Pub Date : 2023-01-01DOI: 10.1504/ijista.2023.133694
Mingzhi Gao, Selin Ahipasaoglu, Kristin Schuster
Automated poetry scoring is an emerging task in automated text scoring, which is receiving increasing attention in AI for education. Poetry is distinct from other text in its complexity and specialty in language feature moreover, poems are usually rated from multiple criteria besides the overall impression. However, few existing methods to the best of our knowledge have considered a tailored text representation model for encoding poetry. Moreover, the lack of large poetry corpus and extensive labelled data is another major constraint to construct an effective poetry scoring model. To address such problems, we proposed BERT-based models with multi-scale poetry representation. In addition, we employ multiple losses and R-Drop strategy to align the distribution of manual and model scoring and mitigate the tendency of consistent score in poems. Experiment results demonstrate that our model with multi-scale poetry representation stands out when comparing with single-scale representation model.
{"title":"Automated poetry scoring using BERT with multi-scale poetry representation","authors":"Mingzhi Gao, Selin Ahipasaoglu, Kristin Schuster","doi":"10.1504/ijista.2023.133694","DOIUrl":"https://doi.org/10.1504/ijista.2023.133694","url":null,"abstract":"Automated poetry scoring is an emerging task in automated text scoring, which is receiving increasing attention in AI for education. Poetry is distinct from other text in its complexity and specialty in language feature moreover, poems are usually rated from multiple criteria besides the overall impression. However, few existing methods to the best of our knowledge have considered a tailored text representation model for encoding poetry. Moreover, the lack of large poetry corpus and extensive labelled data is another major constraint to construct an effective poetry scoring model. To address such problems, we proposed BERT-based models with multi-scale poetry representation. In addition, we employ multiple losses and R-Drop strategy to align the distribution of manual and model scoring and mitigate the tendency of consistent score in poems. Experiment results demonstrate that our model with multi-scale poetry representation stands out when comparing with single-scale representation model.","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135844973","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 : 2023-01-01DOI: 10.1504/ijista.2023.10058063
Derar Elyyan, Yousef-Awwad Daraghmi, Faisal Khamayseh, R. Saffarini, Muath N Sabha
{"title":"Survey of Road Anomalies Detection Methods","authors":"Derar Elyyan, Yousef-Awwad Daraghmi, Faisal Khamayseh, R. Saffarini, Muath N Sabha","doi":"10.1504/ijista.2023.10058063","DOIUrl":"https://doi.org/10.1504/ijista.2023.10058063","url":null,"abstract":"","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"1 1","pages":"280-302"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67209236","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 : 2023-01-01DOI: 10.1504/ijista.2023.133702
Siti Noraini Sulaiman, Ajmal Hadi Ahmad Hishamuddin, Iza Sazanita Isa, Muhammad Khusairi Osman, Zainal Hisham Che Soh
Cervical cancer is a significant global issue, with Pap smear tests being a common screening tool for precancerous stages. This study aims to develop a computer-aided diagnostics system that can classify precancerous cells from Pap smear images. The project employs convolutional neural networks (CNNs) trained using pre-processed images, adaptive fuzzy K-means (AFKM), and fuzzy C-means (FCM) to classify cervical cancer cell data as normal or abnormal. The datasets used in the project include normal, low-grade squamous intraepithelial lesion (LSIL), and high-grade squamous intraepithelial lesion (HSIL) categories. CNN1, CNN2, and CNN3 have been developed and CNN2 was chosen due to its highest accuracy of 87.71%. The CNN2 trained with AFKM outperformed other networks with an accuracy of 89.53%, precision of 0.870, recall of 0.870, specificity of 0.935, and F1-score of 0.870. This study demonstrates the potential of deep learning-based approaches for identifying and classifying cervical cell pre-cancerous stages.
{"title":"Classification of cervical cancer from Pap smear images: a convolutional neural network approach","authors":"Siti Noraini Sulaiman, Ajmal Hadi Ahmad Hishamuddin, Iza Sazanita Isa, Muhammad Khusairi Osman, Zainal Hisham Che Soh","doi":"10.1504/ijista.2023.133702","DOIUrl":"https://doi.org/10.1504/ijista.2023.133702","url":null,"abstract":"Cervical cancer is a significant global issue, with Pap smear tests being a common screening tool for precancerous stages. This study aims to develop a computer-aided diagnostics system that can classify precancerous cells from Pap smear images. The project employs convolutional neural networks (CNNs) trained using pre-processed images, adaptive fuzzy K-means (AFKM), and fuzzy C-means (FCM) to classify cervical cancer cell data as normal or abnormal. The datasets used in the project include normal, low-grade squamous intraepithelial lesion (LSIL), and high-grade squamous intraepithelial lesion (HSIL) categories. CNN1, CNN2, and CNN3 have been developed and CNN2 was chosen due to its highest accuracy of 87.71%. The CNN2 trained with AFKM outperformed other networks with an accuracy of 89.53%, precision of 0.870, recall of 0.870, specificity of 0.935, and F1-score of 0.870. This study demonstrates the potential of deep learning-based approaches for identifying and classifying cervical cell pre-cancerous stages.","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"286 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135844992","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 : 2023-01-01DOI: 10.1504/ijista.2023.10059172
Zhisong Li, Zhiping Fan, Jiaxing Sun
{"title":"A novel approach for intelligent introduction of optimal system error in spatial carrier technology","authors":"Zhisong Li, Zhiping Fan, Jiaxing Sun","doi":"10.1504/ijista.2023.10059172","DOIUrl":"https://doi.org/10.1504/ijista.2023.10059172","url":null,"abstract":"","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135496957","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 : 2023-01-01DOI: 10.1504/ijista.2023.10056521
Kristin Schuster, Selin Ahipasaoglu, Mingzhi Gao
{"title":"Automated Poetry Scoring Using BERT with Multi-Scale Poetry Representation","authors":"Kristin Schuster, Selin Ahipasaoglu, Mingzhi Gao","doi":"10.1504/ijista.2023.10056521","DOIUrl":"https://doi.org/10.1504/ijista.2023.10056521","url":null,"abstract":"","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"1 1","pages":"250-261"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67209266","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}
In today's digital world with a rapid increase in e-commerce portals, the consumers are more oriented towards seeking out online reviews, feedback, or ratings over a product during the online buying process. In this research work, we tried to investigate the relationship between the review ratings and the sentiment of reviews in the form of their polarity. We have tried to predict the sentiments over the given reviews by implementing various machine learning techniques, i.e., logistic regression, support vector machine (SVM), k-nearest neighbours (KNN), and recurrent neural network (RNN). The machine learning techniques predict the sentiments of provided reviews in two scenarios, i.e., scenario 1 - negative (-) and positive (+) and scenario 2 - negative (-), neutral (0) and positive (+). In this paper, we have proposed the architecture for predicting the sentiments with better accuracy over other techniques.
{"title":"Sentiment analysis using RNN model with LSTM","authors":"Liang Zhou, Arpit Kumar Sharma, Kishan Kanhaiya, Amita Nandal, Arvind Dhaka","doi":"10.1504/ijista.2023.133701","DOIUrl":"https://doi.org/10.1504/ijista.2023.133701","url":null,"abstract":"In today's digital world with a rapid increase in e-commerce portals, the consumers are more oriented towards seeking out online reviews, feedback, or ratings over a product during the online buying process. In this research work, we tried to investigate the relationship between the review ratings and the sentiment of reviews in the form of their polarity. We have tried to predict the sentiments over the given reviews by implementing various machine learning techniques, i.e., logistic regression, support vector machine (SVM), k-nearest neighbours (KNN), and recurrent neural network (RNN). The machine learning techniques predict the sentiments of provided reviews in two scenarios, i.e., scenario 1 - negative (-) and positive (+) and scenario 2 - negative (-), neutral (0) and positive (+). In this paper, we have proposed the architecture for predicting the sentiments with better accuracy over other techniques.","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135844731","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 : 2023-01-01DOI: 10.1504/ijista.2023.10058066
Arvind Dhaka, Amita Nandal, K.S.S. Kanhaiya, A. Sharma, Liang Zhou
{"title":"Sentiment Analysis using RNN Model with LSTM","authors":"Arvind Dhaka, Amita Nandal, K.S.S. Kanhaiya, A. Sharma, Liang Zhou","doi":"10.1504/ijista.2023.10058066","DOIUrl":"https://doi.org/10.1504/ijista.2023.10058066","url":null,"abstract":"","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"1 1","pages":"229-249"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67209305","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 : 2023-01-01DOI: 10.1504/ijista.2023.10058137
Lv Lei, Zhijun Fang, Peipei Li, Bo Huang, Chenming Wang
{"title":"Heterogeneous Graph Convolutional Neural Network for Short Text Classification","authors":"Lv Lei, Zhijun Fang, Peipei Li, Bo Huang, Chenming Wang","doi":"10.1504/ijista.2023.10058137","DOIUrl":"https://doi.org/10.1504/ijista.2023.10058137","url":null,"abstract":"","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67209626","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 : 2023-01-01DOI: 10.1504/ijista.2023.10057286
M. SayedElahl
{"title":"A novel edge detection filter based on fractional order Legendre-Laguerre functions","authors":"M. SayedElahl","doi":"10.1504/ijista.2023.10057286","DOIUrl":"https://doi.org/10.1504/ijista.2023.10057286","url":null,"abstract":"","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"1 1","pages":"321-343"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67209617","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 : 2023-01-01DOI: 10.1504/ijista.2023.10059590
B.Vinoth Kumar, Sridevi S, Karpagam G. R
{"title":"Knowledge-based Genetic Algorithm (KBGA) approach to optimize to Gated Recurrent Unit for Semantic Web Service Classification","authors":"B.Vinoth Kumar, Sridevi S, Karpagam G. R","doi":"10.1504/ijista.2023.10059590","DOIUrl":"https://doi.org/10.1504/ijista.2023.10059590","url":null,"abstract":"","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135955966","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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