Data Mining Approach Research Articles

Prediction of fracture initiation in cohesive soils based on data mining modelling and large-scale laboratory verification

Many empirical and analytical methods have been proposed to predict fracturing pressure in cohesive soils. Most of them take into account three to four specific influencing factors and rely on the assumption of a failure mode. In this study, a novel data-mining approach based on the XGBoost algorithm is investigated for predicting fracture initiation in cohesive soils. This has the advantage of handling multiple influencing factors simultaneously, without pre-determining a failure mode. A dataset of 416 samples consisting of 14 distinct features was herein collected from past studies, and used for developing a regressor and a classifier model for fracturing pressure prediction and failure mode classification respectively. The results show that the intrinsic characteristics of the soil govern the failure mode while the fracturing pressure is more sensitive to the stress state. The XGBoost-based model was also tested against conventional approaches, as well as a similar machine learning algorithm namely random forest model. Additionally, several large-scale triaxial fracturing tests and an in-situ case study were carried out to further verify the generalization ability and applicability of the proposed data mining approach, and the results indicate a superior performance of the XGBoost model.

Mapping the dynamics of intensive forage acreage during 2008–2022 in Google Earth Engine using time series Landsat images and a phenology-based algorithm

The increasing demand for livestock feed in China has led to a remarkable increase in forage acreage, particularly in forage fields under intensive agricultural management. However, there are no maps currently available to demonstrate and document the spatial and temporal patterns of the continuous expansion of intensive forage. Here we proposed a pixel- and phenology-based approach to map intensive forage by utilizing time series Landsat images and the Google Earth Engine platform. Given the challenge of accurately identifying intensive forage fields in diverse regions using fixed multi-temporal Landsat data, along with the distinct phenological characteristics of intensive forage fields that undergo multiple annual harvests, we proposed an innovative data mining approach based on dynamic monthly composite images (DMCI). This approach aims to enhance the algorithm’s efficiency and precision in identifying intensive forage. As a pilot study, we analyzed all available Landsat images (1732 scenes) from 2008 to 2022 in a county located in the farming-pastoral ecotone of northern China and tracked the historical dynamics of intensive forage expansion over five epochs at three-year intervals. The accuracy assessment of the intensive forage maps for the five epochs using validation sample points showed that the overall accuracy and Mathews correlation coefficient ranged from 97.1 % to 98.9 % and 0.93 to 0.98 respectively. The intensive forage acreage in the study area exhibited a dramatic expansion from 2008 to 2022, particularly after the 2012s. This study demonstrates the potential of our DMCI- and phenology-based algorithm and time series Landsat images for tracking the dynamics of intensive forage acreage at 30-m resolution in temperate single-cropping regions.

Communication network security situation analysis based on time series data mining technology

AbstractCommunication network security situation analysis prevents unauthorized users from accessing and stealing sensitive information. Network security analysis aims to monitor, discover, and eradicate security flaws by carefully examining the network’s architecture, data, and traffic to ensure safety. In time series data mining analysis by cyber terrorism, specialists must pay attention to cyber security, which involves identifying the elements contributing to long-term trends or systemic patterns via pattern-matching algorithms and other types of inferential processing on large datasets. The challenging characteristics of communication network security situation analysis are data loss, security breaches, hacking, and viruses. Hence, in this research, attention mechanism-based convolutional neural network-enabled practical byzantine fault tolerant (AMBCNN-PBFT) has been designed to improve communication network security situation analysis in time series data mining. AMBCNN-PBFT helps to increase communication network security usage and support the expansion during the evaluation system by optimizing the time series data mining. AMBCNN-PBFT effectively predicts the rise in the communication network, associated with faster times series benefits data mining approach. The study concludes that the AMBCNN-PBFT efficiently indicates and validates the communication network security in time series data mining during the evaluation system. The experimental analysis of AMBCNN-PBFT outperforms the data mining time series in terms of accuracy, efficiency, performance, and prediction.

Text Classification on Customer Feedback: A Systematic Literatures Review

User feedback in text classification serves multiple purposes, including refining models, enhancing datasets, adapting to user preferences, identifying emerging topics, and evaluating system performance, all of which contribute to the creation of more effective and user-centric classification systems. Many text classification techniques, including data mining, machine learning, and deep learning approaches, have been employed in previous literature, each making significant contributions to the field. This paper aims to contribute by guiding researchers seeking commonly used classification techniques and evaluation metrics in text processing. Additionally, it identifies the classification technique that generates higher accuracy and works as a basis for researchers to synthesize studies within their respective fields. PRISMA methodology is adapted to systematically review 28 current literatures on text classification on user feedback. The results obtained are guided by four research questions; paper distribution year, dataset source and size; evaluation metric and model accuracy. The review has shown that support vector machines (SVM) are frequently employed and consistently achieve high levels of accuracy as high as 97.17% with various datasets used. The future direction of this work could explore models that integrate sentiment analysis and natural language understanding to more accurately capture nuanced user opinions and preferences.

ENHANCED NETWORK SECURITY: A DATA MINING APPROACH TO INTRUSION DETECTION

In the ever-evolving landscape of network security, the role of Intrusion Detection Systems (IDSs) is critical. These systems serve as the guardians of digital networks, defending against a relentless tide of cyber threats. However, the efficacy of IDSs in accurately detecting and preventing these threats remains a critical concern. The emergence of new attack vectors and the growing sophistication of cyberattacks underscore the need for innovative approaches to intrusion detection. Nonetheless, there is an information vacuum about the efficacy of different data mining approaches in intrusion detection. This study investigates the deployment of seven data mining models, which include Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, and two types of Recurrent Neural Networks (RNN) on five (5) IDS benchmark datasets. Based on the undertaken experiments, it is evident that the RNN is the best classifier as it obtained 100% accuracy for all datasets. Due to its strength in modelling time-dependent and sequential data problems, RNN has become powerful in predicting future attacks. As our digital world evolves, so must our cyber defences; hence, this study strives to equip network security professionals with the knowledge and tools needed to fortify their networks, ensuring resilience against the ever-adaptive landscape of cyber threats.

Comprehensive safety profile of dipeptidyl peptidase-4 inhibitors: a post-marketing study based on FAERS database using signal detection algorithms

ABSTRACT Background Dipeptidyl peptidase-4 inhibitors (DPP-4 inhibitors) have acquired a foothold in managing type 2 diabetes mellitus, but few concerns have arisen regarding their overall safety profile. The aim of this study is to assess the potential risk of DPP-4 inhibitors by analyzing data from the FDA Adverse Event Reporting System (FAERS) database. Research design and methods This is a retrospective study which explored the FAERS database till March 2023 for the collection of safety reports. The disproportionality analysis was performed using signal detection algorithms (SDAs) incorporating frequentist-based data mining approach such as relative reporting ratio (RRR), reporting odds ratio (ROR) and proportional reporting ratio (PRR) with 95% confidence interval (CI). Results A total of 14,573 adverse event reports were reported in the FAERS public dashboard associated with all the included DPP-4 inhibitors. The computed PRR, ROR, and RRR indicated positive signals for DPP-4 inhibitors with cardiac failure, pancreatitis, pemphigoid, hypoglycemia, acute kidney injury and lactic acidosis. Saxagliptin showed a higher signal score for cardiac failure, while sitagliptin was more associated with pancreatitis. Moreover, alogliptin exhibited an elevated signal score associated with pancreatic carcinoma. Conclusion Several significant disproportionality signals were observed with DPP-4 inhibitors. However, clinicians have to consider the comorbidities and concomitant drugs while prescribing these drugs.

Voice as an Indicator for Laryngeal Disorders Using Data Mining Approach

Introduction: Laryngeal disorders are a common health problem that affects people of all ages, genders, and races. One of the main symptoms of laryngeal disorders is changes in the voice, which can be used as an indicator for the presence of such disorders. In this paper, we present a data mining approach for using voice as an indicator for laryngeal disorders.Material and Methods: We collected a dataset of voice recordings from individuals with and without laryngeal disorders including 434 people from two clinical centers in Tehran. The dataset was created using a powerful signal processing program and then based on the difference between male and female voice, the dataset was separated into two datasets. Finally, a Deep Neural Network was implemented for modelling using Python programming language and F1-score, Accuracy, Sensitivity, Specificity, and AUC as the model’s evaluation metrics were reported.Results: Among all the acoustic features, 23 features were selected for the male dataset and 25 features for the female data set. For the male dataset the final model achieved F1-Score of 0.915 and Accuracy of 0.910. For the female dataset the result was 0.884 of F1-Score and 0.896 of Accuracy.Conclusion: Our results show that machine learning algorithms can accurately classify voice recordings into two groups: individuals with laryngeal disorders and those without. The high accuracy achieved by the algorithms suggests that voice can be used as an objective and automated diagnostic tool for laryngeal disorders.

Investigating Employee Satisfaction and Corporate Performance: Mining From Employer Branding

This study investigates the relationship between employee satisfaction and corporate performance using data mining techniques on anonymous employee feedback. The study tries to determine how employee satisfaction affects company performance. Textual analysis of employee reviews was carried out using a data mining approach in order to find relationships between satisfaction and performance. The investigation highlighted major employee satisfaction elements such as salary and benefits, work-life balance, workload, career development, and management, all of which have a significant impact on business success. Interestingly, work-life balance and workload were found to be negatively correlated with performance. These findings offer useful insights for employer branding efforts, underlining the need of addressing certain components of employee happiness in order to improve overall corporate performance.

Uric acid is associated with type 2 diabetes: data mining approaches.

Several blood biomarkers have been related to the risk of type 2 diabetes mellitus (T2D); however, their predictive value has seldom been assessed using data mining algorithms. This cohort study was conducted on 9704 participants recruited from the Mashhad Stroke and Heart Atherosclerotic disorders (MASHAD) study from 2010 to 2020. Individuals who were not between the ages of 35 and 65 were excluded. Serum levels of biochemical factors such as creatinine (Cr), high-sensitivity C reactive protein (hs-CRP), Uric acid, alanine aminotransferase (ALT), aspartate aminotransferase (AST), direct and total bilirubin (BIL.D, BIL.T), lipid profile, besides body mass index (BMI), waist circumference (WC), blood pressure, and age were evaluated through Logistic Regression (LR) and Decision Tree (DT) methods to develop a predicting model for T2D. The comparison between diabetic and non-diabetic participants represented higher levels of triglyceride (TG), LDL, cholesterol, ALT, BIL.D, and Uric acid in diabetic cases (p-value < 0.05). The LR model indicated a significant association between TG, Uric acid, and hs-CRP, besides age, sex, WC, and blood pressure, hypertension and dyslipidemia history with T2D development. DT algorithm demonstrated dyslipidemia history as the most determining factor in T2D prediction, followed by age, hypertension history, Uric acid, and TG. There was a significant association between hypertension and dyslipidemia history, TG, Uric acid, and hs-CRP with T2D development, along with age, WC, and blood pressure through the LR and DT methods.

Identifying disputants’ attitudinal variations in family mediations: A data mining approach

This article combines linguistic analysis and data mining methods to explore variations in speakers’ evaluative meaning-making in conflict talks. It focuses on conflict style construction through evaluative language, specifically how disputants advance attitudes. The corpus consists of 230 minutes of family mediation talks involving 12 divorcing spouses. The research draws from the Appraisal framework to analyse evaluative meaning-making at a discourse semantics level, capturing both explicit and implicit attitudes, as well as the scaling and dialogic framing of attitudes. Data exploration uses clustering algorithms via RStudio to identify variations in disputants’ discursive behaviour. The findings uncover three conflict styles based on disputants’ preference for attitude advancement formulations, with varying degrees of assertiveness and forcefulness. This study’s contributions include a holistic treatment of evaluative meaning-making, the marriage of digital tools to nuanced linguistic annotation, and a novel interpretation for conflict style. The findings offer fresh insights into disputants’ discursive self-presentation in confrontational exchanges.

Heart Attack Prediction using Machine Learning Algorithms

One of the most crucial parts of the human body is the heart. When the heart's blood supply is cut off, a heart attack happens. The most frequent cause of a blockage is the buildup of fats, cholesterol, and other substances inside the coronary arteries that provide blood to the heart (which eventually results in plaque growth). This study sought to identify which anthropometric characteristics had a high likelihood of having an impact on a person having a heart attack in order to design a program for heart attack analysis using machine learning algorithms. The researchers were able to acquire data from a variety of sources to identify the variables that may be used to create the output in order to fulfill the study's objectives. The existing heart attack analysis was reviewed by the researchers. They discovered that the researchers' algorithms were hardly noteworthy. Another ongoing study uses a variety of data mining and machine learning approaches to analyze vast volumes of patient data in an effort to predict heart attacks before they happen, ultimately helping professionals and the healthcare industry. The software for Heart Attack Prediction was created by taking into account various data from previous studies.

Comparing data mining methods for predicting cost construction projects: A case study of cost management datasets from Thailand

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

A novel linear time clustering using heuristically improved mrk-medoids based on modified squirrel search algorithm

ABSTRACT The rapid development of different techniques and the data are accumulated with distinctive properties with high dimensions and huge size. The most essential approach in data mining is clustering, which groups a set of data into clusters. The training of high-dimensional data and huge volume data in clustering models exhibits low computational efficiency and high computational cost. These clustering methods clarify the inherent properties and discover new information from the data. This proposal plans to design and develop the novel Heuristic-mrk-medoids (H-mrk-medoids) clustering for handling the linear time clustering of big data. The aggregation of data into chunks and optimisation of the centroid is done in the map phase, and clustering is performed by optimising the weighted centroid in the reduce phase. As a main contribution of the paper, the mrk-medoids are optimally tuned by the Modified Squirrel Search Algorithm (M-SSA), which ensures efficient clustering based on the fitness quality measure. The accuracy rate of the designed method is 99% and also the RMSE rate of the offered approach is 0.393095%. The result of the proposed approach has shown major improvements in the efficiency of clustering when compared to conventional linear time clustering models.

Abstract LB009: Unveiling the significance of DARPP-32 and its partners, including GRB7 and DKK1, in breast cancer: Integrative insights from patient data and cell line transcriptomics

Abstract DARPP-32 plays a crucial role in regulating both protein-phosphatase-1 (PP-1) and protein kinase A (PKA), its function being reliant on its phosphorylation status. We have previously demonstrated that low DARPP-32 expression is associated with adverse survival of breast cancer patients, particularly those with oestrogen receptor (ER) positive disease; however, there is limited information on how DARPP-32 may alter breast cancer cell behaviour. We have recently published an integrative approach using patient data and cell line transcriptomics to understand the role of DARPP-32 in breast cancer. We analysed the transcriptome of T47D ER positive breast cancer cells following DARPP-32 knockdown, including treatments in combination with 17β-estradiol or PKA inhibitor fragment (6-22) amide. 202 differentially expressed transcripts were identified in DARPP-32 knock down cells and treatment of these cells with 17β-estradiol or PKA inhibitor fragment (6-22) amide led to the differential expression of 193 and 181 transcripts respectively. Differentially expressed transcripts were compared with genes identified as associated with DARPP-32 expression in 1980 breast cancer patients within the METABRIC cohort using an Artificial Neural Network approach (ANN). Several genes overlapped with those identified from cell line transcriptomics, including PTK7, TRAF5, and KLK6. Both DKK1 and GRB7 were identified as genes associated with DARPP-32 expression in patients using ANN, therefore protein expression was determined in a large cohort of early-stage breast cancer patients (n&amp;gt;1000). We demonstrated that DKK1 and GRB7 protein expression was significantly correlated with DARPP-32 Threonine-34 phosphorylation levels within the same samples (all P&amp;lt;0.05). We have now applied an ANN-based integrative data mining approach to identify genes with the largest effect on other genes resulting in an interactome map, which was based on genes identified within the cell line transcriptomics and original ANN. 1878 genes were identified for inclusion within an interactome map in the total patient cohort, whereas 1003 were identified in ER negative disease alone and 2144 in ER positive disease alone. Pathway enrichment analysis identified the ubiquitin proteosome pathway, the insulin pathway-protein kinase B signalling cascade and the RAS pathway. This study underscores the significance of DARPP-32, a central molecular switch, in breast cancer, elucidating its impact on patient prognosis and highlighting potential molecular targets for therapeutic intervention. The integrative approach combining patient data with cell line transcriptomics provides insights into the complex mechanisms underlying breast cancer progression and opens avenues for further research and targeted interventions. Citation Format: Behnaz Saidy, Richa Vasan, Megan-Rose Greener, Rosie Durrant, Adelynn Immanuel, Andrew Green, Emad Rakha, Ian Ellis, Graham Ball, Stewart Martin, Sarah J. Storr. Unveiling the significance of DARPP-32 and its partners, including GRB7 and DKK1, in breast cancer: Integrative insights from patient data and cell line transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB009.

Maintaining Schools as Socio-Educational Environments for Teachers During the COVID-19 Pandemic

Objective: The objective of this study is to examine the impact of schools and key stakeholders in the socio-educational environment, particularly in assisting high school teachers during remote learning amid the COVID-19 pandemic. Method: This study employs a case study methodology with a qualitative approach, utilizing interview and observation tools for data mining. The research employed purposive sampling as a data mining method, and an interactive model was used for data analysis. Results and Discussion: The research findings indicate that teachers encountered social and educational challenges throughout the pandemic and the use of remote learning. Significant discoveries pertaining to this matter can be classified into two categories. Firstly, teachers saw profound disruptions due to the closure of schools during the pandemic. The rapidly evolving circumstances pose significant difficulties for teachers in the socio-educational domain, particularly due to the transition from in-person instruction to online learning and the substantial surge in technology-driven education. In this scenario, teachers find themselves facing a challenging predicament. They must simultaneously navigate the transition to digital learning while also effectively facilitating interactive learning with their students. The second discovery pertains to significant stakeholders inside schools and their respective roles in upholding the significance of educational institutions throughout the pandemic. The individuals encompassed in this group are teachers, principal, students, and parents. Research Implications: Despite the dire circumstances of the pandemic, the decision to close schools does not diminish their crucial function as institutions that are vital for ensuring the continuity of education and the involvement of key stakeholders. Originality/Value: This study offers a teacher's viewpoint on examining many factors that contribute to preserving the crucial function of schools during the pandemic. The topic of the COVID-19 pandemic, which encompasses environmental background, institutions, actors, education, and social factors, has not been extensively examined in scientific studies.

Perbandingan Akurasi Algoritma Naive Bayes dan Algoritma Decision Tree dalam Pengklasifikasian Penyakit Kanker Payudara

Cancer is one of the deadliest diseases in the world with a high increase in the number of cases every year Cancer disease with significant growth in cases, is a serious global challenge. The main focus of this research is breast cancer in Indonesia. Using a data mining approach, this study compares two main classification algorithms, namely Naive Bayes and Decision Tree, to identify breast cancer. Naive Bayes is a simple probabilistic approach, calculating probabilities assuming attribute independence. Decision Tree, as a popular algorithm, represents decision rules in the form of a tree. Through comparison with previous research on algorithms in other contexts, this study aims to find the algorithm with the highest accuracy in breast cancer classification. With the final result, the decision tree has a higher accuracy of 92.04% and naïve Bayes has an accuracy of 91.15%.This result proves that the decision tree is superior in the classification of breast cancer disease compared to naïve Bayes. The results of the study are expected to make an important contribution to the development of effective approaches for the diagnosis and treatment of breast cancer.

Data Mining in Different Fields: A Study

A revolutionary technique for managing massive amounts of data from databases is data mining. These days, information is more commonplace in all types of businesses and industries. For a range of business objectives, fields choose data mining approaches, and data mining is what makes it possible for fields to establish the ideal customer baseline and cultivate long-lasting relationships. Statistical analysis, machine learning, predictive modelling, and database approaches are all combined in knowledge discovery. This review research focuses on and quickly discusses the various domains in which data mining is applied. Here, we discuss several sectors where data mining is relevant in order to comprehend its significance. We also discuss the numerous technologies used in related fields, which depend on user desire. To learn about it, many algorithms are described.

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques

With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax. Using the Mask R-CNN model and medical transfer learning, the proposed work offers•A novel method for pneumothorax segmentation from chest X-rays.•A method that takes advantage of the Mask R-CNN architecture's for object recognition and segmentation.•A modified model to address the issue of segmenting pneumothoraxes and then polish it using a sizable dataset of chest X-rays.The proposed method is tested against other pneumothorax segmentation techniques using a dataset of ‘chest X-rays’ with ‘pneumothorax annotations. The test findings demonstrate that proposed method outperforms other cutting-edge techniques in terms of segmentation accuracy and speed. The proposed method could lead to better patient outcomes by increasing the precision and effectiveness of pneumothorax diagnosis and therapy. Proposed method also benefits other medical imaging activities by using the medical transfer learning approaches which increases the precision of computer-aided diagnosis and treatment planning.

Unveiling the Negative Customer Experience in Diagnostic Centers: A Data Mining Approach.

This study aims to identify the negative customer experiences reflected in complaints against diagnostic centers using data mining tools. Analyzing customer complaints from a consumer complaints website, the Apriori algorithm was employed to uncover frequent patterns and identify key areas of concern. The frequency and distribution of terms used in complaints were also analyzed, and word clouds were generated to visualize the findings. The study revealed that major areas of unfavorable customer experience included delayed test reports, erroneous test results, difficulties scheduling appointments, staff incivility, subpar service, and medical negligence. These findings and the proposed model can guide diagnostic centers in incorporating data mining tools for customer experience analysis, enabling managers to proactively address issues and view complaints as opportunities for service improvement rather than legal liabilities.

MACHINE LEARNING-BASED APPROACHES FOR CREDIT CARD DEBT PREDICTION

The primary concern in the stock market and banks that offer credit cards has been a problem over time. Regardless of their capacity to pay, most card users abuse their credit cards and accrue debt from cash cards. The most significant issue facing cardholders and banks alike is this calamity. Predicting credit card customers' default payments became vital to lowering this risk. Data mining approaches, including decision tree, logistic regression, and Naïve Bayes with feature selection methods, were applied to secondary credit card debt data to identify the significant factors that impact credit card default and to enhance the prediction of credit card default. As a result, the decision tree with Gini index splitting criteria forward selection wrapper method was identified as the best model with the highest percentages of accuracy, precision, sensitivity, and area under ROC of 76.39%, 72.02%, 85.08%, and 0.891 respectively. Additionally, the significant factors that impact credit card default are gender, education level, repayment status in July 2005, repayment status in August 2005, status of repayment in September 2005, and the amount paid in June 2005 and May 2005. This study may help financial institutions assess creditworthiness and give consumers insights into their financial behaviors.