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.

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.

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 EVALUATION OF MUTUAL INFORMATION ESTIMATORS FOR TIME SERIES

2009 ◽  
Vol 19 (12) ◽  
pp. 4197-4215 ◽  
Author(s):  
ANGELIKI PAPANA ◽  
DIMITRIS KUGIUMTZIS
Keyword(s):  
Time Series ◽  
Nonlinear Systems ◽  
Mutual Information ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Series Length ◽  
K Nearest Neighbors ◽  
Data Adaptive ◽  
The Comparative Study ◽  
Selection Of

We study some of the most commonly used mutual information estimators, based on histograms of fixed or adaptive bin size, k-nearest neighbors and kernels and focus on optimal selection of their free parameters. We examine the consistency of the estimators (convergence to a stable value with the increase of time series length) and the degree of deviation among the estimators. The optimization of parameters is assessed by quantifying the deviation of the estimated mutual information from its true or asymptotic value as a function of the free parameter. Moreover, some commonly used criteria for parameter selection are evaluated for each estimator. The comparative study is based on Monte Carlo simulations on time series from several linear and nonlinear systems of different lengths and noise levels. The results show that the k-nearest neighbor is the most stable and less affected by the method-specific parameter. A data adaptive criterion for optimal binning is suggested for linear systems but it is found to be rather conservative for nonlinear systems. It turns out that the binning and kernel estimators give the least deviation in identifying the lag of the first minimum of mutual information from nonlinear systems, and are stable in the presence of noise.

scholarly journals COMPATIBILITY OF SELECTION OF STUDENT DEPARTMENTS USING k-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFIER IN INFORMATICS PRIVATE VOCATIONAL SCHOOL, SERANG CITY

2021 ◽  
Vol 4 (1) ◽  
pp. 33-39
Author(s):  
Budi Pangestu ◽  
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Prospective Students ◽  
Naïve Bayes Classifier ◽  
K Nearest Neighbors ◽  
Selection Of

Selection of majors by prospective students when registering at a school, especially a Vocational High School, is very vulnerable because prospective students usually choose a major not because of their individual wishes. And because of the increasing emergence of new schools in cities and districts in each province in Indonesia, especially in the province of Banten. Problems experienced by prospective students when choosing the wrong department or not because of their desire, so that it has an unsatisfactory value or value in each semester fluctuates, especially in their Productive Lessons or Competencies. To provide a solution, a departmental suitability system is needed that can provide recommendations for specialization or major suitability based on students' abilities through attributes that can later assist students in the suitability of majors. The process of classifying the suitability of majors in data mining uses the k-Nearest Neighbor and Naive Bayes Classifier methods by entering 16 (sixteen) criteria or attributes which can later provide an assessment of students through this test when determining the majors for themselves, and there is no interference from people. another when choosing a major later. Research that has been carried out successfully using the k-Nearest Neighbors method has a higher recall of 99%, 81% accuracy and 82% precision compared to the Naïve Bayes Classifier whose recall only yields 98% while the accuracy and precision is the same as the k- Nearest Neighbors.

FASICA Framework: Service Selection Using K-d Tree and Cache

2015 ◽  
Vol 25 (05) ◽  
pp. 807-828
Author(s):  
Aimrudee Jongtaveesataporn ◽  
Shingo Takada
Keyword(s):  
Nearest Neighbor ◽  
Service Oriented Architecture ◽  
Service Selection ◽  
K Nearest Neighbor ◽  
Search Results ◽  
Key Points ◽  
Service Oriented ◽  
Selection For ◽  
Selection Of

The selection of services is a key part of Service Oriented Architecture (SOA). Services are primarily selected based on function, but Quality of Service (QoS) is an important factor when choosing among several services with the same function. But current service selection approaches often takes time to unnecessarily recompute requests. Furthermore, if the same service is chosen as having the "best" QoS for multiple selections, this may result in that service having too much load. We thus propose the FASICA (FAst service selection for SImilar constraints with CAche) Framework which chooses a service with satisfactory QoS as quickly as possible. The key points are (1) to use a cache which stores previous search results, (2) to use K-Nearest Neighbor (K-NN) algorithm with K-d tree when a satisfactory service does not exist in the cache, and (3) to distribute the service request according to a distribution policy. Results of simulations show that our framework can rapidly select a service compared to a conventional approach.

Effect of information gain on document classification using k-nearest neighbor

2022 ◽  
Vol 8 (1) ◽  
pp. 50
Author(s):  
Rifki Indra Perwira ◽  
Bambang Yuwono ◽  
Risya Ines Putri Siswoyo ◽  
Febri Liantoni ◽  
Hidayatulah Himawan
Keyword(s):  
Feature Selection ◽  
Test Data ◽  
Nearest Neighbor ◽  
Intelligent System ◽  
Information Gain ◽  
Training Data ◽  
State Universities ◽  
Features Selection ◽  
K Nearest Neighbor ◽  
Support Students

State universities have a library as a facility to support students’ education and science, which contains various books, journals, and final assignments. An intelligent system for classifying documents is needed to ease library visitors in higher education as a form of service to students. The documents that are in the library are generally the result of research. Various complaints related to the imbalance of data texts and categories based on irrelevant document titles and words that have the ambiguity of meaning when searching for documents are the main reasons for the need for a classification system. This research uses k-Nearest Neighbor (k-NN) to categorize documents based on study interests with information gain features selection to handle unbalanced data and cosine similarity to measure the distance between test and training data. Based on the results of tests conducted with 276 training data, the highest results using the information gain selection feature using 80% training data and 20% test data produce an accuracy of 87.5% with a parameter value of k=5. The highest accuracy results of 92.9% are achieved without information gain feature selection, with the proportion of training data of 90% and 10% test data and parameters k=5, 7, and 9. This paper concludes that without information gain feature selection, the system has better accuracy than using the feature selection because every word in the document title is considered to have an essential role in forming the classification.

Implementasi K-Nearest Neighbor pada Decission Support System Pemilihan Satuan Pengamanan Event Perguruan Tinggi

2020 ◽  
Vol 5 (1) ◽  
pp. 67
Author(s):  
Aries Setiawan ◽  
Budi Widjajanto ◽  
Achmad Wahid Kurniawan ◽  
Setyo Budi
Keyword(s):  
Support System ◽  
Nearest Neighbor ◽  
Selection Process ◽  
College Level ◽  
K Nearest Neighbor ◽  
Tertiary Institutions ◽  
Security Guards ◽  
Approval Rate ◽  
Level Of Importance ◽  
Selection Of

Routine events required by tertiary institutions require escort from selected security guards. Elections based on personal subjectivity will lead to results that are not in accordance with the purpose of the security itself. However, if the selection is based on the objectives will give results that are in accordance with professionalism. Each security unit has a different level of importance, so that at the level of security the event needs a level of professionalism in accordance with the level of importance at the college level. In detail the selection of security units on several criteria, namely event, years of service, cooperation, service, personality, skills and responsibilities. The method used in this selection process is the K-Nearest Neighbor, with the final result approval rate of  0.88%

Feature Selection for Helicopter Swashplate Bearing Fault Diagnosis

2017 ◽  
Author(s):  
Yong Wang ◽  
Lin Li
Keyword(s):  
Nearest Neighbor ◽  
Ball Bearing ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
Bearing Fault Diagnosis ◽  
Feature Extraction And Selection ◽  
Frequency Domains ◽  
Selection For ◽  
Selection Of

This paper provides a case study of diagnosing helicopter swashplate ball bearing faults using vibration signals. We develop and apply feature extraction and selection techniques in the time, frequency, and joint time-frequency domains to differentiate six types of swashplate bearing conditions: low-time, to-be-overhauled, corroded, cage-popping, spalled, and case-overlapping. With proper selection of the features, it is shown that even the simple k-nearest neighbor (k-NN) algorithm is able to correctly identify these six types of conditions on the tested data. The developed method is useful for helicopter swashplate condition monitoring and maintenance scheduling. It is also helpful for testing the manufactured swashplate ball bearings for quality control purposes.

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