scholarly journals Where do I Go: Sistema de Recomendações Turísticas

2019 ◽  
Author(s):  
Mauricio Freitas ◽  
Anita Fernandes ◽  
Mônica Da silva

Abstract. This paper describes the development of Where do I Go, a Mobile Tourist Recommendations System that uses recommendation of interest points according to user profile, temporal and semantic constraints, using Case Based Reasoning (CBR). The application aims to make recommendations to tourists during the experimentation of the city, which are in accordance with their tourism preferences and the context at the time of recommendation. CBR stores knowledge around a particular domain in case format, where each case has a problem part and another solution. CBR is premised on the fact that similar problems have similar solutions, where the basis for solving new problems is previously solved problems.

2017 ◽  
Vol 13 (7) ◽  
pp. 1
Author(s):  
Paul Kingsley

Universally true generalizations, from which specific conclusions can be deduced, are often unavailable to the practitioner, defined as anyone carrying out an occupation or profession. Theoretical shortcomings in the body of knowledge presented by academics can be counteracted by the practitioner using his or her knowledge of problem solutions. These can be stored as particular cases or as more generalized design patterns. They will typically contain information about cause-and-effect relationships and normative information about acceptable solutions. Use can be made of these solutions by employing reasoning by analogy and case-based reasoning. Similar problems require similar solutions. Cause-and-effect theory can be generated by practitioners using abstraction from particular cases, as an alternative to enumerative induction. The difference between this theory and that of the academic can be largely one of degree of generality. There is a continuum of cause-and-effect relationships at different levels of abstraction, which does not justify the abrupt separation of the academic and practitioner worlds, which has been encouraged by a reasonable interpretation of Bernsteins work. The study of exemplary problems in vocational education can be made more effective if it is accompanied by an examination of the actual outcomes of previously proposed solutions.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
P. S. Szczepaniak ◽  
A. Duraj

The present paper applies the case-based reasoning (CBR) technique to the problem of outlier detection. Although CBR is a widely investigated method with a variety of successful applications in the academic domain, so far, it has not been explored from an outlier detection perspective. This study seeks to address this research gap by defining the outlier case and the underlining specificity of the outlier detection process within the CBR approach. Moreover, the case-based classification (CBC) method is discussed as a task type of CBR. This is followed by the computational illustration of the approach using selected classification methods, that is, linear regression, distance-based classifier, and the Bayes classifier.


1997 ◽  
Vol 06 (04) ◽  
pp. 511-536 ◽  
Author(s):  
Igor Jurisica ◽  
Janice Glasgow

Classification involves associating instances with particular classes by maximizing intra-class similarities and minimizing inter-class similarities. Thus, the way similarity among instances is measured is crucial for the success of the system. In case-based reasoning, it is assumed that similar problems have similar solutions. The case-based approach to classification is founded on retrieving cases from the case base that are similar to a given problem, and associating the problem with the class containing the most similar cases. Similarity-based retrieval tools can advantageously be used in building flexible retrieval and classification systems. Case-based classification uses previously classified instances to label unknown instances with proper classes. Classification accuracy is affected by the retrieval process – the more relevant the instances used for classification, the greater the accuracy. The paper presents a novel approach to case-based classification. The algorithm is based on a notion of similarity assessment and was developed for supporting flexible retrieval of relevant information. Case similarity is assessed with respect to a given context that defines constraints for matching. Context relaxation and restriction is used for controlling the classification accuracy. The validity of the proposed approach is tested on real-world domains, and the system's performance, in terms of accuracy and scalability, is compared to that of other machine learning algorithms.


2021 ◽  
Vol 9 (02) ◽  
pp. 68-74
Author(s):  
Edwin Febriansyah ◽  
Edy Winarno

In this day and age, motorbikes have an important role in transportation facilities, motorbike users are increasingly dense, especially in the city of Semarang, which is not accompanied by information media. The lack of media information regarding damage to the motorbike makes it difficult for someone to know the cause of the damage to the motorbike, not to mention the Kawasaki KLX150 which happens a lot of engine damage. For this reason, the expert system diagnoses motor damage by knowing the type of motor damage, after that diagnostics and alternative solutions to the problem are carried out. With this, the method and algorithm used is Case-Based Reasoning (CBR) using the Similarity 3W-Jaccard calculation, this second method and algorithm can be used to diagnose damage from the symptoms in the database. Each symptom has a weighted value of each, including a value of 5 (five) for severe symptoms, relating to engine and electrical parts, a value of 3 (three) for moderate symptoms, relating to braking and chains, a value of 1 (one) mild symptom, relating to with the indicator on the speedometer. The system will display 5 (five) types of damage calculated using the 3W-Jaccard Algorithm sorted by the highest value. The revision process will appear if the similarity calculation results are less than 0.6 (zero point six) because it is considered that the results are not sufficiently similar to the solution to be repaired, it needs to be reviewed and will be entered into the review table, then the expert will find a solution.


Author(s):  
Juan Camilo Romero Bejarano ◽  
Thierry Coudert ◽  
Elise Vareilles ◽  
Laurent Geneste ◽  
Michel Aldanondo ◽  
...  

AbstractThis paper addresses the fulfillment of requirements related to case-based reasoning (CBR) processes for system design. Considering that CBR processes are well suited for problem solving, the proposed method concerns the definition of an integrated CBR process in line with system engineering principles. After the definition of the requirements that the approach has to fulfill, an ontology is defined to capitalize knowledge about the design within concepts. Based on the ontology, models are provided for requirements and solutions representation. Next, a recursive CBR process, suitable for system design, is provided. Uncertainty and designer preferences as well as ontological guidelines are considered during the requirements definition, the compatible cases retrieval, and the solution definition steps. This approach is designed to give flexibility within the CBR process as well as to provide guidelines to the designer. Such questions as the following are conjointly treated: how to guide the designer to be sure that the requirements are correctly defined and suitable for the retrieval step, how to retrieve cases when there are no available similarity measures, and how to enlarge the research scope during the retrieval step to obtain a sufficient panel of solutions. Finally, an example of system engineering in the aeronautic domain illustrates the proposed method. A testbed has been developed and carried out to evaluate the performance of the retrieval algorithm and a software prototype has been developed in order to test the approach. The outcome of this work is a recursive CBR process suitable to engineering design and compatible with standards. Requirements are modeled by means of flexible constraints, where the designer preferences are used to express the flexibility. Similar solutions can be retrieved even if similarity measures between features are not available. Simultaneously, ontological guidelines are used to guide the process and to aid the designer to express her/his preferences.


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