Towards a Pervasive Intelligent System on Football Scouting - A Data Mining Study Case

Intense competition in the business sector motivates every company to manage services to regular consumers to the fullest. Increase customer loyalty can be done by grouping customers into several groups and determine appropriate and effective marketing strategies for each group. This study aims to propose the right targeting of discounts that can increase customer loyalty in the retail business. Customer grouping uses data mining techniques with the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, which is divided into six phases, namely business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The formation of this cluster uses k-means clustering method and is based on RFM (recency, frequency, monetary) analysis. From the results of the silhouette test on 2734 transaction data from 210 customers of PT. XYZ from October 2019 to March 2020, three customer clusters were formed. From these three clusters, one cluster that has the best frequency and monetary values is chosen so that it is considered the worthiest group to be given a discount in order to maintain its loyalty.

Cybercrime represents a growing global threat with adverse impacts on citizen security, the digital economy, and quality of life. In this context, an optimized hybrid model was developed that combines autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) for the monthly forecast of cybercrime complaints, applying the cross industry standard process for data mining (CRISP-DM) methodology and applying Python based data science techniques. The model combines the capabilities of the ARIMA statistical approach to capture linear components with the power of LSTM neural networks to address nonlinear temporal relationships. The architecture was trained on a set of 60,378 official records of complaints registered by the National Police of Peru between 2018 and 2023, achieving a mean absolute percentage error (MAPE) of 10.73%, which represents a significant improvement over the singular ARIMA and LSTM predictive models. Compared to previous studies in crime, health, and agriculture, this approach showed a greater ability to generalize over complex time series. It is concluded that the application of the proposed model is a relevant contribution for the police and other security agencies to anticipate crime trends and design preventive and effective strategies to combating cybercrime.

This study aims to implement a classification method using the Nave Bayes Classifier (NBC) algorithm on Lake Toba visitor review text data. The Cross Industry Standard Process for Data Mining (CRISP-DM) methodology comprises the following stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The findings of this study indicate that during the phase of business comprehension, the context of the discussion focuses on the tourism sector, specifically tourist perceptions of the quality of products and services at Lake Toba tourist destinations. At the data comprehension stage, the source of review data used was the Tripadvisor website, which contained as many as 858 reviews with the following rating classification: 8 reviews with abysmal ratings; 22 reviews with poor ratings; 81 reviews with neutral ratings; 304 reviews with good ratings; 443 reviews with excellent ratings. Data cleansing is performed at the data preparation stage so that 382 data are processed by dividing training data by 70 percent and test data by 30 percent. During the modeling phase, the performance of the NBC and DT algorithms was evaluated using and without SMOTE UPsampling operators. The comparison of NBC and DT algorithm values indicates that the model with the best performance is DT using SMOTE UPsampling operators with accuracy values (98.27 percent), precision values (98.83 percent), recall values (97.71 percent), f-measure values (98.26 percent), and AUC values (98.27 percent) (0.982). At the evaluation stage, the importance of excellent service (Quality Human Resources) and supporting infrastructure was highlighted by analyzing the results of ranking the five most frequently used terms in Lake Toba visitor review data (tourism facilities and infrastructure). At the deployment stage, it is necessary to balance the development of attractions, accessibility, lodging, and tourism-supporting amenities to generate visiting intention and revisit motivation to Lake Toba.

Loss of blood circulation to the brain causes a stroke, which is also known as a brain attack. In addition, blood clots are also the leading cause of stroke in the brain. Based on the WHO report, stroke is the leading cause of death in Indonesia in 2024, with a death rate reaching 131.8 per 100,000 population. This study aims to classify early detection of stroke disease by applying the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology using the Random Forest algorithm. The data used is public through the website www.kaggle.com, with a total of 4981 records consisting of 11 attributes. The data composition is unbalanced, with 4733 negative stroke data (95%) and 248 positive strokes (5%). Handling imbalanced data using the Synthetic Minority Oversampling Technique (SMOTE). The total data from SMOTE is 5981 records, with 4733 negative data and 1248 positive. After exploring several models, the best model was obtained using Random Forest with the SMOTE approach, producing an accuracy of 80.14%, AUC 0.836, recall 63.33%, and precision 11.42%.

Outpatient clinics are integral to healthcare, offering vital services without the need for hospital admission. However, appointment scheduling in these settings remains challenging due to uncertainties such as patient no-shows and variable service times. This study proposes a data-driven approach to minimize physician idle time and patient waiting time by analyzing an eight-year (2016-2023) dataset from primary and specialized care for American veterans. Adopting the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, four predictive models: Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Artificial Neural Networks (ANN), were developed for both classification (noshow) and regression (service time) tasks. The ANN model demonstrated superior predictive performance in both domains. The key predictors of no-shows included waiting time, type of care and care provider, while type of care, care provider, and veteran ZIP code were the most influential in forecasting service time. These findings highlight the potential of machine learning to improve appointment scheduling in outpatient clinics.

Automatic detection of knee medial collateral ligament (MCL) tear from magnetic resonance imaging using deep neural network.

ObjectivesTo develop and to propose a machine learning model for predicting glaucoma and identifying its risk factors.MethodData analysis pipeline is designed for this study based on Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The main steps of the pipeline include data sampling, preprocessing, classification and evaluation and validation. Data sampling for providing the training dataset was performed with balanced sampling based on over-sampling and under-sampling methods. Data preprocessing steps were missing value imputation and normalization. For classification step, several machine learning models were designed for predicting glaucoma including Decision Trees (DTs), K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Random Forests (RFs), Extra Trees (ETs) and Bagging Ensemble methods. Moreover, in the classification step, a novel stacking ensemble model is designed and proposed using the superior classifiers.ResultsThe data were from Shahroud Eye Cohort Study including demographic and ophthalmology data for 5190 participants aged 40-64 living in Shahroud, northeast Iran. The main variables considered in this dataset were 67 demographics, ophthalmologic, optometric, perimetry, and biometry features for 4561 people, including 4474 non-glaucoma participants and 87 glaucoma patients. Experimental results show that DTs and RFs trained based on under-sampling of the training dataset have superior performance for predicting glaucoma than the compared single classifiers and bagging ensemble methods with the average accuracy of 87.61 and 88.87, the sensitivity of 73.80 and 72.35, specificity of 87.88 and 89.10 and area under the curve (AUC) of 91.04 and 94.53, respectively. The proposed stacking ensemble has an average accuracy of 83.56, a sensitivity of 82.21, a specificity of 81.32, and an AUC of 88.54.ConclusionsIn this study, a machine learning model is proposed and developed to predict glaucoma disease among persons aged 40-64. Top predictors in this study considered features for discriminating and predicting non-glaucoma persons from glaucoma patients include the number of the visual field detect on perimetry, vertical cup to disk ratio, white to white diameter, systolic blood pressure, pupil barycenter on Y coordinate, age, and axial length.

The rapid growth of AI-generated content from ChatGPT, Gemini, and many others poses significant challenges in digital media. There are increasing instances of text generated by machine learning models being indistinguishable from human-written text. This calls for an effective way of detecting and distinguishing text generated by AI from human text. Using a Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, a Bidirectional Long Short-Term Memory (Bi-LSTM) model for classifying AI-generated text in a digital media context was developed. A carefully curated dataset of human and AI-generated text samples was used. The Bi-LSTM model without an embedding layer was implemented, optimizing the model to capture complex linguistic patterns apparent in each text type. An experimental setup was used to evaluate the effectiveness of the model. It was noted that the model achieved a remarkable test accuracy of 99.79%, with a loss of 0.009. To facilitate practical implementation, we developed a web application using Next.js with our model served from a Flask server that enables real-time AI text detection. Our results highlight the model’s ability to accurately identify AI-generated text, providing valuable insights into deploying such models to verify content on media platforms. This study highlights the potential of neural network-based classifiers to address the pressing need for automated AI text detection in an increasingly AI-influenced digital ecosystem.

Scientometrics is the field of study and evaluation of scientific measures such as the impact of research papers and academic journals. It is an important field because nowadays different rankings use key indicators for university rankings and universities themselves use them as Key Performance Indicators (KPI). The purpose of this work is to propose a semantic modeling of scientometric indicators using the ontology Statistical Data and Metadata Exchange (SDMX). We develop a case study at Tecnologico de Monterrey following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. We evaluate the benefits of storing and querying scientometric indicators using linked data as a mean for providing flexible and quick access knowledge representation that supports indicator discovery, enquiring and composition. The semi-automatic generation and further storage of this linked data in the Neo4j graph database enabled an updatable and quick access model.

BackgroundCurrently, the diagnosis of Alzheimer’s disease dementia (ADD) is determined based on clinical criteria, as well as specific imaging and cerebrospinal fluid (CSF) biomarker profiles. However, healthcare professionals face a variety of challenges that hinder their application, such as the interpretation and integration or large amounts of data derived from neuropsychological assessment, the importance attributed to each source of information and the impact of unknown variables, among others. Therefore, this research focuses on the development of a computerized diagnostic tool based on Artificial Intelligence (AI), to strengthen the capacity of healthcare professionals in the identification and diagnosis of ADD.MethodDuring this research, all phases of the Cross‐Industry Standard Process for Data Mining (CRISP‐DM) methodology were applied. Specifically, seven types of machine learning (ML) classifier algorithms were trained with data derived from the medical and neuropsychological evaluation of 870 people with and without ADD. Additionally, genetic algorithms (GAs) were used to choose the hyperparameters of six of the models and the best performing model was deployed through a graphical user interface.ResultA total of 147 ML models were trained to differentiate or classify between people with and without ADD. Using a random forest (RF) algorithm and 31 predictor variables we achieved a sensitivity of 98%, a specificity of 100% and an area under the curve (AUC) of 0,988.ConclusionWe trained and evaluated a variety of ML models, highlighting the accuracy of a RF algorithm together with the use of GAs, to distinguish people with and without ADD. Additionally, we deployed the model with the best performance metrics through a graphical user interface in order to promote its future use in clinical settings.Although these results are promising, the ongoing need to evaluate and re‐train the model, as well as the ethical considerations of its implementation, are well acknowledged.

This research aims to explore tourist behaviour patterns and develop data-driven tourism route recommendations based on Association Rule Mining (ARM), particularly using the Frequent Pattern Growth (FP-Growth) algorithm. The study focuses on identifying co-occurrence patterns among tourist attractions based on real behavioral data, with the goal of supporting tourism route design and enhancing recommendations for self-guided tourists. The study employed a structured data mining methodology. Raw data was collected from real-world tourist check-ins and GPS traces in Songkhla City, Thailand; encompassing 45 attraction points. The study used the Cross-Industry Standard Process for Data Mining (CRISP-DM) for research methodology. The FP-Growth algorithm was used to discover association rules, using parameters set at support > 0.10, confidence > 0.5, lift > 1.0, and conviction > 1.0 to ensure the relevance and reliability of the extracted rules. The evaluation phase included interpretation of rule quality metrics such as support, confidence, lift, and conviction indicators. Visualizations presented the route recommendations on geographic maps. The results revealed 13 valid association rules involving only 5 out of the 45 locations, suggesting strong behavioral patterns and repeat co-visitation. Interestingly, some of the top-visited locations based on frequency alone did not appear in any of the 13 rules, indicating that frequency-based route planning may not reflect actual tourist behavior. Further analysis showed that all route combinations extracted from the rules had a total travel distance of under 88 kilometers. With Google Map data, this study demonstrates that ARM with FP-Growth can provide insightful patterns for route planning and destination clustering. The proposed method supports smart tourism development by bridging behavioral analytics and operational tourism planning. Stakeholders can apply this approach to periodically update route recommendations to ensure alignment with current tourist behaviors and preferences.

Purpose – This paper deals with the identification and diagnosis of operational variability in chemical processes, which is a common problem in mills but little explored in literature. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely used approach in problem solving. The purpose of this paper is to: first, contribute to the body of knowledge on applying CRISP-DM in a pulp mill production process and the special issues that need to be considered in this context. Exact amounts of a cost increase due to variation in pulp production have not been reported previously. Second, to quantify the cost of variation. Design/methodology/approach – In the case studied, the variation in a pulp mill batch cooking process had increased. In order to identify the causes of variation, CRISP-DM was applied. Findings – The cycle of variation was identified and found to be related to the batch cooking process cycle time. By using information from this analysis it was possible to detect otherwise unobserved defective steam nozzles. The defective equipment was repaired and improved. Further improvement was achieved when the fouling of a heat exchanger was found by analysis to be the root cause of long-term variability parameters. By applying CRISP-DM, equipment defects and fouling were identified as the root causes of the higher manufacturing costs due to increased variation were detected and estimated. The Taguchi loss function is a possible tool for estimating the cost of variation in pulp manufacturing. Originality/value – This paper provides new knowledge in the context of implementing CRISP-DM and the Taguchi loss function in the pulp and paper manufacturing process.

This study examines the effectiveness of the Naïve Bayes and C4.5 algorithms in analyzing book borrowing patterns at the Pringsewu Muhammadiyah University Library. As libraries increasingly serve as vital educational hubs, understanding user borrowing behavior is essential for effective collection management and service enhancement. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM), which includes stages of business understanding, data understanding, preparation, modeling, evaluation, and implementation. A dataset consisting of 5,586 records and ten attributes related to book lending was utilized, with comprehensive data cleaning and preprocessing conducted. The performance of both algorithms was assessed using K-fold cross-validation, yielding an accuracy of 96.26% for C4.5, compared to 91.44% for Naïve Bayes. These results demonstrate that C4.5 is more adept at capturing complex relationships within the data, providing deeper insights into user preferences and enhancing library services. This research underscores the potential of data mining techniques to optimize library management and proposes avenues for future investigation, such as exploring advanced machine learning algorithms and expanding datasets for use in broader library contexts.

For retail companies such as Gramedia stores, promotion and strategies to sell books are important, so tools are needed to analyze past sales data. Gramedia does not yet have tools to analyze shopping cart patterns that aim to carry out product promotions appropriately. To promote what books should be promoted using the market basket analysis method or shopping basket analysis. The algorithm used in the data mining process is Frequent Pattern Growth (FP Growth) because it is faster in processing large data. The data analyzed is historical data on book sales from January to March 2020 which is taken randomly (random sampling). The framework used in the data mining process is the Cross Industry Standard Process for Data Mining (CRISP-DM) and the tool used is the Rapid Miner using a market basket analysis framework. With a minimum support of 0.003 and a minimum confidence 0.3 using the FP-Growth algorithm to produce an item set of 7 rules to recommend product promotions. The algorithm results are also in accordance with the business understanding phase of CRISP-DM.

A Data Mining Based Approach to a Firm's Marketing Channel