis. With the use of CDSS, a pre-
diagnosis could be extracted which can strongly support the
physician’s decision. Hence, lesser time could only be
consumed in the provision of diagnosis to the patient. Along
with this CDSS is a logic which provides probabilistic
conditions that leads to a specific outcome, which is the
Fuzzy Logic; another one is the method to be taken, which
is the rule-based method.
II. RELATED WORK
Complex event processing is supported by systems from
various domains. These include ODE, Snoop and others for
active databases and the Situation Manager Rule Language,
a general purpose event language. Event management was
also introduced in the area of business process management
[3] and service-based systems [7]. An excellent introductory
book to complex event processing is also available. A recent
book introduces principles and applications of distributed
event based systems [6]. Architectures for complex event
processing were proposed, both generic and by extending
middleware.
The majority of existing models do not support event
uncertainty. Therefore, solutions adopted in the active
database literature, such as the Rete network algorithms fail
to provide an adequate solution to the problem, since they
cannot estimate probabilities. An initial rule-based approach
for managing uncertain events was proposed in [14]. This
work was followed by a probabilistic event language for
Dynamic Rule Base Construction and Maintenance Scheme for
Disease Prediction
M. Mythili1
Prof. K. Sampath kumar2
1
ME (CSE), 2
HOD/CSE
PGP College of Engineering and Technology, Namakkal
2. Dynamic Rule Base Construction and Maintenance Scheme for Disease Prediction
(IJSRD/Vol. 1/Issue 5/2013/0051)
All rights reserved by www.ijsrd.com 1247
supporting RFID readings. Our proposed model shares some
commonalities with, namely uncertainty specification at
both the event occurrence and the rule level, and providing
an algorithm for uncertain event derivation. However, unlike
[4], our framework does not limit the type of temporal
expressions it can handle nor does it limit attribute
derivation types. We extend the work by providing
algorithms for deriving uncertain events and empirical
evidence to the scalability of the approach.
Recent works on event stream modelling propose formal
languages to support situations such as event negations.
Various aspects of event-oriented computing were discussed
at CIDR 2007 (e.g., [2]). Our focus in this work is on
modelling and efficiently managing uncertainty in complex
event systems, which was not handled by these works.
Modelling probabilistic data and events was suggested in
[8]. This work extends those models by proposing a model
and a probability space representation of rule uncertainty. A
common mechanism for handling uncertainty reasoning is a
Bayesian network, a method for graphically representing a
probability space, using probabilistic independencies to
enable a relatively sparse representation of the probability
space. Qualitative knowledge of variable interrelationships
is represented graphically, while quantitative knowledge of
specific probabilities is represented as Conditional
Probability Tables (CPTs). The network is, in most
applications, manually constructed for the problem at hand.
In our framework, we automatically construct a Bayesian
network from a set of events and rules, following the
Knowledge Based Model Construction (KBMC) paradigm.
This paradigm separates uncertain knowledge representation
from inference, which is usually carried out by transforming
the knowledge into a Bayesian network that can model
knowledge at the propositional logic level knowledge. The
work also follows the KBMC paradigm. In this work, the
quantitative knowledge, as well as the quantitative
deterministic knowledge, are represented as a set of Horn
clauses and the qualitative probabilistic knowledge is
captured as a set of CPTs. Our framework, however, in that
our framework need not be restricted to first order
knowledge. For example, second order knowledge, or
knowledge that can be expressed using any procedural
language, can also be captured. In addition, in our
framework, probabilities may have a functional dependency
on the events themselves. For example, the probability
assigned to a flu outbreak could be specified to be min (90 +
(X - 350)=10, 100). In addition, our framework enables the
uncertainty associated with such event derivation to be
captured by different probability spaces at different points in
time. Existing works [9] for processing complex events in
specific domains tailor probabilistic models or direct
statistical models to the application. No general framework
was defined there to derive uncertain events. Some
details of our framework were presented in [13] with two
significant extensions in this work. We discuss in details
efficiency aspects of our algorithms, and in particular event
Selectability. We also significantly extended our empirical
analysis to include more cases.
III. EVENT DRIVEN SYSTEMS (EDS)
In recent years, there has been a growing need for event
Driven systems, i.e., systems that react automatically to
events. The earliest event-driven systems in the database
realm impacted both industry and academia. New
applications in areas such as Business Process Management
(BPM) [5]; sensor networks [11]; security applications;
engineering applications; and scientific applications all
require sophisticated mechanisms to manage and react to
events.
Some events are generated externally and deliver data across
distributed systems, while other events and their related data
need to be derived by the system itself, based on other
events and some derivation mechanism. In many cases, such
derivation is carried out based on a set of rules. Carrying out
such event derivation is hampered by the gap between the
actual occurrences of events, to which the system must
respond, and the ability of event-driven systems to
accurately generate events. This gap results in uncertainty
and may be attributed to unreliable event sources, an
unreliable network, or the inability to determine with
certainty whether a phenomenon has actually occurred given
the available information sources. Therefore, a clear trade-
off exists between deriving events with certainty, using full
and complete information, and the need to provide a quick
notification of newly revealed events. Both responding to a
threat without sufficient evidence and waiting too long to
respond may have undesirable consequences.
In this work, we present a generic framework for
representing events and rules with uncertainty. We present a
mechanism to construct the probability space that captures
the semantics and defines the probabilities of possible
worlds using an abstraction based on a Bayesian network. In
order to improve derivation efficiency we employ two
mechanisms: The first mechanism, which we term
Selectability, limits the scope of impact of events to only
those rules to which they are relevant, and enables a more
efficient calculation of the exact probability space. The
second mechanism we employ is one of approximating the
probability space by employing a sampling technique over a
set of rules. We show that the approximations this
mechanism provides truly represent the probabilities defined
by the original probability space. We validate the
approximation solution using a simulation environment,
simulating external event generation, and derive events
using our proposed sampling algorithm.
IV. CHALLENGES IN EDS
A generic model is used for representing the derivation of
new events under uncertainty. Uncertain derivations of
events are performed on uncertain data values. Relevance
estimation is a more challenging task under uncertain event
analysis. Selectability and sampling mechanism are used to
improve the derivation accuracy. Selectability filters events
that are irrelevant to derivation by some rules. Selectability
algorithm is applied to extract new event derivation. A
Bayesian network representation is used to derive new
events given the arrival of an uncertain event and to
compute its probability. A sampling algorithm is used for
efficient approximation of new event derivation. The
following drawbacks are identified from the existing system.
1. Static rule base model
2. Limited accuracy in rule probability estimation
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3. Manages limited incoming events only
4. Rule class and structure are not generalized
V. EVENT DERIVATION MODELS
The challenges associated with event derivation; note first
that such events only suggest a high probability of a flu
outbreak, which does not necessarily mean that such an
event should be derived. We term this type of uncertainty
derivation uncertainty, as it stems from the inability to
derive such events with certainty from the available
information. In addition, there is uncertainty regarding the
data itself, because the provided data are rounded to the
nearest ten. For example, the increase from November 30 to
December 4 is of 350 units, yet rounding may also suggest a
smaller increase of 341 units. This is considered uncertainty
at the source, resulting from inaccurate information
provided by the event source. Additional details regarding
the different types of uncertainty can be found in [1].
EID Date Daily Sales (Rounded)
113001 Nov 30 600
120101 Dec 1 700
120201 Dec 2 800
120301 Dec 3 900
120401 Dec 4 950
120501 Dec 5 930
Table 1: Over-The-Counter Sales Relation
Fig. 1: Anthrax rule
To highlight the complexity of the processing uncertain
events and rules we note that the rule presented above is a
simplified version of rules expected to exist in the real
world. For example, instead of just specifying a single
certainty level of 90 percent, one could compute the
probability of an outbreak as a function of the amount of
increase. Note that while such a rule may be sufficient to
capture in some settings the derivation uncertainty, it does
not capture uncertainty at the source.
We also note that real-life applications need to
process multiple rules with multiple data sources [10]. To
illustrate, consider Fig. 1, where we need to consider the
possibility of an anthrax attack, in addition to the flu
outbreak. In this setting, data must be combined from events
that come from different data sources. A lower probability
should be assigned to an anthrax attack whenever a flu
outbreak is recognized and a higher probability otherwise.
As a result, a flu outbreak event is not derived; a significant
increase of hospital emergency department visits with
respiratory complaints is enough to assign a probability of
60 percent to the occurrence of an anthrax attack event.
Other examples of more complex settings may involve
correlating data between pharmacies or across regions.
Therefore, it must be possible to specify a set of rules that
captures this complexity.
VI. PROBABILISTIC EVENT MODEL
A. Event Model
An event is an actual occurrence or happening that is
significant and atomic. Examples of events include
termination of workflow activity, daily OtCCMS and a
person entering a certain geographical area. We differentiate
between two types of events. Explicit events are signaled by
external event sources. Derived events are events for which
no direct signal exists, but rather need to be derived based
on other events, e.g., Flu Outbreak and Anthrax Attack
events. Data can be associated with an event occurrence.
Some data types are common to all events, while others are
specific. The data items associated with an event are termed
attributes.
B. Derivation Model
Derived events in our model are inferred using rules. For
ease of exposition, we refrain from presenting complete rule
language syntax. Rather, we represent a rule by a quintuple,
r = defining the necessary conditions for the derivation of
new events. Such a quintuple can be implemented in a
variety of ways, such as a set of XQuery statements, Horn
clauses, and CPTs such as in [12], or as a set of procedural
statements. We detail next each of the rule elements,
illustrating them with the rule r1: “If there is an increase in
OtCCMS for four sequential days to a total increase of 350,
then the probability of a flu outbreak is 90 percent.” Recall
that OtCCMS events contain the volume of the daily sales.
C. Event Detection Algorithms
Selectability, as defined by function sr in a rule
specification, plays an important role in event derivation, in
both the deterministic and the uncertain settings. In our
setting, the relevance of an EID to derivation according to
rule r is determined by the function sr. sr is a function sr : h
hr that receives an event history as input, and returns a
subset hr h. As an example of a function sr consider the
selection function corresponding. Given an event history h,
it returns a set consisting of all possible events e that
indicate an increase in OtCCMS in two consecutive days.
Formally, this would be defined as all possible events e such
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that e may be one of a pair of events {e1, e2} in h such that
both e1 and e2 are OtCCMS events, e2 occurred one day
after e1, and the number of cough medication sold as
indicated by e2 is greater than the number of cough
medication sold as indicated by e1. It is the role of the
selection expression to filter out events that are irrelevant for
derivation according to r. An event e h to be relevant to
derivation according to rule r iff e sr (h).We also require
that for every pair of event histories h, h’ such that h h0 sr
(h) it must hold that sr (h) = sr (h’). (1)
Note that from (1) we have the following special case: sr
(h) = sr (sr (h)). (2)
1) Selectability Algorithm
As in the uncertain setting derivation is carried out on EIDs,
algorithms are required to compute which EIDs, from a
given system event history H, are selectable. Deciding
whether an EID E is selectable by rule r may, by itself, incur
significant computational effort. This is because,
selectability depends on the possible event histories in
which the event corresponding to E participates. Therefore,
a naive algorithm for finding all selectable EIDs involves
scanning all event histories, and for each event history,
finding all events selectable by rule r using the function sr.
However, this may be extremely inefficient, as the number
of possible event histories may be exponential in the size of
the state space of the events. Therefore, if the system event
history H contains n EIDs, and the size of the state space of
the largest EID is m, then the number of possible event
histories is O(mn).
We propose an efficient algorithm based on a decomposition
of sr. We decompose sr into two functions as follows: Let =
{e1 ,. . . , en}, then sr may be described by csr (esr (e1)) esr
(e2) ….. esr (en)), such that esr : e {{}, {e}.esr receives a
single event as input, and returns as output either the empty
set if it is clear from the attributes of this event that this
event is not selectable by the rule, or the set containing the
single event received as input otherwise. This representation
enables distinguishing between a selection that can be
carried out only by looking at an individual event and its
attributes using the function e sr, and filtering that requires
looking at combinations of events in h, which is carried out
by function c sr. Note that such decomposition into e sr and
c sr can always be carried out, since e sr can, in the worst
case, return the set containing the input event. In such a
case, sr = csr.
Based on this decomposition we provide an algorithm that,
for each rule r, provides the subset of EIDs in a system event
history that is selectable by r. In this algorithm the function
esr is first used to test selectability at the individual EID
level. A set of EIDs will be constructed such that EID E is in
the set iff one of the possible states of E corresponds to an
event e that esr returns as selectable. Following this, only
the set of event histories defined by this subset of EIDs is
checked for selectability, by using the c sr function. The
pseudocode of this algorithm—function
calculateSelectableEIDs—appears in Algorithm 1.
Algorithm 1. calculateSelectableEIDs(H,r)
1. selectableEIDs
2. esEIDs ,
3. for all E H
4. for all e E
5. if esr (e) = {{e}}
6. esEIDs esEIDs E
7. end if
8. end for
9. end for
10.for all h esEIDs
11. for all e sr (h)
12. E getCorrespondingEID(e)
13. selectableEIDs selectableEIDs E
14. end for
15. end for
16. return selectableEIDs
2) Sampling Algorithm
The algorithm described in the generates a Bayesian
network from which the exact probability of each event can
be computed. Given an existing Bayesian network, it is also
efficiently possible to approximate the probability of an
event occurrence using a sampling algorithm, as follows:
Given a Bayesian network with nodes E1, . . . , En, we
calculate an approximation for the probability that Ei =
{occurred} by first generating m independent samples using
a Bayesian network sampling algorithm. Then, Pr(Ei =
{occurred}) is approximated by , where #(Ei = {occurred})
is the number of samples in which Ei has received the value
occurred.
Algorithm 2. RuleSamp, triggered by a new event arrival
1. h
2. for all E H0
3. e probSampling(E)
4. h h e
5. end for
6. order obtainTriggeringOrder
7. while order
8. r deleteNextRule(order)
9. h’
10. selEvents sr (h)
11. if pr(selEvents) = true
12. assocT uples ar (selEvents)
13. for all tuple assocT uples
14. {s1, . . . , sn} mr (tuple)
15. prob prr (tuple, mr (tuple))
16. probSamp
sampleBernoulli(prob)
17. if probSamp = 1
18. e {occurred, s1,. , sn}
19. else
20. e {notOccurred}
21. end if
22. h h’ {e}
23. end for
24. end if
25. h h h’
26. end while
27. return h
VII. DISEASE PREDICTION WITH HYBRID RULE BASE
MODEL
The event derivation system is enhanced to map dynamic
rules on uncertain data environment. The rule base
construction and maintenance operations are handled by the
system. Rule probability estimation is carried out using the
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Apriori algorithm. The rule derivation process is optimized
for domain specific model. The system is designed to detect
events on uncertain data environment. Dynamic rule base
model is used in the system. The system integrates the rule
base update process. The system is divided into six major
modules. They are patient diagnosis, rule base management,
sampling process, selection process, event detection and rule
base update.
The patient diagnosis module is designed to collect patient
symptoms. The rule base is constructed with experts support
under rule base management module. The sampling process
module is designed to select item samples. The selection
process module is designed to select item combinations. The
rule base comparison is performed under event detection
module. The rule base update module is designed to update
new events with rules.
A. Patient Diagnosis
The system uses TB patient diagnosis information. Patient
diagnosis data can be imported from external databases. The
user can also update new patient diagnosis details. Diagnosis
list shows the patient symptom levels.
B. Rule Base Management
Rule base is used to manage the rules and event names. Rule
base details are collected from domain experts. The rules are
composed with attribute combination and values. Three
types of rules are used in the system. Static, dynamic and
hybrid rules are maintained under the rule base.
C. Sampling Process
The sampling process is performed to select event derivation
models. The attribute values are sampled from uncertain
data collections. The sampling algorithm is used to select
sample data derivations. Sampled data values are passed to
rule analysis process.
D. Selection Process
The selection process is applied to filter irrelevant events.
Item combinations are selected under the selection process.
The Selectability algorithm is used in the selection process.
Event derivations are used in the rule analysis process.
E. Event Detection
The rule base analysis is performed with user data
collections. The Bayesian network is used in the event
detection process. The attribute combinations are compared
with rule base information. Event detection is carried out
with rank and priority information.
F. Rule Base Update
The dynamic rules are derived from user data and static rule
base information. The dynamic rules are updated with event
name and priority values. Rule ranking is performed with
frequency information. Rules are updated with priority
details.
VIII. CONCLUSION
Rule bases are built to manage activities and their class
information. Event derivation is carried out with rule bases.
Dynamic rule base update model introduced to improve the
event detection process. Selective and sampling algorithms
are enhanced to derive events under dynamic and uncertain
domain environments. The system integrates the experts
knowledge and local domain information for rule base
construction. Rule base generation and maintenance
operations are done using machine learning models. Event
classes and its structures are generalized. Association rule
mining methods are used to extract rule patterns.
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