A model to integrate Data Mining and On-line Analytical Processing: with application to Real Time Process Control

Since the widespread use of computers in business and industry, a lot of research has been done on the design of computer systems to support the decision making task. Decision support systems support decision makers in solving unstructured decision problems by providing tools to help understand and analyze decision problems to help make better decisions. Artificial intelligence is concerned with creating computer systems that perform tasks that would require intelligence if performed by humans. Much research has focused on using artificial intelligence to develop decision support systems to provide intelligent decision support. Knowledge discovery from databases, centers around data mining algorithms to discover novel and potentially useful information contained in the large volumes of data that is ubiquitous in contemporary business organizations. Data mining deals with large volumes of data and tries to develop multiple views that the decision maker can use to study this multi-dimensional data. On-line analytical processing (OLAP) provides a mechanism that supports multiple views of multi-dimensional data to facilitate efficient analysis. These two techniques together can provide a powerful mechanism for the analysis of large quantities of data to aid the task of making decisions. This research develops a model for the real time process control of a large manufacturing process using an integrated approach of data mining and on-line analytical processing. Data mining is used to develop models of the process based on the large volumes of the process data. The purpose is to provide prediction and explanatory capability based on the models of the data and to allow for efficient generation of multiple views of the data so as to support analysis on multiple levels. Artificial neural networks provide a mechanism for predicting the behavior of nonlinear systems, while decision trees provide a mechanism for the explanation of states of systems given a set of inputs and outputs. OLAP is used to generate multidimensional views of the data and support analysis based on models developed by data mining. The architecture and implementation of the model for real-time process control based on the integration of data mining and OLAP is presented in detail. The model is validated by comparing results obtained from the integrated system, OLAP-only and expert opinion. The system is validated using actual process data and the results of this verification are presented. A discussion of the results of the validation of the integrated system and some limitations of this research with discussion on possible future research directions is provided

PREDICTIVE DIAGNOSIS THROUGH DATA MINING FOR CARDIOVASCULAR DISEASES

Abstract Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, and early detection and accurate diagnosis are critical for effective treatment and prevention. Data mining techniques have emerged as powerful tools for analyzing large datasets to extract meaningful patterns and make predictions. This research paper aims to explore the application of data mining in predictive diagnosis for cardiovascular diseases. The study will start by collecting a comprehensive dataset comprising patient information, including demographics, medical history, lifestyle factors, and diagnostic test results. Various data mining techniques, such as classification, clustering, and association rule mining, will be applied to uncover hidden patterns and relationships within the data. Feature selection methods will be employed to identify the most relevant attributes for accurate prediction. The research will investigate different predictive models, including decision trees, support vector machines, and neural networks, to develop a reliable diagnostic system. Model performance will be evaluated using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Additionally, the study will employ cross-validation techniques to ensure the generalizability and robustness of the developed models. The research will explore the integration of advanced techniques, such as deep learning and ensemble methods, to enhance the predictive accuracy of the diagnosis. The use of explainable AI techniques will also be considered to provide interpretable insights into the predictive models' decision-making process. The findings of this research will contribute to the advancement of predictive diagnosis for cardiovascular diseases by leveraging data mining techniques. The developed diagnostic models will assist healthcare professionals in making accurate and timely predictions, leading to improved patient outcomes, personalized treatment plans, and effective preventive measures

Analysis of consumer behavior using an intelligent multi-source system

PURPOSE: This work aims to develop an innovative system that analyzes multi-source data and human behavior, ultimately creating and sharing improved procedures and solutions. It focuses on building an IT system prototype for behavior analysis, optimizing the data mining process, and generating innovative business processes.DESIGN/METHODOLOGY/APPROACH: The application aims to optimize processes, analyze data, and reveal relationships between data and processes. Business models will be created using external data, data warehouses (such as ERP systems), and data from online resources (web mining). A process database will support computational intelligence algorithms, with an agent responsible for gathering online data. New data management methods were developed and implemented, while algorithms were designed for efficient web data searching. The system will leverage artificial neural networks, statistical and stochastic methods, fuzzy sets, genetic algorithms, and combinations to build an intelligent computing system.FINDINGS: The innovative system will contribute new data management methods and algorithms for web data searching and analysis. The algorithms will advance methods and concepts for capturing, transmitting, collecting, and extracting information while providing suitable data presentation formats.PRACTICAL IMPLICATIONS: The insights from this system have the potential to revolutionize the way businesses identify and optimize new processes, generate innovative business models, and strengthen their decision-making. By comprehensively analyzing multi-source data, this system can inspire and motivate professionals in the field of data analysis and process optimization.ORIGINALITY/VALUE: This research is at the forefront of developing and implementing a system for analyzing multi-source data and human behavior. By using cutting-edge techniques such as artificial neural networks, statistical and stochastic methods, fuzzy sets, and genetic algorithms, this work provides an intelligent and robust framework for mining data and optimizing business processes, which is sure to intrigue and interest academic researchers, data analysts, and business professionals in the audience.peer-reviewe

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data