scholarly journals Service identification using k-NN machine learning

2018 ◽  
Vol 7 (2.4) ◽  
pp. 182
Author(s):  
Travis Joseph Poulose ◽  
S Ganesh Kumar
Keyword(s):  
Machine Learning ◽  
Web Service ◽  
Service Discovery ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Service Request ◽  
Service Identification ◽  
Daunting Task ◽  
K Nearest Neighbor Algorithm

Web service categorization is a daunting task since it requires semantic descriptions of those services which are not provided to the majority of those websites. The proposal of a Semantic based automated service discovery requires a request from the user that can be analyzed which then provides the user with a list of related webs services based on the request that instigated the search. The problem with these service categorizations listed in the Universal description Discovery and Integration (UDDI) is the way the information is related to one another. The relations follow a syntactic method. Semantic based service descriptions is necessary for accurate web categorization. With the help of machine learning we can also predict the user’s service request automatically based on previous searches and also select the best web service for a particular request that the user has made using a k-nearest neighbor algorithm. By doing this we can distinguish between the various types of user requests, provide services that are suitable for that particular request as well as suggest other services that might potentially suit the needs of the user.  

scholarly journals Sistem Pakar Diagnosa Kerusakan VGA dengan Metode Certainty Factor dan Algoritma K-Nearest Neighbor (K-NN)

2020 ◽  
Vol 4 (2) ◽  
pp. 79
Author(s):  
Rizal Maulana Yusuf Effendi ◽  
Septi Andryana ◽  
Ratih Titi Komala Sari
Keyword(s):  
Machine Learning ◽  
Expert System ◽  
Visual Processing ◽  
Nearest Neighbor ◽  
Diagnostic System ◽  
K Nearest Neighbor ◽  
Certainty Factor ◽  
Nearest Neighbor Algorithm ◽  
Factor Method ◽  
K Nearest Neighbor Algorithm

VGA (Video Graphics Array) is a Video adapter which is very useful for improving the performance and quality of the visual process on a computer, but sometimes there is often a malfunction that cannot be identified the type of damage. The problem is the lack of media to identify the damage that occurs during visual processing. Therefore, the authors created an expert system that can diagnose the type of damage to VGA using the Certainty Factor method as a calculation, using UML modeling as the work process flow of the system on the website, and also equipped with the KNN (K-Nearest Neighbor) algorithm as machine learning. so that it can build an expert system with the PHP programming language MySQL database. The method used in testing is the black box method in testing the system used. The results that can be concluded from this study are; 1) The diagnostic system for detecting damage to the VGA uses the K-Nearest Neighbor Algorithm as machine learning and the Certainty Factor Method as a calculation medium in determining the distance from the type of damage and has suggestions for further actions to deal with and prevent the damage from occurring and also has other possible damage things that are similar to the damage suffered can be accessed quickly and easily to understand, in making scientific research carried out sequentially to facilitate the process, and 2) In addition to diagnosing, there are several additional menus that can be accessed such as the Prediction menu which functions to displays the max and min limits of the temperature of a product, Product Info which functions as a quality product recommendation, and a description that contains a post of details of the damage that can be studied and is expected to help users find solutions to their problems.Keywords:Expert System, PHP, Certainty Factors, Machine learning, K-NN.

scholarly journals Physiological Stress Prediction using Machine Learning Classifiers

2019 ◽  
Vol 9 (1) ◽  
pp. 675-677
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Physiological Stress ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Stress Prediction ◽  
K Nearest Neighbor Algorithm

The aim of this study is to predict the stress of a person using Machine Learning classifiers. This system classifies the stress of a person as either High or Low. There are various classification algorithms present, out of which 9 classification algorithms have been chosen for this study. The algorithms implemented are K-Nearest Neighbor classifier, Support Vector Machine with an RBF kernel, Decision Tree algorithm, Random Forest algorithm, Bagging Classifier, Adaboost algorithm, Voting classifier, Logistic Regression and MLP classifier. The different algorithms are applied on the same dataset. The dataset is obtained from a GitHub repository labelled Stress classifier with AutoML. The different accuracies of each algorithm are found, and the classification algorithm with the best accuracy is determined. On comparison, it was found that the K-Nearest Neighbor algorithm has the best accuracy with an accuracy rate of 79.3% for physiological stress prediction. While other algorithms had varying accuracies, K-Nearest Neighbor algorithm was the most consistent.

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

scholarly journals Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

2021 ◽  
Vol 11 (15) ◽  
pp. 7132
Author(s):  
Jianfeng Xi ◽  
Shiqing Wang ◽  
Tongqiang Ding ◽  
Jian Tian ◽  
Hui Shao ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Operating Data ◽  
Detection Model ◽  
Driving Behaviors ◽  
K Nearest Neighbor Algorithm ◽  
Fatigue Driving ◽  
Freight Vehicles

Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.

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