Knowledge Discovery in Aerodynamic Design Space for Flyback-Booster Wing Using Data Mining

The advanced design of a centrifugal compressor with high efficiency and wide operating range is a challenging task due to the complex flow field arising from the three-dimensional geometry, especially for the high-speed, highly loaded centrifugal compressor stage, which typically has a relatively narrow operating range. A great effort has been undertaken recently to solve the time-costly three-dimensional design problem with the assistance of a metamodel. Some effort has been done to gain insight into the design space with the assistance of the data mining method. However, the published works lack any study that systematically performs the data mining between the performance and three-dimensional geometry data due to two unsolved issues, i.e., lack of reliable systematic data mining model and unresolved high-dimensional data problem in the centrifugal compressor community. To tackle these issues, a systematic metamodel-driven data mining (MDDM) model including six general modules (i.e., problem understanding, data understating, metamodeling, data set preparation, knowledge discovery, and deployment) has been proposed and implemented to the knowledge discovery of the well-known Radiver high-speed centrifugal compressor stage. Particular attention has been paid to develop the design principle of operating range extension for the examined high-speed stage. Four specific data mining techniques, i.e., descriptive statistics, self-organization map, k–d tree, and Sobol index, were used for the statistical, correlation, cluster, and sensitivity analysis. The results showed the performance improvement probabilities, the trade-off relationships between efficiency and pressure ratio/operating range, and the characteristic variation of the performance. Specifically, the wide operating range design subspace and the narrow operating range design subspace were split away from the whole design space. In these subspaces, the two most sensitive geometry parameters that controlled the meridional curvature made a large contribution to the stage performance, especially for the meridional curvature at the shroud side near the impeller outlet. The appropriate variation ranges of the two sensitive geometry parameters were recommended, and the flow mechanism behind them was clarified. The statistical results showed that over 90% of the design stages in the recommended variation ranges had a wide operating range. A design case was chosen randomly in the recommended range to verify the performance improvement via computational fluid dynamics (CFD) simulations. The outcomes of this work are particularly relevant for the advanced design of compressors with high efficiency and a wide operating range for flexibility.

In this study, high efficient design tool is developed with several informatics approaches for Multidisciplinary Design Optimization (MDO) and knowledge discovery of supersonic wing design. In this design, Multi-Objective Genetic Algorithm (MOGA) is applied as an optimizer, while Kriging model is also used to reduce computational cost. To obtain the information of the design space, functional ANalysis Of VAriance (ANOVA) and Parallel Coordinate Plot (PCP) is applied. For Kriging model construction, 107 sample points are evaluated. This tool is applied to the multidisciplinary design problem of supersonic wing. The objective functions are to maximize lift to drag ratio and to minimize sonic boom intensity at supersonic cruise, and to minimize wing weight. According to the results, there are trade-off relationships among three objective functions. The ANOVA results indicate that the cambers of the wing section at the root and the kink have an influence on the lift to drag ratio, the inner wing sweep back angle affects the sonic boom intensity, and the camber of wing section at the kink and aspect ratio affect the wing weight. The design space information could be visualized quantitatively from the sampling results with PCP technique. Since the design space exploration using MOGA is carried out based on Kriging surrogate models, the proposed MDO process is effective in terms of computational cost.

The thriving medical applications of data mining in the fields of medicine and public health has led to the popularity of its use in knowledge discovery in databases (KDD). Data mining has revealed novel biomedical and healthcare acquaintances for clinical decision making that has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Disease diagnosis is one of the applications where data mining tools are establishing the successful results. Data mining intends to endow with a systematic survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research. Discussion is made to enable the disease diagnosis and the breakthrough of hidden healthcare patterns from related databases is offered. Also, the use of data mining to discover such relationships as those between health conditions and a disease is presented. It further discusses about the tools that can be used for the processing and classification of data. This paper summarizes various technical articles on medical diagnosis and prognosis. It has also been focused on current research being carried out using the data mining techniques to enhance the disease(s) forecasting process. This research paper provides future trends of current techniques of KDD, using data mining tools for healthcare. It also confers significant issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths. The root causes of all diseases get closer towards drugs i.e. the foremost risk factor of all hilarious diseases. Drug addiction using WEKA has been used that brings into light concerning majority of drug abusers started abusing drugs at age below 20yrs.It is to make aware the druggist about the various diseases that are caused with heavy or long term intake of drugs in their life. So, to make an expert system that will awake the youth about precarious use of drugs and also alert the affected person.

Design candidates obtained from optimization techniques may have meaningful information, which provides not only the best solution, but also a relationship between object functions and design variables. In particular, trade-off studies for optimum airfoil shape design involving various objectives and design variables require the effective analysis tool to take into account a complexity between objectives and design variables. In this study, for the multiple-conflicting objectives that need to be simultaneously fulfilled, the real-coded Adaptive Range Multi-Objective Genetic Algorithm code, which represents the global and stochastic multi-objective evolutionary algorithm, was developed for an airfoil shape design. Furthermore, the PARSEC method reflecting geometrical properties of airfoil is adopted to generate airfoil shapes. In addition, the Self-Organizing Maps, based on the neural network, are used to visualize trade-offs of a relationship between the objective function space and the design variable space obtained by evolutionary computation. The Self-Organizing Maps that can be considered as data mining of the engineering design generate clusters of object functions and design variables as an essential role of trade-off studies. The aerodynamic data for all candidate airfoils is obtained through Computational Fluid Dynamics. Lastly, the relationship between the maximum lift coefficient and maximum lift-to-drag ratio as object functions and 12 airfoil design parameters based on the PARSEC method is investigated using the Self-Organizing Maps method.

In view of present advancements in computing, with the development of distributed environment, many problems have to deal with distributed input data where individual data privacy is the most important issue to be addressed, for the concern of data owner by extending the privacy preserving notion to the original learning algorithms. Privacy Preserving Data Mining has become an active research area in addressing various privacy issues while bringing out solutions for them. There has been lot of progress in developing secure algorithms and models, able to preserve privacy using various data mining techniques like association, classification and clustering, where as importance of privacy preserving techniques applied for learning algorithms related to neural networks for mining problems are still in infancy. Our work in this paper focused on preserving privacy of an individual, using self organizing map (SOM) adopted for collaborative clustering of distributed data between multiple parties. We present Privacy Preserving Collaborative Clustering method using SOM (PPCSOM) for Horizontal Data Distribution, which allows multiple parties perform clustering in a collaborative approach using SOM neural network, without revealing their data directly to each other, in order to preserve privacy of all parties. Our simulation results shows that implementation of PPCSOM method achieves comparable accuracy and assured Privacy than the original non privacy preserving SOM algorithm.

Data mining is one of the important technologies in data warehouse and one of the fields with the fasted development in computer industry. The skill boosts database technology into a more advanced stage and quickly forming a separate area. The rough set theory and data mining techniques are briefly discussed on the basis of analyzing the existent problem in colleges and universities currently. Take the college student score as an example, the solution to the score analysis is proposed based on rough set theory and data mining techniques.Keywordsdata miningscore analysisrough set theorydata warehouse

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume XVI includes extensions of papers from the Rough Sets and Knowledge Technology Conference which was held in Banff, Canada, in October 2011. In addition this book contains a long paper based on a PhD thesis. The papers cover both theory and applications of rough, fuzzy and near sets. They offer a continuation of a number of research streams which have grown out of the seminal work by Zdzislaw Pawlak during the first decade of the 21st century.

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

In recent years, the exponential growth of both structured and unstructured data has highlighted the importance of efficient knowledge discovery techniques. Among these, image mining and data mining play pivotal roles in extracting meaningful patterns and insights from different forms of information. At the same time, data mining focuses on structured datasets such as databases and transaction records, while image mining deals with unstructured visual content, requiring advanced feature extraction, pattern recognition, and machine learning methods. This paper presents a comparative analysis of image mining and data mining techniques, emphasising their methodologies, applications, and challenges. The comparison explores key dimensions, including data preprocessing, feature selection, algorithmic approaches, and application domains such as healthcare, security, business intelligence, and multimedia. Experimental results show clustering accuracy of 87% for data mining and image classification accuracy of 92% for image mining, highlighting the effectiveness of specialised approaches. The study highlights both the similarities and differences in knowledge discovery processes, demonstrating how integrating image mining and data mining can enhance decision-making in diverse fields. The findings provide a comprehensive understanding of their complementary roles, offering valuable insights for researchers and practitioners aiming to develop hybrid approaches to knowledge discovery.

Multi-objective genetic algorithm (MOGA) and a data mining technique were applied to the multidisciplinary design optimization (MDO) of a hybrid rocket. In this study, a swirling-oxidizer-type hybrid rocket engine with a single cylindrical grain port was designed. MOGA was applied to solve the multi-objective problem using real-number cording and the Pareto ranking method. In this study, two objective functions were considered: one maximized the flight altitude and the other minimized the gross weight. Each objective function was empirically estimated. Many non-dominated solutions were obtained using MOGA, and a trade-off was observed between the two objective functions. To better understand the design problem, the MOGA results were visualized using a scatter plot matrix (SPM) and a data mining technique.

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

Data mining or knowledge discovery in database (KDD) refers to the non-trivial process of discovering interesting, implicit, and previously unknown knowledge from databases.Such a task implies to be able to perform analyses both on high-dimensional input data and large dataset.The most popular models used in KDD are the symbolic models.Unfortunately, these models suffer of very serious limitations.Rule generation is a highly time-consuming process that generates a huge number of rules, including a large ratio of redundant rules.Hence, this prohibits any kind of rule computation and selection as soon as data are numerous and they are represented by very high-dimensional description space.This latter situation is very often encountered with documentary data.To cope with these problems, preliminary KDD trials using numerical models have been made.An algorithm for knowledge extraction from self-organizing network has been proposed in [8].This approach is based on a supervised generalized relevance learning vector quantization (GRLVQ) which is used for extracting decision trees.The different paths of the generated trees are then used for denoting rules.Nevertheless, the main defect of this method is to necessitate training data.On our own side, we have proposed a hybrid classification method for mapping an explicative structure issued from a symbolic classification into an unsupervised numerical self-organizing map (SOM) [15].SOM map and Galois lattice are generated on the same data.The cosine projection is then used for associating lattice concepts to the SOM classes.Concepts properties act as explanation for the SOM classes.Furthermore, lattice pruning combined with migration of the associated SOM classes towards the top of the pruned lattice is used to generate explanation of increasing scope on the SOM map.Association rules can also be produced in such a way.Although it establishes interesting links between numerical and symbolic worlds this approach necessitates the time-consuming computation of a whole Galois lattice.In a parallel way, in order to enhance both the quality and the granularity of the data analysis and to reduce the noise which is inevitably generated in an overall classification approach, we have introduced the multi-viewpoint analysis and multilevel clustering approach based on a significant extension of the SOM model, named MultiSOM [19][25].The viewpoint building principle consists in separating the description of the data into several sub-descriptions corresponding different property subsets or even different data subsets.In MultiSOM each viewpoint is represented by a single SOM map.www.intechopen.com

The explosion of very large databases has created extraordinary opportunities for monitoring, analyzing and predicting global economical, geographical, demographic, medical, political, and other processes in the world. Statistical analysis and data mining techniques have emerged for these purposes. Data mining is the process of discovering previously unknown but potentially useful patterns, rules, or associations from huge quantity of data. Data mining can be performed on different data repositories such as relational databases, data warehouses, transactional databases, sequence databases, spatial databases, spatio-temporal databases, and text databases, etc. Typically, data mining functionalities can be classified into two categories: descriptive and predictive. Descriptive mining tasks aim at characterizing the general properties of the data in the databases, while predictive mining tasks perform inherence on the current data in order to make prediction in future.KeywordsRoot Mean Square ErrorData MiningAssociation RuleIntrusion DetectionAnomaly DetectionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Data mining and machine learning techniques can be applied to computer system design to aid in optimizing design decisions, improving system runtime performance. Data mining techniques have been investigated in the context of branch prediction. Specifically, a comparison of traditional branch predictor performance has been made to data mining algorithms. Additionally, the possiblity of whether additional features available within the architectural state might serve to further improve branch prediction has been evaluated. Results show that data mining techniques indicate potential for improved branch prediction, especially when register file contents are included as a feature set.