Country Corruption Analysis with Self Organizing Maps and Support Vector Machines

Medical data mining and knowledge discovery can benefit from the experience and knowledge of clinicians, however, the implementation of this data mining system is challenging. Unlike traditional data mining methods, in this class of applications we process data with some posterior knowledge and the target function is more complex and even may include the opinion of user. Despite the success of the classical reasoning algorithms in many common data mining applications, they failed to address medical record processing where we need to extract information from incomplete, small samples along with an external rulebase to generate ‘meaningful’ interpretation of biological phenomenon. Swarm intelligence is an alternative class of flexible approaches that is promising in data mining. With full control over the rule extraction target function, particle swarm optimization (PSO) is a suitable approach for data mining subject to a rulebase which defines the quality of rules and constancy with previous observations. In this chapter we describe a complex clinical problem that has been addressed using PSO data mining. A large group of temporal lobe epilepsy patients are studied to find the best surgery candidates. Since there are many parameters involved in the decision process, the problem is not tractable from traditional data mining point of view, while the new approach that uses the field knowledge could extract valuable information. The proposed method allows expert to interaction with data mining process by offering manual manipulation of generated rules. The algorithm adjusts the rule set with regard to manipulations. Each rule has a reasoning which is based on the provided rulebase and similar observed cases. Support vector machine (SVM) classifier and swarm data miner are integrated to handle joint processing of raw data and rules. This approach is used to establish the limits of observations and build decision boundaries based on these critical observations.

Students starting university have different characteristics, which can impact their performance in the classroom. In this study, 743 freshmen were surveyed. The collected variables are grouped into five categories: demographic data, learning approach, personality, emotional intelligence, and perceived social support. These characteristics provide a profile of the student that will impact their behavior and academic performance during their university life. Based on these data, we have applied data mining techniques in order to build patterns of behavior that represent correlations between the characteristics of the students. Our results highlight the importance of using pattern mining techniques on data associated with the psychological evaluation of new university students.

Functional and embedded dependency inference: a data mining point of view

Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.

The decision making process in any field related to human behavior is an integral part of complex environment. Education has become a major concern of the development process in every field. The competent decision making to plan, execute and evaluate policies in this field became a necessity of this field which may be achieved by applying data mining techniques to educational environment. EDM uses many techniques such as decision trees, neural networks, k-nearest neighbor, naive bays, support vector machines and many others. The principal purpose of the study is identifying how each of these traditional processes can be improved through data mining techniques. And also to analyze the student behavior for future benefit by applying Data mining technique and using this valuable information for Decision Support to Prognostication of Academic Intervention for Higher Education using Data Mining Techniques. The study has suggested models and algorithms for every educational sphere we found while analyzing Education database. Data modeling processes is our main outcome, and enhanced processes achieved through data mining have been presented as research outcome. The techniques appropriate in achieving this enhanced processes has also been presented as modeling processes for finding the behavioral aspects and to effectively implement the organizational policies.

Water contains many chemical, physical, and biological impurities. Some impurities are benign while others are toxic. The quality of water is defined in terms of its physical, chemical, and biological parameters and ascertaining its quality is crucial before use for various intended purposes such as potable water, agricultural, industrial, etc. Various water analysis methods are employed to determine water quality parameters such as DO, COD, BOD, pH, TDS, salinity, chlorophyll-a, coli form, and organic contaminants such as pesticides. The list of potential water contaminants is exhaustive and impractical to test for in its entirety. Such water testing is sometimes costly and time consuming. This paper attempts to present application of data mining technique to build a model to predict a widely used gross water quality parameter called Biochemical oxygen demand (BOD). BOD is a measure of the amount of dissolved oxygen used by microbial oxidation of organic matter in wastewater. The standard method for measuring BOD is a 5-day process. Dilution of sample, constant pH and nutrient content besides the temperature of 20 °C and dark area are required for correct results. High levels of nitrogen compounds yield false BOD results. Winkler titration which is also used to measure BOD is a chemical intensive process. Hence an automatic prediction model for BOD has been sought for accurate, cost-effective and time saving measurement. Based on data available for BOD measurements, this paper describes the development of a prediction model for BOD using a technique of data mining, namely, support vector machines (SVM). A correlation coefficient of 0.9471 and RMSE of 0.5019 was obtained for the BOD prediction model on river water quality data. The performance of the proposed model was also compared with two other models namely artificial neural network (ANN) and regression by discretization. Simulation results show that the proposed model performs better than the other two in terms of correlation coefficient and RMSE.

Decision making by applying data mining methods is being used in many service organizations. Educational bodies gradually started to use the business intelligence techniques to identify the current progress in their institutions. Numerous factors which have an impact in academia will be vivid to the educationalists while applying data mining techniques on the academic data. By employing the data mining methodologies, we could identify different patterns which aid institutions to take strategic decisions to improve the students’ academic performance. Potential graduate students will have a dilemma on identifying the universities for their post graduate admissions and on the other hand an average graduate student would be uncertain on getting post graduate admission in a reputed university based on their academic scores. In this study, we applied the classification techniques such as Logistic Regression, KNN Classification, Support Vector Classification, Naive Bayes Classification, Decision Tree Classification and Random Forest Classification on the given academic admission dataset. By comparing the accuracy and mean absolute error of each model, the Logistic Regression classifier outperformed others with an accuracy of 99%.

A review on application of data mining techniques to combat natural disasters

Big Data Analytics (BDA) is going to be discussed using data mining algorithms in this paper. Develop security in BDA fields for data security to achieve confidential storage. Big Data refers to data sets that are very vast, very complicated, and very dynamic and that have many independent sources. Big data is quickly implemented in all research and engineering fields, including physical, biological, and biomedical science, thanks to rapid developments in networking, data storage, and data gathering capacities. From a data mining point of view, this study gives a theorem characterizing the aspects of the Big Data revolution and suggests a Big Data processing model. This data-driven paradigm considers security and privacy concerns, data mining and analysis, and the modeling of user interests. Here, takes a look at a few of the knottier aspects of big data and the data-driven approach, and calculate the precisions, recalls, and measures, which are 90% of different data mining algorithms.

Sequence analysis is a major area in bioinformatics encompassing the methods and techniques for studying the biological sequences, DNA, RNA, and proteins, on the linear structure level. The focus of this area is generally on the identification of intra– and inter–molecular similarities. Identifying intra–molecular similarities boils down to detecting repeated segments within a given sequence, while identifying inter–molecular similarities amounts to spotting common segments among two or multiple sequences. From a data mining point of view, sequence analysis is nothing but string- or pattern mining specific to biological strings. For a long time, this point of view, however, has not been explicitly embraced neither in the data mining nor in the sequence analysis text books, which may be attributed to the co-evolution of the two apparently independent fields. In other words, although the word “data-mining” is almost missing in the sequence analysis literature, its basic concepts have been implicitly applied. Interestingly, recent research in biological sequence analysis introduced efficient solutions to many problems in data mining, such as querying and analyzing time series [49,53], extracting information from web pages [20], fighting spam mails [50], detecting plagiarism [22], and spotting duplications in software systems [14].

Forest fire is one of the most complex phenomena which can cause great economic losses and make eco-environment seriously disordered. Forest fire has caused the loss of many green acres in Lebanon due to the lack of governmental policies in order to mange forest fires. This paper presents an overview of the exciting applications of data mining techniques in different fields. This study aims to predict forest fires in North Lebanon in order to reduce fire occurrence based on 4 meteorological parameters (Temperature, Humidity, Precipitation and Wind speed) using different data mining techniques: Neural networks, decision tree (J48), fuzzy logic, support vector machine (SVM) and linear discriminant analysis (LDA). A comparative study is then made to find the best performing technique tending to manage such a natural crisis. Decision tree (J48) recorded the best accuracy in forest fire prediction (97.8%).

The application of data mining techniques to model visual distraction of bicyclists

Nowadays, the representation of many real word problems needs to use some type of relational model. As a consequence, information used by a wide range of systems has been stored in multi relational tables. However, from a data mining point of view, it has been a problem, since most of the traditional data mining algorithms have not been originally proposed to handle this type of data without discarding relationship information. Aiming to ameliorate this problem, we propose a hierarchical approach for handling relational data. In this approach the relational data is converted into a hierarchical structure (the main table as the root and the relations as the nodes). This hierarchical way to represent relational data can be used either for classification or clustering purposes. In this paper, we will use it in clustering algorithms. In order to do so, we propose a hierarchical distance metric to compute the similarity between the tables. In the empirical analysis, we will apply the proposed approach in two well-known clustering algorithms (k-means and agglomerative hierarchical). Finally, this paper also compares the effectiveness of our approach with one existing relational approach.

The article "Political Corruption and Its Impact on the Rule of Law: A Comparative Analysis of Countries with Islamic Law" examines the relationship and impact of political corruption on the rule of law in countries with Islamic law. The primary objective of this article is to investigate the role and influence of political corruption in establishing the rule of law in these countries.
 The research methodology employed in this study is content analysis, which examines the impact of political corruption on the rule of law in countries with Islamic law through the study of legal texts and political documents. In this research, the level of political corruption in countries is considered as an independent variable using secondary data from Transparency International and Universidad Passau, and its impact on the rule of law is examined through qualitative research methods by studying case studies and analyzing legal and political relationships concurrently.
 The research findings indicate that political corruption leads to a violation of the rule of law in countries with Islamic law. Moreover, this article demonstrates that political corruption has a significant influence on the rule of law and the effectiveness of the legal system in these countries. Therefore, reducing and controlling political corruption in these countries is crucial for improving the rule of law and strengthening the legal system.

Due to the advancement of technology in this digital era, academic institutions are bringing out graduates as well as generating enormous amounts of data from their systems. Hidden information and hidden patterns in large datasets can be efficiently analyzed with data mining techniques. Application of data mining techniques improves the performance of many organizational domains and the concept can be applied in the education sectors for their performance evaluation and improvement. Understanding the business value of the collected data it can be used for classifying and predicting the students’ behavior, academic performance, dropout rates, and monitoring progression and retention. This paper discusses how application of data mining can help the higher education institutions by enabling better understanding of the student data and focuses to consolidate clustering algorithms as applied in the context of educational data mining.