Learning Classifiers from Large Databases Using Statistical Queries

Manufacturing industries facing problem in optimal selection of process parameters in machining process. Finding optimum process parameters for achieving maximum Material Removal Rate and minimum Surface Roughness is a challenging task and it requires lot of time and energy for experimentation trails or experience. It wastes lot of resources and money, sometimes ends up with negative results. To overcome the above issue, this paper presents an algorithm for prediction of Surface Roughness and Material Removal rate using Decision Tree Algorithm and Naive Bayes Algorithm without experimentation. Lot of resources and time can be saved using these machine learning algorithms. In this paper, Material removal rate and Surface roughness of EDM machining of Aluminum composites is predicted using Decision tree algorithm and Naive Bayes algorithm. Then the model can be used to predict the Material Removal Rate and Surface finish of any combination process parameters before machining process.

The data sets used in scientific studies pose a very complex structure from time to time. At this point, data mining is making a big contribution in terms of improving the quality of services by revealing useful information from large databases. Generally on studies, to predict future data trends utilization of the methods, data mining techniques in one of the most widely used are classification and regression models. In this study, among data mining methods, classification and regression models most commonly used ones are decision tree algorithms. By comparing Classification and Regression Trees (CART) algorithm which belongs to decision trees and logistic regression shows classification characteristics on real data set and success rates of these two methods. In this context, taken by the Social Security Administration pharmacy provision system, from the respiratory disease which is one of 11 diagnoses for 6,772,313 entries in the prescribed antibiotics in the penicillin group was used to analyze that profiling the patients and the analysis found the CART analysis has better classification success than logistic regression analysis

Accurate privacy-preserving record linkage for databases with missing values

When preparing large databases, obtaining quality data for analysis without any missing values is almost impossible in many cases. Integration of raw data from multiple heterogeneous sources often results in some values missing leading to loss of valuable information. Even though many methods have been introduced by researchers, only less effort has been spent on handling missing values in heterogeneous attributes (both discrete and continuous) under Missing At Random pattern, the common scenario where missing values have dependency on covariates in the dataset. Also, only few techniques are capable of dealing with missing values in large databases and this demands immediate attention of researchers. This paper addresses both these problems by introducing a single technique called Bayesian Ant colony Optimization (BACO) which combines the searching capability of Ant Colony Optimization with probabilistic nature of Bayesian principles. The algorithm is designed in such a way that missing values in both discrete and continuous attributes in large datasets are efficiently imputed. BACO is implemented in six large real datasets, and it is observed that its imputation accuracy outperforms than that of existing standard techniques. The statistical tests conducted also prove the superior results of BACO in the imputation process.

The purpose of this article was to discuss common issues associated with large databases and present possible solutions to improve the quality and usefulness of large database research. The volume of electronic healthcare-related data is growing exponentially. Some of these data are being stored in registries and administrative databases. These data repositories are increasingly common and can serve as sources of nurse-driven research and quality improvement activities. Although these large databases have a wealth of useful information, they have limitations that may bias results. These include missing data and cases, data accuracy and validity, and the statistical effect of large samples. Researchers using large databases to address quality, safety, clinical, or systems issues have a variety of available techniques to deal with data issues. Proper data cleaning activities such as screening, visualization, and outlier/inlier identification are essential for addressing inaccurate values within large data sets. Common methods for addressing missing data include case analyses and various imputation techniques. Statistical approaches such as risk reductions and effect size are also useful when working with large sample sizes. Registries and administrative databases provide healthcare researchers with increasing opportunities to address a wide variety of important practice and patient care questions. Healthcare researchers are encouraged to explore large data sets as they look for ways to improve patient safety and quality care, develop evidence-based practice guidelines, and fulfill regulatory and accreditation requirements.

Computerisation in healthcare is more general and monitoring intensive care units (ICUs) is more significant and life critical. Accurate monitoring in ICU is essential. Failing to take the right decision at the right time may prove fatal. Similarly, a timely decision can save people's lives in various critical situations. In order to increase the accuracy and timeliness in ICU monitoring, two major technologies can be used, namely parallel processing through vectorisation of ICU data and data communication through internet of things (IoT). With our approach, we can improve efficiency and accuracy in data processing. This paper proposes a parallel decision tree algorithm in ICU data to take faster and accurate decisions on data selection. The uses of parallelised algorithms optimise the process of collecting a large set of patient information. Decision tree algorithm is used for examining and extracting knowledge-based data from large databases. Finalised information will be transferred to concerned medical experts in case of medical emergency using IoT. Parallel implementation of decision tree algorithm is implemented with threads and output data is stored in local IoT table for further processing.

Development of predictive model for post-stroke depression at discharge based on decision tree algorithm: A multi-center hospital-based cohort study

In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of nonoverlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. These approaches have their own drawbacks. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive experiments show that the proposed method outperforms the preprocessing approach, the regression tree approach, and several nontree-based algorithms.

Nowadays most of the real databases are often big and need preprocessing before making any analysis from them. Data analytics will be valid only when the databases are complete. But, values of attributes in the databases may be missing due to several reasons and most of the methods developed were intended to deal with missing values in homogeneous attributes which may be either discrete or continuous. Only few literatures have dealt with missing values in heterogeneous attributes. Ant colony optimisation (ACO) is an evolutionary algorithm which works based on behaviour of ants commonly used to solve combinatorial problems. For estimating missing heterogeneous (discrete and continuous) attribute values, the paper introduces a new algorithm called Bayesian based Parallel Max-Min Ant System (BPMMAS) by hybridising Bayesian principles with Max-Min Ant System (MMAS), an enhanced version of ACO. The algorithm is executed in parallel with subsets of the original dataset and the imputation results are combined. MMAS is chosen since it works better for solving combinatorial problems. Bayesian principles reflect well the influence of covariates for estimation of missing values. BPMMAS is implemented in real datasets and it is observed that its estimation accuracy is better than that of other missing data handling methods with less computational time.

Machine learning methods are widely used in automated technologies. Classification prediction is a machine learning based on data mining. Today, many technological devices can make new predictions by gaining experience from past data with machine learning methods. Machine learning is widely studied in two types, supervised and unsupervised. The limits of the objectives in supervised learning are predetermined. In unsupervised learning, there are no predetermined targets. In this learning, the machines are required to determine the targets automatically. Prediction process is one of the basic components of machine learning. Machines need to use some algorithms in order to perform the prediction process on the basis of data mining. k nearest neighbor (kNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) algorithms are used mostly. k nearest neighbor (kNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) algorithms are used mostly. These algorithms can be applied with the help of some tools on data sets. In this study, kNN algorithm was used to estimate Iris data set classification using Orange tool. The success of the KNN algorithm depends on using the correct attribute and changing the optimum k value. As a result of the tests, it was determined that when the k neighbor value was selected as 15, it was the most suitable k neighbor value, providing 98.67% classification prediction success in the Iris dataset.

Aim: Chronic kidney disease (CKD) is among the main 20 reasons for death worldwide and influences around 10% of the world grown-up populace. CKD is an issue that upsets typical kidney work. The main objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. We will be comparing Novel Decision Tree with Linear Regression to find out which of these can give us the best accuracy. Material and Methods: The study used 322 samples with Novel Decision Tree and Linear Regression is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Linear Regression (85.25%) is obtained. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.000 (p<0.05) by performing independent samples t-tests. Conclusion: This study concludes that the Prediction of Kidney disease using the Novel Decision Tree (DT) algorithm appears to be significantly better than the Linear Regression(LR) with improved accuracy.

The virtualization of computing resources provided by containers has gained increasing attention and has been widely used in cloud computing. This new demand for container technology has been growing and the use of Docker and Kubernetes is considerable. According to recent technology surveys, containers are now mainstream. However, currently, one of the major challenges rises from the fact that multiple containers, with different owners, may cohabit on the same host. In container-based multi-tenant environments, security issues are of major concern. In this paper we investigate the performance of container-level anomaly-based intrusion detection systems for multi-tenant applications. We investigate the use of Bag of System Calls (BoSC) technique and the sliding window with the classifier and we consider eight machine learning algorithms for classification purposes. We show that among the eight machine learning algorithms, the best classification results are obtained with Decision Tree and Random Forest which lead to an F-Measure of 99.8%, using a sliding window with a size of 30 and the BoSC algorithm in both cases. We also show that, although both Decision Tree and Random Forest algorithms leads to the best classification results, the Decision Tree algorithm has a shorter execution time and consumes less CPU and memory than the Random Forest.

This study aims to develop an effective predictive model for identifying students at risk of academic dropout using the Decision Tree and Linear Regression algorithms. The data used are sourced from the public Kaggle dataset Students Dropout and Academic Success, which includes demographic, socioeconomic, and academic performance variables for each semester. The research method includes data preprocessing stages, such as data cleaning, label encoding for categorical variables, numeric feature normalization, and target class adjustment to focus on binary classification, namely Dropout and Graduate. The modeling process is carried out by comparing the performance of the two algorithms using evaluation metrics of accuracy, precision, and recall. The results show that the Decision Tree algorithm has superior performance compared to Linear Regression in mapping non-linear patterns in student data. Feature importance analysis revealed that the number of curricular units in the second semester and tuition payment status are the main predictors of dropout risk. These findings are expected to assist educational institutions in implementing early interventions to improve student academic success.

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.

Comparing decision tree algorithms to estimate intercity trip distribution