scholarly journals The Prediction of Student Failure Using Classification Methods : A Case study

2018 ◽  
Author(s):  
Mashael Al luhaybi ◽  
Allan Tucker ◽  
Leila Yousefi
Keyword(s):  
Classification Methods ◽  
Student Failure

ER rule classifier with an optimization operator recommendation

2021 ◽  
pp. 1-13
Author(s):  
Xiaoyan Wang ◽  
Jianbin Sun ◽  
Qingsong Zhao ◽  
Yaqian You ◽  
Jiang Jiang
Keyword(s):  
Classification Accuracy ◽  
Comprehensive Analysis ◽  
Small Sample ◽  
Empirical Knowledge ◽  
Classification Methods ◽  
Turbofan Engine ◽  
Training Samples ◽  
Optimization Operator ◽  
Recommendation Strategy

It is difficult for many classic classification methods to consider expert experience and classify small-sample datasets well. The evidential reasoning rule (ER rule) classifier can solve these problems. The ER rule has strong processing and comprehensive analysis abilities for diversified mixed information and can solve problems with expert experience effectively. Moreover, the initial parameters of the classifier constructed based on the ER rule can be set according to empirical knowledge instead of being trained by a large number of samples, which can help the classifier classify small-sample datasets well. However, the initial parameters of the ER rule classifier need to be optimized, and choosing the best optimization algorithm is still a challenge. Considering these problems, the ER rule classifier with an optimization operator recommendation is proposed in this paper. First, the initial ER rule classifier is constructed based on training samples and expert experience. Second, the adjustable parameters are optimized, in which the optimization operator recommendation strategy is applied to select the best algorithm by partial samples, and then experiments with full samples are carried out. Finally, a case study on a turbofan engine degradation simulation dataset is carried out, and the results indicate that the ER rule classifier has a higher classification accuracy than other classic classifiers, which demonstrates the capability and effectiveness of the proposed ER rule classifier with an optimization operator recommendation.

scholarly journals Identification of Heracleum sosnowskyi-Invaded Land Using Earth Remote Sensing Data

2020 ◽  
Vol 12 (3) ◽  
pp. 759
Author(s):  
Jūratė Sužiedelytė Visockienė ◽  
Eglė Tumelienė ◽  
Vida Maliene
Keyword(s):  
Remote Sensing ◽  
Invasive Plant ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Economic Losses ◽  
Classification Methods ◽  
Earth Remote Sensing ◽  
Heracleum Sosnowskyi

H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to human health, and causes considerable economic losses. In order to find effective and complex measures against this invasive plant, it is very important to identify places and areas where H. sosnowskyi grows, carry out a detailed analysis, and monitor its spread to avoid leaving this process to chance. In this paper, the remote sensing methodology was proposed to identify territories covered with H. sosnowskyi plants (land classification). Two categories of land cover classification were used: supervised (human-guided) and unsupervised (calculated by software). In the application of the supervised method, the average wavelength of the spectrum of H. sosnowskyi was calculated for the classification of the RGB image and according to this, the unsupervised classification by the program was accomplished. The combination of both classification methods, performed in steps, allowed obtaining better results than using one. The application of authors’ proposed methodology was demonstrated in a Lithuanian case study discussed in this paper.

scholarly journals A Comparative Analysis for 2D Object Recognition: A Case Study with Tactode Puzzle-Like Tiles

2021 ◽  
Vol 7 (4) ◽  
pp. 65
Author(s):  
Daniel Silva ◽  
Armando Sousa ◽  
Valter Costa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Object Recognition ◽  
Template Matching ◽  
Recognition Performance ◽  
Regions Of Interest ◽  
The Other ◽  
Classification Methods

Object recognition represents the ability of a system to identify objects, humans or animals in images. Within this domain, this work presents a comparative analysis among different classification methods aiming at Tactode tile recognition. The covered methods include: (i) machine learning with HOG and SVM; (ii) deep learning with CNNs such as VGG16, VGG19, ResNet152, MobileNetV2, SSD and YOLOv4; (iii) matching of handcrafted features with SIFT, SURF, BRISK and ORB; and (iv) template matching. A dataset was created to train learning-based methods (i and ii), and with respect to the other methods (iii and iv), a template dataset was used. To evaluate the performance of the recognition methods, two test datasets were built: tactode_small and tactode_big, which consisted of 288 and 12,000 images, holding 2784 and 96,000 regions of interest for classification, respectively. SSD and YOLOv4 were the worst methods for their domain, whereas ResNet152 and MobileNetV2 showed that they were strong recognition methods. SURF, ORB and BRISK demonstrated great recognition performance, while SIFT was the worst of this type of method. The methods based on template matching attained reasonable recognition results, falling behind most other methods. The top three methods of this study were: VGG16 with an accuracy of 99.96% and 99.95% for tactode_small and tactode_big, respectively; VGG19 with an accuracy of 99.96% and 99.68% for the same datasets; and HOG and SVM, which reached an accuracy of 99.93% for tactode_small and 99.86% for tactode_big, while at the same time presenting average execution times of 0.323 s and 0.232 s on the respective datasets, being the fastest method overall. This work demonstrated that VGG16 was the best choice for this case study, since it minimised the misclassifications for both test datasets.

scholarly journals Assessing Urban Forest Canopy Cover Using Airborne or Satellite Imagery

2008 ◽  
Vol 34 (6) ◽  
pp. 334-340
Author(s):  
Jeffrey Walton ◽  
David Nowak ◽  
Eric Greenfield
Keyword(s):  
Remote Sensing ◽  
New York ◽  
Forest Canopy ◽  
Urban Forest ◽  
Canopy Cover ◽  
Classification Methods ◽  
Remote Sensing Techniques ◽  
Many Sources ◽  
Forest Managers

With the availability of many sources of imagery and various digital classification techniques, assessing urban forest canopy cover is readily accessible to most urban forest managers. Understanding the capability and limitations of various types of imagery and classification methods is essential to interpreting canopy cover values. An overview of several remote sensing techniques used to assess urban forest canopy cover is presented. A case study comparing canopy cover percentages for Syracuse, New York, U.S. interprets the multiple values developed using different methods. Most methods produce relatively similar results, but the estimate based on the National Land Cover Database is much lower.

scholarly journals Morphological classification of conspecific birds from closely situated breeding areas – A case study of the Common Nightingale

2015 ◽  
Vol 23 (2) ◽  
pp. 20-30 ◽  
Author(s):  
Dávid Kováts ◽  
Andrea Harnos
Keyword(s):  
Environmental Parameters ◽  
Classification Methods ◽  
Morphological Comparison ◽  
Linear Discriminant ◽  
The North ◽  
North Eastern ◽  
Morphological Differences ◽  
Bill Length

Abstract In this paper, a complex morphological comparison of four Common Nightingale groups (Luscinia megarhynchos) is demonstrated. In total, 121 territorial nightingales were mist-netted and measured individually on four study areas called ‘Bódva’, ‘Felső-Tisza’, ‘Szatmár-Bereg’ and ‘Bátorliget’ in the North-Eastern part of Hungary in 2006–2013. To distinguish groups by morphology, Classification and Regression Trees (CART), Random Forest (RF) and Linear Discriminant Analysis (LDA) methods were used. Comparison of the four studied Common Nightingale groups shows substantial morphological differences in the length of the second, the third and the fourth primaries (P2, P3, P4), in bill length (BL) and bill width (BW), while other characteristics showed greater similarities. Based on the results of all the applied classification methods, birds originated from Szatmár-Bereg were clearly distinguishable from the others. The differences in morphology can be explained by interspecific competition or phenotypic plasticity resulting from the change of ecological, environmental parameters. Our case study highlights the advantageous differences of the classification methods to distinguish groups with similar morphology and to choose important variables for classification. In conclusion, broad application of the classification methods RF and CART is highly recommended in comparative ecological studies.

NEWS CLASSIFICATION WITH HUMAN ANNOTATORS: A CASE STUDY

2015 ◽  
Vol 74 (10) ◽  
Author(s):  
Aini Fuddoly ◽  
Jafreezal Jaafar ◽  
Norshuhani Zamin
Keyword(s):  
Online News ◽  
Research Field ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Combined Method ◽  
Human Evaluation ◽  
News Websites ◽  
Manual Classification ◽  
Language Corpus

The need to classify textual documents has become an increasingly vibrant research field due to the development of online news. While most of the news in news websites are categorised manually, the task becomesmore strenuous considering the tremendous surge of data updates every day. This paper addresses the question of how text classification algorithms can substitute the particular task over manual classification methods. A combined method using Bracewell's algorithm and top-n method is demonstrated and tested using Indonesian language corpus. The experiment also uses human evaluation as the benchmark. The result from the human evaluation is further investigated in order to understand how the annotators classify documents and the aspects that can be improved to enhance the method in the future. The results indicate that the method can outperform human annotators by 13% in terms of accuracy.

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