Minimum Confidence Research Articles

Fuzzy Association Rule Mining for Personalized Chinese Language and Literature Teaching from Higher Education

Due to rapid information technology growth, teaching Chinese in higher education has changed, and Chinese literary majors have vigorously evolved. The key teaching difficulties are scalability, individualized teaching, and a lack of resources and methodologies. Research shows individualized education improves topic comprehension, cultural engagement, and learner interest. Fuzzy association rule mining uses fuzzy linguistic values and membership functions to provide more realistic results. Hence, an algorithm, EF-PCL2T, has been proposed to improve personalized Chinese language and literature teaching (PCL2T) using enhanced fuzzy (EF) Apriori association rule mining integrated with the genetic algorithm. Fuzzy Apriori association rule mining identified frequent itemsets with relevant learning patterns and produced applicable association rules from datasets with fuzzy or unclear information, capturing fluctuating itemset importance and providing a flexible representation of relationships to determine student preferences. From fuzzy-related data, a genetic algorithm optimizes skill sets and creates individualized lesson plans considering each student’s competency and preferences for adjusting to personalized teaching tactics. Testing shows that fuzzy enhancement association rule mining for the PCL2T model improves student retention, PET (personalized teaching efficiency), minimal support and confidence update with fuzzy rules, and student involvement compared to other state-of-the-art methods. Students agree that tailored Chinese language and literary instruction is possible. The improvement results show fuzzy rules with minimum confidence levels of 50% to 100%, highly correlated in this model, student retention ratio of 96%, improved assessment grade of various language skills by 40 marks, PTE analysis of 93%, and student involvement ratio of 97%.

Pemodelan Data Mining Transaksi Penjualan Menggunakan Algoritma Apriori (Studi Kasus: Kedai Ngodeng & Smoothies)

The role of UMKM is very large in the growth of the Indonesian economy, Kedai Ngodeng & Smoothies, as one of the UMKM that is active in the food and beverage industry. This shop has not made maximum use of sales transaction data. The transaction data is only used as an archive and is not used to organize sales strategies. In this research, the method used is the a priori algorithm to determine the relationship between one product item and other product items so that it can be used as a promotional package or bundling to increase sales profits. The data used is sales transaction data for the Ngodeng & Smoothies shop from January to May 2024. The research began with initial data collection, followed by data description and evaluation of data selection. After that, attribute selection is carried out. The CRISP-DM method is applied in this research which includes six stages, namely business understanding, data understanding, data preparation, modeling, evaluation and dissemination. Based on the results of the research, there are 95 Association Rules based on previously determined parameters, namely the minimum support value is 50% and the minimum confidence is 90%. One of the rules that was formed is that Smoothies Mangga Large, Twister, Kembang Cumi --> Odeng Spicy has a confidence value of 100%. The information obtained can provide important insights into customer preferences and can be used to design more effective product bundling promotions.

Analisis Perbandingan Algoritma Apriori dan FP-Growth untuk Menentukan Strategi Penjualan Pada Maestro Jakarta Cafe & Space

There has been a decline in sales at Maestro Jakarta Cafe & Space due to a lot of competition and not optimal management of transaction data so that innovation is needed to increase sales. This study aims to compare the performance and results of the Apriori and FP-Growth algorithms in analyzing sales transaction data to determine the optimal sales strategy. This research uses the Apriori and FP-Growth methods to analyze sales transaction data by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM). The data used is product sales transaction data from November 2023 to April 2024. The results of performance comparisons in processing time speed and memory usage that have been carried out show that in processing time speed the FP-Growth algorithm is slightly faster than the Apriori algorithm while in the use of memory capacity the Apriori algorithm requires a larger memory capacity than the memory capacity used by the FP-Growth algorithm. This shows that the performance of the FP-Growth algorithm is better than the Apriori algorithm. The analysis results of the Apriori and FP-Growth algorithms on sales transaction data using a minimum support value of 1% and a minimum confidence value of 100% resulted in 22 association rules. Both algorithms produce identical rules, with the only difference being the occurrence index. The results of this analysis can be used by Maestro Jakarta Cafe & Space in determining sales strategies.

Intelligent Identification of Hidden Dangers in Hydrogen Pipeline Transmission Station Using GWO-Optimized Apriori Algorithm

This work proposes an intelligent grey-wolf-optimizer-improved Apriori algorithm (GWO-Apriori) to mine the association rules of hidden dangers in hydrogen pipeline transmission stations. The optimal minimum support and minimum confidence are determined by GWO instead of the time-consuming trial approach. Experiments show that the average support and average confidence of association rules using GWO-Apriori increase by 29.8% and 21.3%, respectively, when compared with traditional Apriori. Overall, 59 ineffective association rules out of the total 105 rules are filtered by GWO, which dramatically improves data mining effectiveness. Moreover, 23 illogical association rules are excluded, and 12 new strong association rules ignored by the traditional Apriori are successfully mined. Compared with the inefficient and labor-intensive manual investigation, the intelligent GWO-Apriori algorithm dramatically improves pertinency and efficiency of hidden danger identification in hydrogen pipeline transmission stations.

Efficacy and Mechanism of Core Traditional Chinese Medicines for Treating Malignant Lymphoma based on Efficacy Studies: A Study Supported by Network Pharmacology and Molecular Docking.

The burden of malignant lymphoma in China is greater than the global equivalent. The randomized controlled trials provide medical evidence that TCM can improve the response and survival in patients with lymphoma. However, the mechanisms underlying remain undefined. Evidence-based data mining for traditional Chinese medicine (TCM) on improving response and survival in malignant lymphoma treatment was performed in this study. In addition, the mechanisms of TCM through network pharmacology and molecular docking were explored. The China national knowledge infrastructure, Wanfang Data, China Science and Technology Journal Database, PubMed, and Web of Science databases were searched to select TCM formulas with response and survival benefits in the treatment of malignant lymphomas. We then analyzed and visualized the tropism of taste, frequency of drug use, dosage, clustering, association rules mining (minimum support threshold as 0.20, the minimum confidence threshold as 0.80 and lift >1), and complex networks for potential core herb compositions using Excel, IBM SPSS Statistics 26, and IBM SPSS Modeler 18. TCM systems pharmacology, GeneCards, Online Mendelian Inheritance in Man, and other databases were used to screen potential core active ingredients and malignant lymphoma-related targets. The intersection targets were used to construct a protein interaction network using Cytoscape to obtain the key targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment were used to analyze the core target, and molecular docking of key components and targets was performed using CB-Dock2. Twenty-four Chinese herbal formulae were included, encompassing 107 herbs with mainly cold and warm properties and bitter and sweet flavors. They were associated with the yin meridians of the liver, spleen, and lungs. The TCMs underwent association rule analysis, identified 27 association rules, including 12 herb pairs and 13 angle medicine, and clustered into eight classes by clustering analysis. Combined with the results from mining analysis, Pinelliae (Ban-xia), Poria (Fu-ling), Atractylodis macrocephalae (Bai-zhu), Curcumae (E-zhu), and Sparganii (San-leng) were the potential core herbs According to network pharmacology and molecular docking, the main core components of the potential core drugs are hederagenin, cerevisterol, 14- acetyl-12-senecioyl-2E,8E,10E-atractylentriol, 12,13-epoxy-9-hydroxynonadeca-7,10-dienoic acid, cavidine, and baicalein. These core drugs are mainly involved in the pathways of EGFR tyrosine kinase inhibitor resistance, PD-1/L1, natural killer cell-mediated cytotoxicity, NF-κB, epithelial cell signaling in H. pylori infections, and Th17 cell differentiation. They aid in regulating the transmembrane receptor protein tyrosine kinase signaling pathway, ERBB signaling pathway, PI3K signaling pathway, and phosphorylation process. Ten key components and eight key targets, including baicalein and hederagenin, demonstrated strong binding activity. Collectively, some core herbs exerted anti-tumor effects through immune and inflammatory pathway modulation, inhibition of immune escape, and induction of cell apoptosis. These findings support future evidence-based research on malignant lymphoma treatment using TCM.

Association Relationship Analysis in Finding Sales of Goods With Apriori Algorithm

Technology can be designed to help human life from all aspects ranging from agriculture, health science, industry and daily life. Toko Intan, a business engaged in the sale of basic and daily necessities. Every day, Toko Intan records every sales transaction in an archive stored in Microsoft Excel, containing data on goods sold every day. The purpose of this research is to find out what items are bought simultaneously by consumers to manage inventory, with the data mining method used in this research is the Association Rules method. Association Rules is one of the data mining techniques from the a priori algorithm which functions to find a combination pattern of an item. Tests carried out to process data in this study using the RapidMiner application, from tests carried out with the specified parameters, namely minimum support 30% and minimum confidence 65%, resulted in a lift ratio validation rule of 1.206. Personal Care Biscuits with 30.8% support and 90.9% confidence with a validation lift ratio of 1.206. Sales transaction data analysis can be applied well, and is able to generate a new association rule from the sales transaction dataset. With this research, it is hoped that it can provide information to the owner of the Intan store in providing the stock of goods needed by consumers and to find out the combination of item sets from the sales transaction dataset.

Implementation of The Apriori Algorithm in Managing Stock Items at Drl.Rumahan Retail

Drl.Rumahan is a retail store that sells a variety of motorcycle lamp modifications. Drl.Rumahan is still struggling with determining stock levels and understanding customer purchases. Additionally, they are not utilizing transaction data as a valuable information source. Without leveraging this data, Drl.Rumahan will fall behind its business competitors and lose customers because the products they seek are unavailable. This situation will inevitably become a significant problem if it continues. This study aims to utilize sales transaction data as valuable information and identify customer purchasing patterns from the sales transaction data. The algorithm used is the Apriori algorithm to identify purchasing patterns from the transaction data set. The results of this study identified the three highest rules: if someone buys a pass beam switch, they will buy a shroud with a support value of 5.8% and a confidence value of 47.6%; if someone buys a shroud, they will buy a pass beam switch with a support value of 5.8% and a confidence value of 45.5%; and if someone buys a shroud, they will buy a relay with a support value of 5.2% and a confidence value of 40.9%. These results can inform business strategy decisions by increasing the inventory of products that form rules and serve as a guide for promotional product packages for products that have rules above the minimum support and minimum confidence.

Clustering of uninhabitable houses using the optimized apriori algorithm

Clustering is one of the roles in data mining which is very popularly used for data problems in solving everyday problems. Various algorithms and methods can support clustering such as Apriori. The Apriori algorithm is an algorithm that applies unsupervised learning in completing association and clustering tasks so that the Apriori algorithm is able to complete clustering analysis in Uninhabitable Houses and gain new knowledge about associations. Where the results show that the combination of 2 itemsets with a tendency value for Gas Stove fuel of 3 kg and the installed power meter for the attribute item criteria results in a minimum support value of 77% and a minimum confidence value of 87%. This proves that a priori is capable of clustering Uninhabitable Houses to help government work programs.

Clustering of uninhabitable houses using the optimized apriori algorithm

Clustering is one of the roles in data mining which is very popularly used for data problems in solving everyday problems. Various algorithms and methods can support clustering such as Apriori. The Apriori algorithm is an algorithm that applies unsupervised learning in completing association and clustering tasks so that the Apriori algorithm is able to complete clustering analysis in Uninhabitable Houses and gain new knowledge about associations. Where the results show that the combination of 2 itemsets with a tendency value for Gas Stove fuel of 3 kg and the installed power meter for the attribute item criteria results in a minimum support value of 77% and a minimum confidence value of 87%. This proves that a priori is capable of clustering Uninhabitable Houses to help government work programs.

Implementation of the Fp-Growth Algorithm on Spare Parts Supply Requests

Manufacturing companies rely on machines for operational activities to produce finished goods. Common factors constraining the demand and supply of spare parts are the high number of spare parts managed and irregular patterns of demand for spare parts. These varying quantities also require investment in spare parts inventory and longer response times than predicted. The research aims to apply the FP-Growth algorithm approach to find association rules and produce patterns of demand and supply of spare parts in lightweight brick manufacturing companies based on transaction data on demand and supply of spare parts from January – March 2023. The approach used is associated with the applied algorithm. In this research, the primary process of the FP-Growth algorithm is to create a combination of each item until no more combinations are formed using minimum support and minimum confidence parameters. Based on the results of making association rules using spare parts demand data from the machine maintenance department, it is stated that the regulations formed from processing the RapidMiner application with a confidence value of 100% recommend FD Regular Bolt spare parts, then the next rating with a confidence value of 94% is Steel Nuts, seven rules recommend Nuts. Steel. Therefore, it is recommended that FD Regular Bolts and Steel Nuts carry out safety stock to maintain stock availability and place them on shelves included in the fast-moving inventory category.

YOLOv7-Based Intelligent Weed Detection and Laser Weeding System Research: Targeting Veronica didyma in Winter Rapeseed Fields

In recent years, rapeseed oil has received considerable attention in the agricultural sector, experiencing appreciable growth. However, weed-related challenges are hindering the expansion of rapeseed production. This paper outlines the development of an intelligent weed detection and laser weeding system—a non-chemical and precision agricultural protection method of weeding Veronica didyma in winter rapeseed fields in the Yangtze River Basin. A total of 234 Veronica didyma images were obtained to compile a database for a deep-learning model, and YOLOv7 was used as the detection model for training. The effectiveness of the model was demonstrated, with a final accuracy of 94.94%, a recall of 95.65%, and a mAP@0.5 of 0.972 obtained. Subsequently, parallel-axis binocular cameras were selected as the image acquisition platform, with binocular calibration and semi-global block matching used to locate Veronica didyma within a cultivation box, yielding a minimum confidence and camera height values of 70% and 30 cm, respectively. The intelligent weed detection and laser weeding system was then built, and the experimental results indicated that laser weeding was practicable with a 100 W power and an 80 mm/s scanning speed, resulting in visibly lost activity in Veronica didyma and no resprouting within 15 days of weeding. The successful execution of Veronica didyma detection and laser weeding provides a new reference for the precision agricultural protection of rapeseed in winter and holds promise for its practical application in agricultural settings.

A deep learning-based surrogate model for probabilistic analysis of high-speed railway tunnel crown settlement in spatially variable soil considering construction process

In reality, many high-speed railway tunnels are built by sequential excavation method in spatially variable soil. This study introduces an efficient deep learning-based solution for the probabilistic analysis of tunnel crown settlements considering the spatial variability of surrounding soil. The two-dimensional convolutional neural network (2D-CNN) is employed to uncover the implicit correlation between the soil elastic modulus random field and tunnel crown settlements. The correlation coefficient (R) of the surrogate model is greater than 0.9664. Meanwhile, the mean relative error of the predictions remains within 2.38%, under various scales of fluctuation (SOFs). An additional 10,000 samples were produced to assess the practical performance of the trained model in probabilistic analysis. The relative errors of predictions generally fall within 5%, with a minimum confidence interval of 98.15%. These results demonstrate that the developed 2D-CNN model can effectively substitute the traditional method in predicting tunnel crown settlements considering soil spatial variability.

An interpretable online prediction method for remaining useful life of lithium-ion batteries

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is advantageous for maintaining the stability of electrical systems. In this paper, an interpretable online method which can reflect capacity regeneration is proposed to accurately estimate the RUL. Firstly, four health indicators (HIs) are extracted from the charging and discharging process for online prediction. Then, the HIs model is trained using support vector regression to obtain future features. And the capacity model of Gaussian process regression (GPR) is trained and analyzed by Shapley additive explanation (SHAP). Meanwhile, the state space for capacity prediction is constructed with the addition of Gaussian non-white noise to simulate the capacity regeneration. And the modified predicted HIs and noise are obtained by unscented Kalman filter. Finally, according to SHAP explainer, the predicted HIs acting as the baseline and the modified HIs containing information on capacity regeneration are chosen to predict RUL. In addition, the bounds of confidence intervals (CIs) are calculated separately to reflect the regenerated capacity. The experimental results demonstrate that the proposed online method can achieve high accuracy and effectively capture the capacity regeneration. The absolute error of failure RUL is below 5 and the minimum confidence interval is only 2.

Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

1.Passive acoustic monitoring has become increasingly popular as a practical and cost-effective way of obtaining highly reliable acoustic data in ecological research projects. Increased ease of collecting these data means that, currently, the main bottleneck in ecoacoustic monitoring projects is often the time required for the manual analysis of passively collected recordings. In this study we evaluate the potential and current limitations of BirdNET-Analyzer v2.4, the most advanced and generic deep learning algorithm for bird recognition to date, as a tool to assess bird community composition through the automated analysis of large-scale ecoacoustic data.2.To this end, we study 3 acoustic datasets comprising a total of 629 environmental soundscapes collected in 194 different sites spread across a 19° latitude span in Europe. We analyze these soundscapes using both BirdNET and manual listening by local expert birders, and we then compare the results obtained through the two methods to evaluate the performance of the algorithm both at the level of each single vocalization and for entire recording sequences (1, 5 or 10 min).3.Since BirdNET provides a confidence score for each identification, minimum confidence thresholds can be used to filter out identifications with low scores, thus retaining only the most reliable ones. The volume of ecoacoustic data used in this study did not allow us to estimate species-specific minimum confidence thresholds for most taxa, so we instead used and evaluated global confidence thresholds selected for optimized results when consistently applied across all species.4.Our analyses reveal that BirdNET identifications can be highly reliable if a sufficiently high minimum confidence threshold is used. However, the inevitable trade-off between precision and recall does not allow to obtain satisfactory results for both metrics at the same time. We found that F1-scores remain moderate (<0.5) for all datasets and confidence thresholds studied, and that acoustic datasets of extended duration seem to be currently necessary for BirdNET to provide a reliable and minimally comprehensive picture of the target bird community. We estimate, however, that the usage of species- and context-specific minimum confidence thresholds would allow to substantially improve the global performance benchmarks obtained in this study.5.We conclude that a judicious use of AI-based identifications provided by BirdNET can represent a powerful method to assist in the assessment of bird community composition through the automated analysis of ecoacoustic data, especially when applied to acoustic datasets of extended duration.

Utilization of the FP-Growth Algorithm on MSME Transaction Data:Recommendations for Small Gifts from The Padang Region

The existence of adequate transaction data turns out to have a similar sales transaction pattern for MSMEs, so it would be a shame if it were left like that. Moreover, this data can be used to increase efficiency in MSMEs in the culinary sector, one of which is as a recommendation for small gifts. The study uses the Association Rules technique, whereas fp-growth is used to obtain a combination of elements. The goal is to facilitate MSMEs' ability to suggest small gifts to clients. The fp-growth algorithm calculation was implemented to process 2043 data originating from transaction data in MSMEs, with the specified minimum support value being 15%, while the minimum confidence value determined was 55%. The results of the trial obtained the two best rules, namely, "If a customer buys a list of small gifts from Balado Sanjai Chips, then the customer will buy Jangek Crackers" and "If a customer buys Jangek Crackers, then the customer will buy Sanjai Balado Chips".

PENERAPAN DATA MINING TERHADAP DATA PENJUALAN DENGAN MENGGUNAKAN ALGORITMA APRIORI PADA TOKO CITRA UTAMA

Citra Utama Store is a retail and wholesale business aimed at meeting the needs of local residents for their daily household shopping. The problem lies in the difficulty of identifying consumer shopping trends for products in the store, and the inefficiency of the stock placement system. In a competitive business environment, it is crucial for us to obtain information that can help develop our business. One source of such information is the sales transaction history. Using data mining with the apriori algorithm and association rule, we can extract sales transaction data to derive information that can be used to identify frequent itemset combinations, which can then be analyzed to determine products frequently sold together, the most popular items, and customer preferences. The analysis of association rules formed from the apriori algorithm calculation yielded the highest itemset combination pattern, namely EGGS → AQUA GLN, with a support value of 2.84% and a confidence value of 34.13%. By setting minimum support &gt;1.5% and minimum confidence &gt;30%, this research can assist the store in devising sales strategies and managing stock inventory, and uncover association rules that can be used as purchasing patterns by consumers

Shopping Cart Analysis to Support Business Management with the Apriori Algorithm

The increasingly advanced development of the business world has led to increasingly fierce competition. One way to maintain the company's survival is to maintain good relationships with customers. Each market has its own way of increasing sales. Serambi Mart is one of the minimarkets operating in Batusangkar City. This minimarket is relatively new because it has been operating since 2020. Even though it is new, this minimarket is quite busy with consumers visiting for shopping, this is due to Serambi Mart management choosing the right location. Based on observations made, product layout arrangements are still based on subjective management, so there are several products that are not suitable to be compared. The layout seems messy, causing difficulties for consumers in shopping. By utilizing sales transaction data at Serambi Mart. This research uses the Apriori algorithm. The Apriori algorithm is an association rule and looks for relationship patterns between one or more items in data, using Association Rules with Minimum Support of 30% and Minimum Confidence of 50%. Therefore, an application is needed that can help Serambi Mart to get information. One way to get this information is to utilize data mining techniques. By using the Apriori Algorithm method for arranging the product layout at Serambi Mart, it is hoped that it can provide convenience to consumers who shop

Application of the FP-Growth Algorithm in Consumer Purchasing Pattern Analysis

Technology is currently used in various ways, one of which is businesses engaged in selling daily products. The right marketing strategy makes knowledge of consumer shopping patterns important to study because consumers are the main actors in carrying out transactions. The more diverse the types of goods sold in a company, the more diverse the resulting consumer spending patterns will be. Data mining is an analysis process that is carried out automatically on complex and large amounts of data to obtain patterns or trends that are generally not realized. The FP-Growth algorithm is an alternative algorithm that can be used to determine the data set that appears most frequently (frequent itemset) in a data set. The method used in this research is the FP-Growth method which is implemented in the PHP programming language and MySQL as the database. Designing a data mining program using the FP-Growth method can analyze and manage consumer purchasing patterns based on goods purchased simultaneously. The data processed in this research is transaction data that has been processed into information so as to gain knowledge in calculating stock of goods sourced from the owner of Toko Asra. From testing this method, results were obtained from the 10 transactions in December 2021, by limiting the minimum support value to 0.2 and minimum confidence to 0.75, 33 patterns of consumer shopping habits were obtained, meaning that 33 products were most frequently purchased by consumers. Designing a data mining program using the FP-Growth method can help analyze consumer purchasing patterns based on items purchased simultaneously. The results of frequent itemset calculations can help find a sequence of combinations that can be used as product recommendations in business decision

Apply and improve the association rules in predicting national high school admission scores to Nha Trang University

The paper discusses how the traditional method of association rule mining based on user-defined minimum support and confidence can result in either too many or too few association rules. This could lead to valuable information being missed or redundant rules being generated, which is not practical and can be costly to implement. The paper proposes an improvement of DARIT algorithm to mine association rules without redundancy and applies it to data from previous years of National High School Exams to Nha Trang University. The goal is to use the results to build an admissions counseling system that can help students increase their chances of being admitted to universities based on their exam results. Keywords: Data mining, Association rule, Missing value, DARIT, consulting system.

Unveiling the Secrets of Obesity with Machine Learning (ML) Techniques/Algorithms

Obesity is the most significant health threat all over the world as it is considered the mother of all diseases. Recent decades have witnessed an extensive shift towards sedentary lifestyles including adaptation to luxury modes of transportation, processed food, lack of physical activities, and prolonged screen times. The trend of using cars and motorcycles to travel even short distances has emerged as a critical component in Pakistan's obesity pandemic. Whenever the causes of obesity are discussed, the focus remains always on processed food and genetic factors. This study is an effort to exploit a machine-learning-based pathway to predict the major causes of obesity using machine learning (ML) algorithms. For this research, a dataset of 2,111 instances of various age groups and lifestyles was employed. For analysis, three efficient ML algorithms known as k-nearest neighbor (k-NN or KNN), support vector machine (SVM), and decision tree (DT) were utilized. It was observed that the excessive use of personal transport and comparatively limited availability and use of public transport has been a significant cause of obesity in Class II and III obese individuals. From the experimental results, strong association rules with minimum confidence value of 25% and maximum support value of 80% were obtained. Also, DT with random forest technique was found to have the highest accuracy of 90.36%, with the recall factor value of 98.73%. This signifies the fact that sedentary lifestyle and mode of transportation have a significant impact on rising obesity level.