scholarly journals Case-Based Reasoning Using The Nearest Neighbor Method For Detection Of Equipment Damage To PLN Power Plant

2020 ◽  
Vol 14 (4) ◽  
pp. 377
Author(s):  
Riska Amalia Praptiwi ◽  
Nur Rokhman ◽  
Wahyono Wahyono
Keyword(s):  
Power Plant ◽  
Test Data ◽  
Nearest Neighbor ◽  
Intelligent System ◽  
Confusion Matrix ◽  
Threshold Value ◽  
Case Based Reasoning ◽  
Periodic Monitoring ◽  
Total Data ◽  
Case Based

Predictive Maintenance (PdM) at the PLN Power Plant is a periodic monitoring of equipment activities before the equipment is damaged in more severe conditions. According to an expert or PdM owner that maintenance analysis is not appropriate and efficiency has an impact on maintenance costs that are not small. In real conditions, the PdM owner analyzes equipment damage based on previous cases of damage equipment. Then we need a computer-based intelligent system that can help detect damage to equipment.Based on the Literature Review that has been done, Case-Based Reasoning can solve new problems using answers or experiences from old problems such as imitating human abilities. Case-Based Reasoning Process there is the most important step, which is to find the highest similarity value or the level of similarity between new cases and old cases by adapting solutions from old cases that have occurred (Sankar, 2004). In this study the process of similarity or approach using Nearest Neighbor.Testing on the system uses 20 test data and the measurement of system performance uses confusion matrix. Evaluation of testing using confusion matrix can be seen how accurately the system can classify data correctly that is equal to 97.98%. Then the precision value of 95% represents the number of positive categorized data that is correctly divided by the total data classified as positive. Furthermore, the test results of the equipment damage detection test data at the PLN plant with a threshold value of 0.75 using the nearest neighbor, the system has a performance with a 95% sensitivity level.

scholarly journals Diagnosis Tahapan Pengguna Narkoba Menggunakan Metode K-Nearest Neighbor

2020 ◽  
Vol 6 (1) ◽  
pp. 101
Author(s):  
Tursina Tursina ◽  
Hafiz Muhardi ◽  
Dian Aulia Sari
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Fold Cross Validation ◽  
Case Based

Narkoba merupakan bahan yang sangat bermanfaat untuk pengobatan, namun jika disalahgunakan akan memberikan dampak buruk yang luar biasa seperti gangguan kesehatan, gangguan kejiwaan hingga kematian. Seorang pengguna narkoba cenderung tertutup dan tidak ingin berkonsultasi langsung ke dokter maupun rehabilitasi dikarenakan pengguna malu dengan kondisinya, biaya yang relatif mahal, jarak dan waktu yang ditempuh, takut dilaporkan dan tanggapan negatif dari masyarakat. Tujuan dilakukannya penelitian ini adalah untuk membantu seorang pengguna narkoba ataupun bagi seseorang yang dicurigai sebagai pengguna narkoba dalam mendiagnosis tahapan pengguna narkoba dan memberikan solusi serta saran terhadap pengguna narkoba tersebut. Case based reasoning merupakan penalaran yang digunakan untuk menyelesaikan kasus baru dengan cara mengadaptasi solusi yang terdapat pada kasus-kasus sebelumnya, yang mempunyai permasalahan yang mirip dengan kasus baru. Pada tahapan retrieve, terjadi proses menghitung similaritas antara kasus baru dan kasus lama. Perhitungan similaritas kasus pada penelitian ini menggunakan metode k-nearest neighbor. Pengujian hasil akhir sistem menggunakan pengujian tahapan CBR dan pengujian kinerja metode k-nearest neighbor. Hasil pengujian mengukur kinerja dari metode k-nearest neighbor dengan nilai k=7, tingkat akurasi untuk 10-fold cross validation sebesar 98,333%, confusion matrix sebesar 100% dan termasuk excellent classification karena memiliki nilai AUC 1,000.

A Case Based Reasoning System For Recommendation Of Restaurant In Jimbaran Using K-Nearest Neighbor

2020 ◽  
Vol 9 (2) ◽  
pp. 267
Author(s):  
I Gede Teguh Mahardika ◽  
I Wayan Supriana
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
K Nearest Neighbor ◽  
Reasoning System ◽  
Human Ability ◽  
And Function ◽  
Case Based ◽  
Selection Of

Culinary is one of the favorite businesses today. The number of considerations to choose a restaurant or place to visit becomes one of the factors that is difficult to determine the restaurant or place to eat. To get the desired place to eat advice, one needs a recommendation system. Decisions made by the recommendation system can be used as a reference to determine the choice of restaurants. One method that can be used to build a recommendation system is Case Based Reasoning. The Case Based Reasoning (CBR) method mimics human ability to solve a problem or cases. The retrieval process is the most important stage, because at this stage the search for a solution for a new case is carried out. The study used the K-Nearest Neighbor method to find closeness between new cases and case bases. With the selection of features used as domains in the system, the results of recommendations presented can be more suggestive and accurate. The system successfully provides complex recommendations based on the type and type of food entered by the user. Based on blackbox testing, the system has features that can be used and function properly according to the purpose of creating the system.

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

2005 ◽  
Author(s):  
Guanghsu A. Chang ◽  
Cheng-Chung Su ◽  
John W. Priest
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Feature Weight ◽  
Similar Part ◽  
Case Base ◽  
Case Based

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

Integrating fuzzy case-based reasoning, parametric and feature-based cost estimation methods for machining process

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fentahun Moges Kasie ◽  
Glen Bright
Keyword(s):  
Cost Estimation ◽  
Intelligent System ◽  
Machining Process ◽  
Estimation Methods ◽  
Case Based Reasoning ◽  
Cost Estimates ◽  
Content Type ◽  
Estimation Techniques ◽  
Feature Based ◽  
Case Based

Purpose This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers. Design/methodology/approach The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space. Findings The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques. Research limitations/implications The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations. Originality/value Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.

The Study of Hybrid Gaussian CBR Technique and Using in Camshaft Design System

2010 ◽  
Vol 108-111 ◽  
pp. 603-607
Author(s):  
Wei Yan ◽  
Xue Qing Li ◽  
Xu Guang Tan ◽  
De Hui Tong ◽  
Qi Gao
Keyword(s):  
Expert System ◽  
Decision Model ◽  
Nearest Neighbor ◽  
Design System ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Engine Design ◽  
Cam Profile ◽  
Aided Design ◽  
Case Based

In this paper, we propose a hybrid decision model using case-based reasoning augmented the Gaussian and k nearest neighbor methods for aided design camshaft in engine. The hybrid Gaussian k-NN (HGKNN) CBR scheme is designed to compute memberships between cam profile and engine parameters, which provides a more flexible and practical mechanism for reusing the decision knowledge. These methods were implemented in the database application and expert system following the examples of Cam Profile. To get the designed case, the retrieved results were compared and analyzed by HGKNN and k-NN algorithm in the CBR database. It proves the validity of HGKNN and CBR design system is used successfully in engine design process.

scholarly journals Demand Prediction of Railway Emergency Resources Based on Case-Based Reasoning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jianping Sun ◽  
Hantao Cao ◽  
Biao Geng ◽  
Zhaoping Tang ◽  
Xiaopeng Li
Keyword(s):  
Nearest Neighbor ◽  
Structural Similarity ◽  
Empirical Method ◽  
Case Based Reasoning ◽  
Local Similarity ◽  
Demand Prediction ◽  
Railway Traffic ◽  
Proposed Model ◽  
Similarity Calculation ◽  
Case Based

The demand prediction of emergency resources is helpful for rational allocation and optimization of emergency resources for railway rescue when emergency incident occurs. In this paper, a case base containing China railway traffic accident that has occurred since 1978 is established, and the case-based reasoning (CBR) method is applied in railway emergency resource demand predicting research. The core case attributes of railway emergencies are described. In view of the attribute types of railway emergency cases, five types of attributes, including enumeration, numerical, interval, character and fuzzy type, are considered, and the local similarity calculation models of different attributes are given. In order to avoid the problem of missing attribute in the traditional nearest neighbor algorithm, a global case similarity calculation method based on structural similarity and attribute similarity is designed. The empirical results show that case 3 is the most similar to the target case, and the calculating quantities of the proposed model are closer to the actual usage quantity and more accurate in the demand prediction of railway emergency resources, compared with the traditional empirical method. The relative errors of demand forecasts for the 9 resources have been, respectively, reduced by 15.9884%, 15.1471%, 6.4286%, 17.1429%, 66.6667%, 38.8889%, 27.5%, 0%, and 17.7778%. Therefore, the proposed model is both reasonable and applicable. The research results are of great significance to effectively deal with railway emergencies.

scholarly journals Cased Based Reasoning to Identify Cause Conflicts in Marriage

2019 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Arief Ichwani ◽  
Suprapto Suprapto
Keyword(s):  
Test Data ◽  
Nearest Neighbor ◽  
Threshold Value ◽  
Base Case ◽  
Value Similarity ◽  
Consulting Services ◽  
A Value ◽  
Data Source ◽  
Bayes Algorithm

The function of KUA in the activities surrounding the religion of Islam, including providing service and guidance in the area of present services in terms of marriage and reconcilement for Muslims, provide services and guidance in the field of development of Sakina, family consultation conflict or household problems, and so on. Integration between the computer and artificial intelligence into the post-wedding consulting services is one approach in overcoming the limitations of the expert (religious instructor).This research aims to identify conflict in marriage by applying Naive Bayes algorithm at the stage of determining the groups of test data (retrieve), then entered the stage of the search process of the highest similarity value by using the Nearest Neighbor algorithm (reuse). The data source and the test data used are divided into two groups, namely marriage, and history data consultation, While the group conflicts are identified will be divided into five classes, namely an employment factor, the factor of age, educational factors, factors the number of weddings, and social status.Testing is performed by the use of 12 data, consisting of 11 data cases and 1 test data. At the stage of determination of group conflict acquired test data included in group one i.e. F001 (factor of the job), so at the stage of looking for value similarities used only the base case of the class F001 i.e. KK001, KK003, and KK008. The KK001 similarity has a value of 0476, KK003 of 0882, and KK008 of 0142. The case with most high similarity value will be stored as a base case. If the value similarity obtained less than the threshold value that is 0.8, then the solution of the case will be revised by experts. The results of the calculation accuracy, using 35 new test data that gets the value of 82.86%.

scholarly journals Sistem Cerdas Permainan Papan The Battle Of Honor dengan Decision Making dan Machine Learning

2021 ◽  
Vol 12 (2) ◽  
pp. 136
Author(s):  
Arnan Dwika Diasmara ◽  
Aditya Wikan Mahastama ◽  
Antonius Rachmat Chrismanto
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Intelligent System ◽  
Third Party ◽  
Case Based Reasoning ◽  
Board Game ◽  
Rule Based ◽  
Case Based ◽  
Rule Based Systems

Abstract. Intelligent System of the Battle of Honor Board Game with Decision Making and Machine Learning. The Battle of Honor is a board game where 2 players face each other to bring down their opponent's flag. This game requires a third party to act as the referee because the players cannot see each other's pawns during the game. The solution to this is to implement Rule-Based Systems (RBS) on a system developed with Unity to support the referee's role in making decisions based on the rules of the game. Researchers also develop Artificial Intelligence (AI) as opposed to applying Case-Based reasoning (CBR). The application of CBR is supported by the nearest neighbor algorithm to find cases that have a high degree of similarity. In the basic test, the results of the CBR test were obtained with the highest formulated accuracy of the 3 examiners, namely 97.101%. In testing the AI scenario as a referee, it is analyzed through colliding pieces and gives the right decision in determining victoryKeywords: The Battle of Honor, CBR, RBS, unity, AIAbstrak. The Battle of Honor merupakan permainan papan dimana 2 pemain saling berhadapan untuk menjatuhkan bendera lawannya. Permainan ini membutuhkan pihak ketiga yang berperan sebagai wasit karena pemain yang saling berhadapan tidak dapat saling melihat bidak lawannya. Solusi dari hal tersebut yaitu mengimplementasikan Rule-Based Systems (RBS) pada sistem yang dikembangkan dengan Unity untuk mendukung peran wasit dalam memberikan keputusan berdasarkan aturan permainan. Peneliti juga mengembangkan Artificial Intelligence (AI) sebagai lawan dengan menerapkan Case-Based reasoning (CBR). Penerapan CBR didukung dengan algoritma nearest neighbour untuk mencari kasus yang memiliki tingkat kemiripan yang tinggi. Pada pengujian dasar didapatkan hasil uji CBR dengan accuracy yang dirumuskan tertinggi dari 3 penguji yaitu 97,101%. Pada pengujian skenario AI sebagai wasit dianalisis lewat bidak yang bertabrakan dan memberikan keputusan yang tepat dalam menentukan kemenangan.Kata Kunci: The Battle of Honor, CBR, RBS, unity, AI

scholarly journals Intelligent CBR system for automation of the search process for efficient methods for cleaning exhaust gases

2021 ◽  
pp. 44-50
Author(s):  
Liudmyla Bugaieva ◽  
Yurii Beznosyk
Keyword(s):  
Nitrogen Oxides ◽  
Nearest Neighbor ◽  
Intelligent System ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Stream Data ◽  
Exhaust Gases ◽  
Intelligent Decision Making ◽  
Decision Making System ◽  
Selection Of

In this study, the objective is to develop an intelligent system for making decisions on the choice of methods for cleaning exhaust gases from sulfur and nitrogen oxides using the Case-Based Reasoning- (CBR). The task of automating the selection of effective methods for cleaning waste gases is urgent and meets the paradigm of sustainable development. A database on methods for cleaning exhaust gases from nitrogen and sulfur oxides was created. The potential use of intelligent inference on precedents from the database to select the most appropriate cleaning method for new emission stream data is considered. The work of the CBR method is represented as a life cycle, which has four main stages: Retrieving, Reusing, Revising and Retaining. The following characteristics of precedents were considered: degree of purification, initial concentration, temperature, presence of impurities, obtained product, material consumption, and energy consumption. All of these characteristics (in CBR attributes), except for the fourth and fifth, are given by numerical values with respective units of measurement and can be easily normalized. The presence of impurities and the product are categorical attributes with a certain set of values (classes). One of the main problems in CBR was solved: the problem of choosing the type of indexes. A set of all input characteristics of the precedent as indices is suggested to be used for the proposed decision support system (DSS) for methods of cleaning gas emissions. The first two phases of the CBR lifecycle use the k-nearest neighbor method to Retrieving and Reusing. The Euclidean metric is used to estimate the distances between precedents in the developed system. During the third and fourth phases of CBR, the intervention of the decision maker is provided. The process finishes with the adoption of the found solution and the possible storage of this solution in the base of use cases. An intelligent decision-making system has been developed for the selection of methods for cleaning exhaust gases from sulfur and nitrogen oxides based on the method of inference by precedents (CBR), which has been done for the first time for such tasks of chemical technology.

scholarly journals Designing Intelligent Tutoring Systems: A Personalization Strategy using Case-Based Reasoning and Multi-Agent Systems

2013 ◽  
Vol 2 (1) ◽  
pp. 41-54
Author(s):  
Carolina González ◽  
Juan Carlos Burguillo ◽  
Martín Llamas ◽  
Rosalía Laza
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Agents ◽  
Intelligent System ◽  
Intelligent Tutoring ◽  
Educational Systems ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Tutoring Systems ◽  
Student Profile ◽  
Case Based

Intelligent Tutoring Systems (ITSs) are educational systems that use artificial intelligence techniques for representing the knowledge. ITSs design is often criticized for being a complex and challenging process. In this article, we propose a framework for the ITSs design using Case Based Reasoning (CBR) and Multiagent systems (MAS). The major advantage of using CBR is to allow the intelligent system to propose smart and quick solutions to problems, even in complex domains, avoiding the time necessary to derive those solutions from scratch. The use of intelligent agents and MAS architectures supports the retrieval of similar students models and the adaptation of teaching strategies according to the student profile. We describe deeply how the combination of both technologies helps to simplify the design of new ITSs and personalize the e-learning process for each student

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