scholarly journals An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5118 ◽  
Author(s):  
Zhai ◽  
Martínez Ortega ◽  
Beltran ◽  
Lucas Martínez
Keyword(s):  
Intelligent Systems ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Traditional Methods ◽  
Case Representation ◽  
Reasoning System ◽  
Case Base ◽  
Representation Method ◽  
Associated Representation ◽  
Case Based

As an artificial intelligence technique, case-based reasoning has considerable potential to build intelligent systems for smart agriculture, providing farmers with advice about farming operation management. A proper case representation method plays a crucial role in case-based reasoning systems. Some methods like textual, attribute-value pair, and ontological representations have been well explored by researchers. However, these methods may lead to inefficient case retrieval when a large volume of data is stored in the case base. Thus, an associated representation method is proposed in this paper for fast case retrieval. Each case is interconnected with several similar and dissimilar ones. Once a new case is reported, its features are compared with historical data by similarity measurements for identifying a relative similar past case. The similarity of associated cases is measured preferentially, instead of comparing all the cases in the case base. Experiments on case retrieval were performed between the associated case representation and traditional methods, following two criteria: the number of visited cases and retrieval accuracy. The result demonstrates that our proposal enables fast case retrieval with promising accuracy by visiting fewer past cases. In conclusion, the associated case representation method outperforms traditional methods in the aspect of retrieval efficiency.

scholarly journals Case-based Reasoning for Crisis Response: Case Representation and Case Retrieval

2020 ◽  
Vol 176 ◽  
pp. 1063-1072
Author(s):  
Walid Bannour ◽  
Ahmed Maalel ◽  
Henda Hajjami Ben Ghezala
Keyword(s):  
Crisis Response ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Case Representation ◽  
Case Based

scholarly journals An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

Agriculture ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 387
Author(s):  
Zhaoyu Zhai ◽  
José-Fernán Martínez Ortega ◽  
Néstor Lucas Martínez ◽  
Huanliang Xu
Keyword(s):  
Poor Performance ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Reasoning Systems ◽  
Case Base ◽  
Low Efficiency ◽  
Association Table ◽  
Smart Agriculture ◽  
Case Based

Case-based reasoning has considerable potential to model decision support systems for smart agriculture, assisting farmers in managing farming operations. However, with the explosive amount of sensing data, these systems may achieve poor performance in knowledge management like case retrieval and case base maintenance. Typical approaches of case retrieval have to traverse all past cases for matching similar ones, leading to low efficiency. Thus, a new case retrieval algorithm for agricultural case-based reasoning systems is proposed in this paper. At the initial stage, an association table is constructed, containing the relationships between all past cases. Afterwards, attributes of a new case are compared with an entry case. According to the similarity measurement, associated similar or dissimilar cases are then compared preferentially, instead of traversing the whole case base. The association of the new case is generated through case retrieval and added in the association table at the step of case retention. The association table is also updated when a closer relationship is detected. The experiment result demonstrates that our proposal enables rapid case retrieval with promising accuracy by comparing a fewer number of past cases. Thus, the retrieval efficiency of our proposal outperforms typical approaches.

scholarly journals Case Base Reasoning Sebagai Alat Bantu Diagnosis Penyakit Ternak Sapi menggunakan Sorenson Coeficient (Studi Kasus: Di Kabupaten Pati)

Respati ◽  
2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Agus Sugihandono ◽  
Kusrini Kusrini ◽  
Hanif Al Fatta
Keyword(s):  
System Analysis ◽  
Agricultural Sector ◽  
Swot Analysis ◽  
Large Population ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
Cattle Farming ◽  
Case Base ◽  
Case Base Reasoning ◽  
Case Based

INTISARIIndonesia adalah Negara agraris dengan jumlah penduduk yang besar dengan usaha peternakan merupakan sub sector penting dari sector pertanian.Salah satu jenis peternakan di Indonesia adalah peternakan sapi. Jenis sapi yang cocok dan menguntungkan di Indonesia adalah sapi perah. Dalam memenuhi kebutuhan sapi yang tinggi Masalah yang dihadapi para peternak adalah adanya penyakit tetapi dokter hewan yang dapat mendiagnosa penyakit sapi jumlahnya terbatas. Untuk itu perlu suatu alat bantu yang dapat mendiagnosia penyakit ternak. Salah satunya dengan metode Sorenson coefficient. Hasil dari penelitian ini adalah sebuah system yang dapat mendiagnosa penyakit pada sapi dengan sistem case based reasoning. Sebelum membuat sistem perlu dilakukan analsisi sistem terlebih dahulu dengan analsis SWOT, kemudian dilakukan analsis basis pengetahuan yaitu dengan deskripsi sistem dan akuisisi pengetahuannya. Kemudian mengetahui berbagai jenis penyakit pada sapi, dan mendapatkan kasus-kasus penyakit pada sapi. Kemudian dilakukan tahapan diagnosis dengan case base reasoning, dan merepresentasikan kasus serta proses retrival. Kemudian kasus dengan kasus baru yang ada akan dihitung similaritasnya setelah itu akan didapatkan hasil diagnosis penyakitnya. Dengan penelitian ini dapat disimpulkan bahwa sistem penalaran berbasis kasus  yang telah dibuat mampu menerapkan keahlian seorang pakar(dokter hewan) pada kasus peternakan sapi. Sistem penalaran berbasis kasus dengan metode Sorenson dapat digunakan untuk mendiagnosis penyakit sapi tetapi perlu ditambahkan proses perhitungan similarity dengan batas similarity tertinggi 3.Kata kunci— case based reasonong, sorenson coeficient, penyakit sapi, analisis swot, similarity. ABSTRACTIndonesia is an agricultural country with a large population with livestock business is an important sub sector of the agricultural sector. One type of livestock in Indonesia is cattle farming. The type of cow that is suitable and profitable in Indonesia is dairy cows. In meeting the needs of high cattle The problem faced by farmers is the existence of a disease but a limited number of veterinarians who can diagnose cow disease. For this reason, we need a tool that can diagnose livestock disease. One of them is the Sorenson coefficient method. The results of this study are a system that can diagnose disease in cattle with a case based reasoning system. Before making the system, a system analysis needs to be done first with SWOT analysis, then a knowledge base analysis is carried out, namely with a description of the system and the acquisition of his knowledge. Then find out various types of diseases in cattle, and get cases of disease in cattle. Then do the stages of diagnosis with case base reasoning, and represent the case and retrival process. Then the case with new cases will be calculated for similarity after which the diagnosis of the disease will be obtained. With this study it can be concluded that a case-based reasoning system has been made capable of applying the expertise of an expert (veterinarian) to the case of cattle farming. The case-based reasoning system with the Sorenson method can be used to diagnose cow disease but the process of calculating similarity needs to be added with the highest similarity limit 3Keyword— case based reasonong, sorenson coeficient, penyakit sapi, analisis swot, similarity.

scholarly journals Development of Ground Special Vehicle PHM with Case-Based Reason Model

2021 ◽  
Vol 11 (10) ◽  
pp. 4494
Author(s):  
Qicai Wu ◽  
Haiwen Yuan ◽  
Haibin Yuan
Keyword(s):  
Cross Validation ◽  
Optimization Methods ◽  
Engineering Practice ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Data Set ◽  
Attribute Weights ◽  
Special Vehicle ◽  
Weight Allocation ◽  
Case Based

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications.

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

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