Implementation of RETE algorithm using course finder system

Shanthappa, Pallavi M; Vaisakh, P; Reshna, N P

doi:10.1109/sapience.2016.7684165

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overall rule network consists of a knowledge base, an Alpha network layer, a Beta network layer, and an output node layer [7] . One part of the knowledge base is called the root node, which represents the entrance of the network, from which all instances enter the network, and another part is called the type node, which is used to judge the type of all instances, and if it meets the type of the node, it is passed down to the Alpha network layer [8] ; Beta network layer [9] is the connection network, which is used to check the constraint relationship between different patterns caused by the variables given.…”

Section: Regular Networkmentioning

confidence: 99%

IoT data clustering method based on parallelized subnetworks and PCA-K-means

Du,

Hu,

Chen

et al. 2023

Second International Conference on Electronic Information Technology (EIT 2023)

View full text Add to dashboard Cite

Internet of Things(IoT) data is characterized by large amount of data and high real-time performance. Processing IoT data through complex event processing techniques requires setting complex rules, but the rules tend to change as the business changes. Rule engine can set rules through separate configuration files to filter to data or things matching the rules without modifying device data or management platform code. In this paper, in order to solve the performance of the rule engine at large data volume, we design a rule network-based reduced-dimensional K-Means raw data clustering method to cluster devices by the difference of device operation data while implementing parallelized rule networks according to the rule types. After the experimental analysis of the method, the results show that the reduced-dimensional K-Means raw data clustering method based on rule networks has smaller event loss and effectively reduces redundancy.

show abstract

Section: Regular Networkmentioning

confidence: 99%

IoT data clustering method based on parallelized subnetworks and PCA-K-means

Du,

Hu,

Chen

et al. 2023

Second International Conference on Electronic Information Technology (EIT 2023)

View full text Add to dashboard Cite

show abstract

“…Rules usually represent general knowledge [19] just like knowledge-based recommendation system in a hybrid approach which aims to assist students to select suitable courses based on their skills was used in an inference engine [20] to recommend based on user's preference [21] using user models as implicit and explicit information for personalized learning [22]. This allows the user to achieve the desired goal or interest.…”

Section: Introductionmentioning

confidence: 99%

A Career Track Recommender System for Senior High School Students using Fuzzy Logic

B¹

2019

IJATCSE

View full text Add to dashboard Cite

Rule-based algorithms are mostly used in recommender systems but still, cannot address the issue of uncertainty in decision making particularly to senior high school students in choosing the right career track because of numerous influential factors that may affect their decisions. That is why a career track recommender system using fuzzy logic has been developed to address this issue. In this paper, the significant factors that is most influential to the decision of the students as best attributes were determined using feature selection filtering techniques and used as crisp inputs. The result shows that the developed fuzzy model performs a high predictive accuracy based on the computed mean absolute error (MAE) and root-mean-square error (RMSE) scores which decreases from the training, to the validation and test sets. The recommendation returns the best possible result based on the computed normalized discounted cumulative gain (nDCG) which is 0.948 from the desired to the actual preference of students which is almost near to 1.0. With these, the developed recommender system is highly recommended as perceived by the users in terms of usability, maintainability and portability.

show abstract