scholarly journals Assessing the Similarity of Cyanide-Free Gold Leaching Processes: A Case-Based Reasoning Application

Minerals ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 434 ◽  
Author(s):  
Maria Leikola ◽  
Christian Sauer ◽  
Lotta Rintala ◽  
Jari Aromaa ◽  
Mari Lundström
Keyword(s):  
Relevant Information ◽  
Research Interest ◽  
Raw Material ◽  
Case Based Reasoning ◽  
Free Gold ◽  
Oxidant Concentration ◽  
Time Reading ◽  
Reagent Consumption ◽  
Solid Liquid ◽  
Case Based

Hydrometallurgical researchers, and other professionals alike, invest significant amounts of time reading scientific articles, technical notes, and other scientific documents, while looking for the most relevant information for their particular research interest. In an attempt to save the researcher’s time, this study presents an information retrieval tool using case-based reasoning. The tool was built for comparing scientific articles concerning cyanide-free leaching of gold ores/concentrates/tailings. Altogether, 50 cases of experiments were gathered in a case base. 15 different attributes related to the treatment of the raw material and the leaching conditions were selected to compare the cases. The attributes were as follows: Pretreatment, Overall method, Complexant source, Oxidant source, Complexant concentration, Oxidant concentration, Temperature, pH, Redox-potential, Pressure, Materials of construction, Extraction, Extraction rate, Reagent consumption, and Solid-liquid ratio. The resulting retrieval tool (LeachSim) was able to rank the scientific articles according to their similarity with the user’s research interest. Such a tool could eventually aid the user in finding the most relevant information, but not replace thorough understanding and human expertise.

Case-based reasoning and BIM systems for asset management

2015 ◽  
Vol 5 (3) ◽  
pp. 233-247 ◽  
Author(s):  
Ibrahim Motawa ◽  
Abdulkareem Almarshad
Keyword(s):  
Asset Management ◽  
Information Exchange ◽  
Relevant Information ◽  
Integrated System ◽  
Case Based Reasoning ◽  
Content Type ◽  
Building Maintenance ◽  
Building Models ◽  
Building Information ◽  
Case Based

Purpose – The next generation of Building Information Modelling (BIM) seeks to establish the concept of Building Knowledge Modelling (BKM). The current BIM applications in construction, including those for asset management, have been mainly used to ensure consistent information exchange among the stakeholders. However, BKM needs to utilise knowledge management (KM) techniques into building models to advance the use of these systems. The purpose of this paper is to develop an integrated system to capture, retrieve, and manage information/knowledge for one of the key operations of asset management: building maintenance (BM). Design/methodology/approach – The proposed system consists of two modules; BIM module to capture relevant information and case-based reasoning (CBR) module to capture the operational knowledge of maintenance activities. The structure of the CBR module was based on analysis of a number of interviews and case studies conducted with professionals working in public BM departments. This paper discusses the development of the CBR module and its integration with the BIM module. The case retaining function of the developed system identifies the information/knowledge relevant to maintenance cases and pursues the related affected building elements by these cases. Findings – The paper concludes that CBR as a tool for KM can improve the performance of BIM models. Originality/value – As the research in BKM is still relatively immature, this research takes an advanced step by incorporating the intelligent functions of knowledge systems into BIM-based systems which helps the transformation from the conventional BIM to BKM.

Improving Performance of Case-Based Classification Using Context-Based Relevance

1997 ◽  
Vol 06 (04) ◽  
pp. 511-536 ◽  
Author(s):  
Igor Jurisica ◽  
Janice Glasgow
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Relevant Information ◽  
Classification Systems ◽  
Machine Learning Algorithms ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
Novel Approach ◽  
Similar Solutions ◽  
Case Based

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.

scholarly journals Rancang Bangin Case Based Reasoning untuk Diagnosis Hama dan Penyakit Tanaman Nilam menggunakan Nearest Neighbor Kombinasi Certainty Factor

2020 ◽  
Vol 7 (3) ◽  
pp. 477
Author(s):  
Rabiah Adawiyah ◽  
Fitrianti Handayani
Keyword(s):  
Nearest Neighbor ◽  
Raw Material ◽  
Case Based Reasoning ◽  
Certainty Factor ◽  
Accuracy Rate ◽  
Pests And Diseases ◽  
System Diagnosis ◽  
Case Study Site ◽  
Case Based

<p class="Judul2" align="left"> </p><p>Tanaman nilam menghasilkan minyak nilam (<em>patchouli oil</em>) yang digunakan sebagai bahan baku kosmetik, parfum, antiseptik, sabun, obat, dan insektisida. Dalam pengembangan dan peningkatannya tanaman nilam mengalami beberapa kendala seperti serangan hama dan penyakit yang mengakibatkan rendahnya hasil panen khususnya pada daerah Desa Gunung Sari Kecamatan Watubangga Kabupaten Kolaka. Pengembangan tanaman nilam yang terserang hama dan penyakit seringkali terhambat karena masih banyak petani yang tidak mengetahui jenis hama dan penyakit yang menyerang tanaman petani. Oleh sebab itu sistem pakar berbasis kasus atau <em>Case Based Reasoning (CBR)</em> dibangun untuk mendiagnosis jenis hama dan penyakit tanaman nilam. Pada penelitian ini digunakan 7 jenis penyakit dan 22 gejala berdasarkan studi kasus tempat penelitian. CBR menggunakan metode<em> similarity</em> Nearest Neighbor untuk menemukan kemiripan antar kasus yang berada dalam tahapan <em>retrieve</em>. Pada penelitian ini digunakan juga metode lain yaitu Certainty Factor yang berfungsi untuk mengetahui derajat kepercayaan terhadap hasil diagnosis sistem dalam menghasikan jenis hama dan penyakit tanaman nilam. Berdasarkan hasil penelitian dengan menggunakan kombinasi dua metode Nearest Neighbor dan Certainty factor maka dihasilkan sistem mampu melakukan diagnosis hama dan penyakit tanaman nilam dengan nilai <em>similarity</em> 0.7 dan tingkat kepercayaaan sebesar 97,2 %  serta menghasilkan tingkat akurasi sistem sebesar 93.82 % dan tingkat kesalahan sistem 3 %</p><div><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Patchouli oil is used as a raw material for cosmetics, perfumes, antiseptics, soaps, medicines, and insecticides. In the development and improvement of patchouli plants experienced several obstacles such as pests and diseases which resulted in low yields, especially in the area of Gunung Sari Village, Watubangga District, Kolaka Regency. The development of patchouli plants attacked by pests and diseases is often hampered because there are still many farmers who do not know the types of pests and diseases that attack farmers' crops. Therefore a case based reasoning (CBR) expert system was built to diagnose patchouli plants and pests. In this study 7 types of diseases were used and 22 symptoms were based on the case study site. CBR uses the similarity Nearest Neighbor method to find similarities between cases that are in the retrieval stage. In this study, another method is used, namely Certainty Factor, which functions to determine the degree of trust in the results of system diagnosis in producing patchouli species and diseases. Based on the results of the study by using a combination of the two Nearest Neighbor and Certainty factor methods, the system was able to diagnose patchouli pests and diseases with a similarity value of 0.7 and a confidence level of 97.2% and produce a system accuracy rate of 93.82% and a system error rate of 3%</em></p><p><em><strong><br /></strong></em></p></div>

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

Feature level intentions based product recommendations with case-based reasoning

2018 ◽  
Vol 6 (1) ◽  
pp. 266-274
Author(s):  
D. Teja Santosh ◽  
◽  
K.C. Ravi Kumar ◽  
P. Chiranjeevi ◽  
◽  
...  
Keyword(s):  
Case Based Reasoning ◽  
Product Recommendations ◽  
Case Based
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