Early Prediction of Hydrocephalus Using Data Mining Techniques

Smart meter monitoring and data mining techniques for predicting refrigeration system performance

Analysing datasets using data mining techniques can enhance decision making in organizations. However, to ensure that the full potential of these techniques is realised it is important that decision makers understand there are Knowledge Discovery and Data Mining (KDDM) processes that are mature enough to be adopted. This paper demonstrates the benefits of using a KDDM process to evaluate survey data for internet banking users in Jamaica which includes demographic as well as attitudinal and behavioral variables. The major benefits of following this process include the selection of a set of models, rather than a single model, which are more relevant to the business/research objectives and use of a more targeted knowledge discovery process as the data mining analyst is now directed to consider the effects the decisions in each phase will have on subsequent phases. This leads to more relevant knowledge being extracted from the data mining process.

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.

Crop yield is affected by climatic, management, geographical, biological and other such factors. Data mining techniques can be used to analyse the effect of these factors on crop yield and to predict crop yield based on these factors. The current paper focuses on the sequence of steps to be followed in data mining process for prediction of crop yield - starting from the determination of research goals to the application of the data mining techniques to build a model. The study applies the defined data mining process to build a model for the prediction of paddy yield based on different climatic factors. The current research also provides an insight to the different metrics that can be used to evaluate various supervised data mining techniques. The metrics have been divided into three categories - threshold evaluation metrics, numerical evaluation metric, and built time and size metrics. Comparative analysis of five supervised data mining techniques has been carried out on the basis of their performance in these three categories of metrics.

The data mining experiments from this chapter are a component of a larger research which is to be used to make several correlations, analysis in order to integrate data mining process in the managerial system for optimal decision support. I offered an insight of how data mining processes are being applied in the large spectrum of education by presenting recent applications and studies published in the scientific literature, considered to be relevant to the development of this emerging science. I presented my work through a number of experiments conducted over questionnaires data and scholastic databases at Faculty of Economics and Business Administration Cluj-Napoca, using classification learning and data clustering methods. In the last part of the chapter I introduced the concept of Academic Intelligence Management, and illustrated the integration of data mining processes and their particular role in higher education management and decision support. The studies will continue with deeper mining of educational topics, such as performance in scientific research, correlations between the students' knowledge and the competences demanded on the labor market, academic failure, to perceive what and how much the students know, to realize learning gaps, and also improve teaching methods and educational management processes.

This research study analyzes the different data mining techniques used for the detection of credit card frauds. Additionally, different data mining techniques are properly described in this study. The steps of data mining are clearly explained in this study, which are helpful for recognizing the fraudulent activities. This study is discussing that Bayesian network, and decision tree are the effective techniques of data mining. The issues of data mining are also discussed in this study, which is creating performance issues, and user interaction issues in the financial institutions. Therefore, this study is providing the importance of RF algorithm in the determination of the credit card fraud. Data cleaning, and data visualization, therefore, the machine learning process is allowed to be developed with the support of the data mining process of credit cards. Clustering is the main process that is takes place in this data mining process. Additionally, the sequential pattern is being highlighted by this process, which helps to improve the fraud detection process of credit cards.

Stray current interference causes severe electrochemical corrosion to buried pipelines and metal structure in DC mass transit systems. The corrosion current density is an important parameter that affecting the characteristic of stray current corrosion (SCC). A method combining electrochemical experiment with the data mining technique was utilized in this research to study the corrosion current density during stray current process. An improved PSO-NN model was proposed to predict the corrosion current density during the SCC process. In order to improve the forecasting performance, the improved particle swarm optimization (IPSO) algorithm was used to optimize the updating of weights and biases in the neural network (NN). The data mining process was conducted based on the stray current corrosion database, which was recorded by electrochemical corrosion experiment. The data mining results show that the accuracy of IPSO-NN model is better than back-propagation neural network (BPNN) and PSO-NN model. In this way, an accurate mapping between input variables and corrosion current density is successfully established using IPSO-NN algorithm as a data mining tool. The presented model provides a new approach for the prediction and monitoring of the stray current corrosion in DC mass transit system.

In this paper, a detailed analysis of several major works related to study and analysis of Electroencephalography (EEG) signals using imaging and data mining techniques have been presented. The state-of-the-arts methods like spatial analysis, wavelet analysis, frequency domain analysis, artificial neural networks, deep neural networks etc have been discussed with associated contributions in the literature. A comprehensive comparison of all such mechanisms have been done and presented in a tabular manner. This paper also discusses about the existing challenges, shortfalls in present techniques, and future demands of EEG analysis through imaging and data mining techniques.

Clustering technique is critically important step in data mining process. Clustering is a significant task in data analysis and data mining applications. It is the task of arrangement a set of objects so that objects in the identical group are more related to each other than to those in other groups (clusters). Data mining can do by passing through various phases. Mining can be done by using supervised and unsupervised learning. The clustering is unsupervised learning. A good clustering method will produce high superiority clusters with high intra-class similarity and low inter-class similarity. Its main distinctiveness is the fastest processing time. In this paper, an analysis of clustering and its different techniques in data mining is done. Results were quite encouraging and had shown high accuracy.

Objective: Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) are two broadly used for rule generation process in data mining. ANN have been widely used for pattern extraction from medical databases. Methods: Heart disease is one of the leading death cause disease in all over the world in the present situation. A large number of research works have been carried out for diagnosing heart failures using data mining techniques. As the number of heart disease features increasing along with a number of instances, the traditional approaches such as PSO, ANN and BPSO are fail to predict the exact disease causing features. Findings: In this research work, an improved ANN approach model called Optimized Artificial Neural Network (OANN) was implemented on the medical data sets. In this paper, an Optimized Particle Swarm Optimization (PSO) technique is used for disease dimension reduction. Filter based Artificial Neural Network is used for classifying the disease type as positive or negative based on the disease features. Applications/Improvements: The performance of the proposed algorithm is analyzed by using the traditional approaches of Performance plot, Regression, ROC Value and Confusion Matrix. It is proved that the performance of the whole ANN network is optimized after the inclusion of proposed PSO for Feature Reduction.

Data mining techniques have been extensively applied in bioinformatics to analyze biomedical data. In this paper, we choose the Rapid-I’s RapidMiner as our tool to discover decision tree based diabetes prediction model from a Pima Indians Diabetes Data Set, which collects the information of patients with and without developing diabetes. Following the data mining process, our discussion will focus on the data preprocessing, including attribute identification and selection, outlier removal, data normalization and numerical discretization, visual data analysis, hidden relationships discovery, and a diabetes prediction model construction.

This paper introduces a novel methodology for the optimization, analysis and decision support in production systems engineering. The methodology is based on the innovization procedure, originally introduced to unveil new and innovative design principles in engineering design problems. The innovization procedure stretches beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. By integrating the concept of innovization with simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis. The uniqueness of the approach introduced in this paper lies in that decision rules extracted from the multi-objective optimization using data mining are used to modify the original optimization. Hence, faster convergence to the desired solution of the decision-maker can be achieved. In other words, faster convergence and deeper knowledge of the relationships between the key decision variables and objectives can be obtained by interleaving the multi-objective optimization and data mining process. In this paper, such an interleaved approach is illustrated through a set of experiments carried out on a simulation model developed for a real-world production system analysis problem.

Data mining techniques have been extensively applied in bioinformatics to analyze biomedical data. In this paper, we choose the Rapid-I's RapidMiner as our tool to analyze a Pima Indians Diabetes Data Set, which collects the information of patients with and without developing diabetes. The discussion follows the data mining process. The focus will be on the data preprocessing, including attribute identification and selection, outlier removal, data normalization and numerical discretization, visual data analysis, hidden relationships discovery, and a diabetes prediction model construction.

Objectives: Data mining techniques are implemented in many organizations as a standard procedure for analyzing the large volume of available data, extracting useful information and knowledge to support the major decision-making processes. Data mining can be applied to wide variety of applications in the educational sector for the purpose of improving the performance of students as well as the status of the educational institutions. Educational data mining is rapidly developing as a key technique in the analysis of data generated in the educational domain. Methods: The aim of this study presents an analysis of final year results of UG degree students using data mining technique, which carried out in three of the private colleges in Tamil Nadu state of India. The primary objective of this research work is to apply the classification techniques to the prediction of the performance of students in end semester university examinations. Particularly, the decision tree algorithm C4.5 (J48), Bayesian classifiers, k Nearest Neighbor algorithm and two rule learner’s algorithms namely OneR and JRip are used for classifying the performance of students as well as to develop a model of student performance predictors. Results: The result of this study reveals that overall accuracy of the tested classifiers is above 60%. In addition classification accuracy for the different classes reveals that the predictions are worst for distinction class and fairly good for the first class. The JRip produces highest classification accuracy for the Distinction. Classification of the students based on the attributes reveals that prediction rates are not uniform among the classification algorithms. Also shows that selected data attributes have found to be influenced the classification process. The results showed to be satisfactory. Improvements: The study can be extended to draw the performance of other classification techniques on an expanded data set with more distinct attributes to get more accurate results.

Feature extraction from biological data is a very important discipline in bioinformatics. The aim of this work is to classify protein sequences automatically. To do this, it seemed appropriate to use a data mining process: the process of Knowledge Discovery and Data mining (KDD) from biological data. We are interested in the first phase of the KDD, that consists in the preprocessing and we focus on the step: Feature extraction. Feature extraction is translated by the generation of a set of feature that is presented to a supervised learning algorithm for classification. An extraction method that we have adopted is the method of N-grams. The algorithm of n-grams consists in extracting feature of fixed size of length n. In this paper, we propose a hierarchical algorithm of construction of n-grams to obtain feature of variable sizes. This algorithm of extraction is used to meet the needs of biologists. By using the linear classifier SVM, the experiments on real protein banks show the efficiency of our algorithm while presenting a comparison of our work to previous works.