Data Mining Approach Research Articles

Revolutionizing Network Intelligence: Innovative Data Mining and Learning Approaches for Knowledge Management in Next-Generation Networks

The Information and Communication (ICT) industry, a global giant among service sectors, is known for both its massive scale and its unforgiving demands. Here, downtime is unacceptable, requiring constant high availability – often at the stringent Sigma Six standard. Redundancy is a common solution, but it comes at a cost. To meet these demands proactively, the ability to predict load and growth becomes crucial. This project aims to develop a prototype, or proof of concept, that utilizes data mining to provide early warnings and growth forecasts for the ICT industry with good accuracy. Big data is key to making discoveries in any data analysis project. Normally, this data comes from real-time system logs. However, for this initial test, I used a dataset called MIT Reality Mining. This dataset is useful because real-world companies, especially in the tech industry (ICT), are often hesitant to share their current information. By using MIT Reality Mining, I could still find trends and potential reasons behind them in the ICT industry. It's important to remember that this is a limited functionality prototype. While it can serve as a guideline for Telcos looking to implement data warehouses, the actual implementation details will need to adapt to the specific needs of each industry.

AI-based data mining approach to control the environmental impact of conventional energy technologies

Environmental pollution remains one of the foremost existential threats to human well-being, despite the concerted efforts and implementation of various programmes aimed at fostering cleaner air. The contemporary global economic and energy landscape, characterised by multifaceted challenges, has undeniably hindered the efficacy of efforts to kerb air pollutant emissions. Solid fuels persist as primary sources of energy production in numerous countries, serving both the residential and industrial sectors. However, combustion of such fuels, particularly within domestic heating units (DHUs), engenders the release of a diverse array of organic compounds characterised by intricate structures and potent mutagenic and environmentally hazardous properties.However, the combustion process, if properly regulated, can be carried out in an environmentally sustainable manner. The intricate interplay of myriad factors that influence the composition and quality of chimney flue gases underscores the complexity inherent in controlling the combustion process. Artificial intelligence (AI) has emerged as a versatile tool with applications that span various domains, including environmental monitoring systems. In this study, we posit the utilisation of artificial neural networks (ANNs) as a sophisticated data mining technique to control the emission of flue gases contingent on the specific boiler and fuel utilised. Feed forward predictive models with back propagation were utilized for AI-based data mining aiming at the prediction of the concentration of flue gas components. The highest coefficients of model fit goodness were obtained for CO2, NOx and SO2 with R2 equal to 0.99, 0.98 and 0.99, respectively. The study demonstrated the feasibility and effectiveness of using AI-based data mining to predict emissions from conventional energy technologies. By leveraging the predictive capabilities of ANNs, it is possible to significantly reduce the environmental impact of solid fuel combustion, contributing to cleaner air and improved public health.

Integration of Price Data in Wind Power Sales Systems Using Association Rule Mining Combined with Deep Learning Algorithms

With the growing attention of the global population towards renewable energy sources such as wind power derived from natural resources such as Solar, Geothermal, and Wind energy, there is a growing need for genuine estimation of wind energy. Wind energy is important in the supply of electricity in the global energy markets because of its enormous importance in the delivery of renewable energy. As such, it is crucial for accurate appreciation of the power of the wind to ably respond to issues that relate to the trading of power while at the same time addressing issues to do with planning, scheduling and strategic positioning of wind power generation. Therefore, the present work aims to develop a new model known as the Association Rule with DL-based Wind Power Generation and Price Prediction (ARDL-WPGPP). It utilizes two datasets: An Energy dataset includes various columns like tie, wind onshore forecast, and price actual, whereas the weather features dataset includes only wind speed. These datasets are combined to create a dataset where each transaction represents 2 hours of wind generation. A data mining approach is employed to uncover hidden patterns, rules, concepts, and correlations within these datasets, operating on various types of data including quantitative, textual, and multimedia formats. To efficiently extract rules from the dataset, an improved Apriori algorithm is introduced. Subsequently, the generated rules incorporating wind speed are passed into an improved LSTM model, which learns by comparing the label data. The price actual value serves as the label data, assigning labels to data points based on their actual price value. High price actual values indicate high wind power prices, while low values indicate low wind power prices.

Natural language processing applications in social network analysis: a data mining approach

Natural language processing applications in social network analysis: a data mining approach

Visualization and Analysis of Oil and Gas Pipeline Corrosion Research: A Bibliometric Data-Mining Approach

Visualization and Analysis of Oil and Gas Pipeline Corrosion Research: A Bibliometric Data-Mining Approach

A Panoramic View on International Higher Education Studies from a Global Perspective

With globalization, international education has become much more accessible to more people around the world. International higher education (IHE) has been among the most studied topics in higher education in recent years. In this context, the present study aims to investigate the studies conducted on IHE through a systematic literature review by analyzing studies published between 1965 and 2021 and employs social network analysis (SNA) and text-mining techniques. All these findings show that global, national, and institutional strategies are effective in the increase of interest in international education and especially in the increase of research. In this connection, this study provides an overview of trends; academic production in IHE has been researched with the data mining and analytics approaches, bibliometric method and the analysis of social networks. Thus, a better understanding and interpretation of the leading themes in the literature of internationalization in higher education is possible to achieve a better understanding of the field of intellectual scientific knowledge in the literature on globalization and IHE.

Developing an intelligent and sustainable model to improve E-learning satisfaction based on the learner’s personality type: data mining approach in high education systems

PurposeAlthough E-learning has been in use for over two decades, running parallel to traditional learning systems, it has gained increased attention due to its vital role in universities in the wake of the COVID-19 pandemic. The primary challenge within E-learning pertains to the maintenance of sustainable effectiveness and the assurance of stakeholders' satisfaction. This research focuses on an intelligence-driven solution to recommend the most effective approach to education policymakers by considering the unique characteristics of all components within the educational system (course type, student and teacher characteristics, and technological features) to achieve a sustainable E-learning system.Design/methodology/approachThrough a systematic literature review and qualitative content analysis, a conceptual model of the critical components influencing E-learning quality and satisfaction has been developed. The proposed model comprises six main dimensions: usage, service quality, learning system quality, content quality, perceived usefulness, and individual characteristics. These dimensions are further divided into 15 components and 114 sub-components. A data mining process encompassing two scenarios has been designed to prioritize the components impacting E-learning success.FindingsIn the first scenario, data mining techniques identified the most influential components based on the features outlined in the conceptual model. According to the results, the components affecting E-learning quality enhancement in the studied case are “usage purpose, system loyalty, technical and supportive system quality, and student characteristics.” The second scenario examines the impact of individuals' personality types and learning styles on E-learning satisfaction across various aspects (Average System Satisfaction, Overall System Satisfaction, Efficiency and Effectiveness, Skill Enhancement, and Personal Enhancement). The findings reveal that, with an accuracy of over 70%, E-learning satisfaction for diplomat and guard learners is influenced by the alignment between “course learning style” and “student-suggested course learning style.” Conversely, for analyzer and researcher types, satisfaction levels are impacted by the “learning style compatible with their personality type.”Originality/valueImplementing a dynamic model founded on data mining enables educational institutions to personalize the E-learning experience for each individual as much as possible. The study’s findings indicate that “achieving higher satisfaction levels in the E-learning process is not necessarily contingent upon providing a learning style congruent with learners' personality types.” Rather, perceived and suggested learning styles exert a more profound influence. Consequently, providing prescriptive principles for higher education institutions seeking to enhance E-learning quality is inadvisable. Instead, adopting a dynamic, knowledge-based process that furnishes recommendations to policymakers for each course—tailored to the specific course type, teaching records, current processes and technology, and student type—is highly recommended.

On being green in the social media interactivity’s information seeking-sharing: Case of Generation Z in Indonesia

Social media interactivity creates consumer’s space of information seeking-sharing where its intensity could produce knowledge, creates new values and changes behavior. The aim of this study is to exploratory investigate the dual role of Generation Z’s information seeking-sharing behavior within green context through the interactive space of social media as a resource for the development of social media marketing strategy. The research employs mixed-method approach of qualitative-explorative data mining, quantitative cross-tabulation Chi-Square test, and integration. Two findings of this research are elaborated. First, consumer’s space of information-seeking leads to the process of green awareness rationalization, i.e., how environment-oriented actions can be rationalized. Second, consumer’s space of information-sharing leads to green social values, i.e., How environment-oriented actions can be socially recognized. The marketing implications of these two findings are business’ efforts to develop green-oriented strategic mindset through space of social media marketing “customer engagement” where the dual role of information seeking-sharing within green context is facilitated.

Using Machine Learning to Explore the Impact of Borderline Hyponatremia on Adverse Outcomes in Heart Failure Patients: A Data Mining Approach

Using Machine Learning to Explore the Impact of Borderline Hyponatremia on Adverse Outcomes in Heart Failure Patients: A Data Mining Approach

A cross-omics data analysis strategy for metabolite-microbe pair identification.

Given the pivotal roles of metabolomics and microbiomics, numerous data mining approaches aim to uncover their intricate connections. However, the complex many-to-many associations between metabolome-microbiome profiles yield numerous statistically significant but biologically unvalidated candidates. To address these challenges, we introduce BiOFI, a strategic framework for identifying metabolome-microbiome correlation pairs (Bi-Omics). BiOFI employs a comprehensive scoring system, incorporating intergroup differences, effects on feature correlation networks, and organism abundance. Meanwhile, it establishes a built-in database of metabolite-microbe-KEGG functional pathway linking relationships. Furthermore, BiOFI can rank related feature pairs by combining importance scores and correlation strength. Validation on a dataset of cesarean-section infants confirms the strategy's validity and interpretability. The BiOFI R package is freely accessible at https://github.com/chentianlu/BiOFI.

An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction.

Stroke poses a significant health threat, affecting millions annually. Early and precise prediction is crucial to providing effective preventive healthcare interventions. This study applied an ensemble machine learning and data mining approach to enhance the effectiveness of stroke prediction. By employing the cross-industry standard process for data mining (CRISP-DM) methodology, various techniques, including random forest, ExtraTrees, XGBoost, artificial neural network (ANN), and genetic algorithm with ANN (GANN) were applied on two benchmark datasets to predict stroke based on several parameters, such as gender, age, various diseases, smoking status, BMI, HighCol, physical activity, hypertension, heart disease, lifestyle, and others. Due to dataset imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied to the datasets. Hyperparameter tuning optimized the models via grid search and randomized search cross-validation. The evaluation metrics included accuracy, precision, recall, F1-score, and area under the curve (AUC). The experimental results show that the ensemble ExtraTrees classifier achieved the highest accuracy (98.24%) and AUC (98.24%). Random forest also performed well, achieving 98.03% in both accuracy and AUC. Comparisons with state-of-the-art stroke prediction methods revealed that the proposed approach demonstrates superior performance, indicating its potential as a promising method for stroke prediction and offering substantial benefits to healthcare.

A Predictive Model for Academic Performance of Students in MAT101 at Federal University Birnin Kebbi

The research addresses the problem of low academic performance of students taking General Mathematics 1 (MAT101) at the Federal University Birnin Kebbi in Nigeria. MAT101 is a foundational course for several science-related disciplines. However, many students fail to understand its concepts, translating to one of the lowest academic performances, leading to high discontinuation of students’ studies in most science and related fields. To this end, the problem is addressed through a predictive model using data science and machine learning techniques to identify students who are academically at risk. It seeks to answer the appropriateness of the selected statistical and data mining approaches, whether or not they are predictor variables, and validating the model developed. The significance of this study lies in its potential to help instructors identify at-risk students and improve overall performance in MAT101. The research uses data collected from academic records and conducts exploratory data analysis to determine the factors influencing students’ performance. Several classifiers such as Decision Tree and Random Forest were analyzed by evaluating their accuracy. In detail, the difference in the accuracy for the Decision Tree algorithm was 52% and for the Random Forest was 67%. Based on achieved indicators, the Random forest algorithm is more effective in this type of predictive modeling. However, the choice of algorithm depends on specific task requirements, considering factors like speed and accuracy.

A Multi-dimensional Data Mining-based Study on the Prescriptions Developed by Professor Xu Zhiyin in Treating Thyroid Nodules.

In this study, we employed a multi-dimensional data mining approach to examine the clinical instances where Professor Xu Zhiyin treated thyroid nodules. Our aim is to understand the patterns of symptoms, underlying causes, and treatment approaches used for thyroid nodules. By doing so, the intention is to distill the essential aspects, compile Professor Xu Zhiyin's clinical insights, and investigate his scholarly perspectives. Professor Xu Zhiyin's clinical diagnoses and treatments spanning from 2009 to 2019 were entered into Microsoft Excel. Subsequently, the collected data was imported into the Medcase V5.2 system to facilitate data mining. Various techniques, such as frequency-based method, association rule analysis, and clustering, including a decentralized system clustering approach, were employed on a set of 346 cases involving patients with thyroid nodules that conformed to the specified criteria. The primary focus was on extracting insights regarding symptoms and the underlying causes from the medical records. By integrating these findings with Professor Xu Zhiyin's clinical expertise, we examined and summarized the outcomes of the data mining process. The fundamental prescriptions were successfully extracted using the techniques for mining across multiple dimensions. Utilizing the scattered grouping of these prescriptions and with reference to the cluster analysis of the frequency-linked system, the fundamental prescriptions proposed by Professor Xu Zhiyin for addressing thyroid nodules encompass the following ingredients: Glycyrrhiza uralensis Fisch, Cortex Moutan, Paeoniae radix rubra, Curcuma longa L., Radix Curcumae, persica seed, Citri Reticulatae Viride Pericarpium, Pinellia ternata, Spica Prunellae, Ostreae concha, Gleditsia sinensis spine, Tuckahoe and Radix Codonopsis. The fundamental prescriptions were acquired using the frequency approach, association rule technique, k-means clustering approach, and systematic clustering approach. The research findings corroborate one another, demonstrating that Professor Xu Zhiyin's approach to distinguishing and treating thyroid nodules is embodied in distinct prescriptions tailored to specific diseases.

Determining consumer demand patterns for production planning using a data mining approach

Determining consumer demand patterns for production planning using a data mining approach

Modeling performance evaluation in badminton sports: a fuzzy logic approach

Spectators and many young students have flocked to badminton matches in recent years. Badminton practice has received a lot of media coverage. The current state of badminton evaluation methods is lacking in reliability. This article's overarching goal is to examine the many applications of fuzzy logic in badminton performance evaluation and improvement. Data on the badminton technique's flexion and extension phases are mapped into the suggested model using a fuzzy inference system (FIS). This study suggests a fuzzy logic-based badminton-specific objective fuzzy inference system (Bmt-FIS) to evaluate team sports. Despite the gravity of the situation, decisions involving performance reviews often use subjective data. These common decision-making problems may be realistically addressed by fuzzy logic models. Fuzzy logic has the potential to be an effective tool in situations where both quantitative and qualitative data interpretation are allowed. To do this, it accounts for the inherent variability in athletic performance by taking into consideration the 'hazy' or 'uncertain' limitations of data. By taking limitations into account, a rule-based approach makes performance evaluation more precise. Here, a fuzzy inference system (FIS) uses the input variables to evaluate the student's performance. While data mining approaches have been studied, the adaptive neural fuzzy method outperforms others because of its exceptional accuracy.This method eloquently and clearly conveys the many levels of integrity and ambiguity. Also, fuzzy logic may be a great tool for evaluating badminton skills. This foundational study connects the dynamic realm of sports with static measures.

Prediction Model of School Drop Out Factors Using Classification Techniques in Selangor

Malaysia has been struggling to sustain the number of students graduating from school, with an increasing number leaving early, which poses significant concerns for the future generation. Several factors contribute to this issue, such as economic constraints, geographical challenges, transportation problems, and sociocultural norms. This paper uses a data mining approach to identify the attributes that lead to school dropouts and to determine the predictive model with the highest accuracy for forecasting dropout rates. The application of data mining approaches has proven effective in predicting students at risk of dropping out in general education. Nevertheless, there is a shortage of data mining-related studies on student attrition in public schools in Malaysia. The study utilized student and school datasets with consent from the respective departments in the Ministry of Education. This data includes information from 2,482 students across various primary schools in Selangor with initially 22 attributes collected from the dataset. After the attributes undergone feature selection process by using InfoGainAttributeEval, there are 12 features left including class attribute Status_DO. The collected data encompasses student demographics, academic performance and socioeconomic background. The experiments for this study used Decision Trees (J48), Naïve Bayes and Random Forest. By using classification techniques that were made available in WEKA, all attributes from the dataset were tested. The results of the analysis shown that Random Forest with the highest accuracy of 79.5729% in term of predicting student drop out hence indicate the reliability of this research as a decision support tool.

Patterns and factors associated with dental service utilization among insured people: a data mining approach

BackgroundInsurance databases contain valuable information related to the use of dental services. This data is instrumental in decision-making processes, enhancing risk assessment, and predicting outcomes. The objective of this study was to identify patterns and factors influencing the utilization of dental services among complementary insured individuals, employing a data mining methodology.MethodsA secondary data analysis was conducted using a dental insurance dataset from Iran in 2022. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was employed as a data mining approach for knowledge extraction from the database. The utilization of dental services was the outcome of interest, and independent variables were chosen based on the available information in the insurance dataset. Dental services were categorized into nine groups: diagnostic, preventive, periodontal, restorative, endodontic, prosthetic, implant, extraction/surgical, and orthodontic procedures. The independent variables included age, gender, family size, insurance history, franchise, insurance limit, and policyholder. A multinomial logistic regression model was utilized to investigate the factors associated with dental care utilization. All analyses were conducted using RapidMiner Version 2020.ResultsThe analysis encompassed a total of 654,418 records, corresponding to 118,268 insured individuals. Predominantly, restorative treatments were the most utilized services, accounting for approximately 38% of all services, followed by diagnostic (18.35%) and endodontic (13.3%) care. Individuals aged between 36 and 60 years had the highest rate of utilization for any dental services. Additionally, families comprising three to four members, individuals with a one-year insurance history, people contracted with a 20% franchise, individuals with a high insurance limit, and insured individuals with a small policyholder, exhibited the highest rate of service usage compared to their counterparts. The regression model revealed that all independent variables were significantly associated with the use of dental services. However, the patterns of association varied among different service categories.ConclusionsRestorative treatments emerged as the most frequently used dental services among insured individuals, followed by diagnostic and endodontic procedures. The pattern of service utilization was influenced by the characteristics of the insured individuals and attributes related to their insurance.

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation.

Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout and adversely affects patient safety and nurse satisfaction. Traditional methods for workload analysis are either administrative measures (such as the nurse-patient ratio) that do not represent actual nursing care or are subjective and limited to snapshots of care (eg, time-motion studies). Observing care and testing workflow changes in real time can be obstructive to clinical care. An examination of EHR interactions using EHR audit logs could provide a scalable, unobtrusive way to quantify the nursing workload, at least to the extent that nursing work is represented in EHR documentation. EHR audit logs are extremely complex; however, simple analytical methods cannot discover complex temporal patterns, requiring use of state-of-the-art temporal data-mining approaches. To effectively use these approaches, it is necessary to structure the raw audit logs into a consistent and scalable logical data model that can be consumed by machine learning (ML) algorithms. We aimed to conceptualize a logical data model for nurse-EHR interactions that would support the future development of temporal ML models based on EHR audit log data. We conducted a preliminary review of EHR audit logs to understand the types of nursing-specific data captured. Using concepts derived from the literature and our previous experience studying temporal patterns in biomedical data, we formulated a logical data model that can describe nurse-EHR interactions, the nurse-intrinsic and situational characteristics that may influence those interactions, and outcomes of relevance to the nursing workload in a scalable and extensible manner. We describe the data structure and concepts from EHR audit log data associated with nursing workload as a logical data model named RNteract. We conceptually demonstrate how using this logical data model could support temporal unsupervised ML and state-of-the-art artificial intelligence (AI) methods for predictive modeling. The RNteract logical data model appears capable of supporting a variety of AI-based systems and should be generalizable to any type of EHR system or health care setting. Quantitatively identifying and analyzing temporal patterns of nurse-EHR interactions is foundational for developing interventions that support the nursing documentation workload and address nurse burnout.

Clustering and Trend Analysis of Priority Commodities in the Archipelago Capital Region (IKN) using a Data Mining Approach

The policy of moving the capital from Jakarta to East Kalimantan planned by the President of the Republic of Indonesia Joko Widodo has caused a lot of polemic among the public. There are quite a few positive and negative comments on social media regarding the policy of moving the capital. The process of moving the capital requires careful preparation. One thing that needs to be considered is food security in IKN. This research provides recommendations for the main food commodities in IKN by applying data mining. We collect food productivity data available on the official website for East Kalimantan province. These data are processed and grouped into two groups, namely horticulture and livestock products using the K-Means method. After grouping, we predict the increase in productivity of each group using the ARIMA method. This research produces output in the form of grouping commodities into horticulture and livestock products. Productivity results for each type of commodity are displayed from 2016 to 2020 based on data on the official East Kalimantan Province website. Based on this data, predictions are made using the ARIMA method to predict productivity results from 2021 to 2025. Commodities with total productivity are grouped into high-priority commodities. Grouping the amount of productivity is carried out using the clustering method by comparing the amount of productivity for each commodity and producing commodities that are low priority, middle priority, priority and top priority based on the highest to lowest productivity numbers. The cluster quality for grouping horticultural commodities is 99.1%, while the cluster quality for grouping livestock commodities is 87.5%. Hasil prediksi terbaik yaitu ketika memprediksi produksi salak dan slaughter cattle dengan model ARIMA (0, 1, 0) dan ARIMA (2, 2, 2).

A Smart IoT Solution for Monitoring and Predicting Grocery Freshness and Quality

According to the statistics,40% of the food produced is wasted before it reaches the consumers. The core problem lies in logistics and infrastructure. Firstly weather varies widely and it makes generalization difficult. This makes it impossible for perishables to naturally survive long-haul transport. Secondly, cold storage facility is not available and affordable to all farmers. Today food spoilage is a crucial problem. The food we consume can affect in any form of contamination that may occur due to storage or chemical changes within the food. This makes it necessary to develop a system that can help people to identify and predict the freshness of food or quality of food items. A smart grocery management system helps users manage groceries in large warehouses and long transportations. Users no longer have to worry about continuous food monitoring. This project aims to ensure the freshness of grocery by predicting the spoilage of grocery in prior and to notify the user about the same so that he can take necessary precautions to prevent grocery spoilage. Continuous analysis of temperature and humidity of the grocery is carried out by using DHT11 Temperature and Humidity sensor to monitor varying parameters of the grocery. ESP8266 Node MCU is used as the main controller of the system which is used to control the sensing action of the sensor. Data collected from the sensor is used to analyse the variations in the values of temperature and humidity when the grocery is fresh and when it is completely spoilt. The humidity and temperature data are sent to the cloud storage by using the Wi-Fi module which is inbuilt to the Node MCU. ThingSpeak cloud platform is set up for the storage purpose which is retrieved back to use it for predicting the spoilage of the grocery which is the main goal of the system. A prediction algorithm is used for this purpose which is basically a combination of machine learning and data mining approach for forecasting or predicting the future values based on some past set of values. The values of fresh grocery has been given as the input to prediction algorithm which is used to predict the future values of temperature and humidity. Predicted values obtained is compared with the spoilt values measured earlier to detect the spoilage of grocery. Predicted status of the grocery has been notified to the user through SMS. Key Words: food, grocery, freshness, prediction