Data Mining Methodology Research Articles

Comparative Analysis of Multicriteria Inventory Classification and Forecasing: A Case Study in PT XYZ

One crucial aspect of supply chain management is inventory management. Inefficient inventory management can lead to various issues, such as product expiration, where a high number of items in the warehouse either have expired or are approaching expiration. This issue is experienced by a distribution SME in Indonesia, PT XYZ. Without such classifications, it becomes challenging to predict demand and manage stock levels efficiently. Therefore, the aim of this study is to classify inventory to identify the most important items to business and make a forecasting model of sales quantity to predict inventory replenishment using machine learning algorithms. To advance our research, we adopted the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For inventory classification, we conducted a hybrid approach that combined TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ABC analysis (A: high-value items, B: medium-value items, and C: low-value items). The data employed in this study comprised secondary data, including purchase orders, sales orders, and stock movement records. The result reveals that 11 of the total 383 items under class A are important items for business. After obtaining labels from the ABC Analysis, we proceed to train models using KNN, SVC, and Random Forest for predicting inventory classification. Notably, the Random Forest model showcased remarkable performance and outperformed the rest of the models, achieving an accuracy of 99.21%. For inventory forecasting ARIMA displays a competitive performance with RMSE value 5.305 and MAE value 3.476, indicating a relatively accurate prediction with lower forecasting errors than two other models

Comparative Study of Forecasting Methods to Predict the Energy Demand for the Market of Colombia

An important challenge for the energy sector worldwide is the selection of the best method for forecasting the energy demand of a country. This research addresses the Colombian electricity market, a developing country where power generation is predominantly hydroelectric. The goal is to compare commonly used and state-of-the-art methods to predict the general short-term demand. The selected methods are the long short-term memory (LSTM) network, the autoregressive integrated moving average (ARIMA) and Facebook Prophet. These methods were used to predict the hourly total electricity demand for Colombia for the next day (24 h). The cross-industry standard process for data mining methodology was partially used, excluding the deployment phase. The hourly time series considers data from 2022 to 2023. Models are evaluated using the root mean square error (RMSE) and the mean absolute percentage error (MAPE). Prophet had the best RMSE with 428.5 MW, followed by ARIMA (441.7 MW) and LSTM (541.3 MW). Prophet and ARIMA achieved the best MAPE (4.1%), outperforming LSTM (4.9%). Results shown that the hourly intraday energy demand of the Colombian market can be predicted accurately for both work and non-work days.

Performance prediction of 304 L stainless steel based on machine learning

With the development of testing technology and data mining methodologies, machine learning (ML) has been widely applied for predicting the performance of materials in various systems including stainless steel with improved performance. To address the limitations of traditional experimental and statistical methods in predicting the thermal deformation of materials, a predictive machine learning model was developed based on the data obtained from thermal simulation compression tests. Specifically, the Arrhenius and the Modified Johnson-Cook (J-C) Constitutive Model were developed utilizing experimental data to initially predict the rheological behavior of 1.52% Cu-304L stainless steel. Then, a physical metallurgy (PM)-guided Back Propagation (BP) model was developed, and Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Mind Evolutionary Algorithm (MEA) were incorporated into the model for optimization purposes, respectively. Finally, the results indicate that the PM-MEA-BP model exhibits superior predictive performance, accurately forecasting the texture evolution of 304L stainless steel with random crystal orientation under rolling deformation conditions. Additionally, the present work also clearly demonstrated that the model not only satisfies precision requirement but also has certain guiding significance for optimizing process parameters and forecasting the microstructure and properties of stainless steel.

A Case Study on the Data Mining-Based Prediction of Students’ Performance for Effective and Sustainable E-Learning

The study explores the application of data analytics and machine learning to forecast academic outcomes, with the aim of ensuring effective and sustainable e-learning. Technological study programs in universities often experience high dropout rates, which makes it essential to analyze and predict potential risks to reduce dropout percentages. Student performance prediction (SPP) offers potential benefits, including personalized learning and early interventions. However, challenges such as (1) data quality and availability and (2) incomplete and inconsistent data complicate this process. Moreover, to support the fourth Sustainable Development Goal (SDG), we focus on the quality of education. A case study approach is used using data mining techniques, particularly classification, regression, and clustering, to predict student performance. The case presented aims to predict risks and ensure academic success and quality. The cross-industry standard process for data mining (CRISP-DM) methodology is used to structure and guide the prediction process. The study shows that using data from student learning processes within an academic success prediction model and data mining can identify at-risk students.

Public attitudes toward higher education using sentiment analysis and topic modeling

This study examines higher education through data-mining methodologies, aiming to uncover key themes and sentiments in global discourse. Utilizing sentiment analysis and topic modeling, the research analyzes 157,943 tweets from 84,423 unique users over a five-month period (January to May 2023). This period was selected, coinciding with the rise of artificial intelligence (AI) tools, particularly ChatGPT. The study investigates the discussions, emotional tones, and dominant topics shaping the global narrative of higher education within X (Twitter) data. Key findings include the geographical distribution of tweets and the most frequent positive and negative perceptions. It also addresses critical issues such as affordability, accessibility, and funding in higher education. Furthermore, the data shows public reactions to AI in higher education are initially negative, while higher education tweets are primarily characterized by positivity and optimism. The higher education tweets are mainly posted on the weekend, with decreased activity during weekdays. This research provides insights into the evolving higher education landscape amid rapid technological advancements.

Analyzing Daytime/Nighttime Pedestrian Crash Patterns in Michigan Using Unsupervised Machine Learning Techniques and their Potential as a Decision-Making Tool

Background Data mining applications are becoming increasingly common across various fields. Numerous data mining methodologies have been utilized in pedestrian crash data analysis. However, association and correspondence analyses have yet to be extensively employed in pedestrian safety literature to support decision-making. Material and Methods Road lighting significantly affects pedestrian crashes, highlighting the importance of examining pedestrian crash patterns during daytime and nighttime. This study analyzed pedestrian fatal and injury crashes in Michigan from 2011 to 2021 under varying road lighting conditions. The study identified pedestrian crash patterns using unsupervised machine-learning techniques based on several multidimensional factors. The Association Rules Learning (ARL) technique was used to determine general associations of patterns leading to pedestrian crashes. At the same time, Multiple Correspondence Analysis (MCA) helped understand crash attribute patterns in two notable scenarios: elderly pedestrian crashes during daytime lighting and high-speed midblock crashes during nighttime. These methods revealed key patterns associated with contributing factors to pedestrian crashes in different lighting conditions. Results In the analysis of cloud combinations involving elderly pedestrians during daylight, it was found that: 1) elderly pedestrians were significantly involved in severe injury crashes at two-lane midblock locations with a speed limit between 45 and 65 mph; 2) improper actions by elderly drivers were significantly associated with elderly pedestrians on wide roads (>5 lanes); and 3) the complex interaction between city streets with 3-5 lanes was significantly correlated with drivers 'failed to yield' actions. Additionally, in the analysis of cloud combinations involving nighttime pedestrian crashes at high-speed midblock locations, it was found that 1) a specific age group of alcohol- or drug-involved pedestrians (19-30) was significantly associated with young drivers and improper driver actions; 2) during the winter season, young pedestrians were significantly involved in moderate injury crashes at undivided midblock locations; and 3) elderly drivers and 'failed to yield' actions were highly correlated with 3-5 lane roadways. Discussion The research findings are expected to raise awareness of Michigan pedestrian crash patterns under daytime and nighttime lighting conditions. They will also recommend safety countermeasures for practitioners to enhance pedestrian safety in walkable cities. The proposed methodologies can uncover relationships that need to be well-documented in the existing literature on pedestrian safety. Additionally, these methods can identify valuable relationships without restricting variables to being either dependent or independent, and they can reveal relationships that might be challenging to detect otherwise. The results will also provide decision rules and visualizations that are easy to understand. Conclusion The results showed that the proposed techniques can analyze pedestrian crash data in a way that aligns with understanding pedestrian crash characteristics. Traffic safety administrators could benefit from this methodology as a decision-support tool.

Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data

Accurate traffic prediction is crucial for urban planning, especially in rapidly growing cities. Traditional models often struggle to account for sudden traffic pattern changes, such as those caused by the COVID-19 pandemic. Neural networks offer a powerful solution, capturing complex, non-linear relationships in traffic data for more precise prediction. This study aims to create a neural network model for predicting vehicle numbers at main intersections in the city. The model is created using real data from the sensors placed across the city of Zilina, Slovakia. By integrating pandemic-related variables, the model assesses the COVID-19 impact on traffic flow. The model was developed using neural networks, following the data-mining methodology CRISP-DM. Before the modelling, the data underwent thorough preparation, emphasising correcting sensor errors caused by communication failures. The model demonstrated high prediction accuracy, with correlations between predicted and actual values ranging from 0.70 to 0.95 for individual sensors and vehicle types. The results highlighted a significant pandemic impact on urban mobility. The model’s adaptability allows for easy retraining for different conditions or cities, making it a robust, adaptable tool for future urban planning and traffic management. It offers valuable insights into pandemic-induced traffic changes and can enhance post-pandemic urban mobility analysis.

Effective Twitter Sentiment Analysis System with Ensemble Classifiers and Feature Selection

One of the most intriguing areas of research these days is sentiment analysis from Twitter. In order to create such systems, it blends data mining methodologies with natural language processing techniques. We presented an effective Twitter sentiment analysis method in this study. A machine learning model was developed using the suggested approach to identify both good and negative tweets. During the training phase, our model employed various methods to represent the input labelled tweets using various feature sets. For more accurate results, the classifier ensemble is shown various basis classifiers throughout the classification phase. The suggested technique may be used to gauge users' opinions based on their tweets, which is highly beneficial for a variety of uses, including product reviews, political polarity identification, and marketing.

Computer vision solution for uniform adherence in gastronomy schools: An artificial intelligence case study

This research study presents an innovative application of computer vision technology in culinary education to ensure consistent student uniform adherence, crucial to accomplishing hygiene, safety, and professionalism standards. The proposed approach utilizes the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to generate a computer vision application prototype to identify specific culinary uniforms components, such as chef's jackets, aprons, hats, and pants. The development process using LandingLens, a code-free platform, involves several stages: business and data understanding, preparation, modeling, evaluation, and deployment. The final model was training with 77 images, and the application deployment was tested using 38 images. Results demonstrate the potential of artificial intelligence to enhance operational efficiency and uphold professional standards in culinary education. Integrating computer vision addresses the challenges associated with manual monitoring and opens opportunities for broader adoption of technology in culinary pedagogy and training.

Using Machine Learning Models to Detect the Increasing Threats of Financial Fraud in the Cyberspace

In the dynamic landscape of the financial sector, the escalating menace of financial fraud presents pervasive implications for businesses and consumers alike. Particularly, detecting credit card fraud in real- time transactions has become a pivotal concern within the financial industry. This abstract delves into the critical role of data mining in addressing the complexities of credit card fraud detection, shedding light on the multifaceted challenges that confront this domain. The realm of financial business is increasingly besieged by the spectre of financial fraud, necessitating robust measures to combat its detrimental effects. As the sophistication and prevalence of fraudulent activities continue to evolve, the imperative of deploying effective strategies for fraud detection becomes more pronounced. Applying data mining techniques in this context is paramount in identifying and mitigating credit card fraud. Leveraging advanced data mining methodologies is essential for scrutinising live transactions and discerning anomalous patterns indicative of fraudulent behaviour. Credit card fraud detection poses formidable challenges, primarily attributable to two compelling factors. Firstly, the inherent dynamism of normal and fraudulent behavioural profiles engenders a perpetual need for adaptive and responsive detection mechanisms. Secondly, the highly imbalanced nature of credit card fraud data sets further complicates accurately identifying fraudulent activities, necessitating nuanced approaches to discern anomalies amidst voluminous transactional data effectively. In light of the foregoing, this abstract underscore the criticality of data mining in addressing the intricate landscape of credit card fraud detection, emphasising the need for agile and sophisticated methodologies to navigate the evolving nature of fraudulent behaviours and the skewed distribution of fraud-related data sets. By comprehensively elucidating these challenges, this abstract provides a foundational understanding of the nuanced complexities inherent in combatting financial fraud through the lens of data mining.

Data-Mining Methodology to Improve the Scientific Production Quality in Turkey Meat and Carcass Characterization Studies.

The present research aims to describe how turkey meat and carcass quality traits define the interest of the scientific community through the quality standards of journals in which studies are published. To this end, an analysis of 92 research documents addressing the study of turkey carcass and meat quality over the last 57 years was performed. Meat and carcass quality attributes were dependent variables and included traits related to carcass dressing, muscle fiber, pH, colorimetry, water-holding capacity, texture, and chemical composition. The independent variables comprised publication quality traits, including journal indexation, database, journal impact factor (JIF), quartile, publication area, and JIF percentage. For each dependent variable, a data-mining chi-squared automatic interaction detection (CHAID) decision tree was developed. Carcass or piece yield was the only variable that did not show an impact on the publication quality. Moreover, color and pH measurements taken at 72 h postmortem showed a negative impact on publication interest. On the other hand, variables including water-retaining attributes, colorimetry, pH, chemical composition, and shear force traits stood out among the quality-enhancing variables due to their low inclusion in papers, while high standards improved power.

A Survey on Malware Detection and Analysis

Malware, or malicious software, poses a significant threat to the security and functionality of computer systems globally. This survey provides a comprehensive analysis of current malware detection and analysis methods, focusing on data mining methodologies. The study categorizes malware detection techniques into signature-based and behaviour-based approaches, highlighting their respective strengths and weaknesses. It explores heuristic techniques enhanced by artificial intelligence, including neural networks and genetic algorithms, to improve detection accuracy. The literature review examines host-based and network-based intrusion detection systems, hybrid systems, and virtual machine introspection. The paper also discusses static and dynamic analysis methods, emphasizing the importance of analysing malware in controlled environments. Through detailed examination, this survey aims to present a thorough understanding of contemporary malware detection strategies and their applications, offering insights for future advancements in the field.

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 Review on Utilizing Data Mining Techniques for Chronic Kidney Disease Detection

This comprehensive study delves into the application of machine learning (ML) and data mining techniques for the prognosis and diagnosis of Chronic Kidney Disease (CKD), a significant global health concern characterized by the gradual loss of kidney function. Through a detailed examination of various predictive models, the research evaluates the efficacy of different ML algorithms and data mining methodologies in classifying and diagnosing CKD. Utilizing datasets from the UCI machine learning repository and other sources, this study explores a range of ML algorithms-including logistic regression, decision trees, support vector machines, random forest, and deep learning networks-alongside feature selection techniques to enhance prediction accuracy and facilitate early diagnosis. Despite facing challenges such as dataset limitations and the need for external validation, the findings reveal remarkable potential in using ML and data mining to improve CKD diagnosis, with some models achieving accuracy rates exceeding 99%. The research underscores the critical role of technology in advancing CKD diagnosis and management, paving the way for more personalized and effective healthcare solutions.

Gated Recurrent Unit (GRU) for Forecasting Hourly Energy Fluctuations

In the current digital era, energy use undeniably supports economic growth, increases social welfare, and encourages technological progress. Energy-related information is often presented in complex time series data, such as energy consumption data per hour or in seasonal patterns. Deep learning models are used to analyze the data. The right choice of normalization method has great potential to improve the performance of deep learning models significantly. Deep learning models generally use several normalization methods, including min-max and z-score. In this research, the deep learning model chosen is Gated Recurrent Unit (GRU) because the computational load on GRU is lighter, so it doesn't require too much memory. In addition, the GRU data is easier to train, so that it can save training time. This research phase adopts the CRISP-DM methodology in data mining as a solution commonly used in business and research. This methodology involves six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. In this research, the model was obtained using five attribute selection, which applied 2 normalization methods: min-max and z-score. With this normalization, the GRU model produces the best MAPE of 3.9331%, RMSE of 0.9022, and R2 of 0.9022. However, when using z-score normalization, the model performance decreases with MAPE of 10.4332%, RMSE of 0.7602, and R2 of 0.4213. Overall, min-max normalization provides better performance in multivariate time series data analysis.

Analysis of the Spread and Evolution of COVID-19 Mutations in Ecuador Using Open Data.

Currently, the analyses of and prediction using COVID-19-related data extracted from patient information repositories compiled by hospitals and health organizations are of paramount importance. These efforts significantly contribute to vaccine development and the formulation of contingency techniques, providing essential tools to prevent resurgence and to effectively manage the spread of the disease. In this context, the present research focuses on analyzing the biological information of the SARS-CoV-2 viral gene sequences and the clinical data of COVID-19-affected patients using publicly accessible data from Ecuador. This involves considering variables such as age, gender, and geographical location to understand the evolution of mutations and their distributions across Ecuadorian provinces. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology is applied for data analysis. Various data preprocessing and statistical analysis techniques are employed, including Pearson correlation, the chi-square test, and analysis of variance (ANOVA). Statistical diagrams and charts are used to facilitate a better visualization of the results. The results illuminate the genetic diversity of the virus and its correlation with clinical variables, offering a comprehensive understanding of the dynamics of COVID-19 spread in Ecuador. Critical variables influencing population vulnerability are highlighted, and the findings underscore the significance of mutation monitoring and indicate a need for global expansion of the research area.

Machine Learning-Based Cardiovascular Disease Detection and Intelligent Healthcare Workflow Automation

Cardiovascular diseases (CVDs) are a major cause of death worldwide, emphasizing the importance of better early detection methods. This study introduces a machine learning model to detect CVDs by analyzing vital sign information. The model, which was trained using a cardiovascular dataset, utilizes three algorithms: Support Vector Machine (SVM), Naïve Bayes, and Decision Tree. It assesses the health status of patients by predicting vital sign values, allowing healthcare providers to receive timely alerts. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, a proof-of-concept web application was created with object-oriented design principles and implemented using Python. This application predicts the likelihood of CVD and streamlines the scheduling of doctor appointments, promoting quick medical intervention. Our findings reveal that the Decision Tree classifier performed the best in accurately identifying patients who are at risk based on abnormalities in vital signs. This method can potentially enhance early detection of CVDs and improve the timing of medical care.

A Review on Classification Algorithm for Customer Churn Classification

Any sector faces a huge obstacle when it comes to retaining existing customers. The percentage of consumers who have quit using a product or service is referred to as customer churn, and it is a vital indication that offers reliable information about this percentage. When it comes to achieving long-term success in a market or industry, one of the most significant challenges that any company must face is the ability to keep their precious clients and to fulfill their needs. A review of the most significant studies on Customer Churn Prediction is presented in this paper so as to furnish the reader with an overview of frequently employed data mining methodologies and their respective performances. We provide the available statistics in addition to customer information in order to approximate customer attrition. The time period encompassing the survey extends from 2003 to 2023. During the process of Customer Churn Prediction, we identified the issues and difficulties that were linked with it and offered guidance and potential remedies.

Providing a hybrid approach to increase the accuracy of intrusion detection systems in computer networks

Intrusion detection is a critical obstacle in the realm of security and data mining methodologies. Consequently, researchers have extensively investigated the quest for the swiftest and most precise means of identifying intrusions. Essentially, intrusion detection systems are tasked with recognizing any unauthorized activities, misuse, or harm inflicted upon a system, be it by internal or external users. Recently, in order to design intrusion detection systems, artificial intelligence and machine learning methods have been used, each of which has its own characteristics and advantages. Accordingly, this article focuses on using machine learning to improve the accuracy of the intrusion detection process. In fact, by using machine learning, trends, and patterns can be easily identified and thus used in a network environment to detect intrusion. It can be very useful. For this purpose, we utilize Radial Basis Function (RBF) neural networks and support vector machine (SVM) algorithm to improve decision-making and intrusion detection. By employing RBF neural networks, important features of the data are extracted, which in turnPlease check if the author details and affiliations are presented correctly. Kindly amend if necessary. enhance the overall performance of the solution and the efficiency of the SVM algorithm. This is because feature reduction ultimately leads to improved effectiveness of the SVM algorithm. In methods lacking this capability, the learning algorithm is compelled to utilize features that have no specific correlation with intrusion and essentially do not contribute to identifying attacks. Such learning approaches essentially learn from noisy data, which negatively impacts the intrusion detection solution. Finally, the proposed solution was evaluated using Python programming language and KDD99 data set. The results of the evaluation indicate that the proposed solution has a higher accuracy and precision than other evaluated solutions. So, the accuracy is 97, and the precision is over 99%.

CONVOLUTIONAL NEURAL NETWORKS: AN APPROACH FOR VISUAL OBSTRUCTION DETECTION IN AUTOMOTIVE REVERSING CAMERAS

In recent years, the study of Artificial Intelligence in the automotive industry has led to the design of intelligent systems applied to road safety, highlighting the importance of improving road safety worldwide, and thus reducing the number of accidents annually. One of the main functions of these systems is, for example, pedestrian detection, which is performed by cameras and radar-type sensors, among others. However, environmental factors cause visibility problems and obstructions that make pedestrian detection difficult and lead to collisions. With the purpose of contributing to the solution of the exposed problem, two case studies using Convolutional Neural Networks are applied in this research. The first using a pre-trained model (Inception V3) and the second, a proposed model (RvlsNet) to detect dirt on the lens of a vehicle's reverse camera. These types of factors directly affect visibility, which leads to an increased risk of collision when reversing the vehicle. Applying a general data mining methodology, we obtained a result of 0.9549 and 0.9416 accuracy, respectively, for the models used. Keywords: Convolutional Neural Networks, Classification, Obstruction, Detection, Reversing camera, Inception V3