Search Results (598)

Association rule mining plays a critical role in uncovering item correlations and hidden patterns within transactional data, particularly in e-commerce environments. Despite the widespread use of Apriori and FP-Growth algorithms, few studies offer a statistically rigorous, tool-based comparison of their performance on real-world e-commerce data. This paper addresses this gap by evaluating both algorithms in terms of execution time, memory consumption, rule generation volume, and rule strength (support, confidence, and lift). Implementations in RapidMiner and an analysis through SPSS establish statistically significant performance differences, particularly under varying support thresholds. Our findings confirm that FP-Growth consistently outperforms Apriori for large-scale datasets due to its ability to bypass candidate generation, while Apriori retains pedagogical and small-scale relevance. The study contributes practical guidance for data scientists and e-commerce practitioners choosing suitable rule-mining techniques based on their data size and performance constraints. Full article

This research addresses IL-28B gene polymorphisms (rs12979860 and rs8099917) to determine their association with HTLV-1-related diseases; it aims to compare genotypic frequencies to identify predisposition or protection, considering population, disease, and controls. Given HTLV-1’s impact on immunity, this study seeks biomarkers for early diagnosis and intervention. A systematic search met inclusion criteria, such as open access bibliographic and experimental studies published in English between 2010 and 2024, and genetic factors linked to susceptibility to pathologies. Regarding exclusion criteria, bibliographic or experimental studies in organisms other than humans, unofficial sources, non-indexed journals, and scientific articles in languages other than English were ruled out. Statistical data analyses were assessed using meta-analysis, including forest plot and Q test of heterogeneity based on the I² statistics. The analyzed data indicate associations between genotypes, such as CT, GG, CC, and TT of the rs12979890 and rs8099917 polymorphisms and the predisposition to various diseases, such as HCV, arthropathy, HAM/TSP, cytomegalovirus and Crimean–Congo hemorrhagic fever associated with HTLV-1; however, the observed inconsistencies, such as high heterogeneity, and deficiency of related information limit the consolidation of the findings. Further research is needed to clarify IL-28B genotype interactions and disease susceptibility in HTLV-1 infections. Full article

Benzo[a]pyrene is an important indicator of polycyclic aromatic hydrocarbons pollution that exhibits complex atmospheric dynamics influenced by meteorological factors and suspended particulate matter (SPM). Herein, the factors influencing B(a)P concentration were elucidated by analyzing the monthly environmental data for Kyoto, Japan, from 2001 to 2021 using an improved association rule algorithm. Results revealed that B(a)P concentrations were 1.3–3 times higher in cold seasons than in warm seasons and SPM concentrations were lower in cold seasons. The clustering performance was enhanced by optimizing the K-means method using the sum of squared error. The efficiency and reliability of the traditional Apriori algorithm were enhanced by restructuring its candidate itemset generation process, specifically by (1) generating C₂ exclusively from frequent itemset L₁ to avoid redundant database scans and (2) implementing the iterative pruning of nonfrequent subsets during L_k → C_k+1 transitions, adding the lift parameter, and eliminating invalid rules. Strong association rules revealed that B(a)P concentrations ≤ 0.185 ng/m³ were associated with specific meteorological conditions, including humidity ≤ 58%, wind speed ≥ 2 m/s, temperature ≥ 12.3 °C, and pressure ≤ 1009.2 hPa. Among these, changes in pressure had the most substantial impact on the confidence of the association rules, followed by humidity, wind speed, and temperature. Under the influence of high SPM concentrations, favorable meteorological conditions further accelerated pollutant dispersion. B(a)P concentration increased with increasing pressure, decreasing temperature, and decreasing wind speed. Principal component analysis confirmed the robustness and accuracy of our optimized association rule approach in quantifying complex, nonlinear relationships, while providing granular, interpretable insights beyond the traditional methods. Full article

Background/Objectives: Immunotherapy is an essential part of metastatic bladder cancer treatment. Our main objective was to study the prognostic value of FDG-PET/CT in early assessment of response to Pembrolizumab in metastatic bladder cancers using PERCIST5, imPERCIST5, and PERCIMT criteria. Methods: A total of 42 patients were evaluated with FDG-PET/CT at baseline and after 3–4 cycles of Pembrolizumab. Treatment response was blindly assessed with PERCIST5, imPERCIST5, and PERCIMT. Imaging and clinical data were collected. Progression was defined clinically using oncologist reports. Results: A total of 37 patients were evaluable with the PERCIST5 and imPERCIST5 criteria and included in the analysis. Median disease-specific progression-free survival (PFS) and overall survival (OS) were 152 and 363 days, respectively. All response criteria were significantly associated with PFS. When response was dichotomized in responders versus non-responders all scores were significantly associated with OS. When response was dichotomized in progressors versus non-progressors, only PERCIST5 (hazard ratio (HR) 2.2) and PERCIMT (HR 2.6) were significantly associated with OS, while imPERCIST was not (HR 1.6). Two patients had pseudoprogression (5%), both being adequately classified as non-progressors with PERCIMT criteria. Conclusions: Early response to immunotherapy as assessed with FDG-PET is a strong prognostic factor in bladder cancer patients, especially using the PERCIST5 or PERCIMT criteria. The latter seems clinically useful as it is simple to perform and its specific definition of metabolic progression correctly ruled-out patients with significant clinical benefit of Pembrolizumab in our study. Full article

This study introduces a novel Recency, Monetary, and Duration (RMD) model for customer classification in the hospitality industry. Using a hybrid approach that integrates data mining with multi-criteria decision-making techniques, this study aims to identify valuable customer segments and optimize marketing strategies. This research applies the K-means clustering algorithm to classify customers from a hotel in Iran based on RMD attributes. Cluster validation is performed using three internal indices, and hidden patterns are extracted through association rule mining. Customer segments are prioritized using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and Customer Lifetime Value (CLV) analysis. The outcomes revealed six distinct customer clusters, identified as new customers; loyal customers; collective buying customers; potential customers; business customers, and lost customers. This study helps hotels to be aware of different types of customers with particular spending patterns, enabling hotels to tailor services and improve customer retention. It also provides managers with appropriate tools to allocate resources efficiently. This study extends the traditional Recency, Frequency, and Monetary (RFM) model by incorporating duration, an overlooked dimension of customer engagement. It is the first attempt to integrate data mining and multi-criteria decision-making for customer segmentation in Iran’s hospitality industry. Full article

The Atlantic puffin (Fratercula arctica) is a seabird species characterized by great diving capabilities and transoceanic migratory behavior. These movements contribute to the dispersion of the species during migration, and episodes of mortality associated with migration may be a normal event in the dynamic of the Atlantic puffin populations. This study aimed to describe the anatomopathological findings of an unusual mortality event of Atlantic puffins observed during the non-breeding period along the coast of the Canary Islands. The most consistent gross finding during necropsy was generalized muscle atrophy and fat depletion. The main histological findings were centered in the urinary tract, with dilation and inflammation of the primary ureter branch and medullary cones, and intraluminal trematodes identified as Renicola sloanei based on morphology and molecular analysis. Influenza virus infection was ruled out. The postmortem investigations performed in this mortality event of Atlantic puffins indicate that the animals were severely emaciated and suffered from nephropathy. The etiopathological investigation performed in relation to this mortality event of Atlantic puffins indicates starvation associated with bad weather conditions during migratory movement as the most likely cause of the unusual mortality event. Full article

Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports. Full article

This study aims to identify the prevalence and patterns of sports injuries in recreational sports activities in South Korea. This study utilized data from the “survey of safety accidents” conducted by the Korea Sports Safety Foundation and finally, 3182 recreational sports participants who experienced injuries were selected for the study. For data processing, data related to recreational sports injuries were first collected and organized using Excel 2015, and frequency analysis was conducted using the SPSS 25.0 program. Furthermore, the association rule method was applied via Python 3.13.3 to analyze the patterns of injury sites and types. First, by investigating the prevalence of injuries in recreational sports, it was found that the injury frequency was highest in soccer, followed by cycling, hiking, and badminton. Second, in soccer, it was found that when ankle injuries, which have a high injury frequency, occur, knee, toe, and sprain injuries also occur together (Lift: 1.843). Additionally, in cycling, when knee injuries occur, toe, sprain, and strain (bruise) injuries also occur together (Lift: 2.420). In mountain biking, when ankle injuries, which have a high injury frequency, occur, cuts, sprains, stab wounds (cuts), sprains, and strains (bruises) also occur together (Lift: 1.808). The current survey on recreational sports injuries is expected to be used as basic data to prevent injuries in advance for participants in recreational sports, and it is expected that this will allow them to participate in sports by recognizing common injury sites before participating in sports. Full article

Background: Oropouche virus (OROV), part of the Peribunyaviridae family, is an emerging pathogen causing Oropouche fever, a febrile illness endemic in South and Central America. Transmitted primarily through midge bites (Culicoides paraensis), OROV has no specific antiviral treatment or vaccine. This study aims to identify host-targeted therapeutics against OROV using computational approaches, offering a potential strategy for sustainable antiviral drug discovery. Methods: Virus-associated host targets were identified using the OMIM and GeneCards databases. The Enrichr and DSigDB platforms were used for drug prediction, filtering compounds based on Lipinski’s rule for drug likeness. A protein–protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape 3.10.3 software. Four key host targets—IL10, FASLG, PTPRC, and FCGR3A—were prioritized based on their roles in immune modulation and OROV pathogenesis. Molecular docking simulations were performed using the PyRx software to evaluate the binding affinities of selected small-molecule inhibitors—Acetohexamide, Deptropine, Methotrexate, Retinoic Acid, and 3-Azido-3-deoxythymidine—against the identified targets. Results: The PPI network analysis highlighted immune-mediated pathways such as Fc-gamma receptor signaling, cytokine control, and T-cell receptor signaling as critical intervention points. Molecular docking revealed strong binding affinities between the selected compounds and the prioritized targets, suggesting their potential efficacy as host-targeting antiviral candidates. Acetohexamide and Deptropine showed strong binding to multiple targets, indicating broad-spectrum antiviral potential. Further in vitro and in vivo validations are needed to confirm these findings and translate them into clinically relevant treatments. Conclusions: This study highlights the potential of using computational approaches to identify host-targeted therapeutics for Oropouche virus (OROV). By targeting key host proteins involved in immune modulation—IL10, FASLG, PTPRC, and FCGR3A—the selected compounds, Acetohexamide and Deptropine, demonstrate strong binding affinities, suggesting their potential as broad-spectrum antiviral candidates. Further experimental validation is needed to confirm their efficacy and potential for clinical application, offering a promising strategy for sustainable antiviral drug discovery. Full article

In response to the increasing frequency of maritime traffic accidents along China’s coast, this study develops an accident-cause analysis framework that integrates an optimized Bidirectional Encoder Representations from Transformers (BERT) with a Bidirectional Long Short-Term Memory network (BiLSTM), combined with the Apriori association rule algorithm. Systematic performance comparisons demonstrate that the BERT + BiLSTM architecture achieves superior unstructured-text-processing capability, attaining 89.8% accuracy in accident-cause classification. The hybrid framework enables comprehensive investigation of complex interactions among human factors, vessel characteristics, environmental conditions, and management practices through multidimensional analysis of accident reports. Our findings identify improper operations, fatigue-related issues, illegal modifications, and inadequate management practices as primary high-risk factors while revealing that multi-factor interaction patterns significantly influence accident severity. Compared with traditional single-factor analysis methods, the proposed framework shows marked improvements in Natural Language Processing (NLP) efficiency, classification precision, and systematic interpretation of cross-factor correlations. This integrated approach provides maritime authorities with scientific evidence to develop targeted accident prevention strategies and optimize safety management systems, thereby enhancing maritime safety governance along China’s coastline. Full article

Human development education often relies on traditional, lecture-based teaching methods, thus limiting opportunities for active engagement. The lack of diverse creative and active teaching approaches hinders psychology students’ ability to fully understand and apply complex concepts. This study examined the effectiveness of a designed series of board games to enhance understanding of infant development and surveillance among psychology students. A mixed-method approach using a randomized matched control group design and qualitative exploration was applied. In the experiment, there were two groups, intervention and conventional learning, in three data collection phases. A design involving 60 students (30 in each group) was employed. The qualitative exploration involved the completion of a weekly journal to explore the students’ learning experiences when playing board games. A 2 × 3 mixed-design Analysis of Variance (ANOVA) with content analysis of the journal texts was performed. The results revealed that the created board games significantly enhanced students’ understanding, with them achieving significantly higher understanding scores in the posttest and follow-up phases in learning development progress. An interaction effect for the treatments and test phases was also noted. Meanwhile, the qualitative findings complemented and reinforced the quantitative results, offering deeper insights into the learning experiences and valuable suggestions for improving the design and rules of the games. In conclusion, by tailoring the board games to learning objectives associated with infant developmental psychology, educators can enhance student engagement, knowledge retention, and real-classroom application. Full article

Large reservoir-induced landslides pose a persistent threat to the safety of the Three Gorges Project and the Yangtze River shipping channel. A comprehensive multi-field monitoring system has been established to observe potential landslide areas within the Three Gorges Reservoir Area. The tasks of effectively utilizing these extensive datasets and exploring the underlying correlation among various monitoring objects have become critical for understanding landslide movement patterns, assessing stability, and informing disaster prevention measures. This study focuses on the No. 1 riverside sliding mass of the Huangtupo landslide, a representative large-scale landslide in the Three Gorges Area. We specifically analyze the deformation characteristics at multiple monitoring points on the landslide surface and within underground tunnels. The analysis reveals a progressive increase in deformation rates from the rear to the front and from west to east. Representative monitoring points were selected from the front, middle, and rear sections of the landslide, along with four hydrological factors, including two reservoir water factors and two rainfall factors. These datasets were classified using the K-means clustering algorithm, while the FP-Growth algorithm was employed to uncover correlations between landslide deformation and hydrological factors. The results indicate significant spatial variability in the impacts of reservoir water levels and rainfall on the sliding mass. Specifically, reservoir water levels influence the overall deformation of the landslide, with medium-to-low water levels (146.32 to 163.23 m) or drawdowns (−18.70 to −2.16 m/month) accelerating deformation, whereas high water levels (165.37 to 175.10 m) or rising water levels (4.45 to 17.33 m/month) tend to mitigate it. In contrast, rainfall has minimal effects on the front of the landslide but significantly impacts the middle and rear areas. Given that landslide deformation is primarily driven by periodic fluctuations in reservoir water levels at the front, the movement pattern of the landslide is identified as retrogressive. The association rules derived from this study were validated using field monitoring data, demonstrating that the data mining method, in contrast to traditional statistical methods, enables the faster and more intuitive identification of reservoir-induced landslide deformation patterns and underlying mechanisms within extensive datasets. Full article

With the global aging population on the rise, the health and safety of elderly individuals living alone have become increasingly critical. This study introduces a novel AIoT-based habit mining algorithm designed to enhance activity monitoring in smart home environments. The proposed method integrates a one-dimensional U-Net neural network for accurate behavioral classification and an FP-Growth-based temporal association rule analysis for uncovering meaningful living patterns. By leveraging environmental sensor data, the algorithm first classifies daily activities and then uses timestamps to detect time-sensitive dependencies in behavior sequences, identifying the long-term habits of the elderly. Experimental validation on CASAS datasets (ARUBA and MILAN) demonstrates superior performance, achieving a precision of 84.77%. Compared to traditional techniques, this approach excels in behavior recognition and habit mining, offering a precise and adaptive framework for AIoT-driven smart home safety and health monitoring systems. The results highlight its potential to improve the quality of life and safety for elderly individuals living alone. Full article

Investing in the stock market has always been an exciting topic for people. Many specialists have tried to develop tools to predict future stock prices in order to make high profits and avoid big losses. However, predicting prices based on the dynamic characteristics of stocks seems to be a non-trivial problem. In practice, the predictive models are not expected to provide the most accurate forecasts of stock prices, but to highlight changes and discrepancies between the predicted and observed values, to warn against threats, and to inform users about upcoming opportunities. In this paper, we discuss the use of frequent sequences as well as association rules in WIG20 stock price prediction. Specifically, our study used two methods to approach the problem: correlation analysis based on the Pearson correlation coefficient and frequent sequence mining with temporal constraints. In total, 43 association rules were discovered, characterized by relatively high confidence and lift. Moreover, the most effective rules were those that described the same type of trend for both companies, i.e., rise ⇒ rise, or fall ⇒ fall. However, rules that showed the opposite trend, namely fall ⇒ rise or rise ⇒ fall, were rare. Full article

The integration of advanced technologies, such as the Metaverse, has the potential to revolutionize the retail industry and enhance the shopping experience. Understanding consumer behavior and leveraging machine learning predictions based on analysis can significantly enhance user experiences, enabling personalized interactions and fostering overall engagement within the virtual environment. In our ongoing research effort, we have developed a consumer behavior framework to predict interesting buying patterns based on analyzing sales transaction records using association rule learning techniques aiming at improving sales parameters for retailers. In this paper, we introduce a validation analysis of our predictive framework that can improve the personalization of the shopping experience in virtual reality shopping environments, which provides powerful marketing facilities, unlike real-time shopping. The findings of this work provide a promising outcome in terms of achieving satisfactory prediction accuracy in a focused pilot study conducted in association with a prominent retailer in Saudi Arabia. Such results can be employed to empower the personalization of the shopping experience, especially on virtual platforms such as the Metaverse, which is expected to play a revolutionary role in future businesses and other life activities. Shopping in the Metaverse offers a unique blend of immersive experiences and endless possibilities, enabling consumers to interact with products and brands in a virtual environment like never before. This integration of cutting-edge technology not only transforms the retail landscape but also paves the way for a new era of personalized and engaging shopping experiences. Lastly, this empowerment offers new opportunities for retailers and streamlines the process of engaging with customers in innovative ways. Full article