Rule Algorithm Research Articles

Asymptotic synchronization of conformable fractional-order neural networks by L’ Hopital’s rule

This paper focuses on the globally asymptotic synchronization (GAS) for delayed drive-response (DR) conformable fractional-order neural networks (CFONNS). Two differential inequalities (DIS) of conformable fractional-order (FO) are established, which play a central role in studying the GAS of DR CFONNS. By applying the new established two DIS of conformable FO and using L’ Hopital’s rule and limit algorithms of functions, two criteria on the GAS for the discussing networks are obtained. Since the studies on the synchronization for the CFONNS are very rare, the studies for the CFONNS are of important meaning in theory.

Hybrid Fuzzy Rule Algorithm and Trust Planning Mechanism for Robust Trust Management in IoT-Embedded Systems Integration

The Internet of Things (IoT) is rapidly expanding and becoming an integral part of daily life, increasing the potential for security threats such as malware or cyberattacks. Many embedded systems (ESs), responsible for handling sensitive data or facilitating secure online activities, must adhere to stringent security standards. For instance, payment processors employ security-critical components as distinct chips, maintaining physical separation from other network components to prevent the leakage of sensitive information such as cryptographic keys. Establishing a trusted environment in IoT and ESs, where interactions are based on the trust model of communication nodes, is a viable approach to enhance security in IoT and ESs. Although trust management (TM) has been extensively studied in distributed networks, IoT, and ESs, significant challenges remain for real-world implementation. In response, we propose a hybrid fuzzy rule algorithm (FRA) and trust planning mechanism (TPM), denoted FRA + TPM, for effective trust management and to bolster IoT and ESs reliability. The proposed system was evaluated against several conventional methods, yielding promising results: trust prediction accuracy (99%), energy consumption (53%), malicious node detection (98%), computation time (61 s), latency (1.7 ms), and throughput (9 Mbps).

Optimization of the College Basketball Teaching Mode Based on the Applied Explainable Association Rule Algorithm and Cluster Analysis in Mobile Computing Environments

ABSTRACT Under the mobile computing environment, a large number of convenient mobile terminals and extensive mobile network application services have been produced. The technology has been used in college sports teaching to optimize the management of sports teaching. In this context, this paper studies the application of the association rule algorithm and cluster analysis in basketball teaching, which will effectively promote the practice and application of the association rule algorithm and cluster analysis in college PE teaching in China. This paper studies basketball teaching in colleges and universities based on the applied explainable association rule algorithm and cluster analysis. It is concluded that the p value of positioning shooting in the basic basketball technology test between the experimental class and the control class before the experiment is 0.883, which is greater than 0.06, indicating that there is no significant difference between the experimental class and the control class before the experiment. The p value of round-trip straight dribble in the whole field is 0.735, which is greater than 0.07, indicating that there is no significant difference between the experimental class and the control class before the experiment. This teaching mode plays a significant role in cultivating beginners’ learning interest and enthusiasm, preliminarily mastering movement skills and establishing solid technical concepts. With the complex emotional experience of the association rule algorithm, it is of great significance for teachers to grasp students’ various emotional experiences in learning, cultivate students’ team consciousness by cluster analysis, guide and dredge their negative emotions, and develop students’ sense of cooperation, team spirit and democratic spirit, personal responsibility and personality.

A Personalized Learning Path Recommendation Method Incorporating Multi-Algorithm

In this era of intelligence, the learning methods of learners have substantially changed. Many learners choose to learn through online education platforms. Although learners may enjoy more high-quality educational resources, when they are faced with an abundance of resource information, they are prone to become lost in knowledge, among other problems. To solve this problem, a multi-algorithm collaborative, personalized, learning path recommendation model is proposed to provide learning guidance for learners of online learning platforms. First, the learner model is constructed from four perspectives: cognitive level, learning ability, learning style, and learning intensity. Second, the association rule algorithm is employed to generate a sequence of knowledge points and to plan the learning sequence of knowledge points for learners. Last, the swarm intelligence algorithm is utilized to ensure that each knowledge point is matched with personalized learning resources with a higher degree of adaptability so that learners can learn using a more targeted approach. The experimental results show that the research results of this paper can, to a certain extent, recommend ideal learning paths to target users, effectively improve the accuracy of recommended resources, and thus improve the learning quality and learning effect of users.

A new complex system fault detection method based on belief rule base for unreliable interval values

Failures to equipment such as milling machines and inertial navigation systems (INSs) can affect their normal operation, resulting in economic losses and personal injury in severe cases. Therefore, fault detection is of great importance. Belief rule base (BRB) is an expert system that plays an important role in fault detection. The traditional BRB has some problems in the explosion of the number of combination rules, the process of model inference, and the process of parameter optimization. To better deal with the above problems, this paper proposes a complex system fault detection method based on an interval-valued BRB fault detection interval-valued (FDIV) and provides the construction and inference process of the method. In the method construction, the form of interval value and disjunction rules are introduced to solve the problem of the number explosion of combination rules, the indicator reliability is added to improve the accuracy of the method, and a new calculation method of rule availability is proposed. In the inference process, twice fusions are made based on evidence reasoning (ER) analysis algorithm and ER rule algorithm respectively to deal with the interval uncertainties. Moreover, the proposed FDIV method is optimized by the projection covariance matrix adaptive evolutionary strategy algorithm projection covariance matrix adaptive evolutionary strategy (P-CMA-ES). Finally, the effectiveness of the proposed method was verified through the research on milling fault detection and the experimental verification of INS fault detection. The superiority of the model was also confirmed through comparative experiments.

Architecture and modular design of power emergency command system based on association rule algorithm

In order to effectively obtain the electric power emergency command plan and reasonably dispatch emergency resources, the electric power emergency command system architecture based on association rule algorithm is designed according to the modularization idea. In the hardware part, the sensing layer is used to collect operation data, and the service layer is used to analyze and mine the data. The tool layer establishes the basic component library and the power industry component library. The network layer transmits relevant information to the application layer. The application layer realizes the three-dimensional simulation and visual operation of power emergency command such as resource scheduling and fault repair by calling the tool set. In the software part, the association rule algorithm combined with online analytical processing technology is used to mine the effective data in the collected data set to realize the analysis and processing of emergency command related work. Experiments show that the system can effectively command the output of the generator and obtain higher transmission margin when the fault occurs. And reasonably and evenly dispatch emergency resources to avoid resource waste.

Construction of a mathematical modeling teaching quality assessment system for universities based on Eviews model

Abstract Quality assessment of university education and teaching is an essential and important part of teaching and learning activities. Over the years, a large amount of data has been accumulated in teaching and management, but the potential value of these data has not been fully utilized. How to use a large amount of data scientifically, automatically, and more effectively for rational analysis and guidance of current teachers’ teaching work has become a key issue in testing the effectiveness of teaching quality assessment, and an in-depth study is urgently needed. In this paper, we use the Eviews model, regression analysis algorithm, Apriori association rule algorithm, and other techniques. It conducts research on the assessment of teaching quality in Chinese universities. Finally, after completing the construction of the system, the main modules in the system, system management, resource management, quality assessment, and assessment query module, focused on testing. A total of 160 test cases were designed, of which 157 test results met the requirements of the system, and 3 results did not meet the requirements, with a success rate of more than 98%. The results show that the system has no major defects, runs normally, has stable performance, and meets the system requirements for teaching quality assessment in universities.

Big data technology for computer intrusion detection

Abstract In order to improve the ability of computer network intrusion detection, the big data technology for computer intrusion detection was studied. This research uses big data technology to build a network intrusion detection model, using clustering algorithms, classification algorithms, and association rule algorithms in data mining to automatically identify the attack patterns in the network and quickly learn and extract the characteristics of network attacks. The experimental results show that the recognition effect of the classification algorithm is obviously better than that of the clustering algorithm and the association rule. With the increase in the proportion of abnormal commands, the accuracy rate can still be maintained at 90%. As a compromise between the classification algorithm and the clustering algorithm, the accuracy rate of the association rule algorithm is basically maintained at more than 75%. It is proved that the big data technology oriented to computer intrusion detection can effectively improve the detection ability of computer network intrusion.

Integrated analysis for treatment scheme of sodium–glucose cotransporter 2 inhibitors in patients with diabetic kidney disease: a real-world study

Sodium–glucose cotransporter 2 inhibitors (SGLT2i) are recommended for type 2 diabetes mellitus patients with impaired renal function, but the actual situation of SGLT2i using is unclear. Therefore, in this real-world study, we analyzed the treatment scheme and clinical characteristics of SGLT2i in patients with diabetic kidney disease (DKD). We included DKD patients hospitalized in the First Affiliated Hospital of Zhengzhou University from October 2017 to March 2020. The Apriori algorithm of association rules was used to analysis treatment scheme prescribing SGLT2i and other different combinations of hypoglycemic drugs. SGLT2i was used in 781 (12.3%) of 6336 DKD patients, both number and proportion of patients using SGLT2i increased from 2017 to 2020 (1.9% to 33%). Nighty-eight percent of all DKD patients using SGLT2i were combined with other glucose-lowering agents, and insulin, metformin and alpha-glucosidase inhibitors are most commonly used in combination with hypoglycemic drugs. Multivariate analysis showed that compared with non-SGLT2i group, patients using SGLT2i were associated with younger age, higher BMI, higher HbA1c, preserved kidney function, dyslipidemia and combined with ACEI/ARB and statins. In this real-world study, use of SGLT2i in DKD patients is still low. Most patients performed younger age and in the early stages of chronic kidney disease with poor glycemic control. Clinical inertia should be overcome to fully exert the cardiorenal protective effects of SGLT2 inhibitors, with attention to rational drug use.

An Intelligent Technique to Predict the Autism Spectrum Disorder Using Big Data Platform

Autism or autism spectrum disorder (ASD) is considered a psychiatric disorder. It is a condition that puts constraints on the use of linguistic, cognitive, communicative, and social skills and abilities. Recently, many data mining techniques have been developed to help autism patients by discovering the main features of the condition and the correlation between them. In this paper, we employ the association classification (AC) technique as a data mining approach to predict whether or not an individual has an autism. The Intelligent Classification Based on Association rules (ICBA) algorithm is proposed for finding the correlations between the features to decide whether an individual has autism in its early stage, especially in childhood. The ICBA algorithm incorporates the chi-square method to select the best feature to make the decision, in addition to proposing new techniques in all phases and increasing number of folds to 2size of data/10. The proposed algorithm is compared against four well-known AC algorithms in terms of accuracy to evaluate their behavior in the prediction task using big data platform. The results show a better performance for the ICBA algorithm in most experiments. Moreover, all of the considered algorithms had an increased level of accuracy when the chi-square method was used.

PENERAPAN ALGORITMA APRIORI DALAM MENINGKATKAN PRODUKSI BUDIDAYA PERIKANAN MENGGUNAKAN ASSOCIATION RULE

Aquaculture production in North Sumatra needs to be increased in order to meet the increasing demand for aquaculture in the area from time to time. In order to optimize aquaculture production, recommendations are made for what types of aquaculture are most in demand by the community so that the North Sumatra Marine and Fisheries Service can prepare fishery supplies optimally. Recommendations for the provision of fish are carried out by utilizing data mining technology with an association rule algorithm. Processed data is fish sales history data. The result of this data processing is the combination of fish data that is most in demand by the community with a minimum amount of support of 40% to 3 itemset. Furthermore, the a priori algorithm is implemented at the marine and fisheries service to determine associations. By utilizing the results of this a priori algorithm analysis, the Department of Marine and Fisheries of North Sumatra can find out what types of aquaculture are the priorities for increasing production so that they can meet the needs of the community.

RETRACTED ARTICLE: Innovative design of an art teaching quality evaluation system based on big data and an association rule algorithm from the perspective of sustainable development

RETRACTED ARTICLE: Innovative design of an art teaching quality evaluation system based on big data and an association rule algorithm from the perspective of sustainable development

Design of Computer-Aided Assessment and Recognition Platform for College Students' Innovative Training Plan with Data Matrix Mining

In this paper, an improved association rule algorithm based on vector product is used. This algorithm only needs to scan the transaction database once and does not generate a large number of frequent item candidate sets. The algorithm first maps the set of data transactions into a matrix of vectors, each column vector representing an attribute. Matrix factorization algorithms usually decompose the original data matrix into two or more low-dimensional matrices. This paper analyzes the common problems existing in society, colleges, and families in labor education, and innovatively proposes a four-dimensional comprehensive evaluation index system for labor education in colleges and universities, including “subject-dominated colleges—family education support—social environment support—college students’ individual characteristics”.

Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A survey

Educational data mining (EDM) is the application of data mining in the educational field. EDM is used to classify, analyze, and predict the students’ academic performance, and students’ dropout rate, as well as instructors’performance in order to improve teaching–learning process. This review article discusses the detailed analysis of 142 research articles from publication year 2010-2020 downloaded from the research databases such as IEEE, Springer, ACM, and Elsevier. Also this review article contains the current happenings related to EDM in year 2021 and 2022. In this review article, the use of classification techniques and classification techniques along with other data mining techniques such as clustering algorithm, association rule algorithms, regression techniques and ensemble techniques in EDM are presented thoroughly. The comparative study is considered for Classification Techniques; Classification and Clustering Technique; Classification ans Association Rule Mining; Classification, Clustering and Association rule mining; Classification, Regression, and Clustering; and Classification, and Ensemble. Analysis in terms of Yearwise Number of Research Articles employing Classification Techniquein EDM; Classification with other Data Mining Technique used in EDM; classifier as per Weka Tool; Classification Techniques; Clustering Techniques; Association Rule Techniques; Selecting the best Classification Technique; Classification performance metric; software used in EDM; Sampling Period; size of dataset; and data mining tools are illustrated.From review of 142 research articles, it is noted that classification techniques are mostly used technique for analyzing students’ performance in EDM. Also classification technique along with clustering techniques are applied to predict the performance of students. It is found that Naïve Bays, Random Forest, Support vector machine and J48 are mostly considered classification techniques while in classification along with clustering techniques, K-means clustering algorithm is used with classification algorithms. The classification algorithms such as Naïve Bays, Random Forest and Support Vector Machine are noted to be the best classification algorithms after comparing various classification algorithms based on various performance parameters. Among various performance parameters, the parameters accuracy, precision, recall, f-measures and k-fold value found to be used by most of the research articles. Programming languages used to build the model in EDM for analyzing the students’ dataset from educational setting, are Java, R and Python programming languages while data mining tools considered to evaluate the performance of classification or clustering or association rule algorithms are Weka, and RapidMiner. Classification algorithms under the classifiers as per Weka tool such as Tree, Bays, Function and PMML classifier are applied in most of the research articles.In addition to comparative analysis and analysis based on various factors, research gaps are also identified and mentioned the same in this article. Future direction for researcher working in EDM related to building the model on the dataset obtained from educational setting to predict students’ performance are discussed so that work in EDM can be carried out to improve the teaching–learning process.

A symbolic algorithm for the case-split rule in solving word constraints with extensions

Case split is a core proof rule in current decision procedures for the theory of string constraints. Its use is the primary cause of the state space explosion in string constraint solving, since it is the only rule that creates branches in the proof tree. Moreover, explicit handling of the case split rule may cause recomputation of the same tasks in multiple branches of the proof tree. In this paper, we propose a symbolic algorithm that significantly reduces such a redundancy. In particular, we encode a string constraint as a regular language and proof rules as rational transducers. This allows us to perform similar steps in the proof tree only once, alleviating the state space explosion. We also extend the encoding to handle arbitrary Boolean combinations of string constraints, length constraints, and regular constraints. In our experimental results, we validate that our technique works in many practical cases where other state-of-the-art solvers fail to provide an answer; our Python prototype implementation solved over 50% of string constraints that could not be solved by the other tools.

Application Research of Data Mining in MES Quality Management

With the increasing market competition, enterprises have continuously raised the requirements for product quality in order to gain a favorable position in the market competition. In industrial production, MES quality information management mainly involves the collection, statistical analysis, and utilization of information data that affect quality in daily production. In actual industrial production, the formation of product quality is the result of the interaction of numerous factors. Due to the many factors and scenarios involved, traditional data statistical analysis methods cannot accurately and effectively analyze the collected relevant data, and fail to fully mine the value of data. This paper proposes the application of data mining technology to MES quality information management systems and elaborates on the use of the K-means algorithm and Apriori association rule algorithm to analyze the related processing rules of parts in the production and processing process. The algorithm model is used to analyze the actual production and processing data of a certain enterprise, and finally, the value and application of parts association processing rules in actual enterprise production management are summarized.

Analysis of Rural Governance and Resource Endowment Modeling Based on Association Rule Algorithm

At present, in the modeling and analysis of rural resource endowment, the internal relationship of elements is ignored, resulting in inaccurate judgment of governance level. Therefore, the modeling and analysis of rural governance and construction resource endowment is based on association rule algorithm. Identify the characteristics of rural governance resource elements as the information basis, design the clustering algorithm to determine the association rules and element attributes, and use the association rules algorithm to mine the internal relationship of resource endowment. Taking the information of rural governance resource endowment as the direction, the evaluation index is selected, and the rural resource endowment measurement model is constructed. The experimental results show that the modeling analysis results based on association rule algorithm are consistent with the actual governance development orientation, while the modeling analysis results based on evolution analysis algorithm and special group analysis algorithm are quite different from the actual governance development orientation. Therefore, the modeling analysis in this paper is more accurate, which is conducive to the accurate implementation of governance policies and rural planning.

Identifying usual food choices with avocados in a clinical trial cohort of overweight and obese adults in Australia.

Consumption of avocados has been suggested to be beneficial for weight control, however, limited research is available about the related food choices. Understanding the food choices associated with avocados at meal occasions may further aid behavioural strategies to lose weight. The present study used a systematic approach to develop an avocado-specific food database, with the aim to explore food choices related to avocados at meal occasions as reported by overweight and obese volunteers in weight loss clinical trials. The avocado-specific database was based on AUSNUT 2011-13 food composition database structure and was developed via a systematic approach, which determined the avocado content of Australian foods and beverages. Baseline usual food intake data was retrospectively pooled from four food-based clinical trials (n = 758). The Apriori algorithm of association rules, a two-step descriptive method was used to identify food choices associated with avocados at different meal occasions using a nested hierarchical food group classification system. The avocado database identified 34 avocados and avocado-containing foods and beverages. The proportion of avocado consumers in the pooled cohort was 51.3% (n = 389), with an average avocado intake of 21.57 ± 36.75 grams per day. Avocados were found to be closely related to other food groups at all of the food group levels at main meal occasions. A total of 68 food items containing avocados were identified for avocado consumers at the breakfast, lunch and dinner meals. The avocado specific database provides a snapshot of the foods and beverages which contain avocados. Enumerating the full range of food choices in relation to avocado consumption should provide examples of food choices that people might consider in their efforts to increase their avocado consumption.

An active equalization method for redundant battery based on deep reinforcement learning

Battery equalization is essential in the battery management system. In this paper, an active equalization method based on redundant battery is proposed. The equalization circuit consists of a battery string composed of multiple batteries connected in series and a redundant battery. During the discharging process, one cell in the string is selected by the switch controller to be paralleled with redundant cells for equalization purposes. On this basis, an optimal switch control strategy based on deep reinforcement learning (DRL) is proposed, which takes into account the battery's state of charge (SOC), state of health (SOH), and current distribution during parallel connection. The proposed optimal switching control strategy can achieve equalization with the least number of switching times. Simulation shows that, compared with the greedy algorithm and the rule algorithm, the strategy proposed in this paper can reduce the SOC inconsistency of the battery string to less than 1% with the minimum number of switching times.

An Electronic System for Summer Training Students Distribution in Organizations with Comparative Study of Association Rule Algorithms

The training of students is considered as one of the most promising forms of training to inform students with the reality of practical environment and what they require from serious and exact work. It may give the chance to other public sector organizations to be acquainted with the students' abilities and skills, in addition to the benefits of informing youths to join summer vocation. In order to solve the problem of students distribution to organizations and guarantee the equivalency between students desires and the capacity of governmental and privates offices, some algorithms were used to mine up data to uncover essential hidden relationships with huge data, & Distributed Database has been designed for summer training . The data mining were also used to set reports that may refer to the delicate number of students required for training according to the specializations in the four departments of the College of Administration and Economics (application environments) with the number of nominee students for training in these departments using (oracle 11g.).