Data Mining Techniques Research Articles

Taekwondo win-loss determining factors using data mining-based decision tree analysis: focusing on game analysis for evidence-based coaching

BackgroundPurpose In this study, the purpose of this study is to identify the determinants of winning and losing in taekwondo by applying decision tree analysis, one of the data mining techniques, based on the 2022 World taekwondo championships women’s competition.Methods272 women’s games in the taekwondo championships in Guadalajara held by the WT in 2022 were used. For data processing, an independent sample t-test was performed for differences in game content variables according to the win/lose group, and a decision tree analysis was performed to confirm game content variables affecting the win/lose group. To check the predictive power of the model, classification accuracy, standard error, and misclassification estimates were calculated. All statistical significance levels were set at 0.05.ResultsFirst, it was found that there was no statistically significant difference only in body attack (attempt) and number of kicking variables according to the winning and losing groups(p > .05), and there were differences in all other game content variables(p < .05). Second, as a result of conducting a decision tree analysis to confirm the determinants of winning and losing in taekwondo sparring, winning situation, tie situation, and number of kicks were identified as important variables.ConclusionThe World taekwondo championships are analyzed in the currently changed taekwondo competition rules to identify important factors, and at the same time, based on this, data-based coaching is expected to improve performance.

An Effective Optimization in Education System using Decision Support Systems

For academics, the process of retrieving information from large datasets, known as data mining, has become a captivating area of research. The concept of utilizing data mining techniques to extract information has been in existence for several decades. The dataset was initially designed to be divided into sections and analyzed using classification and clustering methods to explore its intrinsic characteristics. They make their forecasts based on these features. These predictions have been generated in the field of educational data mining for several purposes, such as forecasting student achievement using individual traits and assisting students in identifying suitable professors and courses. These targets have been derived from the analysis of student attrition and retention. Our study is centered around the aims of student attrition and retention. In addition, we have discovered intriguing indicators that contribute to the prediction of students' success, indicating the most competent instructors, and helping them with their choice of courses.

Review On Educational Academic Performance Analysis and Dropout Visualization by Analyzing Grades of Student

Education is a crucial aspect of a nation's development, and ensuring the success and retention of students is of paramount importance. The current studies show the need for an effective and efficient education prediction system. Education is a pivotal aspect of a country's development. It acts as a powerful tool to change the world. Education is the key to a literate society. In India, it is necessary to have an integrated web platform to analyze the academic performance and dropout rates across school, higher, and technical education. Student dropout is a significant problem for any nation. Discontinuing schooling due to financial, practical, and social reasons, as well as disappointment in examination results, is what is commonly referred to as student dropout. Educational Data Mining (EDM) techniques can help discover insights from data in educational environments, allowing tutors and researchers to predict future trends and student behavior. The use of machine learning and data mining techniques provides valuable tools for understanding the student learning environment. This literature review aims to synthesize the existing research findings on this topic and identify knowledge gaps for future research.

Strengthening The Skill of The Sri Tanjung Batik Community (KBT) Trough Ecoprint Batik Innovation In Bangsring Village Wongsorejo Banyuwangi

Abstract: Bangsring Village Wongsorejo is one of the village that has both tangible and intangible assets, one of the intangible assets is an organizational asset in the form of a batik community founded by women, but several years ago this community was passive so it needs to be reactivated by carrying out assistance to them in producing batik because around them there are other assets which are also very strategic in the form of leaves which are the main ingredient in eco-printing. This cammunity service uses the ABCD method which focuses on assets in the community, data mining techniques in the form of in-depth interviews and Focus Group Discussions. The main activity program in this cammunity service is strengthening the capacity of members of the Tanjung Sari batik group in the form of workshops and field practice. After carrying out the activities, community experienced changes in the knowledge aspect of producing eco-print batik, in attitudes, organizational and increase income.

OSINT (OPEN SOURCE INTELLIGENCE) Exploring the Power of Open Source Intelligence in Modern Decision-Making

Open Source Intelligence (OSINT) has emerged as a powerful tool in the information age, offering valuable insights to individuals, organizations, and governments. This paper explores the significance of OSINT in contemporary decision-making processes, highlighting its role in providing timely, relevant, and actionable information from publicly available sources. The first section elucidates the concept of OSINT, delineating its scope, sources, and methodologies. OSINT encompasses a wide array of publicly accessible information, including social media posts, news articles, government reports, and academic publications. Leveraging advanced data mining, web scraping, and analytical techniques, OSINT practitioners sift through this vast trove of data to extract pertinent insights. The subsequent section delves into the multifaceted applications of OSINT across various domains. In the realm of national security, OSINT aids in threat assessment, geopolitical analysis, and monitoring of adversarial activities. Law enforcement agencies utilize OSINT for criminal investigations, intelligence gathering, and identifying emerging trends. Moreover, businesses employ OSINT for competitive intelligence, market research, and brand monitoring, gaining a competitive edge in dynamic markets. The paper also examines the ethical and privacy implications inherent in OSINT practices. While OSINT offers unprecedented access to information, it raises concerns regarding privacy infringement, misinformation propagation, and algorithmic biases. Safeguarding individual privacy rights and ensuring data accuracy are imperative considerations in the ethical utilization of OSINT. Furthermore, the paper discusses the evolving landscape of OSINT technologies and methodologies. Advancements in artificial intelligence, natural language processing, and machine learning have revolutionized OSINT capabilities, enabling automated data collection, sentiment analysis, and predictive modeling. However, these technological advancements also pose challenges in terms of information overload, data veracity, and algorithmic transparency.

Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques, and GIS.

An integrated approach combining water quality indices (WQIs), multivariate data mining, and geographic information system (GIS) was employed to examine the water quality of Bheemasandra Lake, located adjacent to a sewage treatment plant (STP) in Tumakuru city, India. The analysis of 22 lake water samples, examined before and after the monsoons, revealed that the physicochemical parameters namely - electrical conductivity, biochemical oxygen demand, turbidity, total dissolved solids, ammoniacal nitrogen, nitrates, phosphates, magnesium, total hardness, total alkalinity, and calcium - exceeded the acceptable limits stipulated by national and international standards. The Canadian Council of Ministers of the Environment WQI (pre-monsoon: 25.3; post-monsoon: 33.9) and weighted arithmetic WQI (pre-monsoon: 3398; post-monsoon: 2093) designated the water as unsafe for drinking. Irrigation WQIs (sodium adsorption ratio, sodium percentage, residual sodium carbonate, magnesium hazard, permeability index, and potential salinity) implied water's suitability for irrigation. However, electrical conductivity indicated otherwise. Industrial WQIs (Larson-Skold Index, Langelier Index, Aggressive Index, and Puckorius Scaling Index) illustrated scaling propensity and the chloride sulfate mass ratio alluded galvanic corrosion potential. Hierarchical cluster analysis gathered 22 sampling points into two clusters (cluster 1: relatively lower polluted regions; cluster 2: highly polluted regions) for each season based on similarities in water features. Principal component analysis extracted four (79.07% cumulative variance) and six (87.14% cumulative variance) principal components before and after the monsoons, respectively. These components identified the primary pollution sources as urban sewage and natural lithological processes. WQI maps, created using the inverse distance weighted interpolation technique, enhanced the visualization of spatial-temporal variations. This study highlights the dire consequences of urbanization, STP pollution, and sewage management failures, necessitating that concerned authorities should implement policies and measures to curb the negative impacts on the environment and public health.

A New Agricultural Drought Index to Characterize Agricultural Drought Using Data Mining Techniques

Drought monitoring is a critical task as its occurrence and extent vary according to many factors like drought type, risk, agricultural losses, and impact. Monitoring drought is important because the footprint of this hazard is larger than that of other natural hazards. Many drought indices are developed to monitor complex drought conditions. The intensity and severity of drought in a particular region and at a particular time can be tracked by the drought indicator. In this research, a new agricultural drought index, Yield-Evapotranspiration Drought Index (YEDI) is developed using crop yield, potential, and reference crop evapotranspiration. Data mining and Neural Network techniques have been used to model the drought index. The agricultural and climatic data used is selected from the year 1983 to 2015 (33 years) from the period of June to October (Kharif period) for Maharashtra state in India. The drought index generates the positive values which are further divided into a range of high, medium, and low intensities of drought. SPI and SPEI indices are used for validation against YEDI. Results show that there is a correlation between YEDI and SPEI whereas a low correlation is between YEDI and SPI. YEDI proves to be useful for agricultural drought monitoring.

ANALYSIS USING ANT COLONY OPTIMIZATION TECHNIQUES TO MINE LUNG CANCER DATA FOR THE PURPOSE OF INCREASING OR DECREASING THE DISEASE PREDICTION VALUE

The leading cause of death for both men and women is cancer. The unchecked proliferation of aberrant cells that begin in one or both lung most often in the cell lining the air passageways. Small cell lung cancer and non-small cell lung cancer are the two primary forms. Non smokers Get Lung Cancer at a Rate of 10–15%. Smokers Make Up 50% of the Case. The longer someone smokes and the more cigarettes they smoke, the higher their risk of developing lung cancer. Lung cancer has become more common. Age, sex, wheezing, shortness of breath, and chest pain are among the symptoms that can indicate a patient's likelihood of developing lung cancer. Data mining algorithms, such as classification, decision tables, naïve-based, ant colony optimization, lung cancer prediction, and data mining techniques, are used to detect lung cancer disease in its early stages. According to this paper, early detection of lung cancer can completely cure the disease and help doctors save patients' lives. Ant colony optimization data mining techniques are useful for improving or decreasing the disease prediction value of lung cancer data.

Application of Data Mining Techniques in Biopsy Interpretation and Staging of Carcinoma Cancer Disease: A Case Study of Northeastern Nigeria

This paper introduces an innovative framework tailored for carcinoma cancer staging in northeastern Nigeria, employing data mining techniques and machine learning algorithms, with analysis conducted using the WEKA toolkit. Given the challenges in healthcare systems, especially in cancer diagnosis and treatment, this study aims to enhance diagnostic precision thorough analysis of biopsy reports. By assessing Support Vector Machine (SVM) and decision tree algorithms using datasets from six tertiary health institutions, the framework demonstrates promising outcomes in accurately determining cancer malignancy stage. SVM exhibits high accuracies in staging breast and prostate cancer, while decision tree algorithms show notable accuracy rates in staging skin cancer. These results highlight the potential of machine learning in advancing cancer diagnosis and treatment planning, particularly in resource-constrained settings, and pave the way for the development of computer-aided diagnostic tools tailored for carcinoma cancer staging in northeastern Nigeria.

Predicting insurance claims through a variety of data mining techniques: facing lots of missing values and moderate class-imbalanced levels

Abstract This paper copes with a real-world classification problem related to the management of claims received in an insurance company. The way to obtain the classifier is not easy due to the high amount of missing values as well as the inherent imbalanced scenario within class labels. Once the data partition has been done, the training set is submitted to an intensive double grid search in order to obtain the most promising type of missing value imputation approach and then a step ahead is done using the best method and it starts the next round of data mining strategies which now falls into data rebalancing umbrella. Again, a grid search from an undersampling and oversampling family with different settings is done taking into account only seen data. The training data obtained after the first grid search are now submitted to the second step according the second grid search in order to get the ready training set for the further classifier training. The main objective of the work is to find the best combination of data mining techniques that suits the data set with a pipeline containing two types of data preparation methods coming from different families. As an outcome, first the problem of the presence of missing values has been addressed and then the data rebalancing techniques has been applied. The study focuses on obtaining classifiers based on Bayesian and lazy approaches as well as decision trees, evaluated on metrics such as the area under the ROC curve (AUC), Cohen’s kappa, Accuracy and the F-measure, among others. The imputation by the mean the mode is preferable to the Expectation Maximization Imputation in the scenario faced in this paper taking into account that the amount of missing values is higher than a forty percent for many features.

Assessing the Copyright Infringement Risk of Generative AI Created Works

The rapid evolution of the Internet and big data technology has ushered in significant advancements in data production and utilization. However, this progress has brought to the forefront the intricate issue of copyright protection. This is particularly evident with the widespread adoption of generative artificial intelligence (AI), where concerns regarding data source protection, data processing standards, and the boundaries of data application have garnered considerable attention from both academia and industry. Big data mining techniques coupled with generative AI algorithms offer robust support for data collection and processing, but they also introduce risks of data infringement and pose challenges regarding algorithmic compliance and ownership rights over generated works. Addressing these issues is imperative. This paper recommends enhancing legal compliance standards throughout each stage of the generative AI process, recalibrating the scope of acceptable copyright use, and establishing a regulatory framework for generative AI works. These measures are crucial for fostering the sustainable and orderly advancement of the generative AI industry.

A new approach to predict pump transient phenomena in Molten salt reactor Experiment (MSRE) by missing data identification and regeneration

Molten Salt Reactor Experiment (MSRE) is being extensively used for validating the computational tools for Molten Salt Reactors (MSRs). The pump transient tests were performed during the operation of MSRE to obtain the reactivity response to flow perturbation. The transient flow rate during this set of tests is required as input to predict the reactivity response. Since the primary loop of MSRE was not equipped with a flow rate meter, the transient flow rate, which is the crucial factor for the reactivity change in MSRE, is missing. Achieving an accurate simulation of the reactivity response to the MSRE pump transient tests necessitates a precise estimation of the transient flow rate.This paper endeavors to reconstruct the missing flow rate with the aim of offering a valuable input for simulating reactivity responses in the pump transient test series. In order to obtain an accurate transient flow rate, we employed a centrifugal pump transient model based on the affinity laws and solved the model for the MSRE secondary pump to validate the underlying assumptions of the model. In addition, we constructed the homologous pump head curves based on the water test data for the same purpose. The affinity law approximation is proved to be adequate in predicting the MSRE pump startup transient with the root-mean-square error (RMSE) in the normalized flow rate to be 0.03. On the other hand, for the MSRE pump coastdown transient, the preliminary results indicate that neither the affinity law approximation nor the homologous head curve is sufficient to provide acceptable predictions. To overcome this noticeable modeling deficit, we propose an innovative approach based on a straightforward data mining technique to regenerate the MSRE pump coastdown homologous relations using the measured data of the secondary pump. These relations are transferred to the primary pump and used to simultaneously solve for the impeller speed and the flow rate of the pump in the primary pump coastdown. With the new approach, the estimated RMSE in the normalized pump speed for the primary pump coastdown is reduced 0.0052. This excellent agreement validates the accurate calculations for the flow rate during the pump coastdown transient. We also performed an uncertainty analysis to quantify the confidence interval of the predicted quantities, which further justifies the robustness of the proposed approach.

Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation

Supervised discretisation is widely considered as far more advantageous than unsupervised transformation of attributes, because it helps to preserve the informative content of a variable, which is useful in classification. After discretisation, based on employed criteria, some attributes can be found irrelevant, and all their values can be represented in a discrete domain by a single interval. In consequence, such attributes are removed from considerations, and no knowledge is mined from them. The paper presents research focused on extended transformations of attribute values, thus combining supervised with unsupervised discretisation strategies. For all variables with single intervals returned from supervised algorithms, the ranges of values were transformed by unsupervised methods with varying numbers of bins. Resulting variants of the data were subjected to selected data mining techniques, and the performance of a group of classifiers was evaluated and compared. The experiments were performed on a stylometric task of authorship attribution.

ANALISIS KELAYAKAN KREDIT KOPERASI MITRA TANI MANDIRI DENGAN ALGORITMA NAÏVE BAYES

A cooperative is a financial institution located in each village. Cooperatives can provide solutions to the community when the community's economic needs are decreasing. One of the services provided by cooperatives to the community is credit loans. This research focuses on analyzing the suitability of prospective cooperative member creditors who are eligible for credit applications. This analysis requires attributes or variables that are suitable for use in the credit application process. The attributes used in this research are that prospective creditors must be of sufficient age, disciplined in the payment process according to the monthly due date, have income and have allowances. This research uses data mining techniques using the Naïve Bayes algorithm, and data processing uses the Rapidminer application. The results obtained were based on the results of processing 182 data, namely obtaining an accuracy value of 81.32%, precision of 89.36% and recall of 77.78%. In this case, credit worthiness analysis with four attributes using the Naïve Bayes algorithm is suitable for use with accurate and precise results.

Safety analysis of Oseltamivir and Baloxavir Marboxil after market approval: a pharmacovigilance study based on the FDA adverse event reporting system

Background and objectivesAmidst limited influenza treatment options, evaluating the safety of Oseltamivir and Baloxavir Marboxil is crucial, particularly given their comparable efficacy. This study investigates post-market safety profiles, exploring adverse events (AEs) and their drug associations to provide essential clinical references.MethodsA meticulous analysis of FDA Adverse Event Reporting System (FAERS) data spanning the first quarter of 2004 to the fourth quarter of 2022 was conducted. Using data mining techniques like reporting odds ratio (ROR), proportional reporting ratio, Bayesian Confidence Propagation Neural Network, and Multiple Gamma Poisson Shrinkage, AEs related to Oseltamivir and Baloxavir Marboxil were examined. Venn analysis compared and selected specific AEs associated with each drug.ResultsIncorporating 15,104 Oseltamivir cases and 1,594 Baloxavir Marboxil cases, Wain analysis unveiled 21 common AEs across neurological, psychiatric, gastrointestinal, dermatological, respiratory, and infectious domains. Oseltamivir exhibited 221 significantly specific AEs, including appendicolith [ROR (95% CI), 459.53 (340.88 ∼ 619.47)], acne infantile [ROR (95% CI, 368.65 (118.89 ∼ 1143.09)], acute macular neuroretinopathy [ROR (95% CI), 294.92 (97.88 ∼ 888.64)], proctitis [ROR (95% CI), 245.74 (101.47 ∼ 595.31)], and Purpura senile [ROR (95% CI), 154.02 (81.96 ∼ 289.43)]. designated adverse events (DMEs) associated with Oseltamivir included fulminant hepatitis [ROR (95% CI), 12.12 (8.30-17.72), n=27], ventricular fibrillation [ROR (95% CI), 7.68 (6.01–9.83), n=64], toxic epidermal necrolysis [ROR (95% CI), 7.21 (5.74–9.05), n=75]. Baloxavir Marboxil exhibited 34 specific AEs, including Melaena [ROR (95% CI), 21.34 (14.15–32.18), n = 23], cystitis haemorrhagic [ROR (95% CI), 20.22 (7.57-54.00), n = 4], ileus paralytic [ROR (95% CI), 18.57 (5.98–57.71), n = 3], and haemorrhagic diathesis [ROR (95% CI), 16.86 (5.43–52.40)), n = 3]. DMEs associated with Baloxavir Marboxil included rhabdomyolysis [ROR (95% CI), 15.50 (10.53 ∼ 22.80), n = 26].ConclusionMonitoring fulminant hepatitis during Oseltamivir treatment, especially in patients with liver-related diseases, is crucial. Oseltamivir’s potential to induce abnormal behavior, especially in adolescents, necessitates special attention. Baloxavir Marboxil, with lower hepatic toxicity, emerges as a potential alternative for patients with liver diseases. During Baloxavir Marboxil treatment, focused attention on the occurrence of rhabdomyolysis is advised, necessitating timely monitoring of relevant indicators for those with clinical manifestations. The comprehensive data aims to provide valuable insights for clinicians and healthcare practitioners, facilitating an understanding of the safety profiles of these influenza treatments in real-world scenarios.

Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances

Predicting students' academic success in tertiary institutions in terms of determining accurately whether a student will be a top 10 in his class, an average student or a dropout is a significant issue in higher education. Therefore, to predict the behavior of a learner, many data mining techniques are used, such as clustering, classification, and regression. In this paper, students’ academic performance prediction model and new features are introduced that have a great influence on student’s overall academic achievement. Twenty features were considered from the dataset obtained from about 200 students using questionnaire and information from the institutions database. The Naive Bayes classifier was used and the experiment was carried out in a WEKA implementation workbench. The attributes were first analyzed for relevance, the analysis showed that a student’s first semester grade point average (GPA) has the highest relevance followed by friends’ study affinity, learning facilities, private home lesson, gender and available transport facilities. One hundred and sixty-nine (169) students’ records were used for the analysis, the model recorded 162 instances that were correctly classified which translates to 95.86% accuracy and incorrectly predicted 4.142%. The experiment showed an excellent performance from the Naive Bayes classifier in predicting the dataset correctly Keywords: Educational Data Mining, Students' Performance Management, Tertiary Institutions, Classification, Naive Bayes Classifier. Agwi, C.U. &amp; Akpojaro, J. (2024): Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances. Journal of Advances in Mathematical &amp; Computational Science. Vol. 12, No. 1. Pp 53-66. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P6

Urban flood forecast model: a data-based approach from the Ribeirão dos Padilhas river Basin, Curitiba (PR)

This is a study on natural disasters, with an emphasis on urban floods, which have occurred more often involving relevant socio-economic impacts. Among the measures aimed at minimizing the consequences of floods, the most noteworthy are the empirical models linked with new technologies in a promising way and with lower financial cost compared to the models. Thus, a flood forecast model with applicability improved by new technologies becomes a protagonist in reducing the impacts caused by floods. The aim of this study is to compare urban flood prediction models using data mining, geo-processing (GIS) and machine learning techniques. The results illustrate the feasibility of applying in urban areas from the case study, with the aim of contributing to the management of urban environment disasters. Predicting the occurrence of floods is fundamental for alerting the riverside population and for mobilizing the civil defense action teams. To do so, models for forecasting flooding were drawn up one day in advance, where the results obtained from the models built with the techniques for classifying the rules showed satisfactory results. Therefore, forecasting the occurrence of urban floods can be used as a tool to support decision-making to guide the actions of the public authorities in the management of this type of disaster. The knowledge presented seeks to contribute in the debates on socio-environmental issues and public policies. In addition, the results of this study can be replicated elsewhere and for other types of variable data.

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

Prediction of Engineering Students Learning Outcomes using Machine Learning

Data science and machine learning, over the years have proven very well-organized and significant in many sectors including education. Machine learning is an aspect of artificial intelligence in which a computing system can able to learn from data and make conclusions. The recent development in education sector provides assessment tools to predict the student performance by exploring education data using machine learning and data mining techniques. Student performance assessment is an important measurement metrics in education which affects the university accreditation. Student performance improvement plan must be implemented in those universities, by counselling the low performer students. It helps both students and teachers to overcome the problems experienced by the student during studies and teaching techniques of teachers. In this review paper, different student performance prediction literature related to find out low performer student. The survey results indicated that different machine learning techniques are used to overcome the problems related to predicting student at risk and assessment of student performance. Machine learning techniques plays an important role in progress and prediction of student performance, thus improving student performance prediction system

Data-Driven Decision Making in Scholarship Programs: Leveraging Decision Trees and Clustering Algorithms

Data-Driven Decision Making in Scholarship Programs: Leveraging Decision Trees and Clustering Algorithms