Pub Date : 2021-01-01DOI: 10.1504/ijiids.2021.10033770
Ghada Landoulsi, Khaoula Mahmoudi, Sami Faïz
With the evolving research in geographic information system (GIS) owing to its ability to support decision makers in different fields, there is a strong need to enabling all users; specialists and non-specialists to profit from this technology. Although, the key impediment to non-specialists is the language to interact with the GIS and especially its embedded geographic database (GDB) which require SQL skills. In this paper we explore a new approach which alleviates nomad GIS users from any formatting effort by only using the natural language as a GDB communication mean. The process is generally two-fold: 1) formatting the natural language user query to be processed by the GDB engine; 2) translating the GDB retrieved answer to a text easily interpreted by all GIS users. The resulting implemented system was integrated to the OpenJump GIS and has been evaluated to give satisfactory results.
{"title":"Building natural language responses from natural language questions in the spatio-temporal context","authors":"Ghada Landoulsi, Khaoula Mahmoudi, Sami Faïz","doi":"10.1504/ijiids.2021.10033770","DOIUrl":"https://doi.org/10.1504/ijiids.2021.10033770","url":null,"abstract":"With the evolving research in geographic information system (GIS) owing to its ability to support decision makers in different fields, there is a strong need to enabling all users; specialists and non-specialists to profit from this technology. Although, the key impediment to non-specialists is the language to interact with the GIS and especially its embedded geographic database (GDB) which require SQL skills. In this paper we explore a new approach which alleviates nomad GIS users from any formatting effort by only using the natural language as a GDB communication mean. The process is generally two-fold: 1) formatting the natural language user query to be processed by the GDB engine; 2) translating the GDB retrieved answer to a text easily interpreted by all GIS users. The resulting implemented system was integrated to the OpenJump GIS and has been evaluated to give satisfactory results.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"118 1","pages":"1-25"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87643436","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 : 2021-01-01DOI: 10.11648/j.ijiis.20211006.13
Desta Abayechaw
{"title":"Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model","authors":"Desta Abayechaw","doi":"10.11648/j.ijiis.20211006.13","DOIUrl":"https://doi.org/10.11648/j.ijiis.20211006.13","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90261364","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 : 2021-01-01DOI: 10.1504/IJIIDS.2021.10037625
Dattatray P. Gandhmal, K. Kannan
{"title":"Chronological penguin Adam-based deep long short-term memory classifier for stock market prediction","authors":"Dattatray P. Gandhmal, K. Kannan","doi":"10.1504/IJIIDS.2021.10037625","DOIUrl":"https://doi.org/10.1504/IJIIDS.2021.10037625","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"52 1","pages":"215-238"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73232406","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 : 2021-01-01DOI: 10.1504/ijiids.2021.10033772
Mateusz Piech, M. Los, R. Marcjan
{"title":"Enhancement for graph operations in relational database for criminal intelligence domain","authors":"Mateusz Piech, M. Los, R. Marcjan","doi":"10.1504/ijiids.2021.10033772","DOIUrl":"https://doi.org/10.1504/ijiids.2021.10033772","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"2 1","pages":"49-66"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75991912","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 : 2021-01-01DOI: 10.1504/IJIIDS.2021.10035966
Messaoud Chaa, O. Nouali, P. Bellot
{"title":"Leveraging app features to improve mobile app retrieval","authors":"Messaoud Chaa, O. Nouali, P. Bellot","doi":"10.1504/IJIIDS.2021.10035966","DOIUrl":"https://doi.org/10.1504/IJIIDS.2021.10035966","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"1 1","pages":"177-197"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77470067","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 : 2021-01-01DOI: 10.1504/IJIIDS.2021.10035961
Khaleel W. Mershad, Ali Hamieh
{"title":"SDMS: smart database management system for accessing heterogeneous databases","authors":"Khaleel W. Mershad, Ali Hamieh","doi":"10.1504/IJIIDS.2021.10035961","DOIUrl":"https://doi.org/10.1504/IJIIDS.2021.10035961","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"42 1","pages":"115-152"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78101163","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 : 2021-01-01DOI: 10.1504/ijiids.2020.10033515
Mobin Akhtar, D. Ahamad, Shabi AlamHameed
{"title":"Optimisation algorithm-based recurrent neural network for big data classification","authors":"Mobin Akhtar, D. Ahamad, Shabi AlamHameed","doi":"10.1504/ijiids.2020.10033515","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10033515","url":null,"abstract":"","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"6 1","pages":"153-176"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86505368","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 : 2020-12-31DOI: 10.11648/J.IJIIS.20200906.11
Okoroma Francisca Nwakaego
The enrichment of the national cultural heritage is directly linked to the level of protection given to literary and artistic works. The higher the level of protection, the greater the number of each country’s intellectual output. This calls for an effective copyright information resource management in every nation and organization to optimize access to relevant information on copyright in order to curb the rate of infringement. This paper seeks to address the copyright information resource management in Nigeria, and the way forward. Questionnaire instrument was used for data collection. The last two questions on the questionnaire were open-ended questions, designed to enable the respondents freely express their views and suggestions. The findings identified the benefits of copyright information resource management, both to the authors and the users; include the inhibition of infringement as it delivers quick access to copyright related information in a dynamic and effective way. This is due to the fact that many acts of infringement on copyright are as a result of ignorance on the part of users. The findings further highlighted that copyright information resource management facilitation is dependent on putting the right people in positions of authority, setting up committee in each institution to monitor and establish standards in order to ensure quality assurance in the system, and keeping tracks of publications of each university’s scholar.
{"title":"Copyright Information Resource Management in Nigeria: and the Way Forward","authors":"Okoroma Francisca Nwakaego","doi":"10.11648/J.IJIIS.20200906.11","DOIUrl":"https://doi.org/10.11648/J.IJIIS.20200906.11","url":null,"abstract":"The enrichment of the national cultural heritage is directly linked to the level of protection given to literary and artistic works. The higher the level of protection, the greater the number of each country’s intellectual output. This calls for an effective copyright information resource management in every nation and organization to optimize access to relevant information on copyright in order to curb the rate of infringement. This paper seeks to address the copyright information resource management in Nigeria, and the way forward. Questionnaire instrument was used for data collection. The last two questions on the questionnaire were open-ended questions, designed to enable the respondents freely express their views and suggestions. The findings identified the benefits of copyright information resource management, both to the authors and the users; include the inhibition of infringement as it delivers quick access to copyright related information in a dynamic and effective way. This is due to the fact that many acts of infringement on copyright are as a result of ignorance on the part of users. The findings further highlighted that copyright information resource management facilitation is dependent on putting the right people in positions of authority, setting up committee in each institution to monitor and establish standards in order to ensure quality assurance in the system, and keeping tracks of publications of each university’s scholar.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86896262","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 : 2020-10-28DOI: 10.11648/j.ijiis.20200904.12
Abhishek Agarwal, R. Venkat
Plants have a significant role in every corner, let it be for humans, animals, and the environment. They play a significant role in saving each other lives by providing each one with the necessities. For saving these plants, humans should be able to identify the plants in order to give proper treatment to the plants. The species of the plants can be easily identified by the venation of the leaves. This paper focuses on the Convolution Neural Networks (CNN) classification methodology, which helps to classify the leaves accurately. The work uses leaf images of apple, grape and tomatoes from the plant village dataset for getting the features and further classification of the leaves. The prediction of the leaves will be done by using the deep learning techniques in which the input layer will be the features extracted using the proposed algorithm. The proposed algorithm is based on Local Binary Pattern (LBP), which is a simple yet very efficient method to identify the pixels of the image by threshold in the neighborhood of each pixel and consider the result as a binary number. The proposed algorithm is efficient for its computational simplicity, which makes it possible to analyze images in challenging real-time settings in the field of image processing and computer vision.
{"title":"Prediction of Leaves Using Convolutional Neural Network","authors":"Abhishek Agarwal, R. Venkat","doi":"10.11648/j.ijiis.20200904.12","DOIUrl":"https://doi.org/10.11648/j.ijiis.20200904.12","url":null,"abstract":"Plants have a significant role in every corner, let it be for humans, animals, and the environment. They play a significant role in saving each other lives by providing each one with the necessities. For saving these plants, humans should be able to identify the plants in order to give proper treatment to the plants. The species of the plants can be easily identified by the venation of the leaves. This paper focuses on the Convolution Neural Networks (CNN) classification methodology, which helps to classify the leaves accurately. The work uses leaf images of apple, grape and tomatoes from the plant village dataset for getting the features and further classification of the leaves. The prediction of the leaves will be done by using the deep learning techniques in which the input layer will be the features extracted using the proposed algorithm. The proposed algorithm is based on Local Binary Pattern (LBP), which is a simple yet very efficient method to identify the pixels of the image by threshold in the neighborhood of each pixel and consider the result as a binary number. The proposed algorithm is efficient for its computational simplicity, which makes it possible to analyze images in challenging real-time settings in the field of image processing and computer vision.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"188 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74508773","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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