Pub Date : 2022-06-30DOI: 10.13164/mendel.2022.1.008
A. Sangodiah, Tham Jee San, Yong Tien Fui, Lim Ean Heng, R. Ayyasamy, Norazira A Jalil
Examination is one of the common ways to evaluate the students’ cognitive levels in higher education institutions. Exam questions are labeled manually by educators in accordance with Bloom’s taxonomy cognitive domain. To ease the burden of the educators, several past research works have proposed the automated question classification based on Bloom’s taxonomy using the machine learning technique. Feature selection, feature extraction and term weighting are common ways to improve the accuracy of question classification. Commonly used term weighting method in the past work is unsupervised namely TF and TF-IDF. There are several variants of TF and TFIDF and the most optimal variant has yet to be identified in the context of question classification based on BT. Therefore, this paper aims to study the TF, TF-IDF and normalized TF-IDF variants and identify the optimal variant that can enhance the exam question classification accuracy. To investigate the variants two different classifiers were used, which are Support Vector Machine (SVM) and Naïve Bayes. The average accuracies achieved by TF-IDF and normalized TF-IDF variants using SVM classifier were 64.3% and 72.4% respectively, while using Naïve Bayes classifier the average accuracies for TF-IDF and normalized TF-IDF were 61.9% and 63.0% respectively. Generally, the normalized TF-IDF variants outperformed TF and TF-IDF variants in accuracy and F1-measure respectively. Further statistical analysis using t-test and Wilcoxon Signed also shows that the differences in accuracy between normalized TF-IDF and TF, TF-IDF are significant. The findings from this study show that the Normalized TF-IDF3 variant recorded the highest accuracy of 74.0% among normalized TF-IDF variants. Also, the differences in accuracy between Normalized TF-IDF3 and other normalized variants are generally significant, thus the optimal variant is Normalized TF-IDF3. Therefore, the normalized TF-IDF3 variant is important for benchmarking purposes, which can be used to compare with other term weighting techniques in future work.
{"title":"Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy","authors":"A. Sangodiah, Tham Jee San, Yong Tien Fui, Lim Ean Heng, R. Ayyasamy, Norazira A Jalil","doi":"10.13164/mendel.2022.1.008","DOIUrl":"https://doi.org/10.13164/mendel.2022.1.008","url":null,"abstract":"Examination is one of the common ways to evaluate the students’ cognitive levels in higher education institutions. Exam questions are labeled manually by educators in accordance with Bloom’s taxonomy cognitive domain. To ease the burden of the educators, several past research works have proposed the automated question classification based on Bloom’s taxonomy using the machine learning technique. Feature selection, feature extraction and term weighting are common ways to improve the accuracy of question classification. Commonly used term weighting method in the past work is unsupervised namely TF and TF-IDF. There are several variants of TF and TFIDF and the most optimal variant has yet to be identified in the context of question classification based on BT. Therefore, this paper aims to study the TF, TF-IDF and normalized TF-IDF variants and identify the optimal variant that can enhance the exam question classification accuracy. To investigate the variants two different classifiers were used, which are Support Vector Machine (SVM) and Naïve Bayes. The average accuracies achieved by TF-IDF and normalized TF-IDF variants using SVM classifier were 64.3% and 72.4% respectively, while using Naïve Bayes classifier the average accuracies for TF-IDF and normalized TF-IDF were 61.9% and 63.0% respectively. Generally, the normalized TF-IDF variants outperformed TF and TF-IDF variants in accuracy and F1-measure respectively. Further statistical analysis using t-test and Wilcoxon Signed also shows that the differences in accuracy between normalized TF-IDF and TF, TF-IDF are significant. The findings from this study show that the Normalized TF-IDF3 variant recorded the highest accuracy of 74.0% among normalized TF-IDF variants. Also, the differences in accuracy between Normalized TF-IDF3 and other normalized variants are generally significant, thus the optimal variant is Normalized TF-IDF3. Therefore, the normalized TF-IDF3 variant is important for benchmarking purposes, which can be used to compare with other term weighting techniques in future work.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80105127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-30DOI: 10.13164/mendel.2022.1.055
Joao Paulo Schwarz Schuler, S. Romaní, M. Abdel-Nasser, Hatem A. Rashwan, D. Puig
Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.
{"title":"Color-Aware Two-Branch DCNN for Efficient Plant Disease Classification","authors":"Joao Paulo Schwarz Schuler, S. Romaní, M. Abdel-Nasser, Hatem A. Rashwan, D. Puig","doi":"10.13164/mendel.2022.1.055","DOIUrl":"https://doi.org/10.13164/mendel.2022.1.055","url":null,"abstract":"Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80302311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-030-92127-9_88
Parvin Alizada
{"title":"Conversion of Volatile Time Series into a Fuzzy Time Series by the Example of the Dow Jones Index Dynamics","authors":"Parvin Alizada","doi":"10.1007/978-3-030-92127-9_88","DOIUrl":"https://doi.org/10.1007/978-3-030-92127-9_88","url":null,"abstract":"","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74074863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-030-92127-9_29
S. Ulyanov, A. Shevchenko, A. A. Shevchenko, A. Reshetnikov
{"title":"Imperfect Knowledge Base Self-organization in Robotic Intelligent Cognitive Control: Quantum Supremacy","authors":"S. Ulyanov, A. Shevchenko, A. A. Shevchenko, A. Reshetnikov","doi":"10.1007/978-3-030-92127-9_29","DOIUrl":"https://doi.org/10.1007/978-3-030-92127-9_29","url":null,"abstract":"","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74369919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-030-92127-9_43
Leyla Hasanova
{"title":"Portfolio Selection Model Using Z-Numbers Theory","authors":"Leyla Hasanova","doi":"10.1007/978-3-030-92127-9_43","DOIUrl":"https://doi.org/10.1007/978-3-030-92127-9_43","url":null,"abstract":"","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81282848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-030-92127-9_13
Yucel Inan
{"title":"Quality Metrics of LSB Image Steganography Technique for Color Space HSI","authors":"Yucel Inan","doi":"10.1007/978-3-030-92127-9_13","DOIUrl":"https://doi.org/10.1007/978-3-030-92127-9_13","url":null,"abstract":"","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90177502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-030-92127-9_21
J. Fathi
{"title":"Minimizing Handover Process and Wireless Propagation Lose by Using Multilayer Perception Neural Network","authors":"J. Fathi","doi":"10.1007/978-3-030-92127-9_21","DOIUrl":"https://doi.org/10.1007/978-3-030-92127-9_21","url":null,"abstract":"","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"114 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80705591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-030-92127-9_56
N. Yusupbekov, Shuhrat Gulyamov, N. Usmanova, Jamshid Khoshimov
{"title":"Exploring of the Problematic Industry 4.0 and Platform-Based Economic Development","authors":"N. Yusupbekov, Shuhrat Gulyamov, N. Usmanova, Jamshid Khoshimov","doi":"10.1007/978-3-030-92127-9_56","DOIUrl":"https://doi.org/10.1007/978-3-030-92127-9_56","url":null,"abstract":"","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82029165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-030-92127-9_35
I. Islamov, M. Hasanov, M. H. Abbasov
{"title":"Simulation of Electrodynamic Processes in the Cylindrical-Rectangular Microwave Waveguide Systems Transmitting Information","authors":"I. Islamov, M. Hasanov, M. H. Abbasov","doi":"10.1007/978-3-030-92127-9_35","DOIUrl":"https://doi.org/10.1007/978-3-030-92127-9_35","url":null,"abstract":"","PeriodicalId":38293,"journal":{"name":"Mendel","volume":"142 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74947420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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