{"title":"Plant species identification using leaf biometrics and swarm optimization: A hybrid PSO, GWO, SVM model","authors":"Heba F. Eid, A. Abraham","doi":"10.3233/HIS-180248","DOIUrl":"https://doi.org/10.3233/HIS-180248","url":null,"abstract":"","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"24 1","pages":"155-165"},"PeriodicalIF":0.0,"publicationDate":"2018-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81919567","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}
Semantic-based process mining is a useful technique towards improving information values of process models and analysis by means of conceptualization. The conceptual system of analysis allows the meaning of process elements to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge that can be used to determine useful patterns and predict future outcomes. The work in this paper presents a Semantic-Fuzzy mining approach that makes use of labels within event log about real-time process to provide a method which allows for mining and improved process analysis of the resulting process models through semantic – annotation, representation and reasoning. Qualitatively, the study shows by using a case study of Learning Process – how data from various process domains can be extracted, semantically prepared, and transformed into mining executable formats to support the discovery, monitoring and enhancement of real-time domain processes through further semantic analysis of the discovered models. Also, the paper quantitatively assess the level of accuracy of the classification results to predict behaviours of unobserved instances within the process knowledge-base by determing which traces are fitting or not fitting the discovered model by using a training set and test log for the cross-validation experiment. Accordingly, the work looks at the sophistication of the proposed semantic-based approach and the discovered models, validation of the classification results and their influence compared to other existing benchmark techniques and algorithms for process mining. The experimental results and data validation ends with the supposition that a system which is formally encoded with semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner) has the capability to lift process mining analysis and outcomes from the syntactic level to a much more conceptual level, resulting in a mining approach that is able to induce new knowledge based on previously unobserved behaviours and a more intuitive and easy way to envisage the relationships between the process instances found within the available event data logs and the discovered process
{"title":"Semantic fuzzy mining: Enhancement of process models and event logs analysis from syntactic to conceptual level","authors":"Kingsley Okoye, U. Naeem, Syed Islam","doi":"10.3233/HIS-170243","DOIUrl":"https://doi.org/10.3233/HIS-170243","url":null,"abstract":"Semantic-based process mining is a useful technique towards improving information values of process models and analysis by means of conceptualization. The conceptual system of analysis allows the meaning of process elements to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge that can be used to determine useful patterns and predict future outcomes. The work in this paper presents a Semantic-Fuzzy mining approach that makes use of labels within event log about real-time process to provide a method which allows for mining and improved process analysis of the resulting process models through semantic – annotation, representation and reasoning. Qualitatively, the study shows by using a case study of Learning Process – how data from various process domains can be extracted, semantically prepared, and transformed into mining executable formats to support the discovery, monitoring and enhancement of real-time domain processes through further semantic analysis of the discovered models. Also, the paper quantitatively assess the level of accuracy of the classification results to predict behaviours of unobserved instances within the process knowledge-base by determing which traces are fitting or not fitting the discovered model by using a training set and test log for the cross-validation experiment. Accordingly, the work looks at the sophistication of the proposed semantic-based approach and the discovered models, validation of the classification results and their influence compared to other existing benchmark techniques and algorithms for process mining. The experimental results and data validation ends with the supposition that a system which is formally encoded with semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner) has the capability to lift process mining analysis and outcomes from the syntactic level to a much more conceptual level, resulting in a mining approach that is able to induce new knowledge based on previously unobserved behaviours and a more intuitive and easy way to envisage the relationships between the process instances found within the available event data logs and the discovered process","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"47 1","pages":"67-98"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75569870","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":"Implementation of Genetic Algorithm for developing knowledge centric environment in higher education","authors":"Preeti Gupta, T. Sharma, D. Mehrotra","doi":"10.3233/HIS-170238","DOIUrl":"https://doi.org/10.3233/HIS-170238","url":null,"abstract":"","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"9 1","pages":"13-19"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82006845","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}
Hafiz Muhammad Faisal, Munir Ahmad, S. Asghar, A. Rahman
{"title":"Intelligent quranic story builder","authors":"Hafiz Muhammad Faisal, Munir Ahmad, S. Asghar, A. Rahman","doi":"10.3233/HIS-170241","DOIUrl":"https://doi.org/10.3233/HIS-170241","url":null,"abstract":"","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"84 1","pages":"41-48"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83827185","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":"Estimating influence of threat using Misuse Case Oriented Quality Requirements (MCOQR) metrics: Security requirements engineering perspective","authors":"C. Banerjee, A. Banerjee, S. K. Sharma","doi":"10.3233/HIS-170237","DOIUrl":"https://doi.org/10.3233/HIS-170237","url":null,"abstract":"","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"74 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73949693","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":"Web prediction framework for college selection based on the hybrid Case Based Reasoning model and expert's knowledge","authors":"Bruno Trstenjaka, Dzenana Donkob","doi":"10.3233/HIS-160233","DOIUrl":"https://doi.org/10.3233/HIS-160233","url":null,"abstract":"","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"13 1","pages":"161-171"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/HIS-160233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49231762","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":"A hybrid clustering algorithm and web information foraging","authors":"H. Drias, Amine Kechid, N. Cherif","doi":"10.3233/HIS-160231","DOIUrl":"https://doi.org/10.3233/HIS-160231","url":null,"abstract":"","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"69 1","pages":"137-149"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81632320","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}
Long-term tracking is an expanding field with applications in logistics, ecology and wearable computing. The main challenge for longevity of tracking applications is the high energy consumption of GPS, which has been addressed by using low power sensors to trigger GPS activation upon detecting events of interest. While triggering can reduce power consumption, static thresholds can under-perform in the long-term as context changes. This paper presents a comparison between a dynamic adaptive threshold algorithm and off-line machine learning techniques. We test the algorithms on empirical data from flying foxes to show that off-line machine learning techniques improve the hit rate when compared to the dynamic adaptive threshold algorithm. We then combine the models into an on/off-line hybrid ensemble learning model to improve both hit rate and false alarm rate when compared to the dynamic adaptive threshold algorithm. The hybrid model also has lower false alarm rate and precision when compared to the stand alone machine learning algorithms. We also test the off-line machine learning techniques on unknown data to show that the hit and false alarm rates vary from node to node. This indicates that more consistent performance might be found through the development of on-line machine learning algorithms.
{"title":"Hybrid ensemble learning for triggering of GPS in long-term tracking applications","authors":"Llewyn Salt, R. Jurdak, Erin Oliver, B. Kusy","doi":"10.3233/HIS-160235","DOIUrl":"https://doi.org/10.3233/HIS-160235","url":null,"abstract":"Long-term tracking is an expanding field with applications in logistics, ecology and wearable computing. The main challenge for longevity of tracking applications is the high energy consumption of GPS, which has been addressed by using low power sensors to trigger GPS activation upon detecting events of interest. While triggering can reduce power consumption, static thresholds can under-perform in the long-term as context changes. This paper presents a comparison between a dynamic adaptive threshold algorithm and off-line machine learning techniques. We test the algorithms on empirical data from flying foxes to show that off-line machine learning techniques improve the hit rate when compared to the dynamic adaptive threshold algorithm. We then combine the models into an on/off-line hybrid ensemble learning model to improve both hit rate and false alarm rate when compared to the dynamic adaptive threshold algorithm. The hybrid model also has lower false alarm rate and precision when compared to the stand alone machine learning algorithms. We also test the off-line machine learning techniques on unknown data to show that the hit and false alarm rates vary from node to node. This indicates that more consistent performance might be found through the development of on-line machine learning algorithms.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"8 1","pages":"183-194"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88804491","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":"Intelligent generation of fuzzy rules for network firewalls based on the analysis of large-scale network traffic dumps","authors":"Andrii Shalaginov, K. Franke","doi":"10.3233/HIS-170236","DOIUrl":"https://doi.org/10.3233/HIS-170236","url":null,"abstract":"","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"249 1","pages":"195-206"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74903546","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":"Application design and analysis of different hybrid intelligent techniques","authors":"Koushik Mondal","doi":"10.3233/HIS-160234","DOIUrl":"https://doi.org/10.3233/HIS-160234","url":null,"abstract":"","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"54 1","pages":"173-181"},"PeriodicalIF":0.0,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85819742","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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