Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimizationrahulmonikasharma
Now a day, health information management and utilization is the demanding task to health informaticians for delivering the eminence healthcare services. Extracting the similar cases from the case database can aid the doctors to recognize the same kind of patients and their treatment details. Accordingly, this paper introduces the method called H-BCF for retrieving the similar cases from the case database. Initially, the patient’s case database is constructed with details of different patients and their treatment details. If the new patient comes for treatment, then the doctor collects the information about that patient and sends the query to the H-BCF. The H-BCF system matches the input query with the patient’s case database and retrieves the similar cases. Here, the PSO algorithm is used with the BCF for retrieving the most similar cases from the patient’s case database. Finally, the Doctor gives treatment to the new patient based on the retrieved cases. The performance of the proposed method is analyzed with the existing methods, such as PESM, FBSO-neural network, and Hybrid model for the performance measures accuracy and F-Measure. The experimental results show that the proposed method attains the higher accuracy of 99.5% and the maximum F-Measure of 99% when compared to the existing methods.
ARTIFICIAL INTELLIGENCE BASED DATA GOVERNANCE FOR CHINESE ELECTRONIC HEALTH R...IJDKP
Electronic health record (EHR) analysis can leverage great insights to improve the quality of human
healthcare. However, the low data quality problems of missing values, inconsistency, and errors in the
data setseverely hinder buildingrobust machine learning models for data analysis. In this paper, we
develop a methodology ofartificial intelligence (AI)-based data governance to predict the missing values
or verify if the existing values are correct and what they should be when they are wrong. We demonstrate
the performance of this methodology through a case study ofpatient gender prediction and verification.
Experimental resultsshow that the deep learning algorithm of convolutional neural network (CNN) works
very wellaccording to the testing performance measured by the quantitative metric of F1-Score, and it
outperformsthe support vector machine (SVM) models with different vector representations for documents.
Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimizationrahulmonikasharma
Now a day, health information management and utilization is the demanding task to health informaticians for delivering the eminence healthcare services. Extracting the similar cases from the case database can aid the doctors to recognize the same kind of patients and their treatment details. Accordingly, this paper introduces the method called H-BCF for retrieving the similar cases from the case database. Initially, the patient’s case database is constructed with details of different patients and their treatment details. If the new patient comes for treatment, then the doctor collects the information about that patient and sends the query to the H-BCF. The H-BCF system matches the input query with the patient’s case database and retrieves the similar cases. Here, the PSO algorithm is used with the BCF for retrieving the most similar cases from the patient’s case database. Finally, the Doctor gives treatment to the new patient based on the retrieved cases. The performance of the proposed method is analyzed with the existing methods, such as PESM, FBSO-neural network, and Hybrid model for the performance measures accuracy and F-Measure. The experimental results show that the proposed method attains the higher accuracy of 99.5% and the maximum F-Measure of 99% when compared to the existing methods.
ARTIFICIAL INTELLIGENCE BASED DATA GOVERNANCE FOR CHINESE ELECTRONIC HEALTH R...IJDKP
Electronic health record (EHR) analysis can leverage great insights to improve the quality of human
healthcare. However, the low data quality problems of missing values, inconsistency, and errors in the
data setseverely hinder buildingrobust machine learning models for data analysis. In this paper, we
develop a methodology ofartificial intelligence (AI)-based data governance to predict the missing values
or verify if the existing values are correct and what they should be when they are wrong. We demonstrate
the performance of this methodology through a case study ofpatient gender prediction and verification.
Experimental resultsshow that the deep learning algorithm of convolutional neural network (CNN) works
very wellaccording to the testing performance measured by the quantitative metric of F1-Score, and it
outperformsthe support vector machine (SVM) models with different vector representations for documents.
Patterns discovered from based on collected molecular profiles of patient tumour samples, and also clinical metadata, could be used to provide personalized cancer treatment to patients with
similar molecular subtypes. Computational algorithms for cancer diagnosis, prognosis, and therapeutics that can recognize specific functions and aid in classifiers based on a plethora of
publicly accessible cancer research outcomes are needed. Machine learning, a branch of artificial intelligence, has a great deal of potential for problem solving in cryptic cancer
datasets, as per a literature study. We focus on the new state of machine learning applications in cancer research in this study, illustrating trends and analysing major accomplishments,
roadblocks, and challenges along the way to clinic implementation. In the context of noninvasive treating cancer using diet-based and natural biomarkers, we propose a novel machine learning algorithm.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Comparative Study of Classification Method on Customer Candidate Data to Pred...IJECEIAES
Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k-fold. The C4.5 experiment results also confirm that the most influential instant data attribute in this research is the salary.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
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 health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure 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. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
Implementing Clinical Decision Support System Using Naïve Bayesian Classifierrahulmonikasharma
To speed up the diagnosis time and improve the diagnosis accuracy in today’s healthcare system, it is important to provide a much cheaper and faster way for diagnosis. This system is called as Clinical Decision Support System (CDSS). With various data mining techniques being applied to assist physicians in diagnosing patient diseases with similar symptoms, has received a great attention now a days. The advantages of clinical decision support system include not only improving diagnosis accuracy but also reducing diagnosis time. In this paper, the data mining technique name Naïve Bayesian Classifier, which offers many advantages over the traditional methods of data mining is used that opens a new way for clinicians to predict patient’s diseases. As the system is built on the sensitive data for patient privacy it is necessary to add some features that meets the security requirement. Specifically, with large amounts of data related to healthcare is generated every day, the classification can be utilized to excavate valuable information that improve clinical decision support system. Here the fuzzywuzzy string matching algorithm of naïve bayesian classifier is used to perform prediction from large number of symptoms data. The Result analysis perform in the last section on live data of five patient gives that by using proposed technique we try to make the Clinical Decision Support System more helpful for providing diagnosis of deceases more accurately and efficiently.
DENGUE DETECTION AND PREDICTION SYSTEM USING DATA MINING WITH FREQUENCY ANALYSIScsandit
Clinical documents are a repository of information about patients' conditions. However, this
wealth of data is not properly tapped by the existing analysis tools. Dengue is one of the most
widespread water borne diseases known today. Every year, dengue has been threatening lives
the world over. Systems already developed have concentrated on extracting disorder mentions
using dictionary look-up, or supervised learning methods. This project aims at performing
Named Entity Recognition to extract disorder mentions, time expressions and other relevant
features from clinical data. These can be used to build a model, which can in turn be used to
predict the presence or absence of the disease, dengue. Further, we perform a frequency
analysis which correlates the occurrence of dengue and the manifestation of its symptoms over
the months. The system produces appreciable accuracy and serves as a valuable tool for
medical experts.
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Life is the most precious gift to man and safeguarding this gift is of utmost importance.With
increasing number of diseases and fast paced lives, people have less time to look after themselves and
their family members or to even visit the doctor for regular check-ups.Our E-Health patient
monitoring system can remotely monitor the health of the patients and intimate the doctor of critical
conditions without human intervention. Some of the existing E-Health systems include telemedicine
network for Francophone African countries (RAFT) and LOBIN. RAFT is implemented in java and
uses asymmetric public – private key encryption, however it is expensive, does not support mobility
and is not a context aware system. LOBIN is a hardware/software platform to locate and monitor a set
of physiological parameters and context parameters of several patients within hospital facilities.
Although it is a context aware system it cannot handle high and concurrent data traffic load.
To overcome the above flaws, our proposed system puts forward an idea of patient monitoring
using various knowledge based techniques like K-means clustering, Gaussian kernel function, ANN
and Fuzzy inference engine. In our project we intend to do remote patient health monitoring in which
we will be using three-four machines which will send various sensed health parameters to the
centralised server that will make clusters of the sensed health parameters based on criticality of the
health condition. Then depending upon clusters formed and on comparison with the threshold values
appropriate reports will be generated and send to the doctors and caretakers.
An efficient feature selection algorithm for health care data analysisjournalBEEI
Diabete is a silent killer, which will slowly kill the person if it goes undetected. The existing system which uses F-score method and K-means clustering of checking whether a person has diabetes or not are 100% accurate, and anything which isn't a 100% is not acceptable in the medical field, as it could cost the lives of many people. Our proposed system aims at using some of the best features of the existing algorithms to predict diabetes, and combine these and based on these features; This research work turns them into a novel algorithm, which will be 100% accurate in its prediction. With the surge in technological advancements, we can use data mining to predict when a person would be diagnosed with diabetes. Specifically, we analyze the best features of chi-square algorithm and advanced clustering algorithm (ACA). This research work is done using the Pima Indian Diabetes dataset provided by National Institutes of Diabetes and Digestive and Kidney Diseases. Using classification theorems and methods we can consider different factors like age, BMI, blood pressure and the importance given to these attributes overall, and singles these attributes out, and use them for the prediction of diabetes.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical
research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more
number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well
as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
THE USE OF ARTIFICIAL INTELLIGENCE SYSTEMS AS A TOOL TO DIFFERENTIATE IN QUAL...AM Publications
Expert systems have a major role in medicine. The expert system can: Diagnose and treat diseases by building intelligent database. There are many expert systems used in the treatment of diseases. In this paper, the researcher reviews some of the expert systems used to diagnose diseases.
Comparative Analysis of Different Numerical Methods for the Solution of Initi...YogeshIJTSRD
A mathematical equation which involves a function and its derivatives is called a differential equation. We consider a real life situation, from this form a mathematical model, solve that model using some mathematical concepts and take interpretation of solution. It is a well known and popular concept in mathematics because of its massive application in real world problems. Differential equations are one of the most important mathematical tools used in modeling problems in Physics, Biology, Economics, Chemistry, Engineering and medical Sciences. Differential equation can describe many situations viz exponential growth and de cay, the population growth of species, the change in investment return over time. We can solve differential equations using classical as well as numerical methods, In this paper we compare numerical methods of solving initial valued first order ordinary differential equations namely Euler method, Improved Euler method, Runge Kutta method and their accuracy level. We use here Scilab Software to obtain direct solution for these methods. Vibahvari Tukaram Dhokrat "Comparative Analysis of Different Numerical Methods for the Solution of Initial Value Problems in First Order Ordinary Differential Equations" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45066.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathematics/45066/comparative-analysis-of-different-numerical-methods-for-the-solution-of-initial-value-problems-in-first-order-ordinary-differential-equations/vibahvari-tukaram-dhokrat
Improving Prediction Accuracy Results by Using Q-Statistic Algorithm in High ...rahulmonikasharma
Classification problems in high dimensional information with little sort of observations became furthercommon significantly in microarray information. The increasing amount of text data on internet sites affects the agglomerationanalysis. The text agglomeration could also be a positive analysis technique used for partitioning a huge amount of datainto clusters. Hence, the most necessary draw back that affects the text agglomeration technique is that the presenceuninformative and distributed choices in text documents. A broad class of boosting algorithms is known as actingcoordinate-wise gradient descent to attenuate some potential performs of the margins of a data set. This paperproposes a novel analysis live Q-statistic that comes with the soundness of the chosen feature set to boot to theprediction accuracy. Then we've a bent to propose the Booster of associate degree FS algorithm that enhances theworth of the Q-statistic of the algorithm applied.
A KNOWLEDGE BASED AUTOMATIC RADIATION TREATMENT PLAN ALERT SYSTEMijaia
In radiation therapy, preventing treatment plan errors is of paramount importance. In this paper, an alert system is proposed and developed for checking if the pending cancer treatment plan is consistent with the intended use. A key step in the development of the paper is characterization of various treatment plan fingerprints by three-dimension vectors taken from possibly thousands of variables in each treatment plan. Then three machine learning based algorithms are developed and tested in the paper. The first algorithm is a knowledge-based support vector machine method. If an incorrect treatment plan were offered, the algorithm would tell that the pending treatment plan is inconsistent with the intended use and provide a red flag. The algorithm is tested on the actual patient data sets with 100% successful rate and 0% failure rate. In addition, two algorithms based on the well-known k-nearest neighbour and Bayesian approach respectively are developed. Similar to the support vector machine algorithm, these two algorithms are also tested with 100% success rate and 0% failure rate. The key seems to pick up the right features.
Patterns discovered from based on collected molecular profiles of patient tumour samples, and also clinical metadata, could be used to provide personalized cancer treatment to patients with
similar molecular subtypes. Computational algorithms for cancer diagnosis, prognosis, and therapeutics that can recognize specific functions and aid in classifiers based on a plethora of
publicly accessible cancer research outcomes are needed. Machine learning, a branch of artificial intelligence, has a great deal of potential for problem solving in cryptic cancer
datasets, as per a literature study. We focus on the new state of machine learning applications in cancer research in this study, illustrating trends and analysing major accomplishments,
roadblocks, and challenges along the way to clinic implementation. In the context of noninvasive treating cancer using diet-based and natural biomarkers, we propose a novel machine learning algorithm.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Comparative Study of Classification Method on Customer Candidate Data to Pred...IJECEIAES
Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k-fold. The C4.5 experiment results also confirm that the most influential instant data attribute in this research is the salary.
PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY: CH...ijdms
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 health sector, that has witnessed a great
evolution following the development of new computer technologies that gave birth to multiple fields of
research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful
knowledge from it, predict diseases and anticipate the cure 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. In this paper, we
will present an overview on the evolution of big data in healthcare system, and we will apply three learning
algorithms on a set of medical data. The objective of this research work is to predict kidney disease by
using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5),
and Bayesian Network (BN), and chose the most efficient one.
Implementing Clinical Decision Support System Using Naïve Bayesian Classifierrahulmonikasharma
To speed up the diagnosis time and improve the diagnosis accuracy in today’s healthcare system, it is important to provide a much cheaper and faster way for diagnosis. This system is called as Clinical Decision Support System (CDSS). With various data mining techniques being applied to assist physicians in diagnosing patient diseases with similar symptoms, has received a great attention now a days. The advantages of clinical decision support system include not only improving diagnosis accuracy but also reducing diagnosis time. In this paper, the data mining technique name Naïve Bayesian Classifier, which offers many advantages over the traditional methods of data mining is used that opens a new way for clinicians to predict patient’s diseases. As the system is built on the sensitive data for patient privacy it is necessary to add some features that meets the security requirement. Specifically, with large amounts of data related to healthcare is generated every day, the classification can be utilized to excavate valuable information that improve clinical decision support system. Here the fuzzywuzzy string matching algorithm of naïve bayesian classifier is used to perform prediction from large number of symptoms data. The Result analysis perform in the last section on live data of five patient gives that by using proposed technique we try to make the Clinical Decision Support System more helpful for providing diagnosis of deceases more accurately and efficiently.
DENGUE DETECTION AND PREDICTION SYSTEM USING DATA MINING WITH FREQUENCY ANALYSIScsandit
Clinical documents are a repository of information about patients' conditions. However, this
wealth of data is not properly tapped by the existing analysis tools. Dengue is one of the most
widespread water borne diseases known today. Every year, dengue has been threatening lives
the world over. Systems already developed have concentrated on extracting disorder mentions
using dictionary look-up, or supervised learning methods. This project aims at performing
Named Entity Recognition to extract disorder mentions, time expressions and other relevant
features from clinical data. These can be used to build a model, which can in turn be used to
predict the presence or absence of the disease, dengue. Further, we perform a frequency
analysis which correlates the occurrence of dengue and the manifestation of its symptoms over
the months. The system produces appreciable accuracy and serves as a valuable tool for
medical experts.
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Life is the most precious gift to man and safeguarding this gift is of utmost importance.With
increasing number of diseases and fast paced lives, people have less time to look after themselves and
their family members or to even visit the doctor for regular check-ups.Our E-Health patient
monitoring system can remotely monitor the health of the patients and intimate the doctor of critical
conditions without human intervention. Some of the existing E-Health systems include telemedicine
network for Francophone African countries (RAFT) and LOBIN. RAFT is implemented in java and
uses asymmetric public – private key encryption, however it is expensive, does not support mobility
and is not a context aware system. LOBIN is a hardware/software platform to locate and monitor a set
of physiological parameters and context parameters of several patients within hospital facilities.
Although it is a context aware system it cannot handle high and concurrent data traffic load.
To overcome the above flaws, our proposed system puts forward an idea of patient monitoring
using various knowledge based techniques like K-means clustering, Gaussian kernel function, ANN
and Fuzzy inference engine. In our project we intend to do remote patient health monitoring in which
we will be using three-four machines which will send various sensed health parameters to the
centralised server that will make clusters of the sensed health parameters based on criticality of the
health condition. Then depending upon clusters formed and on comparison with the threshold values
appropriate reports will be generated and send to the doctors and caretakers.
An efficient feature selection algorithm for health care data analysisjournalBEEI
Diabete is a silent killer, which will slowly kill the person if it goes undetected. The existing system which uses F-score method and K-means clustering of checking whether a person has diabetes or not are 100% accurate, and anything which isn't a 100% is not acceptable in the medical field, as it could cost the lives of many people. Our proposed system aims at using some of the best features of the existing algorithms to predict diabetes, and combine these and based on these features; This research work turns them into a novel algorithm, which will be 100% accurate in its prediction. With the surge in technological advancements, we can use data mining to predict when a person would be diagnosed with diabetes. Specifically, we analyze the best features of chi-square algorithm and advanced clustering algorithm (ACA). This research work is done using the Pima Indian Diabetes dataset provided by National Institutes of Diabetes and Digestive and Kidney Diseases. Using classification theorems and methods we can consider different factors like age, BMI, blood pressure and the importance given to these attributes overall, and singles these attributes out, and use them for the prediction of diabetes.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical
research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more
number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well
as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
THE USE OF ARTIFICIAL INTELLIGENCE SYSTEMS AS A TOOL TO DIFFERENTIATE IN QUAL...AM Publications
Expert systems have a major role in medicine. The expert system can: Diagnose and treat diseases by building intelligent database. There are many expert systems used in the treatment of diseases. In this paper, the researcher reviews some of the expert systems used to diagnose diseases.
Comparative Analysis of Different Numerical Methods for the Solution of Initi...YogeshIJTSRD
A mathematical equation which involves a function and its derivatives is called a differential equation. We consider a real life situation, from this form a mathematical model, solve that model using some mathematical concepts and take interpretation of solution. It is a well known and popular concept in mathematics because of its massive application in real world problems. Differential equations are one of the most important mathematical tools used in modeling problems in Physics, Biology, Economics, Chemistry, Engineering and medical Sciences. Differential equation can describe many situations viz exponential growth and de cay, the population growth of species, the change in investment return over time. We can solve differential equations using classical as well as numerical methods, In this paper we compare numerical methods of solving initial valued first order ordinary differential equations namely Euler method, Improved Euler method, Runge Kutta method and their accuracy level. We use here Scilab Software to obtain direct solution for these methods. Vibahvari Tukaram Dhokrat "Comparative Analysis of Different Numerical Methods for the Solution of Initial Value Problems in First Order Ordinary Differential Equations" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45066.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathematics/45066/comparative-analysis-of-different-numerical-methods-for-the-solution-of-initial-value-problems-in-first-order-ordinary-differential-equations/vibahvari-tukaram-dhokrat
Improving Prediction Accuracy Results by Using Q-Statistic Algorithm in High ...rahulmonikasharma
Classification problems in high dimensional information with little sort of observations became furthercommon significantly in microarray information. The increasing amount of text data on internet sites affects the agglomerationanalysis. The text agglomeration could also be a positive analysis technique used for partitioning a huge amount of datainto clusters. Hence, the most necessary draw back that affects the text agglomeration technique is that the presenceuninformative and distributed choices in text documents. A broad class of boosting algorithms is known as actingcoordinate-wise gradient descent to attenuate some potential performs of the margins of a data set. This paperproposes a novel analysis live Q-statistic that comes with the soundness of the chosen feature set to boot to theprediction accuracy. Then we've a bent to propose the Booster of associate degree FS algorithm that enhances theworth of the Q-statistic of the algorithm applied.
A KNOWLEDGE BASED AUTOMATIC RADIATION TREATMENT PLAN ALERT SYSTEMijaia
In radiation therapy, preventing treatment plan errors is of paramount importance. In this paper, an alert system is proposed and developed for checking if the pending cancer treatment plan is consistent with the intended use. A key step in the development of the paper is characterization of various treatment plan fingerprints by three-dimension vectors taken from possibly thousands of variables in each treatment plan. Then three machine learning based algorithms are developed and tested in the paper. The first algorithm is a knowledge-based support vector machine method. If an incorrect treatment plan were offered, the algorithm would tell that the pending treatment plan is inconsistent with the intended use and provide a red flag. The algorithm is tested on the actual patient data sets with 100% successful rate and 0% failure rate. In addition, two algorithms based on the well-known k-nearest neighbour and Bayesian approach respectively are developed. Similar to the support vector machine algorithm, these two algorithms are also tested with 100% success rate and 0% failure rate. The key seems to pick up the right features.
DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
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www.nexgenproject.com
Disease Prediction Using Machine Learning Over Big DataCSEIJJournal
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 CNN-
based unimodal disease risk prediction (CNN-UDRP) algorithm.
Disease Prediction Using Machine Learning Over Big DataCSEIJJournal
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 CNN-
based unimodal disease risk prediction (CNN-UDRP) algorithm.
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.
Intensive care unit deals with data that are dynamic in nature like real time measurement of health condition to laboratory test data that are continuously
changes accordingly with time. Artificial intelligence (AI’s) potential ability to perform complex pattern analyses using large volumes of data. Generated
pattern discovers the new symptoms of the disease in the Intensive care units (ICUs), helps the doctors to prescribe the new drug discovery which is
helpful to intelligent use. Currently research work has been focused in the ICU making more efficient clinical workflow by generation of high-risk
patterns from improved high volumes of data. Emerging area of AI in the ICU includes mortality prediction, uses of powerful sensors, new drug
discovery, prediction of length of stay and legal role in uses of drugs for severity of disease. This review focuses latest application of AI drugs and
other relevant issues for the ICU.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
Machine learning in drug supply chain management during disease outbreaks: a ...IJECEIAES
The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks.
Similar to A comprehensive study on disease risk predictions in machine learning (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.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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/
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.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
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2. INTRODUCTION TO MACHINE LEARNING
Machine learning (ML) is an artificial intelligence (AI) branch that helps analyze the data structure
and fit the information into models correctly. It is one of the computer science fields, and differs from other
computing technology by the way of training the computers based on data inputs and it uses statistical
analysis to get the proper output. For this reason, ML is used in automated decision-making models like
Facial recognition, Recommendation engines, OCR and Self driving car applications.
Machine learning methods are categorized into three classifications according to the training
processes used. The categories are supervised machine learning, unsupervised machine learning and
reinforcement learning. In supervised learning, the data samples with category labels are used in training.
Classification and regression models are examples of supervised learning. The algorithms used in this
approach are decision tree, naïve bayes etc. In unsupervised learning, the data samples are directly used in
training without category label. Clustering techniques and encoders are the basic examples of unsupervised
approach. Reinforcement learning is a mixture of prior two approaches. It uses agent that finds the correct
action to achieve the overall goal of the application [15].
3. PREDICTION MODELS
A prediction model is characterized as a model that provides a way to evaluate the individual danger
of a patient for the outcome of a disease. The question of when, what and how to use these models arises with
the growth of such prediction models. These models can be taught over time, providing the demands of
the company, to react to new information or views.
Two types of prediction models exist. They are models of classification predicting class outcomes,
and models of regression predicting the relationship between a response variable Y and a predictor
variable X. Various basic and advanced algorithm, listed in Figure 1, Figure 2 conducts data analysis and
statistical analysis and determine information trends and patterns. While machine learning and prediction
analysis can provide an opportunity for any implementation, the haphazard implementation of these options
will drastically impede their ability to provide insight into the demands of the organization without
considering how they fit into everyday operations. Organizations need to guarantee that they have
the architecture in place to support these alternatives, as well as high-quality information to feed them and
assist them learn, to make the most of prediction analytics and machine learning [16].
Figure 1. Basic prediction techniques Figure 2. Advanced prediction techniques
4. EXISTING TECHNIQUES FOR DISEASE PREDICTION
Mingyu Pak, Miyoung Shin [17], explored few environmental variables linked to type-2 diabetes
disease and selected some variables to develop an analytical prototype of prediction of disease. For the choice
of important variables, they first pre-processed all external environmental factors into numerical data and
then estimated the maximum/minimum probability ratios of all the sorted exogenous variables.
The top-n positioned variables were then chosen as input variables for the forecast model depending on
ansan/ansung cohort 2 data collected from the Korean National Institute of Health (KNIH), the disease risk
factor prediction model was created using SVM. Their prediction model showed the output of 65.97 percent
precision and showed very identical performance only with genetic factors with particular environmental
variables to the model [17].
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The research shown in [18] focuses on disease forecasting from medical information supplied by
New York's Presbyterian Hospital. Since these are medical information, predicting computerized predictions
is generally unique and simpler than forecasting user text inputs. Nicolae Dragu et al discussed-on forecast of
serious contagious diseases from web material sources, which is also a specific origin where open clinical
terms are used [19]. Many attempts have been made to forecast selective diseases [20, 21]. For instance,
the authors in [20] deal with the prediction by mining content of coronary heart disease. There are also
an important amount of study works on medicinal services debates that have been carried out. Lav petrov
used NLP to evaluate and break down user remarks to predict disease and concentrate uncommon responses
to drugs [22].
Ryan McDonald et al, use a terminology-laden interface (i.e. clients need to explore a long list of
hints). It's an awkward job from the user's view, and the operation is also tedious. Also, if the customers do
not find a certain indication, they are forced to prevent that symptom that is not in any manner desired [23].
Client's directed text input [24, 25]. They rely on negligible symptom-disease relationship system [26, 27] in
any situation and use complete content database. These frameworks begin to search for accurate term match
at the input of the client from each input line in the database. For example, if a client with symptoms
equivalent is not specified in the database, then we cannot match the information correctly. If the user input
should contain a greater number of semi-technical terms than anticipated, it will degrade its performance.
The system used is especially strong and restricted to particular data types.
Xiaoyan Wang et al, proposed an automated system for disease prediction that based on the client's
driven feedback. Their structure requires input from the client such as the names of symptoms and certain
significant parameters and provides a list of likely illnesses (maximum infections are more likely to occur).
The accuracy of the automated disease prediction scheme (ADPS) evaluated the solution of the current
scheme with a standard 14.35 percent greater accuracy in examination [28]. S Manimekala et al, suggested
that the Automatic Disease Prediction technique determine the most probable disease based on the feedback
of the patient that facilitates early detection. The model uses data mining algorithm apriori-frequent pattern
(A-FP) to identify illnesses through health data mining focused on the signs of input. This method is used for
finding medical datasets from which to create association rules. The goal is to identify from the health dataset
relevant and frequent illnesses [29].
Cristinel Ababei et al, discussed the overall context of computer frameworks on multicore processor
systems, they provided a discussion on the most common prediction and classification methods.
They introduced some prediction systems from simple to more complicated, while highlighting one frequent
basic theme: each of these systems misuses the prior history of the variable of interest [30]. Ankita Dewan et
al, in conjunction with the back-propagation method, they suggested a successful genetic algorithm for
predicting heart disease and created a model that could identify and extract unknown data (patterns and
relationships) linked to coronary heart disease from archived database records of heart disease [31]. Table 1
shows the different methods and their advantages and disadvantages. The merits and demerits are specified in
terms of efficiency, prediction accuracy and how easily a model could be implemented.
Table 1. Merits and demerits of prediction techniques
Methods Pros Cons
KNN Simple, high accuracy Local information structure sensitivity. Calculated slowly.
Linear Regression Accuracy of good prediction
Training needs. When fresh information arrive,
coefficients may need to be updated constantly.
SVM Practically efficient, very popular
The size of the training information can improve the
number of bias functions. Selection of parameters
depends on information.
Bayes classifier high accuracy, efficient Training needs
LDA Fast, Implementation is comparatively simple Training needs
4.1. Description of existing techniques for heart disease prediction
P. K. Anooj [32], suggested a decision-making scheme based on fuzzy rules to assess the level of
risk of heart disease. He first pre-processed the information for missing values in his method. He then
performed fuzzy sets generation and created a fuzzy decision-making system. The technique selects
the required attribute based on the number of events in the database to create weighted fuzzy laws.
These weighted fuzzy rules are then used to create a decision-based scheme of assistance. He tested his
suggested scheme on three distinct dataset kinds that are collected from V.A. Cleveland dataset. Medical
centre with 303 cases of training data for 202 documents and test information for 101 documents. P. K.
Anooj contrasted his model with models based on neural networks and obtained the greatest precision [32].
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Tsipouras M.G., et al. [33] suggested an automated system based on fuzzy modeling and
information mining to predict heart disease. His model includes measures such as inferring information
decision tree, extracting guidelines, formulating crisp model, transforming crisp model into fuzzy model, and
optimizing it. The information gathered from Invasive Cardiology, University Department, Ioannina
Hospital. The technique provides 80% precision in awareness and 65% precision in specificity [33].
Chaitrali S. Dangare et al. [34], used heart disease prediction data mining method. He first
pre-processed the missing information using the mean mode technique and used the perceptron multi-layer
model to map the information. To analyse the database of heart disease, Naive bays, neural networks and
decision tree were used. He gathered 303 documents from the repository of Cleveland heart disease and used
it as a training set and gathered 270 documents as test information from the repository of Stat log Heart
Disease. The information set consists of attribute, input and key predictable. His model provides 100%
accuracy for neural networks, Decision tree with 99.62 and Naïve bayes with 90.74% precision [34].
Mai Shouman et al. [35], by incorporating decision tree and k-means clustering, suggested
a technique for diagnosing patients with heart disease. For k-means, the method utilizes centroid selection
technique and decision tree is used to determine the clusters. Thirteen distinct characteristics are gathered
from the Cleveland Clinic Foundation Heart Disease. Compared to the current decision tree, the integrated
model of k-means and decision tree obtained greater outcomes of 83.9% [35].
S. Pal et al [36], suggested using information mining to predict heart disease. He surveyed 3 distinct
classifiers such as CART, ID3 and tree of Decision. The information set from the Cleveland Clinic
Foundation shows that 83.49 percent of the classification and regression tree (CART) precision was much
better than the decision tree and ID3 (Iterative Dichotomized 3) [36]. H. A. Huijer et al. [37], designed
a decision support service to find unrest transition through decision trust measure, trust-based SVM and
trust-based multilevel SVM to discover agitation transition. 240 Samples are gathered via human body
sensors. The patient experiences distinctive tension inventory of state-quality scale (T-STAI), used to
calculate uncomfortable adults. The technique provides 91.4 percent precision when compared to traditional
vector supporting machine with 90.9 percent precision [37].
Latha Parthiban et al. [38], outlined a prediction technique for smart heart disease prediction.
The method is executed using coactive neuro fuzzy inference system, neural network, and genetic algorithm.
The dataset is gathered from UCI and the prototype is simulated using Neuro Solution Software. The mean
square error of CANFIS was 0.000842 [38]. N. Deepika et al. [39], suggested a heart attack patient
classification model. He pre-processed his information sets for missing values and then implemented
the same width binning interval approach. Then numerical parameters are transformed into categorical
parameters and frequent patterns are mined based on pruning-classification rule algorithm linked to heart
disease. His model used an effective forecast of particular class label [39].
T. Turner [40] proposed the idea of diagnosing heart disease by combining naïve bayes and k-means
clustering with distinct choice of centroids. Cleveland clinic foundation collects the data set. The precision of
the embedded k-k-is 84.5 percent compared to the traditional algorithm [40]. Uzma Ansari et al. [41] used
weighted associative classifier to develop a model for predicting heart attacks. The data set is gathered from
the ML database of Irvine University of California (UCI). He used 2 class labels 1 in his model for "No heart
disease" and other one for "Heart disease" rather than getting 5 class labels with 1 for no heart disease and 4
for four heart disease kinds. He used 80% of confidence value and 25% of support value. The prototype
proposed achieves precision of 81.51 percent. He concludes that the measured associative classifier is
the easiest way to acquire efficient important patterns from information setting for cardiac disease [41].
The below Table 2 shows the comparative surveyof heart disease techniques which we have discussed above.
Table 2. The comparative survey of heart disease
Authors Year Data set Methodology Accuracy
Latha Parthiban 2007 University of California Irvine (UCI) Neural network and Genetic algorithm MSE -.000842
Tsipouras M. G 2008 Invasive Cardiology, Hospital of Loannina Fuzzy Modelling and Data Mining 80%
H. A. Huijer 2010 Sensor Data SVM 91.4%
N. Deepika 2011 University of California Irvine (UCI) Pruning-Classification Association rule
Predicts
effectively
Uzma Ansari 2011 University of California Irvine (UCI) Weighted Associative Classifier 81.51%
P. K. Anooj 2012
Cleveland, Hungarian Institute of
Cardiology, University Hospital, Zurich,
Switzerland
Weighted Fuzzy rules 57.85%
Chaitrali S. 2012 Cleveland heart disease database
Naïve bayes, Neural network and
Decision tree
100%
Mai Shouman 2012 Cleveland heart disease database Centroid Selection Technique 83.9%
T. Turner 2012 Cleveland heart disease database Integrated k- means and Naïve bayes 84.5%
S. Pal 2013 Cleveland heart disease database CART, ID3, Decision Tree 83.49%
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4.2. Description of existing techniques for breast cancer prediction
A fuzzy model was created by Yassi et al. [42] to differentiate between normal and malicious breast
cancer. The technique brought disorder into the hierarchical cluster of partial swarm enhancement of
multispecies, prompting the improvement of chaotic hierarchical cluster-based multispecies swarm
enhancement of particles (CHCMSPSE). CHCMSPSE helps to distinguish the form of cancer of the breast
and to enhance the fuzzy rules. The model also discovers fuzzy rules very correctly. The dataset is gathered
from the machine learning database of Irvine University of California (UCI). Thus, for worldwide search
capability, the technique utilizes 11 chaotic maps. Sinusoidal chaotic map acquired 99 percent precision
from those maps because it matched with the position of the issue. The model achieves more than
90% precision [42].
In defining 5, 10 and 15 years of specific breast cancer sustainability, Lundin M et al. [43] provided
a prototype for accessing the precision of ANN. The data source is collected from City Hospital of Turku and
Turku University Central Hospital with 951 instances. In that training set of 651 instances and a validation
set of 300 instances. This prototype compares the outcomes of artificial neural network and logistical
regression. The precision of breast cancer specific survival for 5 years reported as 0.909,10 years reported as
0.086 and 15 years reported as 0.883 [43].
Delen D et al. [44] implemented a data mining technique comparison approach that involves
logistical regression, decision tree, and artificial neural network to predict breast cancer development.
The model utilizes over 200,000 cases of an enormous information repository. Thus, logistical regression
precision is 89.2%, decision tree precision (C5) 93.6% and artificial neural network 91.2%. 10-fold
cross-validation for information testing, unbiased estimation measurement and 3 techniques prediction.
Research indicates that the selection trees is the best determinant method for defining breast cancer growth
relative to the artificial neural network and logistic regression [44].
Bellaachia Abdelghani et al. [45] used information mining techniques to present a model for
predicting breast cancer development. The pre-classification process is carried out in three areas: recovery of
essential status, recovery of survival time and cause of death. Three techniques of machine learning, namely:
neural network propagated back, naïve bayes and C4.5 for classification performance. The data set is
gathered from the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results. There are
151.886 records in the dataset, with 16 characteristics. The model provides roughly 87% precision [45].
H. Koyuncu et al. [46] implemented a biomedical pattern based on artificial neural network
rotational forest algorithm (RF-ANN). Multilayer perceptron was used as the classifier of base and the model
used RF algorithm as the classifier of ensemble. Using the main component assessment, different function
sets are gathered from the information set. The precision of RF-ANN is therefore 98.05 percent [46].
S. M. Jamarani H et al. [47] proposed a method for recognizing the disease and helping radiologists
predict breast cancer. The model combines decomposition of artificial neural network (ANN) and sub-band
picture based on multiwavelet. The technique is studied using mammographic database by mammographic
image analysis society (MIAS). The highest output of biorthogonal Geronimo, Hardin and massopust
multiwavelet 2 (BiGHM2) was among the various kinds of multiwavelet. Thus, BiGHM2 accomplished
precision in the operating characteristic curve of the receiver around 0.96 [47].
T. Nguyen et al. [48] suggested an automatic wavelet-based technique for classifying medical
information and a type-2 fuzzy logic. They carried out execution from the UCI database for machine learning
on 2 medical datasets: Cleveland heart disease and Wisconsin breast cancer. The outcome demonstrates that,
compared to other machine learning methods, the advantage of interval type-2 fuzzy logic scheme is
better [48]. Z. Mahmud et al. [49] suggested a method to use age, marital status and therapy among
Malaysian women to find out about cervical cancer. The records of patients with cervical cancer are gathered
from the medical center of Kebangsaan Malaysia University (UKM). The model has four phases, with 444
records of patients impacted by cervical cancer, and finding out the age and marital status of women's
medical therapy. They discovered that the 46-year-old females are more likely to develop cervical cancer.
So, it is suggested that Malaysian women undergo testing prior to the age of 45 and they also found that
Chinese women under the age of 57 have more likelihood of being diagnosed with radiotherapy in
the original phase of cervical cancer [49].
M. Seera et al. [50] suggested using hybrid smart classification to classify medical information to
predict cancer. The model has a random forest, classified trees, and a regression tree and a min-max neural
network. The technique of random forest is used to create a classification and regression Tree model
ensemble. Fuzzy min-max is used for teaching purposes. The tree of classification and regression is used to
extract the rule. The precision of this model for cancer forecast was 98.84% [50].
W. Kuo et al. [51] suggested a novel technique for breast tumour prediction in clinical ultrasonic
images using the decision tree. The model concentrated on pictures from the United States. The decision tree
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uses the C5.0 algorithm. Machine learning with decision tree algorithm help predict breast tumour illness
with 93.33% responsiveness precision and 96.67% specificity precision [51].
Seon-Hak [52] constructed a prototype using rough set structures for hierarchical classification.
The model is based on the framework of hierarchical granulation to find out the laws of classification and
therefore suggested a discovery of laws. The technique is validated against the Wisconsin breast cancer
(WBC) data gathered dataset. The model still generates excellent efficiency when loaded with simple rules
and brief conditionals. Thus, by creating minimal classification rules, the model was effective in decreasing
the number of dimensions. His model makes it simpler for us to analyze the information system [52].
The below Table 3 shows the comparative study of breast cancer techniques which we have discussed above.
Table 3. The comparative survey of breast cancer
Authors Year Data set Methodology Accuracy
Lundin M 1999
Turku City Hospital and Central Hospital
of Turku University
ANN
5yrs-0.909, 10yrs-0.086
and 15yrs0.883 G
W. Kuo 2001 Machine learning repository C5.0 algorithm 96.67%
Seon-Hak Seo 2001
Wisconsin breast cancer (WBC), UCI
diabetes for Pima Indians
Hierarchical Classification 83.49%
Delen D 2005 Machine learning repository ANN and logistic regression 93.6%
Jamarani S. M. h 2005 Machine learning repository
(ANN), decomposition of
multiwavelet-based picture
sub-band
ROC-0.96
Bellaachia
Abdelghani
2006
National Cancer Institute (NCI)
surveillance, epidemiology and end
results
Data Mining Methods 87%
Uzma Ansari 2013 University of California Irvine (UCI)
Rotation forest algorithm (RF-
ANN)
98.05%
M. Seera 2014
Wisconsin breast cancer (WBC), UCI
diabetes for Pima Indians
Random forest, classified
trees and regression tree
(CART) and fuzzy neural
network min-max
98.84%
A. Yassi 2014 University of California Irvine (UCI) hierarchical-clustering 90%
T. Nguyen 2015 University of California Irvine (UCI)
Wavelets and interval type-2
fuzzy logic system (IT2FLS)
97.40%
5. CHALLENGES AND RESEARCH OPPORTUNITIES
Reviews of data mining methods, classification methods, smart methods and choice of features for
disease prediction were discussed here. As the selection of characteristics enables us to eradicate unnecessary
data, large-dimensional data must be compressed without the loss of data, which improves the effectiveness
of classification. But the difficulty of subset attribute selection is high, which is complicated as it requires
complex interdependence on a wide range of factors. We could incorporate guidelines and feature selection
for better results in the classifiers in the future. Additionally, new feature selection method such as ant colony
optimization, etc. is possible to test to improve quality, and you can attempt experimenting with algorithm
potential for most medical datasets that include distinct features such as noisy information, sparsity, missing
value, etc. to enhance model accuracy.
6. CONCLUSION
This paper's primary focus is to discuss various prediction models and techniques used for
predicting heart disease and breast cancer. The technique also sheds light on the significance in medical
dataset of various classification techniques for disease prediction. The dataset that we have discussed in so
many current methods is linked to heart and breast cancer. As a classifier, the different machine learning
methods are used to construct a value-effective model for predicting disease. It is therefore well recognized
by the exhaustive study that the extraction of the necessary data from the clinical repository helps us to
promote excellently-informed testing and choices.
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A comprehensive study on disease risk predictions in machine learning (G. Saranya)
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BIOGRAPHIES OF AUTHORS
Ms. G. Saranya received B.Tech degree in Information Technology from Sri Venkateswara
College of Engineering, Sriperumbudur, Chennai, India in 2009. M.Tech degree in Information
Technology from Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Avadi,
Chennai, India in 2011.She is currently pursuing her Ph.D in Computer Science and Engineering
at Sathyabama Institute of science and Technology, Chennai, India and works as Assistant
Professor in Information Technology Department at SRM Institute of Science and Technology,
India. She has 7+ years of teaching experience and 2 years of Industry Experience. Her research
interests include Big Data Analytics, Machine learning and Deep learning.
Dr. A. Pravin received the B.E degree in Computer Science & Engineering from Bharath Niketan
Engineering College, Madurai Kamaraj University, Madurai, India in 2003 , M.E degree in
Computer Science & Engineering from Sathyabama University, Chennai, India in 2005 and Ph.D
degree in Computer Science & Engineering at Sathyabama University, Chennai, India in 2014.
He works currently as an Associate Professor for the Department of Computer Science and
Engineering at Sathyabama Institute of Science and Technology, Chennai and he has 14 Years of
teaching experience. He has participated and presented many Research Papers in International and
National Conferences and also published many papers in International and National Journals.
His area of interests includes Software Engineering, Data mining , Internet of Things and Big data.