Correlation Rules Research Articles

A-195 Combination of Lean Mindset, Robotics and Digitalization allowed us to manage a 2.5 times increase in workload in our central reference lab

Abstract Background Our Agilus Diagnostics, Gurugram, India is a Central Reference Laboratory and part of the pan-India network over 400 lab in India. Our Gurugram lab delivers 3,200 tubes in the serum work area in 17 hour operations daily and is ISO 15189 and CAP accredited.In Jan 2023, our lab started to support Govt PHC Program samples and our workload increased to 8000 tubes. This was a clear operational and clinical challenge for us with a 2.5 x jump in workload. To achieve this goal, a lean mindset with process improvement steps were to be applied required across the total testing process. This lean approach was supported by cobas ® Lab Automation systems managed by navify ® Lab Operations informatics automation solution. This poster explains our journey of planning, implementing, and delivering on our goal of predictable operational efficiency when faced with this challenge. Methods We took 4 major steps We redesigned lab processes using lean process improvement operational dashboards. This poster will show the metric we track which help eliminate waste in the system. We optimized our total lab automation solution with smart load balancing across cobas ® Pro integrated analyzers and staff friendly recursive workflow. Clear and strict use of off peak hours for maintenance and QC procedures and standardized predictable schedule of operation. This poster will share our time table with key milestones across the 24 hours We built on the Informatics solution using 5 layered Auto verification rules based decision framework. IQC, Systems Flags check, Sample Quality checks, Biological Reference check, custom Auto verification Range, Critical ranges Delta check Criteria, Clinical Correlation Rule are all part of the auto verification logic framework. Results We were able to deliver the following results.Lean process design led to 53% simplification. Number of process steps reduced to 29 from 62 steps across the total testing process. Total Lab Automation has helped improve 90 percentile production TAT by 46% from 250 mins to 135 mins Our 90 % TAT CV has significantly reduced making our production predictable. Over 17,500 results / day reported are subject to auto verification with 87 % of all results auto-verified and auto-approved. Not a single additional employee was added in the analytical or post analytical areas of the lab operations Conclusions Lean Mindset complemented by Total Laboratory Automation along with Decision Automation on an Informatics solution has enhanced lab performance in terms of TAT, Efficiency, and Staff Motivation. This poster will also demonstrate using incremental effectiveness analysis comparative data of key performance indicators, such predictability in production, and efficiency of workforce, senior staff time saved, before and after technology deployment. The challenge of increase in sample workload is real in India and this approach allows for a sustainable way to ensure patient impact with minimum burden on our workforce.

Construction and Practice of Teaching Evaluation System Based on Data Mining

The teaching quality evaluation system based on data mining technology can accurately and fairly identify the core driving factors to improve teaching quality. This method adopts the analysis of big data correlation rules, including data collection and processing preparation steps, builds the data warehouse of association rules, and then generates an educational quality evaluation framework using the principle of data mining. Based on this, this paper analyzes the construction design and method of the teaching evaluation system under data mining, hoping to provide help for the improvement of the teaching evaluation system and the improvement of teaching quality.

Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

Analysis of Attack Intensity on Autonomous Mobile Robots

Autonomous mobile robots (AMRs) combine a remarkable combination of mobility, adaptability, and an innate capacity for obstacle avoidance. They are exceptionally well-suited for a wide range of applications but usually operate in uncontrolled, non-deterministic environments, so the analysis and classification of security events are very important for their safe operation. In this regard, we considered the influence of different types of attacks on AMR navigation systems to subdivide them into classes and unified the effect of attacks on the system through their level of consequences and impact. Then, we built a model of an attack on a system, taking into account five methods of attack implementation and identified the unified response thresholds valid for any type of parameter, which allows for creating universal correlation rules and simplifies this process, as the trigger threshold is related to the degree of impact that the attack has on the finite subsystem. Also, we developed a methodology for classifying incidents and identifying key components of the system based on ontological models, which makes it possible to predict risks and select the optimal system configuration. The obtained results are important in the context of separating different types of destructive effects based on attack classes. Our study showed that it is sometimes difficult to divide spoofing attacks into classes by assessing only one parameter since the attacker can use a complex attack scenario, mixing the stages of the scenarios. We then showed how adding an attack intensity factor can make classification more flexible. The connections between subsystems and parameters, as well as the attack impact patterns, were determined. Finally, a set of unique rules was developed to classify destructive effects with uniform response thresholds for each parameter. In this case, we can increase the number of parameters as well as the type of parameter value.

AGCM: A multi-stage attack correlation and scenario reconstruction method based on graph aggregation

With an increase in the complexity and scale of networks, cybersecurity faces increasingly severe challenges. For instance, an attacker can combine individual attacks into complex multi-stage attacks to infiltrate targets. Traditional intrusion detection systems (IDS) generate large number of alerts during an attack, including attack clues along with many false positives. Furthermore, due to the complexity and changefulness of attacks, security analysts spend considerable time and effort on discovering attack paths. Existing methods rely on attack knowledgebases or predefined correlation rules but can only identify known attacks. To address these limitations, this paper presents an attack correlation and scenario reconstruction method. We transform the abnormal flows corresponding to the alerts into abnormal states relationship graph (ASR-graph) and automatically correlate attacks through graph aggregation and clustering. We also implemented an attack path search algorithm to mine attack paths and trace the attack process. This method does not rely on prior knowledge; thus, it can well adapt to the changed attack plan, making it effective in correlating unknown attacks and identifying attack paths. Evaluation results show that the proposed method has higher accuracy and effectiveness than existing methods.

Risk identification and management through knowledge Association: A financial event evolution knowledge graph approach

The spread and evolution of financial risk events have endangered the operation of enterprises and financial institutions, and even strike the whole social credit system. To clarify the path of risk events and identify key risk sources by event association, we propose and construct a novel event-driven knowledge graph called “Financial Event Evolution Knowledge Graph (FEEKG)”, with a multi-layer structure of “entity-event-risk”. In the entity layer, the subgraph of about 112,000 entities and 78,500 relationships (including 6 types of entities and 12 types of relations) is constructed based on the event extraction algorithm and topic model. In the event layer, the evolution relationship scores higher than 0.2 is selected as the edge of the event evolution subgraph. The risk layer takes 23 types of topic risk events and 12 types of second-level risk types as nodes and risk transition relations as edges, to generate the dynamic evolution probability subgraphs of topic risk events and risk types. Driven by risk events, we also analyze the characters and rules of entity correlation, event evolution, and risk transmission based on FEEKG; propose a new method for timely mining potential related risk entities and effectively summarizing the regularity of risk evolution and transmission and provide a new perspective for enterprises and financial institutions to find the root of risks and formulate an effective risk management decision in time.

Evaluation of classical correlation functions from 2/3D images on CPU and GPU architectures: Introducing CorrelationFunctions.jl

Correlation functions are becoming one of the major tools for quantification of structural information that is usually represented as 2D or 3D images. In this paper we introduce ▪ open-source package developed in Julia and capable of computing all classical correlation functions based on imaging input data. Images include both binary and multi-phase representations. Our code is capable of evaluating two-point probability S2, phase cross-correlation ρij, cluster C2, lineal-path L2, surface-surface Fss, surface-void Fsv, pore-size P and chord-length p distribution functions on both CPU and GPU architectures. Where possible, we presented two types of computations: full correlation map (correlations of each point with other points on the image, that also allows obtaining ensemble averaged CF) and directional correlation functions (currently in major orthogonal and diagonal directions). Such an implementation allowed for the first time to assemble a completely free solution to evaluate correlation functions under any operating system with well documented application programming interface (API). Our package includes automatic tests against analytical solutions that are described in the paper. We measured execution times for all CPU and GPU implementations and as a rule of thumb full correlation maps on GPU are faster than other methods. However, full maps require more RAM and, thus, are limited to available RAM resources. On the other hand, directional CFs are memory efficient and can be evaluated for huge datasets – this way they are the first candidates for structural data compression of feature extraction. The package itself is available through Julia package ecosystem and on GitHub, the latter source also contains documentation and additional helpful resources such as tutorials. We believe that a single powerful computational tool such as ▪ presented in this paper will significantly facilitate the usage of correlation functions in numerous areas of structural description and research of porous materials, as well as in machine learning applications. We also present some examples as applied to ceramic, soil composite and oil-bearing rock samples based on their 3D X-ray tomography and 2D scanning electron microscope images. Finally, we conclude our paper with discussion of possible ways to further improve presented computational framework. Program summaryProgram Title: CorrelationFunctions.jlCPC Library link to program files:https://doi.org/10.17632/6gb9gfm3dw.1Developer's repository link:https://github.com/fatimp/CorrelationFunctions.jlLicensing provisions: MITProgramming language: JuliaSupplementary material: Numerous Jupiter notebooks with examples are available on the GitHub pageNature of problem: Correlation functions are invaluable universal statistical descriptors of structures used in numerous scientific fields such as astronomy, material science, rock and soil physics, hydrology and biology, to name just a handful of examples. While computational approaches are available in the literature for some functions, they are fragmented and are usually implemented in proprietary interpreted languages for CPU architecture alone.Solution method: We contribute an open source and cross-platform solution with well documented API for computation of all classical correlation functions from both 2D and 3D images on CPU and GPU architectures. The package computes correlation functions using two approaches: computation of correlation maps and computation along predefined directions. These two approaches can be thought of as an execution time - memory trade-off, but the choice may also depend on the application. The computations are based on a) fast Fourier transform with preprocessing steps such as cluster labeling or edge detection, and b) linear scan approach to evaluate correlation functions along predefined directions. Where justified, the algorithms can be executed on both CPU and GPU which results in high execution speed on modern hardware.

Exploration of the methods and rules of syndrome/pattern differentiation and treatment of headache from the acupuncture-moxibustion prescriptions of ancient literature based on the data mining technology.

The study aims to identifying and exploring the methods and rules of the syndrome/pattern differentiation and treatment of headache through collating acupuncture-moxibustion prescriptions recorded earliest in ancient literature. Using Excel2016 software, the structural data table was prepared with "name of disease", "location of disease", "etiology and pathogenesis", "complicated symptoms", "sites for acupuncture and moxibustion" and "techniques of acupuncture and moxibustion" included. The normative approach was conduced on "name of disease", "etiology and pathogenesis", "complicated symptoms" and "nomenclature of acupoint". Using conventional literature statistical method, combined with Apriori algorithm of association rule, the implicit multi-dimensional correlation rules were explored among various elements of syndrome/pattern differentiation of headache and corresponding therapeutic methods. Based on the findings of the study, the regularity was distinct regarding the treatment at "distal acupoints along the affected meridian and the local acupoints at the affected area" after identifying the location of headache; the strong association was presented between "etiology and pathogenesis" and "acupoint selection", and between "etiology and pathogenesis" and "therapeutic methods", including 9 and 12 rules, respectively. Guanyuan (CV 4) selected in treatment of headache was associated with kidney deficiency, the combination of Zhongwan (CV 12) and Zusanli (ST 36) was with phlegm, Fengfu (GV 16), Fengchi (GB 20), Xinghui (GV 22) and Baihui (GV 20) was with wind, and Hegu (LI 4) was with cold. Moxibustion was dominant in treatment if headache was caused by pathogenic cold or related to deficiency syndrome; acupuncture was used specially for the case caused by phlegm, or interaction of wind and phlegm or wind and heat. For heat syndrome, either acupuncture or moxibustion was applicable, in general, acupuncture was more commonly used in comparison with moxibustion for headache. There were 6 association rules regarding the acupoint selection and the techniques of acupuncture and moxibustion. Moxibustion was generally applied to Xinghui (GV 22), Shangxing (GV 23) and Baihui (GV 20) ; and acupuncture was to Fengfu (GV 16), Hegu (LI 4) and Zusanli (ST 36). There were few association rules between the complicated symptoms and acupoint selection. Among nearly 100 complications, there were only 3 feature associations. Zhongwan (CV 12) was selected for the case with poor appetite, Chengjiang (CV 24) was with neck stiffness, and Fengchic (GB 20) combined with Fenglong (ST 40) or Jiexi (ST 41) was used if vertigo was present. In the ancient time, regarding the treatment of headache, acupuncture and moxibustion are delivered based on the three aspects, i.e. the location of illness, the etiology and pathogenesis, and the complicated symptoms. For acupoint selection, in line with the courses of affected meridians, the adjacent and distal acupoints are combined according to the location of headache. The acupoint prescription is composed in terms of the etiology and pathogenesis. The techniques of acupuncture and moxibustion are optimized in consideration of the sites where acupuncture and moxibustion are operated.

Dynamics of quantum coherence and correlations in a transverse Ising spin chain environment

In this paper, we study the dynamical processes of quantum coherence and correlations for two central qubits system coupled with a transverse Ising spin chain. Suppose the initial state of quantum system is the Werner state and the initial state of environment is the ground state of spin chain, and the corresponding time evolution operator, decoherence factor and reduced density matrix are given. We deduce the analytical expressions of evolution of quantum coherence, entanglement and quantum discord. We find that when the spin chain undergoes quantum phase transition (QPT), the entanglement vanished at time [Formula: see text], and the coherence and discord vanished when the decoherence factor decayed to zero. Besides, we also find that the environmental scale N, coupling strength g and parameter P of Werner state do not change the evolution rules of quantum correlation, but accelerate their decay rate with these parameters’ increase.

Fast Kinetics Design for Solid-State Battery Device.

Fast kinetics of solid-state batteries at the device level is not adequately explored to achieve fast charging and discharging. In this work, a leap forward is achieved for fast kinetics in full cells with high cathode loading and areal capacity. This kinetic improvement is achieved by designing a hierarchical structure of electrode composites. In the cathode, the authors' design enables high areal capacities above 3 mAh cm-2 to be stably cycled at high current densities of ≈13-40mA cm-2, yielding a C-rate from 5 to 10 C. In the anode, the authors' design breaks the common rule of the negative correlation between critical C-rate and the discharge voltage that is observed in most other anodes. The overall design enables the fast cycling of such batteries for over 4000 cycles at room temperature and 5 C charge-rate. The design principles unveiled by this work help to understand critical kinetic processes in battery devices that limit the fast cycling at high cathode loading and speed up the design of high-performance solid-state batteries.

Variation in Phosphorus Concentration and Flux at Zhutuo Section in the Yangtze River and Source Apportionment

Phosphorus (P) is a pollutant of great concern in the Yangtze River Basin. The Xiangjiaba Reservoir and Xiluodu Reservoir on the lower reach of the Jinsha River began to operate in 2012 and 2013, respectively, which greatly changed the concentrations of suspended sediment and characteristics of P form and transport in the reservoirs and the downstream reach from Yibin to Jiangjin of the Yangtze River. The Zhutuo section is representative in the water quality of the Yibin-Jiangjin reach, which can not only reflect the comprehensive effects of the formation of the two reservoirs and changes in the aquatic environment in the Min-Tuo Rivers but also reflect the quality of water flowing into the Three Gorges Reservoir. The runoff, concentrations and fluxes of suspended sediments (SS), and P concentrations and fluxes at Zhutuo section were studied during 2002-2019, and the source of P was apportioned based on the principle of river base flow. The results showed that in the past 18 years, the concentrations and fluxes of total phosphorus (TP) and particulate phosphorus (PP) at Zhutuo section in the wet season were higher than those in the level and dry seasons; the rule of positive correlation between PP and SS concentrations remained unchanged. From 2002 to 2019, the concentrations and fluxes of TP, PP, and dissolved P (DP) generally increased first and then decreased, and the operation of the Xiangjiaba Reservoir was a time node for the trend turning. Compared with that in the period from 2002-2012, the SS concentration and flux decreased by 94% and 77%, TP and PP concentrations decreased by 46% and 70%, and TP and PP fluxes decreased by 58% and 74%, respectively, during 2014-2019. The decline mainly occurred in the wet season, followed by that in the level season. After the formation of the two reservoirs, the relationship between water and sediment and the form of P greatly changed, and the proportion of DP in TP increased significantly, whereas the proportion of PP was the opposite. The TP pool in overlying water in the dry and level seasons shifted from mainly particulate to mainly dissolved. The change in water and sediment conditions was the main driving force for the significant change in P concentration, flux, and form. Before the operation of the Xiangjiaba Reservoir, the Jinsha River was the maximum contributor to the whole and diffuse source part of the TP load at Zhutuo section among the contributing catchment sub-basins; however, the Minjiang River became the largest contributor after the operation. The average TP load at Zhutuo section from 2017-2019 was 3.575×104 t·a-1 (after deducting the natural background value), of which the contribution of diffuse sources and point sources accounted for 68% and 32%, respectively. The Minjiang River represented 49%, 43%, and 62% of the total TP load, diffuse source TP load, and point source TP load at Zhutuo section, respectively. Considering the load contribution and pollution intensity, the key area for P pollution control in the area upstream of the Three Gorges Reservoir was the Min-Tuo River Basin.

Research on the Integration and Development of Visual Professional Education and Book Creative Design Combined with Association Rule Mining

Abstract The visual professional education of existing colleges and universities can no longer meet the needs of the development of the times. In view of such a situation, this paper explores the integration and development of visual professional education and book creative design using association rule mining. Based on xAPI technology, a data collection platform has been designed to provide security, easy scalability, and real-time data acquisition for visual professional education and creative design text data acquisition. Afterward, the data is preprocessed so that it can be used for association rule mining in the future. The support degree, confidence degree, and enhancement degree of the two are defined to achieve the purpose of constructing the Apriori association rule mining analysis model for the fusion development of visual professional education and book and cultural and creative design. The analysis of the association rules of visual education and creative design shows that 10 association rules are obtained when the minimum support degree is 0.4. The minimum confidence degree is 0.6, and the confidence degree of the practical activities in visual professional education for the cultural elements and creative elements in book and creative design is 0.657, 0.619. The degree of enhancement is 0.709 and 1.021, and the correlation rules of all professional education and book and creative design show a synchronous enhancement of the correlation rules. Association rules show a synchronized enhancement of correlation.

Research on Intelligent Decision Support System for Student Management and Mental Health Intervention in Higher Education Institutions

Abstract The intelligent management system of colleges and universities is an important display of the informationization construction of colleges and universities. Aiming at the life trajectory and life information of students in colleges and universities, this paper combines data mining and other technologies, adopts Python language and MySQL database technology, and improves on the Flask framework to develop an intelligent decision support system for student management and mental health intervention. The study takes student achievement abnormality detection and mental health intervention as an example, uses the improved K-Means algorithm and Apriori algorithm to mine and analyze the relevant data, and discusses the specific application of the intelligent decision support system. The sample students can be divided into three different types of academic performance: excellent, ordinary, and difficult. Precision teaching can be implemented according to the different types of students. Only 3% of the students had more obvious and serious psychological problems, while 80% of the students had a relatively healthy psychological state. Moreover, the correlation rules between student information and mental health status are analyzed, which can aid in the implementation of mental health interventions. The intelligent system of student management can provide the most direct and effective evidence for academic administrators in making decisions on teaching plans, thus making student education more targeted, personalized, and predictive.

Modular Application of Evaluation System for Innovation and Entrepreneurship Education of College Students--Based on Big Data Platform Analysis

Abstract Based on the objectives of innovation and entrepreneurship education for college students, this paper constructs the evaluation index system, utilizes the improved Apriori algorithm to mine the association rules between the indicators, and reflects the relevant influence between the evaluation indicators through the association rules with higher support and confidence. Meanwhile, based on the big data platform, an evaluation system for innovation and entrepreneurship education for college students is established, and the level of innovation and entrepreneurship education for students is evaluated. The analysis of the correlation rules between the evaluation indicators found that the support degree of the rule that students with stronger independent ability are better at grasping innovation and entrepreneurship opportunities is 29.97%, which provides a reference for the teachers’ teaching objectives and content development. The results of the empirical analysis of the evaluation system of innovation and entrepreneurship education show that the modular application of the evaluation system created in this paper has a positive effect, and there is a highly significant difference in the evaluation of the “Z13 personal willingness” index before and after the application (P=0.000<0.001). The correlation rule of evaluation indexes in this study lays a theoretical foundation for the effective development of innovation and entrepreneurship teaching, and the modularized application of the innovation and entrepreneurship evaluation system constructed in this paper can improve the level of innovation and entrepreneurship education in colleges and universities to a certain extent.

Product Bundling Strategy for Office Supplies Retailer through Association Rules Mining: Comparative Study of Apriori and ECLAT Algorithms

Our study aims to develop an effective bundled product promotion strategy for the office supply store to boost sales. The primary challenge is comprehending which product combinations align with customer preferences and cater to their needs. We leverage the Apriori and ECLAT algorithms for consistent rule generation, revealing robust associations between product purchases. Notably, a strong positive correlation rule emerges at a confidence level of 0.8, while at 0.9, no results are found. The identical rules derived from both algorithms signify their reliability. The shop owner employs two rules for bundled products based on a minimum Lift Ratio of 1.96. The first bundle focuses on 70gsm natural paper in Folio and Quarto sizes, capitalizing on their popularity, even though customers may prefer one size. The second bundle emphasizes notebooks, often bought together but in smaller quantities than paper products, reflecting diverse customer needs and behaviors.

A Meta-Analysis on the process of how to understand Semantic Correlation Rules

The academic training of young professionals plays a particularly important role in the field of technical communication (TC). Since well-trained specialists are in great demand in this domain, it’s important for students to successfully learn semantic correlation rules. But there must be a good interaction between different parties because students face major challenges when it comes to TC topics such as content management, metadata management or semantic technologies. There are several challenges to understanding in the considered case of semantic modelling and technical communication in general. However, this paper does not just show different ways of bringing together the different perspectives of students, lecturers and institutions related to a specialized course on semantic correlation rules. Rather, it develops a new area of work that enables new collaborations and takes the field of technical communication to a new level.

A study on the correlation between physical education courses and students’ health status in colleges and universities based on big data analysis

Abstract Physical education in colleges and universities is of great significance to the development of the whole education chain, to realize the goal of physical education courses in colleges and universities and to promote the physical and mental health development of students, this paper takes the physical education courses and students in Z college and university as the object of research, and proposes a correlation model of the physical education courses in colleges and universities and students’ health status by combining the fuzzy comprehensive evaluation and the Apriori correlation rule algorithm. The weights were calculated after constructing the physical education course quality index system. The quality of physical education courses in college Z was comprehensively evaluated. It was concluded that teaching explanation and demonstration (0.137), venue equipment (0.133) and physical education class schedule (0.113) had the most significant influence on the quality of physical education courses. The quality of physical education courses in the selected colleges and universities was good, scoring 88.93 points. When the quality of physical education courses is excellent and sound, the confidence level of students’ health status corresponding to excellent and sound is greater than 80%, indicating that physical education courses strongly correlate with students’ health. Among the various indicators of courses analyzed using big data, teaching explanation and demonstration, venue equipment, and students’ health status have stronger correlations.

Design and Timeliness Analysis of English Teaching Models in Colleges and Universities Combining Perceptual and Behavioral Correlation Algorithms

Abstract With the continuous development of education, the way English education presents itself has become more and more complex and diversified. This study focuses on the problem of low quality and efficiency in English teaching, and it centers on the characteristics of perception and behavior in English teaching. To collect data on perception and behavior in English teaching, we first utilize the association rule method in data mining technology. Then, we introduce the technology acceptance model and design the English teaching model of “Knowing and Doing” by combining the collected data. The timeliness of the English teaching model in practice is investigated using the fuzzy comprehensive evaluation method on this basis. The English teaching model designed in this paper improved by 10.688 points compared to the previous one, and the improvement was 1.307 points in the third month. In the correlation rule analysis, it can be seen that the probability of English grade A (90 or above) is 70.4% when perceived ease of use, usefulness, and learning behavior occur simultaneously. This study proves the feasibility of the English teaching model in this paper and provides a useful reference for the development of the English teaching model in the future.

Reform of educational management work in higher education based on departmental sociology in the context of intelligent technology

Abstract Based on Apriori algorithm and BP neural network technology, this paper deeply integrates association rules and data mining technology in education and informatization management, which provides a new method and a new way of thinking for reforming education management work in the context of intelligent technology. In real-life settings, education management is being managed using this new method. Taking the university district of DN201 as the research object, the allocation level of basic education resources in the university district is measured and optimized in a balanced way through indicator analysis. The parameter settings described allowed for the search of strong correlation rules for the mastery level of students in each course cluster. The results show that SN201 has the highest level of educational resource allocation at 8.32. SN215 is at the lowest level at 2.62. This shows that there is an imbalance in its resource allocation. The coefficient of variation of educational resource allocation of SN217 is 0.83 before optimization, and after optimization, the coefficient of variation is 0.65, and the variation becomes lower. The courses have a higher level of enhanced strong association than 4.

Physical Fitness Detection System for College Students Based on Correlation Rule Data Mining

Physical Fitness Detection System for College Students Based on Correlation Rule Data Mining