This document describes a two-layer k-means based consensus clustering algorithm for rural health information systems. The algorithm helps partition heterogeneous data and form sub-clusters within main clusters to enable efficient decision-making. It was tested on an MCTS dataset and found to be highly efficient and robust even with incomplete data, outperforming traditional k-means. The algorithm involves applying k-means clustering twice - first to generate main clusters, then to each main cluster to form sub-clusters - in order to better handle outliers and variations within clusters.
IRJET- Missing Data Imputation by Evidence ChainIRJET Journal
This document presents a new method called "evidence chain" for imputing missing data. The method works as follows:
1. It first identifies missing data values marked as "-1" in a dataset.
2. It then combines attribute values associated with each missing data point to form "evidence chains" to estimate potential values.
3. It calculates possible values and their probabilities for each missing data point.
4. It checks if an evidence chain matches values in the data and uses the most probable or highest value to impute the missing data.
5. The imputed values replace the original missing values to complete the dataset. The method is implemented in an application that can generate test data with missing values.
Analysis on Data Mining Techniques for Heart Disease DatasetIRJET Journal
This document analyzes various data mining techniques for classifying heart disease datasets. It compares the performance of classification algorithms like decision trees and lazy learning on aspects like time taken to build models. The algorithms are tested on a heart disease dataset from a public repository using the KEEL data mining tool. Decision trees and k-nearest neighbors are implemented using distance functions like Euclidean and HVDM across different validation modes. The results show that k-nearest neighbors with no validation is the most efficient algorithm for predicting heart disease, taking the least time to build models of the dataset. The study aims to determine the optimal classification algorithm for heart disease prediction systems.
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.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Prediction of Dengue, Diabetes and Swine Flu using Random Forest Classificati...IRJET Journal
This document describes a disease prediction system that uses the Random Forest classification algorithm to predict Dengue, diabetes, and swine flu. The system trains on labeled datasets for each disease. It then takes user-entered symptoms as input and predicts the likelihood of each disease. If a disease is predicted to be positive, the system recommends a specialized doctor. The document discusses related work on disease prediction using data mining techniques. It provides an overview of how the Random Forest algorithm works for classification problems and ensemble learning. The proposed system aims to help users predict diseases and find appropriate doctors for treatment.
This document discusses medical data mining and classification techniques. It begins with an introduction to data mining and its applications in healthcare to improve treatment. Medical data mining can help discover patterns in medical data to aid diagnosis. Classification algorithms like decision trees can categorize medical records and help predict outcomes. Specifically, the document discusses the J48 decision tree algorithm available in the WEKA data mining tool, which implements the C4.5 algorithm for classification. Decision trees work by recursively splitting the data into subsets based on attribute values, forming a tree structure. The document concludes that while data mining can help with medical analysis, results from small medical datasets should be interpreted cautiously.
IRJET-Survey on Data Mining Techniques for Disease PredictionIRJET Journal
This document discusses using data mining techniques to predict disease, specifically focusing on heart disease. It provides an overview of different classification algorithms that can be used for disease prediction, including decision trees, Bayesian classifiers, multilayer perceptrons, and ensemble techniques. These algorithms are analyzed based on their accuracy, time efficiency, and area under the ROC curve. The document also reviews related literature applying various data mining methods like decision trees, KNN, and support vector machines to heart disease prediction. Overall, the document examines using classification algorithms and data mining to extract patterns from medical data that can help predict heart disease and other illnesses.
Column store decision tree classification of unseen attribute setijma
A decision tree can be used for clustering of frequently used attributes to improve tuple reconstruction time
in column-stores databases. Due to ad-hoc nature of queries, strongly correlative attributes are grouped
together using a decision tree to share a common minimum support probability distribution. At the same
time in order to predict the cluster for unseen attribute set, the decision tree may work as a classifier. In
this paper we propose classification and clustering of unseen attribute set using decision tree to improve
tuple reconstruction time.
IRJET- Missing Data Imputation by Evidence ChainIRJET Journal
This document presents a new method called "evidence chain" for imputing missing data. The method works as follows:
1. It first identifies missing data values marked as "-1" in a dataset.
2. It then combines attribute values associated with each missing data point to form "evidence chains" to estimate potential values.
3. It calculates possible values and their probabilities for each missing data point.
4. It checks if an evidence chain matches values in the data and uses the most probable or highest value to impute the missing data.
5. The imputed values replace the original missing values to complete the dataset. The method is implemented in an application that can generate test data with missing values.
Analysis on Data Mining Techniques for Heart Disease DatasetIRJET Journal
This document analyzes various data mining techniques for classifying heart disease datasets. It compares the performance of classification algorithms like decision trees and lazy learning on aspects like time taken to build models. The algorithms are tested on a heart disease dataset from a public repository using the KEEL data mining tool. Decision trees and k-nearest neighbors are implemented using distance functions like Euclidean and HVDM across different validation modes. The results show that k-nearest neighbors with no validation is the most efficient algorithm for predicting heart disease, taking the least time to build models of the dataset. The study aims to determine the optimal classification algorithm for heart disease prediction systems.
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.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Prediction of Dengue, Diabetes and Swine Flu using Random Forest Classificati...IRJET Journal
This document describes a disease prediction system that uses the Random Forest classification algorithm to predict Dengue, diabetes, and swine flu. The system trains on labeled datasets for each disease. It then takes user-entered symptoms as input and predicts the likelihood of each disease. If a disease is predicted to be positive, the system recommends a specialized doctor. The document discusses related work on disease prediction using data mining techniques. It provides an overview of how the Random Forest algorithm works for classification problems and ensemble learning. The proposed system aims to help users predict diseases and find appropriate doctors for treatment.
This document discusses medical data mining and classification techniques. It begins with an introduction to data mining and its applications in healthcare to improve treatment. Medical data mining can help discover patterns in medical data to aid diagnosis. Classification algorithms like decision trees can categorize medical records and help predict outcomes. Specifically, the document discusses the J48 decision tree algorithm available in the WEKA data mining tool, which implements the C4.5 algorithm for classification. Decision trees work by recursively splitting the data into subsets based on attribute values, forming a tree structure. The document concludes that while data mining can help with medical analysis, results from small medical datasets should be interpreted cautiously.
IRJET-Survey on Data Mining Techniques for Disease PredictionIRJET Journal
This document discusses using data mining techniques to predict disease, specifically focusing on heart disease. It provides an overview of different classification algorithms that can be used for disease prediction, including decision trees, Bayesian classifiers, multilayer perceptrons, and ensemble techniques. These algorithms are analyzed based on their accuracy, time efficiency, and area under the ROC curve. The document also reviews related literature applying various data mining methods like decision trees, KNN, and support vector machines to heart disease prediction. Overall, the document examines using classification algorithms and data mining to extract patterns from medical data that can help predict heart disease and other illnesses.
Column store decision tree classification of unseen attribute setijma
A decision tree can be used for clustering of frequently used attributes to improve tuple reconstruction time
in column-stores databases. Due to ad-hoc nature of queries, strongly correlative attributes are grouped
together using a decision tree to share a common minimum support probability distribution. At the same
time in order to predict the cluster for unseen attribute set, the decision tree may work as a classifier. In
this paper we propose classification and clustering of unseen attribute set using decision tree to improve
tuple reconstruction time.
This document outlines the aim, objectives, scope, and structure of a dissertation on using genetic programming to optimize and combine K nearest neighbor classifiers for intrusion detection. The aim is to use genetic programming with the KDD Cup 1999 dataset to develop a numeric classifier that shows improved performance over individual KNN classifiers. The objectives are to determine if a GP-based numeric classifier outperforms individual KNN classifiers, if GP combination techniques produce higher performance than KNN component classifiers, and if heterogeneous KNN classifier combination performs better than homogeneous combination. The document describes the methodology that will be used, including developing an optimal KNN classifier using fitness evaluation in the first phase and combining optimal KNN classifiers based on ROC curves in the second phase.
An exploratory analysis on half hourly electricity load patterns leading to h...acijjournal
Accurate prediction of electricity demand can bring
extensive benefits to any country as the forecaste
d
values help the relevant authorities to take decisi
ons regarding electricity generation, transmission
and
distribution appropriately. The literature reveals
that, when compared to conventional time series
techniques, the improved artificial intelligent app
roaches provide better prediction accuracies. Howev
er,
the accuracy of predictions using intelligent appro
aches like neural networks are strongly influenced
by the
correct selection of inputs and the number of neuro
-forecasters used for prediction. Deshani, Hansen,
Attygalle, & Karunarathne (2014) suggested that a c
luster analysis could be performed to group similar
day types, which contribute towards selecting a bet
ter set of neuro-forecasters in neural networks. Th
e
cluster analysis was based on the daily total elect
ricity demands as their target was to predict the d
aily
total demands using neural networks. However, predi
cting half-hourly demand seems more appropriate
due to the considerable changes of electricity dema
nd observed during a particular day. As such cluste
rs
are identified considering half-hourly data within
the daily load distribution curves. Thus, this pape
r is an
improvement to Deshani et. al. (2014), which illust
rates how the half hourly demand distribution withi
n a
day, is incorporated when selecting the inputs for
the neuro-forecasters.
IRJET- Machine Learning Classification Algorithms for Predictive Analysis in ...IRJET Journal
This document discusses machine learning classification algorithms and their applications for predictive analysis in healthcare. It provides an overview of data mining techniques like association, classification, clustering, prediction, and sequential patterns. Specific classification algorithms discussed include Naive Bayes, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Neural Networks, and Bayesian Methods. The document examines examples of these algorithms being used for disease diagnosis, prognosis, and healthcare management. It analyzes their predictive performance on datasets for conditions like breast cancer, heart disease, and ICU readmissions. Overall, the document reviews how machine learning techniques can enhance predictive accuracy for various healthcare problems.
Effect of Data Size on Feature Set Using Classification in Health Domaindbpublications
In health domain, the major critical issue is prediction of disease in early stage. Prediction of disease is mainly based on the experience of physician so many machine learning approach contribute their work in the prediction of disease. In existing approaches, either prediction or feature selection has been concentrated. The aim of this paper is to present the effect of data size and set of features in the prediction of disease in health domain using Naïve Bayes. This shows how each attribute or combination of attribute behaves on different size of dataset.
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...sushantparte
This document is a research project submission for a MSc in Data Analytics at the National College of Ireland. The project aims to use supervised machine learning models to predict animal shelter outcomes using a dataset from the Austin Animal Center. Four classification models - logistic regression, neural network, XGboost, and random forest - are implemented and evaluated based on metrics like accuracy, logarithmic loss, sensitivity and specificity. The best performing model is found to be XGboost, which achieves an accuracy of 65.33% on the Austin animal shelter outcomes dataset.
IRJET- Human Heart Disease Prediction using Ensemble Learning and Particle Sw...IRJET Journal
The document discusses using ensemble learning and particle swarm optimization to predict heart disease. It aims to select the machine learning algorithm from among AdaBoost, gradient descent, random forest, decision tree and Gaussian naive Bayes that achieves the highest accuracy. Particle swarm optimization is used to select important predictive features from the dataset. The proposed approach uses AdaBoost and particle swarm optimization to achieve an accuracy of 84.88% in predicting heart disease, with an error rate of 4%.
IRJET- Probability based Missing Value Imputation Method and its AnalysisIRJET Journal
This document describes a probability-based method for imputing missing data. It begins with an abstract that outlines the goal of developing an application to identify and replace missing values in a dataset using a probability approach. It then provides background on missing data issues and different imputation techniques. The proposed method uses a probability approach to calculate possible values for missing data based on attributes of known values, stores this information separately, and then imputes values based on probability calculations. It claims this map-reduce approach reduces processing time for large datasets compared to existing methods. The method and imputed dataset will be analyzed using clustering algorithms to examine changes from the original missing data.
Survey paper on Big Data Imputation and Privacy AlgorithmsIRJET Journal
This document summarizes issues related to big data mining and algorithms to address them. It discusses data imputation algorithms like refined mean substitution and k-nearest neighbors to handle missing data. It also discusses privacy protection algorithms like association rule hiding that use data distortion or blocking methods to hide sensitive rules while preserving utility. The document reviews literature on these topics and concludes that algorithms are needed to address big data challenges involving data collection, protection, and quality.
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.
Early Identification of Diseases Based on Responsible Attribute using Data Mi...IRJET Journal
This document describes a proposed method for early identification of diseases using data mining and classification techniques. It begins with an introduction to classification and discusses how it is commonly used in healthcare for tasks like predicting patient risk levels. It then reviews related literature applying classification methods to diseases like heart disease and diabetes. The document outlines the problem of selecting the best classification technique for a given healthcare dataset. It proposes an architecture and method for disease prediction that assigns recommended values to attributes and classifies unknown data based on calculating totals. The method is experimentally analyzed using a heart disease dataset, and its accuracy is compared to Bayesian classification. In conclusion, the proposed method seeks to reduce attributes and complexity while accurately classifying patient data for early disease identification.
CLUSTERING ALGORITHM FOR A HEALTHCARE DATASET USING SILHOUETTE SCORE VALUEijcsit
The huge amount of healthcare data, coupled with the need for data analysis tools has made data mining
interesting research areas. Data mining tools and techniques help to discover and understand hidden
patterns in a dataset which may not be possible by mainly visualization of the data. Selecting appropriate
clustering method and optimal number of clusters in healthcare data can be confusing and difficult most
times. Presently, a large number of clustering algorithms are available for clustering healthcare data, but
it is very difficult for people with little knowledge of data mining to choose suitable clustering algorithms.
This paper aims to analyze clustering techniques using healthcare dataset, in order to determine suitable
algorithms which can bring the optimized group clusters. Performances of two clustering algorithms (Kmeans
and DBSCAN) were compared using Silhouette score values. Firstly, we analyzed K-means
algorithm using different number of clusters (K) and different distance metrics. Secondly, we analyzed
DBSCAN algorithm using different minimum number of points required to form a cluster (minPts) and
different distance metrics. The experimental result indicates that both K-means and DBSCAN algorithms
have strong intra-cluster cohesion and inter-cluster separation. Based on the analysis, K-means algorithm
performed better compare to DBSCAN algorithm in terms of clustering accuracy and execution time.
IRJET- A Detailed Study on Classification Techniques for Data MiningIRJET Journal
This document discusses classification techniques for data mining. It provides an overview of common classification algorithms including decision trees, k-nearest neighbors (kNN), and Naive Bayes. Decision trees use a top-down approach to classify data based on attribute tests at each node. kNN identifies the k nearest training examples to classify new data points. Naive Bayes assumes independence between attributes and uses Bayes' theorem for classification. The document also discusses how these techniques are used for data cleaning, integration, transformation and knowledge representation in the data mining process.
Prognosis of Cardiac Disease using Data Mining Techniques A Comprehensive Surveyijtsrd
The Healthcare exchange generally clinical diagnosis is ended commonly by doctor's knowledge and practice. Computer Aided Decision Support System plays a major task in the medical field. Data mining provides the methodology and technology to modify these rises of data into valuable data for decision making. By utilizing data mining techniques it requires less time for the prediction of the diseases with more accuracy. Among the expanding research on coronary diseases predicting system, it has happened significant to classifications the exploration results and gives readers with a layout of the current coronary diseases forecast strategies in every discussion. Data mining tools can respond to exchange addresses that expectedly being used much time over riding to decide. In this paper we study different papers in which at least one algorithm of data mining used for the prediction of coronary diseases. As of the study it is observed that Naïve Bayes Technique increase the accuracy of the coronary diseases prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are outlined in this paper. D. Haripriya | Dr. M. Lovelin Ponn Felciah "Prognosis of Cardiac Disease using Data Mining Techniques: A Comprehensive Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26605.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26605/prognosis-of-cardiac-disease-using-data-mining-techniques-a-comprehensive-survey/d-haripriya
Correlation of artificial neural network classification and nfrs attribute fi...eSAT Journals
Abstract
Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately.
Keywords: ANN, SVM, PCA, CFS
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Accounting for variance in machine learning benchmarksDevansh16
Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.
Health Care Application using Machine Learning and Deep LearningIRJET Journal
This document presents a study on using machine learning and deep learning techniques for healthcare applications like disease prediction. It discusses algorithms like logistic regression, decision trees, random forests, SVMs and deep learning models like VGG16 applied on various disease datasets. For diabetes, heart and liver diseases, ML algorithms were used while CNN models were used for malaria and pneumonia image datasets. Random forest achieved the highest accuracy of 84.81% for diabetes prediction, SVM had 81.57% accuracy for heart disease and random forest was best at 83.33% for liver disease. The VGG16 model attained accuracies of 94.29% and 95.48% for malaria and pneumonia respectively. The study aims to develop an intelligent healthcare application for predicting different
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
IRJET- Prediction of Heart Disease using RNN AlgorithmIRJET Journal
This document discusses using a recurrent neural network (RNN) algorithm to predict heart disease. It proposes a method called prognosis prediction using RNN (PP-RNN) that uses multiple RNNs to learn from patient diagnosis code sequences in order to predict high-risk diseases. The experimental results show that the proposed PP-RNN method can achieve more accurate results than existing methods for predicting heart disease risk. It also provides background on related works using other techniques like decision trees, clustering, and AdaBoost for heart disease prediction.
This document outlines the aim, objectives, scope, and structure of a dissertation on using genetic programming to optimize and combine K nearest neighbor classifiers for intrusion detection. The aim is to use genetic programming with the KDD Cup 1999 dataset to develop a numeric classifier that shows improved performance over individual KNN classifiers. The objectives are to determine if a GP-based numeric classifier outperforms individual KNN classifiers, if GP combination techniques produce higher performance than KNN component classifiers, and if heterogeneous KNN classifier combination performs better than homogeneous combination. The document describes the methodology that will be used, including developing an optimal KNN classifier using fitness evaluation in the first phase and combining optimal KNN classifiers based on ROC curves in the second phase.
An exploratory analysis on half hourly electricity load patterns leading to h...acijjournal
Accurate prediction of electricity demand can bring
extensive benefits to any country as the forecaste
d
values help the relevant authorities to take decisi
ons regarding electricity generation, transmission
and
distribution appropriately. The literature reveals
that, when compared to conventional time series
techniques, the improved artificial intelligent app
roaches provide better prediction accuracies. Howev
er,
the accuracy of predictions using intelligent appro
aches like neural networks are strongly influenced
by the
correct selection of inputs and the number of neuro
-forecasters used for prediction. Deshani, Hansen,
Attygalle, & Karunarathne (2014) suggested that a c
luster analysis could be performed to group similar
day types, which contribute towards selecting a bet
ter set of neuro-forecasters in neural networks. Th
e
cluster analysis was based on the daily total elect
ricity demands as their target was to predict the d
aily
total demands using neural networks. However, predi
cting half-hourly demand seems more appropriate
due to the considerable changes of electricity dema
nd observed during a particular day. As such cluste
rs
are identified considering half-hourly data within
the daily load distribution curves. Thus, this pape
r is an
improvement to Deshani et. al. (2014), which illust
rates how the half hourly demand distribution withi
n a
day, is incorporated when selecting the inputs for
the neuro-forecasters.
IRJET- Machine Learning Classification Algorithms for Predictive Analysis in ...IRJET Journal
This document discusses machine learning classification algorithms and their applications for predictive analysis in healthcare. It provides an overview of data mining techniques like association, classification, clustering, prediction, and sequential patterns. Specific classification algorithms discussed include Naive Bayes, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Neural Networks, and Bayesian Methods. The document examines examples of these algorithms being used for disease diagnosis, prognosis, and healthcare management. It analyzes their predictive performance on datasets for conditions like breast cancer, heart disease, and ICU readmissions. Overall, the document reviews how machine learning techniques can enhance predictive accuracy for various healthcare problems.
Effect of Data Size on Feature Set Using Classification in Health Domaindbpublications
In health domain, the major critical issue is prediction of disease in early stage. Prediction of disease is mainly based on the experience of physician so many machine learning approach contribute their work in the prediction of disease. In existing approaches, either prediction or feature selection has been concentrated. The aim of this paper is to present the effect of data size and set of features in the prediction of disease in health domain using Naïve Bayes. This shows how each attribute or combination of attribute behaves on different size of dataset.
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...sushantparte
This document is a research project submission for a MSc in Data Analytics at the National College of Ireland. The project aims to use supervised machine learning models to predict animal shelter outcomes using a dataset from the Austin Animal Center. Four classification models - logistic regression, neural network, XGboost, and random forest - are implemented and evaluated based on metrics like accuracy, logarithmic loss, sensitivity and specificity. The best performing model is found to be XGboost, which achieves an accuracy of 65.33% on the Austin animal shelter outcomes dataset.
IRJET- Human Heart Disease Prediction using Ensemble Learning and Particle Sw...IRJET Journal
The document discusses using ensemble learning and particle swarm optimization to predict heart disease. It aims to select the machine learning algorithm from among AdaBoost, gradient descent, random forest, decision tree and Gaussian naive Bayes that achieves the highest accuracy. Particle swarm optimization is used to select important predictive features from the dataset. The proposed approach uses AdaBoost and particle swarm optimization to achieve an accuracy of 84.88% in predicting heart disease, with an error rate of 4%.
IRJET- Probability based Missing Value Imputation Method and its AnalysisIRJET Journal
This document describes a probability-based method for imputing missing data. It begins with an abstract that outlines the goal of developing an application to identify and replace missing values in a dataset using a probability approach. It then provides background on missing data issues and different imputation techniques. The proposed method uses a probability approach to calculate possible values for missing data based on attributes of known values, stores this information separately, and then imputes values based on probability calculations. It claims this map-reduce approach reduces processing time for large datasets compared to existing methods. The method and imputed dataset will be analyzed using clustering algorithms to examine changes from the original missing data.
Survey paper on Big Data Imputation and Privacy AlgorithmsIRJET Journal
This document summarizes issues related to big data mining and algorithms to address them. It discusses data imputation algorithms like refined mean substitution and k-nearest neighbors to handle missing data. It also discusses privacy protection algorithms like association rule hiding that use data distortion or blocking methods to hide sensitive rules while preserving utility. The document reviews literature on these topics and concludes that algorithms are needed to address big data challenges involving data collection, protection, and quality.
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.
Early Identification of Diseases Based on Responsible Attribute using Data Mi...IRJET Journal
This document describes a proposed method for early identification of diseases using data mining and classification techniques. It begins with an introduction to classification and discusses how it is commonly used in healthcare for tasks like predicting patient risk levels. It then reviews related literature applying classification methods to diseases like heart disease and diabetes. The document outlines the problem of selecting the best classification technique for a given healthcare dataset. It proposes an architecture and method for disease prediction that assigns recommended values to attributes and classifies unknown data based on calculating totals. The method is experimentally analyzed using a heart disease dataset, and its accuracy is compared to Bayesian classification. In conclusion, the proposed method seeks to reduce attributes and complexity while accurately classifying patient data for early disease identification.
CLUSTERING ALGORITHM FOR A HEALTHCARE DATASET USING SILHOUETTE SCORE VALUEijcsit
The huge amount of healthcare data, coupled with the need for data analysis tools has made data mining
interesting research areas. Data mining tools and techniques help to discover and understand hidden
patterns in a dataset which may not be possible by mainly visualization of the data. Selecting appropriate
clustering method and optimal number of clusters in healthcare data can be confusing and difficult most
times. Presently, a large number of clustering algorithms are available for clustering healthcare data, but
it is very difficult for people with little knowledge of data mining to choose suitable clustering algorithms.
This paper aims to analyze clustering techniques using healthcare dataset, in order to determine suitable
algorithms which can bring the optimized group clusters. Performances of two clustering algorithms (Kmeans
and DBSCAN) were compared using Silhouette score values. Firstly, we analyzed K-means
algorithm using different number of clusters (K) and different distance metrics. Secondly, we analyzed
DBSCAN algorithm using different minimum number of points required to form a cluster (minPts) and
different distance metrics. The experimental result indicates that both K-means and DBSCAN algorithms
have strong intra-cluster cohesion and inter-cluster separation. Based on the analysis, K-means algorithm
performed better compare to DBSCAN algorithm in terms of clustering accuracy and execution time.
IRJET- A Detailed Study on Classification Techniques for Data MiningIRJET Journal
This document discusses classification techniques for data mining. It provides an overview of common classification algorithms including decision trees, k-nearest neighbors (kNN), and Naive Bayes. Decision trees use a top-down approach to classify data based on attribute tests at each node. kNN identifies the k nearest training examples to classify new data points. Naive Bayes assumes independence between attributes and uses Bayes' theorem for classification. The document also discusses how these techniques are used for data cleaning, integration, transformation and knowledge representation in the data mining process.
Prognosis of Cardiac Disease using Data Mining Techniques A Comprehensive Surveyijtsrd
The Healthcare exchange generally clinical diagnosis is ended commonly by doctor's knowledge and practice. Computer Aided Decision Support System plays a major task in the medical field. Data mining provides the methodology and technology to modify these rises of data into valuable data for decision making. By utilizing data mining techniques it requires less time for the prediction of the diseases with more accuracy. Among the expanding research on coronary diseases predicting system, it has happened significant to classifications the exploration results and gives readers with a layout of the current coronary diseases forecast strategies in every discussion. Data mining tools can respond to exchange addresses that expectedly being used much time over riding to decide. In this paper we study different papers in which at least one algorithm of data mining used for the prediction of coronary diseases. As of the study it is observed that Naïve Bayes Technique increase the accuracy of the coronary diseases prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are outlined in this paper. D. Haripriya | Dr. M. Lovelin Ponn Felciah "Prognosis of Cardiac Disease using Data Mining Techniques: A Comprehensive Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26605.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26605/prognosis-of-cardiac-disease-using-data-mining-techniques-a-comprehensive-survey/d-haripriya
Correlation of artificial neural network classification and nfrs attribute fi...eSAT Journals
Abstract
Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately.
Keywords: ANN, SVM, PCA, CFS
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Accounting for variance in machine learning benchmarksDevansh16
Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.
Health Care Application using Machine Learning and Deep LearningIRJET Journal
This document presents a study on using machine learning and deep learning techniques for healthcare applications like disease prediction. It discusses algorithms like logistic regression, decision trees, random forests, SVMs and deep learning models like VGG16 applied on various disease datasets. For diabetes, heart and liver diseases, ML algorithms were used while CNN models were used for malaria and pneumonia image datasets. Random forest achieved the highest accuracy of 84.81% for diabetes prediction, SVM had 81.57% accuracy for heart disease and random forest was best at 83.33% for liver disease. The VGG16 model attained accuracies of 94.29% and 95.48% for malaria and pneumonia respectively. The study aims to develop an intelligent healthcare application for predicting different
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
IRJET- Prediction of Heart Disease using RNN AlgorithmIRJET Journal
This document discusses using a recurrent neural network (RNN) algorithm to predict heart disease. It proposes a method called prognosis prediction using RNN (PP-RNN) that uses multiple RNNs to learn from patient diagnosis code sequences in order to predict high-risk diseases. The experimental results show that the proposed PP-RNN method can achieve more accurate results than existing methods for predicting heart disease risk. It also provides background on related works using other techniques like decision trees, clustering, and AdaBoost for heart disease prediction.
IRJET- Analyse Big Data Electronic Health Records Database using Hadoop ClusterIRJET Journal
This document discusses using Hadoop clusters and MapReduce programming to analyze large electronic health records databases. It proposes that Hadoop can efficiently process and analyze huge healthcare datasets in a distributed computing environment. Specifically, the document proposes a MapReduce program to analyze electronic health records stored in Hadoop clusters. Analyzing these large datasets with Hadoop could provide personalized treatment planning, assisted diagnosis, and utilization review to improve healthcare outcomes and reduce costs.
This document discusses data mining algorithms for clustering healthcare data streams. It provides an overview of the K-means and D-stream algorithms, and proposes a framework for comparing them on healthcare datasets. The framework involves feature extraction from physiological signals, calculating risk components, and applying the K-means and D-stream algorithms to cluster the data. The results would show the effectiveness and limitations of each algorithm for clustering streaming healthcare data.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict diseases based on patient symptoms. Specifically, it proposes using naive bayes, k-nearest neighbors (KNN), and logistic regression algorithms on structured and unstructured hospital data to predict diseases like diabetes, malaria, jaundice, dengue, and tuberculosis. The system is intended to make disease prediction more accessible to end users by analyzing their symptoms without needing to visit a doctor. It aims to improve prediction accuracy by handling both structured and unstructured data using machine learning models.
IRJET- Extending Association Rule Summarization Techniques to Assess Risk of ...IRJET Journal
The document discusses using association rule mining and k-means clustering to identify risk factors for diabetes from electronic medical records. It reviews existing techniques for summarizing large sets of association rules generated from medical data and proposes using k-means clustering as an improved method. The k-means algorithm clusters patient data into groups based on similarity and identifies representative risk factor patterns within each cluster, providing a concise summary for clinicians to assess diabetes risk.
A comparative analysis of classification techniques on medical data setseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Utilizing Machine Learning, Detect Chronic Kidney Disease and Suggest A Healt...IRJET Journal
The document discusses using machine learning models to detect chronic kidney disease and recommend a healthy diet. It evaluates four machine learning algorithms - decision tree, K-nearest neighbor, random forest, and support vector machine - to predict chronic kidney disease based on patient data. The random forest algorithm achieved the highest accuracy of 100% compared to 96.88% for support vector machine, 81.25% for K-nearest neighbor, and 100% for decision tree. The proposed system uses feature selection and the patient's blood potassium level to recommend an appropriate diet to help patients and clinicians.
ROLE OF CERTAINTY FACTOR IN GENERATING ROUGH-FUZZY RULEIJCSEA Journal
The generation of effective feature-based rules is essential to the development of any intelligent system. This paper presents an approach that integrates a powerful fuzzy rule generation algorithm with a rough set-assisted feature reduction method to generate diagnostic rule with a certainty factor. Certainty factor of each rule is calculated by considering both the membership value of each linguistic term introduced at time of fuzzyfication of data as well as possibility values, due to inconsistent data, generated by rough set theory at time of rule generation. In time of knowledge inferencing in an intelligent system, certainty factor of each rule will play an important role to find out the appropriate rule to be selected. Experimental results demonstrate the superiority of our approach.
Lung cancer disease analyzes using pso based fuzzy logic systemeSAT Journals
Abstract
Main objective of this paper to improve accuracy of lung cancer disease investigation utilizing molecule swarm enhancement
(PSO) in combination with fuzzy expert system. This paper briefly a introduce fuzzy expert systems and this proposed scheme
compared with related methods. Experimental results of the proposed system simulated by MATLAB 2014.
CLUSTERING DICHOTOMOUS DATA FOR HEALTH CAREijistjournal
This document discusses clustering dichotomous health care data using the K-means algorithm after transforming the data using Wiener transformation. It begins with an introduction to dichotomous data and the challenges of clustering medical data. It then describes the K-means clustering algorithm and various distance measures used for binary data clustering. The document proposes using Wiener transformation to first transform binary data to real values before applying K-means clustering. It evaluates the results on a lens dataset using inter-cluster and intra-cluster distances, finding the transformed data yields better clusters than the original binary data according to these metrics.
CLUSTERING DICHOTOMOUS DATA FOR HEALTH CAREijistjournal
Dichotomous data is a type of categorical data, which is binary with categories zero and one. Health care data is one of the heavily used categorical data. Binary data are the simplest form of data used for heath care databases in which close ended questions can be used; it is very efficient based on computational efficiency and memory capacity to represent categorical type data. Clustering health care or medical data is very tedious due to its complex data representation models, high dimensionality and data sparsity. In this paper, clustering is performed after transforming the dichotomous data into real by wiener transformation. The proposed algorithm can be usable for determining the correlation of the health disorders and symptoms observed in large medical and health binary databases. Computational results show that the clustering based on Wiener transformation is very efficient in terms of objectivity and subjectivity.
Predicting disease at an early stage becomes critical, and the most difficult challenge is to predict it correctly along with the sickness. The prediction happens based on the symptoms of an individual. The model presented can work like a digital doctor for disease prediction, which helps to timely diagnose the disease and can be efficient for the person to take immediate measures. The model is much more accurate in the prediction of potential ailments. The work was tested with four machine learning algorithms and got the best accuracy with Random Forest.
Dynamic Rule Base Construction and Maintenance Scheme for Disease Predictionijsrd.com
Business and healthcare application are tuned to automatically detect and react events generated from local are remote sources. Event detection refers to an action taken to an activity. The association rule mining techniques are used to detect activities from data sets. Events are divided into 2 types' external event and internal event. External events are generated under the remote machines and deliver data across distributed systems. Internal events are delivered and derived by the system itself. The gap between the actual event and event notification should be minimized. Event derivation should also scale for a large number of complex rules. Attacks and its severity are identified from event derivation systems. Transactional databases and external data sources are used in the event detection process. The new event discovery process is designed to support uncertain data environment. Uncertain derivation of events is performed on uncertain data values. Relevance estimation is a more challenging task under uncertain event analysis. Selectability and sampling mechanism are used to improve the derivation accuracy. Selectability filters events that are irrelevant to derivation by some rules. Selectability algorithm is applied to extract new event derivation. A Bayesian network representation is used to derive new events given the arrival of an uncertain event and to compute its probability. A sampling algorithm is used for efficient approximation of new event derivation. Medical decision support system is designed with event detection model. The system adopts the new rule mapping mechanism for the disease analysis. The rule base construction and maintenance operations are handled by the system. Rule probability estimation is carried out using the Apriori algorithm. The rule derivation process is optimized for domain specific model.
This document provides an overview of data mining techniques and tools. It discusses data mining processes like predictive and descriptive data mining. It describes various data mining tasks such as classification, clustering, regression, and association rule learning. It then examines specific techniques for prediction using data mining, including classification analysis, association rule learning, decision trees, neural networks, and clustering analysis. Finally, it reviews several popular open-source tools that can be used to implement these data mining techniques, such as RapidMiner, Oracle Data Mining, IBM SPSS Modeler, KNIME, Python, Orange, Kaggle, Rattle, and Weka.
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
Developing predictive modelling solutions for risk estimation is extremely challenging in health-care
informatics. Risk estimation involves integration of heterogeneous clinical sources having different
representation from different health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel
computing tools collectively termed big data tools are in need which can synthesize and assist the physician
to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel
approach for combining the predictive ability of multiple models for better prediction accuracy. We
demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study.
Results show that the proposed multi-model predictive architecture is able to provide better accuracy than
best model approach. By modelling the error of predictive models we are able to choose sub set of models
which yields accurate results. More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
IRJET- A Framework for Disease Risk PredictionIRJET Journal
This document presents a framework for disease risk prediction using machine learning techniques. It proposes using a convolutional neural network model for disease prediction. The system architecture includes an admin module for dataset management and file conversion, and a disease risk prediction module for prediction. The admin module allows uploading medical data, converting files to a compatible format, and performing predictions. The disease risk prediction module takes a test set, generates compatible files, and uses those files and a convolutional neural network for prediction. The goal is to develop an automated disease prediction system to help predict disease and improve healthcare.
Prediction of Diabetes using Probability ApproachIRJET Journal
This document discusses using a Bayesian Network classifier to predict whether individuals have diabetes based on various attributes. It analyzes a Pima Indian Diabetes dataset containing information on individuals with and without diabetes. The study aims to help identify diabetes and improve people's lifestyles by making them aware of the disease and how to treat it. It evaluates the prediction performance of Bayesian algorithms for classifying individuals as diabetic or non-diabetic.
CLASSIFICATION ALGORITHM USING RANDOM CONCEPT ON A VERY LARGE DATA SET: A SURVEYEditor IJMTER
Data mining environment produces a large amount of data, that need to be
analyses, pattern have to be extracted from that to gain knowledge. In this new period with
rumble of data both ordered and unordered, by using traditional databases and architectures, it
has become difficult to process, manage and analyses patterns. To gain knowledge about the
Big Data a proper architecture should be understood. Classification is an important data mining
technique with broad applications to classify the various kinds of data used in nearly every
field of our life. Classification is used to classify the item according to the features of the item
with respect to the predefined set of classes. This paper provides an inclusive survey of
different classification algorithms and put a light on various classification algorithms including
j48, C4.5, k-nearest neighbor classifier, Naive Bayes, SVM etc., using random concept.
Similar to Two Layer k-means based Consensus Clustering for Rural Health Information System (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.