scholarly journals CASE-BASED REASONING UNTUK SISTEM DIAGNOSIS PENYAKIT MALARIA DI RSUD KABUPATEN PULAU MOROTAI

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Miswar Papuangan ◽  
Hean Rakomole
Keyword(s):  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Case Based

Malaria merupakan penyakit menular yang disebabkan oleh parasit Plasmodium. Penyakit ini menyebar lewat gigitan nyamuk yang terinfeksi parasit. Jika tidak ditangani dengan cepat dan tepat, dapat menimbulkan komplikasi berat yang dapat berujung pada kematian. Infeksi malaria dapat terjadi hanya dengan satu gigitan nyamuk saja. Penyakit ini tidak menular secara langsung dari satu individu ke individu lainnya. Penularan dapat terjadi apabila ada kontak dengan darah penderita. Untuk mendiagnosis pasien penderita penyakit malaria dapat diketahui gejala-gejala yang dirasakan dan faktor resiko yang dialami pasien. Penggunaan konsep case-based reasoning sebagai sistem untuk membantu melakukan diagnosis penyakit malaria. Fitur-fitur yang digunakan dalam melakukan diagnosis penyakit malaria meliputi usia, jenis kelamin, gejala yang dirasakan dan faktor resiko yang dialami pasien. Algoritma nearest neighbor digunakan untuk menghitung kemiripan antara permasalahan kasus baru dengan kasus-kasus yang tersimpan dalam basis kasus. Hasil pengujian menggunakan data rekam medik menunjukan bahwa sistem mampu mengenali tiga jenis penyakit malaria secara benar dengan tingkat akurasi sebesar 90,91%.

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.

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.

Case-Based Reasoning Model with Genetic Algorithms, Group Decision-Making and Template Reduction

2016 ◽  
Vol 25 (02) ◽  
pp. 1550032 ◽  
Author(s):  
Aijun Yan ◽  
Hairuo Song ◽  
Pu Wang
Keyword(s):  
Decision Making ◽  
Genetic Algorithms ◽  
Group Decision Making ◽  
Nearest Neighbor ◽  
Group Decision ◽  
Small Samples ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Attribute Weights ◽  
Case Based

Case retrieval, case reuse and case retention are critical to the reasoning performance of the traditional case-based reasoning (CBR) model. In this paper, the integrated use of template reduction technology (TR), genetic algorithms (GA), nearest neighbor (NN) rules and group decision-making (GDM) establishes the CBR-GDM model. First, the TR method of the case base is introduced. Then, an attribute weights optimization using GA is discussed in the case retrieval phase. After that, a case reuse method is carried out with NN and GDM. Finally, 10 data sets from UCI are used to carry out a comparison experiment by 5-fold cross-validation. The classification accuracy rate and the classification efficiency are analyzed under the small samples, before and after the data reduction. The results show that, combined with TR, GA and GDM, the pattern classification performance by CBR can be improved.

scholarly journals A Case-Based Reasoning Method for Remanufacturing Process Planning

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Fan Zhou ◽  
Zhigang Jiang ◽  
Hua Zhang ◽  
Yan Wang
Keyword(s):  
Process Planning ◽  
Nearest Neighbor ◽  
Critical Role ◽  
Influence Factors ◽  
Economic Benefits ◽  
Case Based Reasoning ◽  
Local Similarity ◽  
Failure Characteristics ◽  
Nearest Neighbor Matching ◽  
Case Based

Remanufacturing is a practice of growing importance due to its increasing environmental and economic benefits. Process planning plays a critical role in realizing a successful remanufacturing strategy. This paper presents a case-based reasoning method for remanufacturing process planning, which allows a process planner to rapidly retrieve, reuse, revise, and retain the solutions to past process problems. In the proposed method, influence factors including essential characteristics, failure characteristics, and remanufacturing processing characteristics are identified, and the local similarity of influence factors between the new case and the past cases is determined by nearest neighbor matching method, and then the vector of correction factor for local similarity is utilized in the nearest neighbor algorithm to improve the accuracy and effectiveness of case searching. To assess the usefulness and practicality of the proposed method, an illustrative example is given and the results are discussed.

scholarly journals Penyelesaian Masalah Pengelolaan Lumbung Pangan Desa Menggunakan Case-Based Reasoning dengan Algoritma K-Nearest Neighbor

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Mgs. Afriyan Firdaus ◽  
Dwi Rosa Indah ◽  
Putri Eka Sevtiyuni ◽  
Choirunnisa Qonitah
Keyword(s):  
Problem Solving ◽  
Nearest Neighbor ◽  
Technical Problem ◽  
Case Based Reasoning ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Existing Problems ◽  
K Nearest Neighbor Algorithm ◽  
Case Based

In this paper, we discuss the problem solving of village food barn management using Case-Based Reasoning (CBR) with the K-Nearest Neighbor algorithm. This research was carried out by adopting the stages of the CBR cycle and the nearest neighbor algorithm. The results of the study show that the application of CBR and K-nearest neighbor algorithms can support the resolution of knowledge problems in village food barn management using technical problem solving based on the symptoms and solutions to existing problems. Based on the test results, the problem-solving accuracy was 92%.Keywords - case-based reasoning, K-nearest neighbor, food barn, problem-solving

scholarly journals Twin-Systems to Explain Artificial Neural Networks using Case-Based Reasoning: Comparative Tests of Feature-Weighting Methods in ANN-CBR Twins for XAI

2019 ◽  
Author(s):  
Eoin M. Kenny ◽  
Mark T. Keane
Keyword(s):  
Neural Networks ◽  
Evaluation Method ◽  
Nearest Neighbor ◽  
Image Data ◽  
Black Box ◽  
Feature Weighting ◽  
Case Based Reasoning ◽  
Artificial Neural ◽  
Weighting Methods ◽  
Case Based

In this paper, twin-systems are described to address the eXplainable artificial intelligence (XAI) problem, where a black box model is mapped to a white box “twin” that is more interpretable, with both systems using the same dataset. The framework is instantiated by twinning an artificial neural network (ANN; black box) with a case-based reasoning system (CBR; white box), and mapping the feature weights from the former to the latter to find cases that explain the ANN’s outputs. Using a novel evaluation method, the effectiveness of this twin-system approach is demonstrated by showing that nearest neighbor cases can be found to match the ANN predictions for benchmark datasets. Several feature-weighting methods are competitively tested in two experiments, including our novel, contributions-based method (called COLE) that is found to perform best. The tests consider the ”twinning” of traditional multilayer perceptron (MLP) networks and convolutional neural networks (CNN) with CBR systems. For the CNNs trained on image data, qualitative evidence shows that cases provide plausible explanations for the CNN’s classifications.

scholarly journals Multilayer Perceptron Integrated Fuzzy Nearest Neighbor to Improve the Proficiency of CBR-Retrieval

2020 ◽  
Vol 2 (2) ◽  
pp. 101-110
Author(s):  
Dr. Suma V.
Keyword(s):  
Multilayer Perceptron ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Medical Field ◽  
Data Set ◽  
Standard Data ◽  
Wide Range ◽  
Training Examples ◽  
Case Based

The CBR (case based reasoning) is a problem solving technique following different strategy compared to the major approaches of the artificial intelligence. It develops remedies to certain problem based on the pre-existing solutions of similar nature. So the problem using the CBR is handled by retrieving and reusing the similar previously solved problems and available solutions respectively. This makes the process functioning alike based on the human activities is instinctively attractive and more beneficial compared to the Conventional_AI as begins to reason out the possible solutions form the shallow base. The CBR due to the exceeding performance are popular among a wide range of applications such as the weather fore casting, medical and engineering diagnosis, aerospace etc. Identification or sorting out or classification take a significant role in cases that is the training examples retrieval as the perfect identification results in perfect case retrieval, this further enables the case based reasoning to arrive to at a perfect remedy for the problem. The retrieval of cases are mostly based on the similarity and utilizes the KNN (K-Nearest Neighbor). The proposed method in the paper integrates the multilayer perceptron with the fuzzy nearest neighbor (MLP-NFF) system with the help of WEKA to deliver a perfect classification to make the CBR-retrieval efficient. The evaluation of the proposed method and its comparison with the KNN is done using the standard data set obtained from the medical field.

scholarly journals DIAGNOSA PENYAKIT PADA IKAN AIR TAWAR DENGAN METODE RULE-BASED DAN CASE BASED REASONING

2019 ◽  
Vol 4 (1) ◽  
pp. 67
Author(s):  
Hisma Abduh
Keyword(s):  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Data Input ◽  
Certainty Factor ◽  
Rule Based ◽  
Case Based

Para pelaku usaha budidaya ikan air tawar, khususnya dalam pemeliharaan ikan di kolam air tawar sudah jelas dituntut agar apa yang diusahakan pada usaha pembesaran ikan dapat berkembang atau tumbuh dengan baik, cepat dan tumbuh besar sehingga produksi akan berhasil secara baik dan bisa memuaskan dengan keuntungan yang maksimal. Untuk mencapai hasil tersebut sudah jelas bahwa pelaku usaha  perlu memahami beberapa teknik yang harus dijalankan sehingga apa yang dilakukan dalam usaha tersebut dapat berhasil dengan baik dan ikan bisa tumbuh berkembang secara cepat dan sempurna. Untuk  menghambat kegagalan dalam pemeliharaan ikan maka perlu dibuat sebuah sistem pakar dengan menggunakan penggabungan RBR dan CBR. Sistem pakar dimulai dengan melakukan pengecekan data masukan berdasarkan aturan yang telah tersimpan dalam basis aturan dengan menerapkan metode certainty factor. Bersamaan dengan itu juga sistem akan melakukan pengecekan data input berdasarkan kasus yang tersimpan dalam basis kasus dengan menerapkan metode nearest neighbor. Hasil penelitian menghasilkan bahwa sistem mengenali 24 data yang sesuai dengan data riil hasil diagnosa pakar dan 6 data yang tidak sesuai dengan data riil hasil diagnosa pakar atau bisa dikatakan nilai akurasi sistem sebesar 80 %.

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