Data Mining and Query Answer techniques applied to a bio-nutritional trials focused Expert System

Data is ubiquitous in our cities. However, designing a knowledge network about our cities is an arduous task, given that data sensed cannot be used directly, human significance must be added. Adding human significance can be achieved via an automated “expert system (ES)” in which domain expert knowledge are stored in a knowledge-based repository. The domain expert knowledge is matched with the corresponding data to derive specific inference which can aid decision making for urban stakeholders.This requires amalgamation of various interdisciplinary techniques. This paper presents a survey of existing technologies in order to investigate the emerging issues surrounding the design of a live knowledge network for sustainable urban living. The maps and models of the existing infrastructure of our cities that include a wealth of information such as topography, layout, zoning, land use, transportation networks, public facilities, and resource network grids need to be integrated with real-time spatiotemporal information about the city. Public data in forms of archives and data streams as well as online data from the social network and the Web can be analyzed using data mining techniques. The domain experts need to interpret the results of data mining into knowledge that will augment the existing knowledge base and models of our cities. In addition to the analysis of archived and streamed data sources from the built environment, the emerging state-of-the-art Web 2.0 and mobile technologies are presented as the potential techniques to crowddesign a live urban knowledge network. Data modeling, data mining, crowdsourcing, and social intervention techniques are reviewed in this paper with examples from the related work and our own experiments.

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.

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.

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

Objective: Financial fraud has been a big concern for many organizations across industries; billions of dollars are lost yearly because of this fraud. So businesses employ data mining techniques to address this continued and growing problem. This paper aims to review research studies conducted to detect financial fraud using data mining tools within one decade and communicate the current trends to academic scholars and industry practitioners. Method: Various combinations of keywords were used to identify the pertinent articles. The majority of the articles retrieved from Science Direct but the search spanned other online databases (e.g., Emerald, Elsevier, World Scientific, IEEE, and Routledge - Taylor and Francis Group). Our search yielded a sample of 65 relevant articles (58 peer-reviewed journal articles with 7 conference papers). One-fifth of the articles was found in Expert Systems with Applications (ESA) while about one-tenth found in Decision Support Systems (DSS). Results: 41 data mining techniques were used to detect fraud across different financial applications such as health insurance and credit card. Logistic regression model appeared to be the leading data mining tool in detecting financial fraud with a 13% of usage.In general, supervised learning tool have been used more frequently than the unsupervised ones. Financial statement fraud and bank fraud are the two largest financial applications being investigated in this area – about 63%, which corresponds to 41 articles out of the 65 reviewed articles. Also, the two primary journal outlets for this topic are ESA and DSS. Conclusion: This review provides a fast and easy-to-use source for both researchers and professionals, classifies financial fraud applications into a high-level and detailed-level framework, shows the most significant data mining techniques in this domain, and reveals the most countries exposed to financial fraud.

In the evolving landscape of financial markets, the convergence of data mining, cybersecurity, and algorithmic trading plays a pivotal role in enhancing trading performance and forensic investigations. This study investigates how data mining techniques are leveraged to extract meaningful patterns and trends from vast financial datasets, improving the accuracy and profitability of algorithmic trading strategies. By identifying historical trends, price movements, and trade margins, data mining enables traders to optimize decision-making processes and manage risks more effectively. Cybersecurity emerges as a critical factor in safeguarding both trading algorithms and sensitive financial data from cyber threats. The integration of advanced cybersecurity measures ensures the integrity, confidentiality, and availability of trading systems, reducing vulnerabilities that could be exploited by malicious actors. Additionally, forensic investigation techniques are employed to detect fraudulent trading activities, such as insider trading and market manipulation, thereby protecting market participants and maintaining regulatory compliance. This research highlights the importance of combining secure data mining practices with robust cybersecurity measures to enhance the overall performance of algorithmic trading systems. Furthermore, it explores how forensic methodologies can help detect anomalies and ensure the transparency and fairness of financial markets. Through an integrated approach, this study emphasizes the potential of data mining and cybersecurity in transforming trading operations while mitigating risks associated with cyber threats and financial fraud.

This chapter is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which are predicated by using the data mining techniques, decision tree, association rules and neural networks. Routing algorithms can use this metric for optimal path selection which in turn will affect positively on the system performance. Also, in this chapter management axis using data mining techniques were handled, i.e., check the status of the telecommunication networks, role of data mining in obtaining optimal configuration, how to use data mining technique to assure high level of security for the telecommunication. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry. These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs (Liebowitz, J. 1988). The problem with these expert systems is that they are expensive to develop because it is both difficult and time consuming to elicit the requisite domain knowledge from experts.

In recent years, the convergence of data mining and robotic systems has emerged as a key trend shaping the future of artificial intelligence. This study aimed to explore how data mining techniques can enhance robotic decision-making, enabling robots to learn, adapt, and respond to dynamic environments. A qualitative-analytical research design was used to examine the impact of intelligent data processing on robotic behavior, particularly in social robots. The analysis revealed that real-time data mining significantly improves robot autonomy, adaptability, and human interaction. Moreover, the integration of data mining introduced critical security challenges, such as distributed denial of service (DoS) attacks, sensor data eavesdropping, and manipulation of input data. The study found that implementing advanced security mechanisms—including encryption, authentication, and anomaly detection through artificial intelligence algorithms—helps mitigate these risks effectively. The findings emphasize the transition from task-executing machines to intelligent systems capable of independent decision-making. One notable application discussed in the paper is the development of social robots that interact naturally and empathetically with humans. This research highlights the importance of embedding data mining within robotic architectures not only to enhance performance but also to strengthen security and resilience. The study concludes that the intelligent fusion of data mining and robotics paves the way for scalable, secure, and context-aware robotic systems. Future work is needed to explore real-world implementations and evaluate long-term performance under various operational scenarios.

Decision making tasks are subject to limitation and also depends on human knowledge, experiences, judgments and preferences. In this case, Intelligent Decision System (IDS) technologies can be used to provide realistic and consistent decisions, besides to improve the effectiveness of decision making processes. Intelligent Decision Support System (IDSS) is a contributory of IDS technology to assist decision makers in high level phases of decision making by integrating human knowledge with modeling tools. Nowadays, data mining (DM) techniques is also can be used to support Knowledge Management (KM) tasks especially for knowledge discovery and knowledge engineering. DM is emerging data analysis tool and widely used in order to produce valuable knowledge for decision making as knowledge modeling task. In Human Resource (HR), managing talent is among the challenges of HR professionals which can be handle by using IDSS and data mining technologies. For that reason, in this article, we discussed the potential to uses IDSS approach for talent management using DM techniques by proposing IDSS architecture and a case study on talent classification. This study consists of three parts; the first part is to know the previous works on IDSS, talent management and DM. The second part is discussion on proposed IDSS architecture for talent management. Finally, the third part is a case study on the use of DM classification method for talent forecasting, especially for employee's job promotion.

This study aimed to identify patients with the highest risk of periodontal disease (PD), and to provide recommendations for the effective use and application of data mining (DM) techniques when establishing evidence-based dental-care policies for vulnerable groups at a high risk of PD. This study used the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology to construct DM models based on data acquired from the fifth and sixth Korea National Health and Nutrition Examination Surveys (2000-2015). We analyzed the sociodemographic and comorbidity variables that influence PD by applying the popular DM techniques of decision-tree, neural-network, and regression models, and also attempted to improve the predictive power and reliability by comparing the results obtained by these three models. Our comparisons of the three DM algorithms confirmed that the average squared error, misclassification rate, receiver operating characteristic index, Gini coefficient, and Kolmogorov-Smirnov test results were the most appropriate for the decision-tree model. The analysis of the decision-tree model revealed that age and smoking status exert major effects on the risk of PD, and that stress and education level exert effects in rural areas, whereas education level, sex, hyperlipidemia, and alcohol intake exert effects in urban areas. We demonstrated that the decision-tree model is an effective DM technique for identifying the complex risk factors for PD. These results are expected to be helpful in improving the equality and efficacy of dental-care policies for vulnerable groups at a high risk of PD.

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

Our research transacts with a great various audiology data from National Health System (NHS) facility, including audiograms, structured data such as age, gender, and diagnosis, and a text of specific information about each patient, i.e., clinical reports. This research examines factors related to audiology patients depends on various data by using the mining and analysis of this data. This paper looks for factors affecting the choice between two prevalent hearing aid kinds: BTE (Behind The Ear) or ITE (In The Ear). This choice often done by audiology technicians working in specific clinics for this purpose, based on audiograms results and patient consultation. In many situations, there is an obvious choice, but sometimes the technicians need for the second opinion via an automatic system includes clarification of how to obtain that second opinion. The research deals with diversified specifics and more significant factors for choosing of confirmed hearing aid related to those specifics. We depend on the earlier study data (Bareiss, E. Ray, & Porter, Bruce (1987)). Protos: An Exemplar-Based Learning Apprentice. In the Proceedings of the 4th International Workshop on Machine Learning, 12-23, Irvine, California, which illustrates the database analysis for 180,000 records, for 23,000 patients, by the hearing aid clinic at James Cook University Hospital in Middlesbrough, UK. This data mined to find which factors contribute to the deduction to fit a BTE hearing aid as opposed to an ITE hearing aid. Here we conduct some enhancements on this database and analyze the data depends on medical information to create a new class then we use some intelligent Data Mining (DM) techniques to guess the most correct illness that could be associated with patient's information. Based on the result (according to the patients' diagnosis details), we can obtain right predictions of which type of Hearing Aid (HA) they should use.

A Critical Survey of Data Grid Replication Strategies Based on Data Mining Techniques

The application of Data Mining (DM) techniques for DQ, often called Data Quality Mining (DQM), offers a wide range of possibilities for DQ assessment. The goal of this work is to propose a mechanism for data currency assessment using statistics and DM techniques. The proposed approach consists on estimating the validity period for the entities using a training set and then evaluating the probability of currency of the last known data value for each entity. The proposed scheme helps in two ways to lead to an always up-to-date database: it can warn if a certain data value is becoming obsolete, and it can inform the data manager about the best frequency for updating data.

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