scholarly journals Improving an AI-Based Algorithm to Automatically Generate Concept Maps

2019 ◽  
Vol 12 (4) ◽  
pp. 72
Author(s):  
Sara Alomari ◽  
Salha Abdullah
Keyword(s):  
Text Analysis ◽  
Classification Accuracy ◽  
Concept Maps ◽  
Concept Map ◽  
Nearest Neighbors ◽  
Learning Method ◽  
K Nearest Neighbors ◽  
Intelligent Algorithms ◽  
Effective Learning

Concept maps have been used to assist learners as an effective learning method in identifying relationships between information, especially when teaching materials have many topics or concepts. However, making a manual concept map is a long and tedious task. It is time-consuming and demands an intensive effort in reading the full content and reasoning the relationships among concepts. Due to this inefficiency, many studies are carried out to develop intelligent algorithms using several data mining techniques. In this research, the authors aim at improving Text Analysis-Association Rules Mining (TA-ARM) algorithm using the weighted K-nearest neighbors (KNN) algorithm instead of the traditional KNN. The weighted KNN is expected to optimize the classification accuracy, which will, eventually, enhance the quality of the generated concept map.

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

scholarly journals A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2940
Author(s):  
Luciano Ortenzi ◽  
Simone Figorilli ◽  
Corrado Costa ◽  
Federico Pallottino ◽  
Simona Violino ◽  
...  
Keyword(s):  
Machine Vision ◽  
Nearest Neighbors ◽  
Calibration Method ◽  
Mobile App ◽  
Harvest Time ◽  
Automatic Assessment ◽  
K Nearest Neighbors ◽  
Olive Ripeness ◽  
Olive Ripening

The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method—an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.

Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters

2017 ◽  
pp. 897-914 ◽  
Author(s):  
Ahmed.T. Sahlol ◽  
Aboul Ella Hassanien
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Random Forests ◽  
Classification Accuracy ◽  
Processing Time ◽  
Optimization Algorithms ◽  
Nearest Neighbors ◽  
Benchmark Dataset ◽  
K Nearest Neighbors ◽  
Linear Discriminant

There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a different shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time.

scholarly journals Automatic Classification for Fruits’ Types and Identification of Rotten Ones Using k-NN and SVM

2019 ◽  
Vol 15 (03) ◽  
pp. 47 ◽  
Author(s):  
Ann Nosseir ◽  
Seif Eldin A. Ahmed
Keyword(s):  
Texture Feature ◽  
Texture Features ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
A Value ◽  
Different Types ◽  
Linear Svm ◽  
Occurrence Matrix

Having a system that classifies different types of fruits and identifies the quality of fruits will be of a value in various areas especially in an area of mass production of fruits’ products. This paper presents a novel system that differentiates between four fruits types and identifies the decayed ones from the fresh. The algorithms used are based on the colour and the texture features of the fruits’ images. The algorithms extract the RGB values and the first statistical order and second statistical of the Gray Level Co-occurrence Matrix (GLCM) values. To segregate between the fruits’ types, Fine, Medium, Coarse, Cosine, Cubic, and Weighted K-Nearest Neighbors algorithms are applied. The accuracy percentages of each are 96.3%, 93.8%, 25%, 83.8%, 90%, and 95% respectively.  These steps are tested with 46 pictures taken from a mobile phone of seasonal fruits at the time i.e., banana, apple, and strawberry. All types were accurately identifying.  To tell apart the decayed fruits from the fresh, the linear and quadratic Support Vector Machine (SVM) algorithms differentiated between them based on the colour segmentation and the texture feature algorithms values of each fruit image. The accuracy of the linear SVM is 96% and quadratic SVM 98%.

System Model for Prediction Analytics Using K-Nearest Neighbors Algorithm

2019 ◽  
Vol 16 (10) ◽  
pp. 4425-4430 ◽  
Author(s):  
Devendra Prasad ◽  
Sandip Kumar Goyal ◽  
Avinash Sharma ◽  
Amit Bindal ◽  
Virendra Singh Kushwah
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Research Work ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
System Model ◽  
K Nearest Neighbors ◽  
Prediction Analysis ◽  
Pros And Cons

Machine Learning is a growing area in computer science in today’s era. This article is focusing on prediction analysis using K-Nearest Neighbors (KNN) Machine Learning algorithm. Data in the dataset are processed, analyzed and predicated using the specified algorithm. Introduction of various Machine Learning algorithms, its pros and cons have been discussed. The KNN algorithm with detail study is given and it is implemented on the specified data with certain parameters. The research work elucidates prediction analysis and explicates the prediction of quality of restaurants.

scholarly journals Teacher’s Effort In Improving Student Intrapersonal Intelligence Through Student Facilitator And Explaining (SFAE) Method In IPS Learning

2017 ◽  
Vol 2 (1) ◽  
pp. 136
Author(s):  
Aulia Prisani
Keyword(s):  
Concept Maps ◽  
Concept Map ◽  
Descriptive Study ◽  
Limited Time ◽  
Learning Method ◽  
Learning Time ◽  
Learning Plan ◽  
Class Viii ◽  
Intrapersonal Intelligence ◽  
Using Data

This study is motivated by the low of students intrapersonal intelligence in IPS learning and still lack of implementation of learning method that can improve students intrapersonal intelligence. The formulation of the problem that will be answered in this study is how the learning plan implemented by the teacher to improve students intrapersonal intelligence through Student Facilitator And Explaining method (SFAE), how the implementation of learning by teachers to improve students intrapersonal intelligence through SFAE method and how the obstacles encountered by teachers in attempt to improve students' intrapersonal intelligence through the SFAE method. The purpose of this study is to describe the efforts of teachers in improving students' intrapersonal intelligence in IPS learning through SFAE method. This study method is descriptive study by using data collecting technique of interview, observation and documentation study. The subjects of this study are IPS teachers and 33 students of class VIII-K. Based on the data obtained, the following results are obtained. First, IPS learning planning using SFAE method has been prepared and prepared by teachers well, teachers are guided by the syllabus and pay attention to RPP components. Second, the implementation of the SFAE method in improving the intrapersonal intelligence goes well, the teacher carries out the learning with respect to the provisions and learning steps in accordance with the SFAE method. Third, the obstacles encountered in applying the SFAE method to improve students 'intrapersonal intelligence are the lack of students' knowledge of the SFAE method and concept maps as well as the limited time available for learning execution. The solution to solve the problem is the teacher should introduce the learning method used, introduce the concept map and manage the learning time effectively.

scholarly journals A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 286 ◽  
Author(s):  
Hamid Saadatfar ◽  
Samiyeh Khosravi ◽  
Javad Hassannataj Joloudari ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband
Keyword(s):  
Big Data ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Parametric Classification ◽  
Efficient Data ◽  
Data Pruning ◽  
Selection Of

The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.

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