A Recommendation Model that Combines Self-Organizing Maps and Case-Based Reasoning: A Case of Online Community Recommender Systems

2008 ◽  
Vol 9 (1) ◽  
pp. 309-327 ◽  
Author(s):  
Hyoung-yong Lee
Keyword(s):  
Recommender Systems ◽  
Online Community ◽  
Case Based Reasoning ◽  
Self Organizing Maps ◽  
Case Based ◽  
Self Organizing

scholarly journals Diagnosa Penyakit Kulit Menggunakan Case Based-Reasoning dan Self Organizing Maps

2020 ◽  
Vol 6 (1) ◽  
pp. 53
Author(s):  
Fhatiah Adiba ◽  
Nurul Mukhlisah Abdal ◽  
Andi Akram Nur Risal
Keyword(s):  
Nearest Neighbor ◽  
Skin Diseases ◽  
Case Based Reasoning ◽  
Self Organizing Maps ◽  
Indexing Method ◽  
Case Based ◽  
Self Organizing

This study aims to compare the results of the accuracy and speed of the system in diagnosing skin diseases using the case based reasoning (CBR) method with the indexing method and without using indexing. Self-organizing maps (SOM) are used as an indexing method and the process of finding similarity values uses the nearest neighbor method. Testing is done with two scenarios. The first scenario uses CBR without indexing self-organizing maps, the second scenario uses CBR with indexing self-organizing maps. The accuracy of the diagnosis of skin diseases at a threshold ≥80 for CBR without indexing self-organizing maps is 93.46% with an average retrieve time of 0.469 seconds while CBR testing using SOM indexing is 92.52% with an average retrieve time of 0.155 seconds. The results of comparison of CBR methods without using show higher results than using SOM indexing, but the process of retrieving CBR using SOM is faster than not using indexing

Case-Based Reasoning for Stiffness Changes Detection in Structures

2015 ◽  
pp. 124-152
Author(s):  
Rodolfo Villamizar Mejia ◽  
Jhonatan Camacho Navarro ◽  
Wilmer Alexis Sandoval Caceres
Keyword(s):  
Principal Component ◽  
Element Model ◽  
Discrete Wavelet ◽  
Case Based Reasoning ◽  
Self Organizing Maps ◽  
Time Frequency ◽  
Knowledge Based ◽  
Analysis Methodology ◽  
Changes Detection ◽  
Case Based

This chapter presents an expert monitoring algorithm approach to detect, locate and quantify stiffness variations in structures. The algorithm is based on pattern recognition and artificial intelligence techniques that emulate knowledge based on human reasoning. The expert system (ES) uses time-frequency information about dynamics of structure, which is processed by using discrete wavelet transform (DWT), self-organizing maps (SOM), case-based reasoning (CBR) and principal component analysis (PCA). In addition, two applications are considered in order to evaluate the effectiveness of vibration analysis methodology and CBR in damage detection. The first application (Camacho 2010) uses the environmental excitation to detect and quantify damage in a Mechanical UBC ASCE Benchmark. The second one (Sandoval 2010) uses a predesigned signal to detect geometric damages on a gas pipeline. In both cases, a finite element model (FEM) is used to simulate different damages scenarios, which correspond to stiffness variations in different location.

Music Recommendation System Based on Context Using Case-Based Reasoning and Self Organizing Map

2016 ◽  
Vol 4 (2) ◽  
pp. 459
Author(s):  
Gst Ayu Vida Mastrika Giri ◽  
Agus Harjoko
Keyword(s):  
Recommendation System ◽  
Case Based Reasoning ◽  
Self Organizing Map ◽  
Music Recommendation ◽  
Indexing Method ◽  
System Output ◽  
Context Data ◽  
Music Recommendation System ◽  
Case Based ◽  
Self Organizing

Effective music recommendation can decrease listener’s effort in choosing music that will be listened. Music recommendation is not only can be obtained based on genre or audio similarity, because listener’s music choices are also influenced by the listener’s context (mood, occasion, part of day, date, weather, region, month, and weekday). This research used Case-Based Reasoning (CBR) for determining music recommendation based on listener’s context data and also Self Organizing Map (SOM) which is used as an indexing method in CBR. Inputs given by user to the system are user’s occasion and mood desired by user. The system output is a playlist consists of music that suitable with user’s context and desired mood.

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