Pub Date : 2020-06-26DOI: 10.1504/ijiids.2020.10030209
Phuoc Tran, V. Duong, Dinh Dien, Bay Vo, Huu Nguyen, Long H. B. Nguyen
In natural language processing, the corpora play an important role, particularly labelled corpora, such as labelled part-of-speech corpora, labelled component syntax corpora, and labelled dependency syntax corpora. These labelled corpora are used for corpus-based research and give higher quality results than the non-labelled. In this paper, we have conducted a Vietnamese dependency label tagger based on English-Vietnamese bilingual corpus, in which English was tagged with dependency labels. The experimental results show that our method produces a high tagging result with LAS measurement of 73.5% and UAS measurement of 81.7%.
{"title":"Projecting dependency syntax labels from English into Vietnamese in English-Vietnamese bilingual corpus","authors":"Phuoc Tran, V. Duong, Dinh Dien, Bay Vo, Huu Nguyen, Long H. B. Nguyen","doi":"10.1504/ijiids.2020.10030209","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10030209","url":null,"abstract":"In natural language processing, the corpora play an important role, particularly labelled corpora, such as labelled part-of-speech corpora, labelled component syntax corpora, and labelled dependency syntax corpora. These labelled corpora are used for corpus-based research and give higher quality results than the non-labelled. In this paper, we have conducted a Vietnamese dependency label tagger based on English-Vietnamese bilingual corpus, in which English was tagged with dependency labels. The experimental results show that our method produces a high tagging result with LAS measurement of 73.5% and UAS measurement of 81.7%.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"9 1","pages":"17-32"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75459696","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 : 2020-06-26DOI: 10.1504/ijiids.2020.10030219
Tri-Cong Pham, C. Luong, A. Doucet, Van-Dung Hoang, Diem-Phuc Tran, Duc-Hau Le
Millions of women are suffering from breast cancer pressing burden on their shoulders and the global economy. Meanwhile, general treatment methods are applied without considering personalised health and genetic features. Artificial intelligence appears to be a robust method for breast cancer sub-typing. Most of researches have been implemented on binary classification with limited number of data samples. Multi-classification is much more difficult especially on large number of samples. The study aims to use machine learning to find better ways to subtype breast cancer as well as find new disease causative genes which help facilitate more personalised treatment with limited side effect in the future. This study compares the accuracy of three classification methods in combination with eight feature selection methods on a dataset of 2,682 samples. The study shows that the highest accuracy was 83.96% with the SVM-C005 classifier and percentile feature selection (800 genes). Additionally, our method can predict causative disease genes of breast cancer with four of them known to be associated with breast cancer and 29 promising ones with supporting evidence from the literature. This shows the effectiveness of our research.
{"title":"Meta-analysis of computational methods for breast cancer classification","authors":"Tri-Cong Pham, C. Luong, A. Doucet, Van-Dung Hoang, Diem-Phuc Tran, Duc-Hau Le","doi":"10.1504/ijiids.2020.10030219","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10030219","url":null,"abstract":"Millions of women are suffering from breast cancer pressing burden on their shoulders and the global economy. Meanwhile, general treatment methods are applied without considering personalised health and genetic features. Artificial intelligence appears to be a robust method for breast cancer sub-typing. Most of researches have been implemented on binary classification with limited number of data samples. Multi-classification is much more difficult especially on large number of samples. The study aims to use machine learning to find better ways to subtype breast cancer as well as find new disease causative genes which help facilitate more personalised treatment with limited side effect in the future. This study compares the accuracy of three classification methods in combination with eight feature selection methods on a dataset of 2,682 samples. The study shows that the highest accuracy was 83.96% with the SVM-C005 classifier and percentile feature selection (800 genes). Additionally, our method can predict causative disease genes of breast cancer with four of them known to be associated with breast cancer and 29 promising ones with supporting evidence from the literature. This shows the effectiveness of our research.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"18 1","pages":"89-111"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80474563","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 : 2020-06-26DOI: 10.1504/ijiids.2020.10030218
N. Bhosle, M. Kokare
The classification-based relevance feedback approach suffers from the problem of imbalanced training dataset, which causes instability and degradation in the retrieval results. In order to tackle with this problem, a novel active learning approach based on random forest classifier and feature reweighting technique is proposed in this paper. Initially, a random forest classifier is used to learn the user's retrieval intention. Then, in active learning the most informative classified samples are selected for manual labelling and added in training dataset, for retraining the classifier. Also, a feature reweighting technique based on Hebbian learning is embedded in the retrieval loop to find the weights of most perceptive features used for image representation. These techniques are combined together to form a hypothesised solution for the image retrieval problem. The experimental evaluation of the proposed system is carried out on two different databases and shows a noteworthy enhancement in retrieval results.
{"title":"Random forest-based active learning for content-based image retrieval","authors":"N. Bhosle, M. Kokare","doi":"10.1504/ijiids.2020.10030218","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10030218","url":null,"abstract":"The classification-based relevance feedback approach suffers from the problem of imbalanced training dataset, which causes instability and degradation in the retrieval results. In order to tackle with this problem, a novel active learning approach based on random forest classifier and feature reweighting technique is proposed in this paper. Initially, a random forest classifier is used to learn the user's retrieval intention. Then, in active learning the most informative classified samples are selected for manual labelling and added in training dataset, for retraining the classifier. Also, a feature reweighting technique based on Hebbian learning is embedded in the retrieval loop to find the weights of most perceptive features used for image representation. These techniques are combined together to form a hypothesised solution for the image retrieval problem. The experimental evaluation of the proposed system is carried out on two different databases and shows a noteworthy enhancement in retrieval results.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"28 1","pages":"72-88"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87651907","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}
Earlier smartphones came with fewer functions but Location Based Service (LBS) were confined to simple GPS tracking device. But today, advance wireless communication system provided current smartphones with GPS service and cheaper data service fees. Apparently, LBS applications are emerging technology solutions for most businesses to connect millions of customers within close proximity. However, many e-commerce websites today are still operating without location-based service to easily connect buyers and sellers in the same locality. Nevertheless, the growth of LBS technology and LBS market have raised privacy concerns due to potential abuse of location information. This study aims to highlight the significance of location-based service and how it helped tech companies to drive significant revenue growth. It equally validates the research model focusing on privacy concern as moderator of post adoptive behavior associated with location-based applications. We used risky shift phenomenon research method to conduct an online survey using Google docs on 500 businesses. The study tends to test the effects of major variables Unified Theory of Acceptance and Use of Technology (UTAUT) on LBS usage intention and actual use. We also test the hypothesis on post adoptive behaviorusing risky shift phenomenon research on over66 users of LBS application. The research findings support the hypothesis of moderating effect of privacy concern on performance expectancy and continuous usage is strong.
早期的智能手机功能较少，但基于位置的服务(LBS)仅限于简单的GPS跟踪设备。但是今天，先进的无线通信系统为现在的智能手机提供了GPS服务和更便宜的数据服务费用。显然，对于大多数企业来说，LBS应用是一种新兴的技术解决方案，可以近距离地连接数百万客户。然而，今天的许多电子商务网站仍然没有基于位置的服务来方便地连接同一地区的买家和卖家。然而，由于位置信息可能被滥用，LBS技术和LBS市场的发展引起了人们对隐私的担忧。本研究旨在强调基于位置的服务的重要性，以及它如何帮助科技公司推动显著的收入增长。它同样验证了关注隐私问题的研究模型，该模型是与基于位置的应用程序相关的帖子采用行为的调节因素。我们采用风险倒班现象研究方法，利用谷歌文档对500家企业进行了在线调查。本研究倾向于检验主要变量UTAUT (Unified Theory of Acceptance and Use of Technology)对LBS使用意向和实际使用的影响。我们还通过对66岁以上的LBS应用用户进行风险转移现象研究，验证了采用后行为的假设。研究结果支持了隐私关注对性能期望和持续使用的调节作用的假设。
{"title":"Significance of Location Based Service Applications in Smartphones Using GPS and Web Service","authors":"Akinyele Okedola Akinleye, Sarumi Akingbola Jamiu, Green Olawole Olakunle, Badmus Abdulsamad","doi":"10.11648/J.IJIIS.20190806.11","DOIUrl":"https://doi.org/10.11648/J.IJIIS.20190806.11","url":null,"abstract":"Earlier smartphones came with fewer functions but Location Based Service (LBS) were confined to simple GPS tracking device. But today, advance wireless communication system provided current smartphones with GPS service and cheaper data service fees. Apparently, LBS applications are emerging technology solutions for most businesses to connect millions of customers within close proximity. However, many e-commerce websites today are still operating without location-based service to easily connect buyers and sellers in the same locality. Nevertheless, the growth of LBS technology and LBS market have raised privacy concerns due to potential abuse of location information. This study aims to highlight the significance of location-based service and how it helped tech companies to drive significant revenue growth. It equally validates the research model focusing on privacy concern as moderator of post adoptive behavior associated with location-based applications. We used risky shift phenomenon research method to conduct an online survey using Google docs on 500 businesses. The study tends to test the effects of major variables Unified Theory of Acceptance and Use of Technology (UTAUT) on LBS usage intention and actual use. We also test the hypothesis on post adoptive behaviorusing risky shift phenomenon research on over66 users of LBS application. The research findings support the hypothesis of moderating effect of privacy concern on performance expectancy and continuous usage is strong.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88369687","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 : 2020-01-01DOI: 10.1504/ijiids.2020.10031606
Neha Singh, Sandeep Joshi, S. Birla
{"title":"False-positive free transparent and optimal watermarking for colour images","authors":"Neha Singh, Sandeep Joshi, S. Birla","doi":"10.1504/ijiids.2020.10031606","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10031606","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"31 1","pages":"319-338"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73865358","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 : 2020-01-01DOI: 10.1504/ijiids.2020.10031615
Houssem Lahiani, M. Neji
{"title":"Hand pose estimation system based on combined features for mobile devices","authors":"Houssem Lahiani, M. Neji","doi":"10.1504/ijiids.2020.10031615","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10031615","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"64 1","pages":"436-453"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86049808","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}
{"title":"Fuzzy-based approach to incorporate spatial constraints in possibilistic c-means algorithm for remotely sensed imagery","authors":"Abhishek Singh, Anil Kumar","doi":"10.2139/ssrn.3354465","DOIUrl":"https://doi.org/10.2139/ssrn.3354465","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"31 1","pages":"307-318"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82884677","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 : 2020-01-01DOI: 10.1504/ijiids.2020.10031592
A. Khamparia, S. Singh, A. Luhach, X. Gao
{"title":"Classification and analysis of users review using different classification techniques in intelligent e-learning system","authors":"A. Khamparia, S. Singh, A. Luhach, X. Gao","doi":"10.1504/ijiids.2020.10031592","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10031592","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"50 6 1","pages":"139-149"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90058656","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 : 2020-01-01DOI: 10.1504/ijiids.2020.10031605
Kamalinder Kaur Kaleka, Avneet Kaur, Vijay Kumar
{"title":"A conceptual comparison of metaheuristic algorithms and applications to engineering design problems","authors":"Kamalinder Kaur Kaleka, Avneet Kaur, Vijay Kumar","doi":"10.1504/ijiids.2020.10031605","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10031605","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"1 1","pages":"278-306"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83292700","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 : 2020-01-01DOI: 10.1504/ijiids.2020.10031613
Bharat V. Chawda, J. Patel
{"title":"Winnowing algorithm with enhanced exploration to optimise portfolio weights","authors":"Bharat V. Chawda, J. Patel","doi":"10.1504/ijiids.2020.10031613","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10031613","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"1 1","pages":"411-435"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88078764","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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