Knowledge Discovery Process Research Articles

Hakikat Sains : Pengertian, Fungsi, dan Penerapan dalam Proses Pembelajaran

Science plays a crucial role in human life as it helps us understand the universe through observation, experimentation, and analysis. In education, science not only teaches scientific concepts but also fosters scientific attitudes and critical thinking skills in students. To make science education more meaningful, interactive, and relevant, a deep understanding of the Nature of Science (NOS) is essential. This study aims to explore the understanding, benefits, and application of scientific concepts in the teaching and learning process to shape students' scientific attitudes. Using a literature review method, this research gathers information from various relevant academic sources. The findings indicate that science encompasses elements of philosophy, sociology, history, and psychology that support scientific understanding. By understanding NOS, students will view science as a process of knowledge discovery, rather than merely a collection of facts. The application of science in education also impacts students' ability to apply knowledge in practical contexts, enhance their analytical skills, and adapt to societal changes.

Promoting Machine Abilities of Discovering and Utilizing Knowledge in a Unified Zero-shot Learning Paradigm

Knowledge discovery and utilization are two essential cognitive processes that enable humans to understand the world and extract new insights from their surroundings. These processes have motivated machine learning studies, particularly zero-shot learning (ZS), which seeks to identify unseen concepts through the use of side information. Previous ZS studies primarily focused on utilizing existing knowledge to infer unseen events, yet they overlook the crucial process of knowledge discovery and the integrated modeling of these knowledge-aware processes. In this study, we present a comprehensive ZS learning approach that explores and evaluates the machine's abilities of discovering and utilizing knowledge. More specifically, to emulate human-like knowledge discovery and utilization processes, we propose a novel visual-aware ZS knowledge graph completion task for evaluation, incorporating a traditional ZS image classification task. Technically, we develop a unified ZS learning paradigm named Cognitive Learner (CoLa) to foster the two knowledge-aware abilities. Including a knowledge representation learning (KRL) module and a knowledge adaptation (KA) module, CoLa adapts well to the two specified tasks with the corresponding data. Extensive experiments on large-scale datasets demonstrate CoLa models’ outstanding performance over compared methods in the two ZS tasks, illustrating their superior ability of discovering and utilizing knowledge.

A Model for Predicting Dropout of Higher Education Students

Higher education institutions are becoming increasingly concerned with the retention of their students. This work is motivated by the interest in predicting and reducing student dropout, and consequently in reducing the financial loss of said institutions. From the characterization of the dropout problem, and application of a knowledge discovery process, a model (ensemble) to improve dropout prediction is proposed. The ensemble model combines the results of three models: Logistic Regression, Neural Networks, and Decision Tree. As a result, the model can correctly classify 89% of the students (as enrolled and dropped) and accurately identify 98.1% dropouts. When the proposed model is compared with the ensemble method, Random Forest, the proposed model presented desirable characteristics to assist the management in proposing actions to retain students.

Dynamic knowledge graph applications for augmented built environments through “The World Avatar”

The proliferation of digital building models in recent years has led to a corresponding rise in specialised, non-interoperable models. These models impede sustainable developments by forming data silos that hinder cross-application data exchange and knowledge discovery processes. Although Semantic Web solutions hold promise in addressing these silos, current approaches primarily focus on developing novel ontologies, yielding similar outcomes. But it is unclear how these methodologies could support broader knowledge discovery processes and application requirements. This paper addresses these research challenges by introducing a dynamic knowledge graph as implemented within The World Avatar for interoperable building models. We demonstrate its value through two distinct applications in urban energy management and laboratory automation. The dynamic knowledge graph revolves around a comprehensive structured knowledge model constructed from ontologies and agents. Ontologies semantically annotate data and represent domain knowledge and their relationships with standardised definitions. When augmented with an agent architecture, the resulting knowledge model can align stakeholder perspectives and accommodate the dynamic and scalable nature of urban data. Moreover, the dynamic knowledge graph fosters innovative human-machine interactions through visualisation interfaces to augment knowledge discovery processes in the built environment for greater efficiencies and innovation. As the knowledge model expands, users gain access to a broader spectrum of private and public data sources and technologies, while reducing integration barriers. This is especially pertinent for smaller and less influential entities like municipal and local governments with limited resources, who can realise substantial benefits at reduced costs.

Importance of Characteristic Features and Their Form for Data Exploration.

The nature of the input features is one of the key factors indicating what kind of tools, methods, or approaches can be used in a knowledge discovery process. Depending on the characteristics of the available attributes, some techniques could lead to unsatisfactory performance or even may not proceed at all without additional preprocessing steps. The types of variables and their domains affect performance. Any changes to their form can influence it as well, or even enable some learners. On the other hand, the relevance of features for a task constitutes another element with a noticeable impact on data exploration. The importance of attributes can be estimated through the application of mechanisms belonging to the feature selection and reduction area, such as rankings. In the described research framework, the data form was conditioned on relevance by the proposed procedure of gradual discretisation controlled by a ranking of attributes. Supervised and unsupervised discretisation methods were employed to the datasets from the stylometric domain and the task of binary authorship attribution. For the selected classifiers, extensive tests were performed and they indicated many cases of enhanced prediction for partially discretised datasets.

Evaluation of Single/Multiple Joint Effects of Lipid Profiles on Hypertension, Diabetes Mellitus and Obesity Accompanying Coronary Artery Disease

Objective: Although cardiovascular diseases are among the most prominent causes of mortality/morbidity in the world, they are even more important together with comorbidities. This study aims to reveal the single/multiple effects of total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), and triglyceride (TG) on hypertension (HT), type 2 diabetes mellitus (T2DM) and obesity accompanying coronary artery disease (CAD). Method: The data were retrospectively achieved from the records of CAD patients undergoing coronary bypass surgery at the Department of Cardiovascular Surgery, Medical Center, University. The medical knowledge discovery process (MKDP) was applied to the data concerning HT, DM, obesity, TC, HDL-C, LDL-C, and TG variables. Different methods were used to determine the optimal cut-off points of lipid profiles. Logistic regression analysis (LRA) was examined the single/multiple effects of lipid profiles on HT, T2DM, and obesity. Results: TC, LDL-C, TG, and HDL-C lipid profiles categorized according to the cut-off points determined in the current study were analyzed with LRA models. LDL-C (>117 mg/dL)*TC (>191 mg/dL)*HDL-C (>37.2 mg/dL) in HT and TC (>190 mg/dL)*TG (>197) mg/dL)*HDL-C (>36.3 mg/dL) in T2DM interaction terms had a moderate effect size. LDL-C (>115 mg/dL)*TG (>197 mg/dL)*HDL-C (>36.3 mg/dL) interaction terms in T2DM and TC (>192 mg/dL)*LDL-C (>117 mg/dL)*HDL-C (>36.8 mg/dL), TK (>192 mg/dL)*TG (>193 mg/dL)*HDL-C (>36.8 mg/dL) and LDL-C (>117 mg/dL)*TG (>193 mg/dL)*HDL-C (>36.8 mg/dL) interaction terms in obesity were reported as having a high effect size. Conclusion: In conclusion, it is recommended to use the approach that analyzes the cut-off points proposed in this study for lipid profiles in predicting HT, T2DM, and obesity.

Online Education Big Data Management and Mining Based on Intelligent Technology

The advent of online education platforms has revolutionized traditional learning paradigms, providing unprecedented access to educational resources worldwide. However, the vast amounts of data generated by these platforms present significant challenges in terms of effective management and utilization. This paper proposes a comprehensive approach to address these challenges through the integration of big data management and mining techniques, supported by intelligent technologies. By harnessing the power of advanced data management strategies and cutting-edge machine learning algorithms, our approach aims to unlock valuable insights from online education data, thereby enhancing the learning experience and improving educational results. This paper presents a systematic framework for online education big data management and mining, encompassing data collection, storage, preprocessing, analysis, and visualization. Furthermore, we discuss the role of intelligent technologies such as artificial intelligence, natural language processing, and predictive analytics in optimizing the process of data mining and knowledge discovery. Through case studies and experimental evaluations, we demonstrate the effectiveness and applicability of our approach in real-world online education scenarios. Our research contributes to the growing body of knowledge in the field of educational data mining and underscores the potential of intelligent technology in shaping the future of online learning.

Identification of Students with Similar Performances in Micro-Learning Programming Courses with Automatically Evaluated Student Assignments

The identification of heterogeneous and homogeneous groups of students using clustering analysis in learning analytics is still rare. The paper describes a study in which the students’ performance data stored in the micro-learning platform Priscilla are analyzed using learning analytics methods. This study aims to identify the groups of students with similar performances in micro-learning courses focused on learning programming and uncover possible changes in the number and composition of the identified groups of students. The CRISP-DM methodology was used to navigate through the complexity of the knowledge discovery process. Six different datasets representing different types of graded activities or term periods were prepared and analyzed for that purpose. The clustering analysis using the K-Means method found two clusters in all cases. Subsequently, performance metrics, the internal composition, and transfers of the students between clusters identified in different datasets were analyzed. As a result, this study confirms that analyzing student performance data from a micro-learning platform using learning analytics methods can reveal distinct groups of students with different academic performances, and these groups change over time. These findings align with teachers’ assumptions that the micro-learning platform with automated evaluation of programming assignments highlights how the students perceive the role of learning tools during learning programming in different term periods. Simultaneously, this study acknowledges that clustering, as an exploratory method, provides a solid basis for further research and can identify distinct groups of students with similar characteristics.

Application of Machine Learning and Data Mining in Manufacturing Industry

With the rise of machine learning in various industries, the traditional manufacturing industry is facing a new disruption, which requires the use of different technologies and tools to achieve its production targets; In this regard, machine learning (ML) and data mining (DM) play a key role. This paper provides a statistical understanding of the main methods and algorithms used to improve manufacturing processes over the past 20 years by dividing them into four main themes: Scheduling, Monitoring, Quality and Failure, presents previous ML research and the latest advances in manufacturing, followed by a comprehensive discussion of existing problem solutions in manufacturing from multiple aspects, It includes tasks (i.e., clustering, classification, regression), algorithms (i.e., support vector machines, neural networks), learning types (i.e., ensemble learning, deep learning), and performance indicators (i.e., accuracy, mean absolute error). In addition, the main steps of database knowledge discovery (KDD) process that should be followed in manufacturing applications are described in detail, and the methods to overcome some problems and the advantages of machine learning applied to manufacturing industry are briefly described. Finally, the paper summarizes and further looks forward to the future development direction.

Use of Artificial Intelligence Tools for Research by Medical Students: A Narrative Review.

With the advent of the era of digitalization, a new door has opened for the development of artificial intelligence (AI)-driven tools/algorithms that can help analyse the huge amount of data uploaded onto the cloud. AI-based tools/algorithms have created a niche in the field of research. AI has enabled researchers and practitioners to access and evaluate an enormous number of scientific papers more effectively. This can link similar studies from the past and highlight research gaps, thus accelerating the literature review, evidence generation, and knowledge discovery process. Medical students can obtain help from various AI-based solutions for literature organization and citations. These tools/algorithms facilitate secure information exchange, collaborative research efforts, and communication among multiple research centres. However, AI-driven research requires the guidance and supervision of human experts for better accuracy, coherence, and credibility of the content entering scientific databases. The key objective of this review is to discuss and evaluate various AI-based tools/algorithms and their key features that can assist medical students in medical research.

Sub-suppliers model 4.0 and knowledge discovery in industry supply chain

Web 4.0 revolution and the abundance of industrial data in the current business environment allow each industrial company to innovate in a diversity of ways. The novelty approaches for the industry are often related to Industry 4.0, where data plays a key role. This research paper aims to develop a new model of the Sub-Supplier process description „Sub-Suppliers Model 4.0“. The model presents the role of knowledge management and Web 4.0 as having a key impact on the processes for business value creation for the industrial organisation. The paper is structured into three chapters. The introduction presents the methods used along with the research question. Chapter one starts with the theoretical bases and state of the art of the Industrial Sub-Supplier Process (ISSP). Chapter two makes a link between the ISSP, knowledge management and knowledge discovery process using data from Supply Chain Management Software for management decision-making. Chapter three of the research starts with web 4.0 tools and proposes a Sub-Suppliers Model 4.0, while trying to explain how the ISSP can be optimized based on the KPIs used. The main research contribution is the novelty integration of the knowledge discovery concept and the implementation of web 4.0 tools into the ISSP.

Predicting students’ academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes

BackgroundDropout and poor academic performance are persistent problems in medical schools in emerging economies. Identifying at-risk students early and knowing the factors that contribute to their success would be useful for designing educational interventions. Educational Data Mining (EDM) methods can identify students at risk of poor academic progress and dropping out. The main goal of this study was to use machine learning models, Artificial Neural Networks (ANN) and Naïve Bayes (NB), to identify first year medical students that succeed academically, using sociodemographic data and academic history.MethodsData from seven cohorts (2011 to 2017) of admitted medical students to the National Autonomous University of Mexico (UNAM) Faculty of Medicine in Mexico City were analysed. Data from 7,976 students (2011 to 2017 cohorts) of the program were included. Information from admission diagnostic exam results, academic history, sociodemographic characteristics and family environment was used. The main dataset included 48 variables. The study followed the general knowledge discovery process: pre-processing, data analysis, and validation. Artificial Neural Networks (ANN) and Naïve Bayes (NB) models were used for data mining analysis.ResultsANNs models had slightly better performance in accuracy, sensitivity, and specificity. Both models had better sensitivity when classifying regular students and better specificity when classifying irregular students. Of the 25 variables with highest predictive value in the Naïve Bayes model, percentage of correct answers in the diagnostic exam was the best variable.ConclusionsBoth ANN and Naïve Bayes methods can be useful for predicting medical students’ academic achievement in an undergraduate program, based on information of their prior knowledge and socio-demographic factors. Although ANN offered slightly superior results, Naïve Bayes made it possible to obtain an in-depth analysis of how the different variables influenced the model. The use of educational data mining techniques and machine learning classification techniques have potential in medical education.

Real-world validation of a framework for automated knowledge driven feature engineering inspired by medical domain experts

Knowledge discovery from real-world data in health care can be demanding due to unstructured data and low registration quality in electronic health records (EHRs). This requires close collaboration of domain experts and data scientists. To perform the knowledge discovery process more effectively and efficiently, a framework for automatic Knowledge Driven Feature Engineering (aKDFE) has been developed. Central to aKDFE is an automated feature engineering (FE), i.e., an automated construction of new, highly informative variables, referred to as features, from those directly observed and recorded, e.g., in EHRs. The framework learns and aggregates domain knowledge to generate features that are more informative compared to those recorded in EHRs or manually engineered (manual FE) as done in medical research projects today.Manual KDFE is a systematic manual FE process, which improves prediction performance without loss of explainability of the predictions. But the following research questions remained open: (i) is it possible to automate KDFE, (ii) are aKDFE features more informative than features from a manual FE process, and (iii) does aKDFE produce explainable and transparent results?To summarize the present study, aKDFE is (i) more efficient than manual FE since it automates the manual knowledge discovery and FE processes. It is (ii) more effective due to its higher predictive power compared to manual KDFE. This was evaluated on a real-world medical research project by comparing the classification ability when using manual and aKDFE features in machine learning (ML) models, measured as the area under the receiver operating characteristic curve (AUROC). The project included 26,992 patients regarding “Negative bone structure effects of antiepileptic drug consumption”. The baseline features (manual FE) used in studied project were compared with features generated by aKDFE; aKDFE-generated features resulted in higher AUROC than baseline features, with a p-value <0.05. Finally, aKDFE (iii) applies and describes data pivoting and feature generation as explicit and transparent operation sequences on EHR features. Inefficiency issues remain, mainly regarding non-automatic FE and baseline generation.Threat of validity to the aKDFE framework exists in the selection of the pivoting method, the generalization of used FE operations and rules, and usage of evaluation metrics.

Collection of Weather Data from Authentic Websites and Secondary Data Sources for Rainfall Prediction

The field of data mining and machine learning has been grown many folds from the last two decades. Almost every other problem can be solved using data mining and this becomes the most tempting part of it for the scientist and researchers all over the world. Data mining can be viewed as a process of discovering knowledge. This discovery of knowledge starts with the collection of data and ends with the acquired knowledge in the form of patterns. Data collection lays the foundation for the process of knowledge discovery. In this paper, various secondary data sources from where data can be collected for rainfall prediction are deeply studied and analyzed. Some of these authentic websites and secondary data sources are NCDC (National climate data center), Kaggle, Datahub.io, UCI machine learning repository, Earth Data etc. The data collected from these secondary data sources for rainfall prediction have been critically analyzed and compared on the parameters of Accuracy, Completeness, reliability, relevance, and timeliness.

SKATEBOARD: Semantic Knowledge Advanced Tool for Extraction, Browsing, Organisation, Annotation, Retrieval, and Discovery

This paper introduces Semantic Knowledge Advanced Tool for Extraction Browsing Organisation Annotation Retrieval and Discovery (SKATEBOARD), a tool designed to facilitate knowledge exploration through the application of semantic technologies. The demand for advanced solutions that streamline Knowledge Extraction, management, and visualisation, characterised by abundant information, has grown substantially in the current era. Graph-based representations have emerged as a robust approach for uncovering intricate data relationships, complementing the capabilities offered by AI models. Acknowledging the transparency and user control challenges faced by AI-driven solutions, SKATEBOARD offers a comprehensive framework encompassing Knowledge Extraction, ontology development, management, and interactive exploration. By adhering to Linked Data principles and adopting graph-based exploration, SKATEBOARD provides users with a clear view of data relationships and dependencies. Furthermore, it integrates recommendation systems and reasoning capabilities to augment the knowledge discovery process, thus introducing a serendipity effect generated by the SKATEBOARD interface exploration. This paper elucidates SKATEBOARD’s functionalities while emphasising its user-centric design. After reviewing related research, we provide an overview of the SKATEBOARD pipeline, demonstrating its capacity to bridge RDF and LPG representations. Subsequent sections delve into Knowledge Extraction and exploration, culminating in the evaluation of the tool. SKATEBOARD empowers users to make informed decisions and uncover valuable insights within their data domains, with the added dimension of serendipitous discoveries facilitated by its interface exploration capabilities.

Development of Artificial Intelligence of Ensembles of Software and Hardware Agents by Natural Intelligence on the Basis of Self-Organization

Natural intelligence is the totality of acquired knowledge and intellectual skills of a person. Intellectual skills are the ability to think creatively and gracefully, communicate and learn universally. From the point of view of the Orthodox tradition, there are three types of thinking, learning and communication: carnal, creative and grace-filled (spiritual). Creative thinking, learning and communication develop and improve rational intelligence. Gracious thinking, learning and communication develops and improves spiritual intelligence. Strong natural intelligence generates superior knowledge relative to the knowledge of society. It expands and deepens the knowledge of society. Knowledge is a social product. The dynamic process of discovering knowledge and broadcasting about the works of the Creator is described in (18:3-5) parables: “Day imparts speech to day, night reveals knowledge to night. There is no language and no dialect where their voice is not heard. Their voice goes throughout the whole earth, and their words to the ends of the world.” As part of the dynamic process of knowledge discovery, the natural intelligence of each person develops. Natural intelligence began to develop artificial intelligence. Artificial intelligence can be possessed by a software-hardware operating process capable of creating poetry and essays, painting pictures, developing recommendations and solutions for goals set by humans, managing production and systems in various fields of activity based on the existing knowledge of the natural intelligence of mankind. Artificial intelligence cannot develop without human participation. A very promising use of artificial intelligence is carried out by ensembles of software and hardware agents using proven methods based on self-organization in various spheres of life. Ensembles of software and hardware agents can be trained using the knowledge and skills of natural intelligence.

Convolutional neural networks applied to data organized as OLAP cubes

AbstractThis paper presents a Convolutional Neural Network (CNN) architecture named OlapNet, which incorporates implicit operations of OLAP cubes (or data cubes). OLAP cubes are produced from database tables or spreadsheets and they allow particular operations that support performing complex queries efficiently. OlapNet permits evaluating various combinations of these OLAP operations in its search space and thus, it enables, in part, to automate the data transformation step in the knowledge discovery process. A sample of data from an actual database containing anonymized data on the debt history of customers of a financial institution has been used to evaluate our proposal. A predictive classification problem to estimate the probability of any given customer contracting new credits in the next three months has been modelled from these data. Then, traditional methods of Machine Learning and CNN were applied. The results showed that CNN, using the OlapNet architecture, outperforms traditional methods in almost all cases, indicating that the proposed architecture is quite promising.

Human resources analytics in practice: A knowledge discovery process

AbstractExisting scholarship offers a comprehensive understanding of the concept and purposes of human resource analytics (HRA). However, how HRA is carried out in practice in organisations is still under‐researched. We examine the practice of HRA through a systematic review across three disciplines, namely, human resource management, business analytics and management information systems while using a process lens: the knowledge discovery process (KDD) model. A hundred and three high‐end quality manuscripts were analysed. Our findings show that the scope of HRA is expanding both in the use of HR and business data and certain sophisticated statistical techniques. However, much needs to be done to uncover the measurable impact of HRA on HR and business outcomes.

A Study on Parkinson's Disease Parameters Using Data Mining with Machine Learning Approaches

Parkinson's disease is a long-term condition that demands continuous attention and care. While it can substantially affect an individual's quality of life, with appropriate treatment and support, people diagnosed with Parkinson's can enjoy meaningful lives for numerous years. Research into Parkinson's disease remains active, with ongoing progress in comprehending the condition and discovering innovative treatment options. Data mining, also known as Knowledge Discovery in Database (KDD), is a highly valuable technique employed by entrepreneurs, researchers, and individuals for extracting valuable insights from extensive data collections. The knowledge discovery process encompasses several key steps, including data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge presentation.This paper considers Parkinson’s disease Data Set.The machine learning approaches which is used to analysis and predict the dataset usinglinear regression, multilayer perceptron, SMOreg, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

CAPTO - A method for understanding problem domains for data science projects

Data Science aims to infer knowledge from facts and evidence expressed from data. This occurs through a knowledge discovery process (KDD), which requires an understanding of the application domain. However, in practice, not enough time is spent on understanding this domain, and consequently, the extracted knowledge may not be correct or not relevant. Considering that understanding the problem is an essential step in the KDD process, this work proposes the CAPTO method for understanding domains, based on knowledge management models, and together with the available/acquired tacit and explicit knowledge, proposes a strategy for construction of conceptual models to represent the problem domain. This model will contain the main dimensions (perspectives), aspects and attributes that may be relevant to start a data science project. As a case study, it will be applied in the Type 2 Diabetes domain. Results show the effectiveness of the method. The conceptual model, obtained through the CAPTO method, can be used as an initial step for the conceptual selection of attributes.