Pub Date : 2019-07-01DOI: 10.4018/ijdai.2019070102
Jimmy Singla
In this work, a fuzzy expert system (FES) is designed and developed to help customers in selection of a car. The work is supported on fuzzy expert system (FES) that was implemented with the data bases and expertise of customers. The input variables taken in this fuzzy expert system are same as used in literature. All these factors give an efficient car evaluation.
{"title":"A Fuzzy Expert System for Car Evaluation","authors":"Jimmy Singla","doi":"10.4018/ijdai.2019070102","DOIUrl":"https://doi.org/10.4018/ijdai.2019070102","url":null,"abstract":"In this work, a fuzzy expert system (FES) is designed and developed to help customers in selection of a car. The work is supported on fuzzy expert system (FES) that was implemented with the data bases and expertise of customers. The input variables taken in this fuzzy expert system are same as used in literature. All these factors give an efficient car evaluation.","PeriodicalId":176325,"journal":{"name":"International Journal of Distributed Artificial Intelligence","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116590566","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 : 2019-07-01DOI: 10.4018/ijdai.2019070105
Krishan Tuli, Amanpreet Kaur, Meenakshi Sharma
Cloud computing is offering various IT services to many users in the work on the basis of pay-as-you-use model. As the data is increasing day by day, there is a huge requirement for cloud applications that manage such a huge amount of data. Basically, a best solution for analyzing such amounts of data and handles a large dataset. Various companies are providing such framesets for particular applications. A cloud framework is the accruement of different components which is similar to the development tools, various middleware for particular applications and various other database management services that are needed for cloud computing deployment, development and managing the various applications of the cloud. This results in an effective model for scaling such a huge amount of data in dynamically allocated recourses along with solving their complex problems. This article is about the survey on the performance of the big data framework based on a cloud from various endeavors which assists ventures to pick a suitable framework for their work and get a desired outcome.
{"title":"A Survey on Comparison of Performance Analysis on a Cloud-Based Big Data Framework","authors":"Krishan Tuli, Amanpreet Kaur, Meenakshi Sharma","doi":"10.4018/ijdai.2019070105","DOIUrl":"https://doi.org/10.4018/ijdai.2019070105","url":null,"abstract":"Cloud computing is offering various IT services to many users in the work on the basis of pay-as-you-use model. As the data is increasing day by day, there is a huge requirement for cloud applications that manage such a huge amount of data. Basically, a best solution for analyzing such amounts of data and handles a large dataset. Various companies are providing such framesets for particular applications. A cloud framework is the accruement of different components which is similar to the development tools, various middleware for particular applications and various other database management services that are needed for cloud computing deployment, development and managing the various applications of the cloud. This results in an effective model for scaling such a huge amount of data in dynamically allocated recourses along with solving their complex problems. This article is about the survey on the performance of the big data framework based on a cloud from various endeavors which assists ventures to pick a suitable framework for their work and get a desired outcome.","PeriodicalId":176325,"journal":{"name":"International Journal of Distributed Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128565559","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 : 2019-07-01DOI: 10.4018/ijdai.2019070103
Meenakshi Sharma, A. Garg
The World Wide Web is immensely rich in knowledge. The knowledge comes from both the content and distinctive characteristics of the web like its hyperlink structure. The problem comes in digging the relevant data from the web and giving the most appropriate decision to solve the given problem, which can be used for improving any business organisation. The effective solution of the problem depends on how efficiently and effectively the analysis of the web data is done. In analysing the data on web, not only relevant content analysis is essential but also the analysis of web structure is important. This article gives a brief introduction about the various terminologies and measures like centrality, Page Rank, and density used in the web networking analysis. This article will also give a brief introduction about the various supervised ML techniques such as classification, regression, and unsupervised machine learning techniques such as clustering, etc., which are very useful in analysing the web network so that user can make quick and effective decision making
{"title":"An Insight of Machine Learning in Web Network Analysis","authors":"Meenakshi Sharma, A. Garg","doi":"10.4018/ijdai.2019070103","DOIUrl":"https://doi.org/10.4018/ijdai.2019070103","url":null,"abstract":"The World Wide Web is immensely rich in knowledge. The knowledge comes from both the content and distinctive characteristics of the web like its hyperlink structure. The problem comes in digging the relevant data from the web and giving the most appropriate decision to solve the given problem, which can be used for improving any business organisation. The effective solution of the problem depends on how efficiently and effectively the analysis of the web data is done. In analysing the data on web, not only relevant content analysis is essential but also the analysis of web structure is important. This article gives a brief introduction about the various terminologies and measures like centrality, Page Rank, and density used in the web networking analysis. This article will also give a brief introduction about the various supervised ML techniques such as classification, regression, and unsupervised machine learning techniques such as clustering, etc., which are very useful in analysing the web network so that user can make quick and effective decision making","PeriodicalId":176325,"journal":{"name":"International Journal of Distributed Artificial Intelligence","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116928883","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 : 2019-01-01DOI: 10.4018/ijdai.2019010105
Smiley Gupta, Jagtar Singh
A large volume of user-generated data is evolving on a day-to-day basis, especially on social media platforms like Twitter, where people express their opinions and emotions regarding certain individuals or entities. This user-generated content becomes very difficult to analyze manually and therefore requires a need for an intelligent automated system which mines the opinions and organizes them using polarity metrics, representing the process of sentiment analysis. The motive of this review is to study the concept of sentiment analysis and discuss the comparative analysis of its techniques along with the challenges in this field to be considered for future enhancement.
{"title":"Review of Sentiment Detection","authors":"Smiley Gupta, Jagtar Singh","doi":"10.4018/ijdai.2019010105","DOIUrl":"https://doi.org/10.4018/ijdai.2019010105","url":null,"abstract":"A large volume of user-generated data is evolving on a day-to-day basis, especially on social media platforms like Twitter, where people express their opinions and emotions regarding certain individuals or entities. This user-generated content becomes very difficult to analyze manually and therefore requires a need for an intelligent automated system which mines the opinions and organizes them using polarity metrics, representing the process of sentiment analysis. The motive of this review is to study the concept of sentiment analysis and discuss the comparative analysis of its techniques along with the challenges in this field to be considered for future enhancement.","PeriodicalId":176325,"journal":{"name":"International Journal of Distributed Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132749277","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 : 2019-01-01DOI: 10.4018/ijdai.2019010103
S. Chhabra, Neeraj Kumar Jain, V. Tomar
In this article, a reversible data hiding technique is proposed to embed multiple segments of a single message into a single cover image. This multiple message segment technique uses a pixel value ordering approach to embed the secret message. The splitting and randomization of the original secret message provides security from an attacker There are many digital formats for data hiding, like images, audio, and video, of which the digital image is the simplest format. Data hiding in image processing refers to inserting the secret message into digital images. Reversible data hiding (RDH) is a lossless technique, in which both the embedded secret message and the cover image is extracted by the receiver. The applications of RDH include medical and military imaging.
{"title":"PVO-Based Multiple Message Segment Reversible Data Hiding","authors":"S. Chhabra, Neeraj Kumar Jain, V. Tomar","doi":"10.4018/ijdai.2019010103","DOIUrl":"https://doi.org/10.4018/ijdai.2019010103","url":null,"abstract":"In this article, a reversible data hiding technique is proposed to embed multiple segments of a single message into a single cover image. This multiple message segment technique uses a pixel value ordering approach to embed the secret message. The splitting and randomization of the original secret message provides security from an attacker There are many digital formats for data hiding, like images, audio, and video, of which the digital image is the simplest format. Data hiding in image processing refers to inserting the secret message into digital images. Reversible data hiding (RDH) is a lossless technique, in which both the embedded secret message and the cover image is extracted by the receiver. The applications of RDH include medical and military imaging.","PeriodicalId":176325,"journal":{"name":"International Journal of Distributed Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131164259","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 : 2018-07-01DOI: 10.4018/ijdai.2018070103
Leila Zemmouchi-Ghomari
Any information used to characterize the situation of an entity: a person, a place, or an object, can be considered as context. Indeed, context is crucial to avoid semantic ambiguity in data interpretation. However, linking data to its context is a recognized research issue. Adopting an ontology-based approach to model formally the context enables automatic interpretation and reasoning capabilities. This article discusses the main context modeling approaches based ontology by highlighting their principles, scenarios, use cases, benefits, and challenges to explore the use of ontologies to represent contexts.
{"title":"Current Development of Ontology-Based Context Modeling","authors":"Leila Zemmouchi-Ghomari","doi":"10.4018/ijdai.2018070103","DOIUrl":"https://doi.org/10.4018/ijdai.2018070103","url":null,"abstract":"Any information used to characterize the situation of an entity: a person, a place, or an object, can be considered as context. Indeed, context is crucial to avoid semantic ambiguity in data interpretation. However, linking data to its context is a recognized research issue. Adopting an ontology-based approach to model formally the context enables automatic interpretation and reasoning capabilities. This article discusses the main context modeling approaches based ontology by highlighting their principles, scenarios, use cases, benefits, and challenges to explore the use of ontologies to represent contexts.","PeriodicalId":176325,"journal":{"name":"International Journal of Distributed Artificial Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124473955","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 : 2018-07-01DOI: 10.4018/ijdai.2018070101
G. Musumba, R. Wario, P. K. Wamuyu
Business collaborations have gained prominence in many domains mediated by information technology platforms. These collaborations, normally referred to as virtual enterprises (VEs) consider varying core competencies of participants. The VEs' dynamic nature requires participants to be dynamically selected and engaged. This requires a flexible systematic approach, lacking in existing literature, to handle varying forms of VEs. This study aims to consider a VE from an enterprise integration viewpoint and to develop an agent-based model that supports the VE's formation and operation phases. This model will provide support to business managers in making decisions efficiently by delegating part of the processes to software agents. An agent-based VE (ABVE) model prototype is developed. Case studies from various domains are used in the demonstration of the model's applicability and possible generalization. After evaluation it is shown that users are motivated to use the model as an effective tool for VE formation and collaborations in diverse domains with an 88.86% acceptance rate.
{"title":"Towards an Agent-Oriented Business Collaboration Model","authors":"G. Musumba, R. Wario, P. K. Wamuyu","doi":"10.4018/ijdai.2018070101","DOIUrl":"https://doi.org/10.4018/ijdai.2018070101","url":null,"abstract":"Business collaborations have gained prominence in many domains mediated by information technology platforms. These collaborations, normally referred to as virtual enterprises (VEs) consider varying core competencies of participants. The VEs' dynamic nature requires participants to be dynamically selected and engaged. This requires a flexible systematic approach, lacking in existing literature, to handle varying forms of VEs. This study aims to consider a VE from an enterprise integration viewpoint and to develop an agent-based model that supports the VE's formation and operation phases. This model will provide support to business managers in making decisions efficiently by delegating part of the processes to software agents. An agent-based VE (ABVE) model prototype is developed. Case studies from various domains are used in the demonstration of the model's applicability and possible generalization. After evaluation it is shown that users are motivated to use the model as an effective tool for VE formation and collaborations in diverse domains with an 88.86% acceptance rate.","PeriodicalId":176325,"journal":{"name":"International Journal of Distributed Artificial Intelligence","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120937649","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 : 2018-07-01DOI: 10.4018/ijdai.2018070102
F. Lahlou, H. Benbrahim, I. Kassou
Context aware recommender systems (CARS) are recommender systems (RS) that provide recommendations according to user contexts. The first challenge for building such a system is to get the contextual information. Some works tried to get this information from reviews provided by users in addition to their ratings. However, all of these works perform important feature engineering in order to infer the context. In this article, the authors present a new CARS architecture that allows to automatically use contextual information from reviews without requiring any feature engineering. Moreover, they develop a new CARS algorithm that is tailored to textual contexts, that they call Textual Context Aware Factorization Machines (TCAFM). An empirical evaluation shows that the proposed architecture allows to significantly improve recommendation accuracy using state of the art RS and CARS algorithms, whereas TCAFM leads to additional improvements.
{"title":"Review Aware Recommender System","authors":"F. Lahlou, H. Benbrahim, I. Kassou","doi":"10.4018/ijdai.2018070102","DOIUrl":"https://doi.org/10.4018/ijdai.2018070102","url":null,"abstract":"Context aware recommender systems (CARS) are recommender systems (RS) that provide recommendations according to user contexts. The first challenge for building such a system is to get the contextual information. Some works tried to get this information from reviews provided by users in addition to their ratings. However, all of these works perform important feature engineering in order to infer the context. In this article, the authors present a new CARS architecture that allows to automatically use contextual information from reviews without requiring any feature engineering. Moreover, they develop a new CARS algorithm that is tailored to textual contexts, that they call Textual Context Aware Factorization Machines (TCAFM). An empirical evaluation shows that the proposed architecture allows to significantly improve recommendation accuracy using state of the art RS and CARS algorithms, whereas TCAFM leads to additional improvements.","PeriodicalId":176325,"journal":{"name":"International Journal of Distributed Artificial Intelligence","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115943937","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 : 1900-01-01DOI: 10.4018/ijdai.2019010101
Pinki Sharma, J. Sengupta, P. K. Suri
Cloud computing is the internet-based technique where the users utilize the online resources for computing services. The attacks or intrusion into the cloud service is the major issue in the cloud environment since it degrades performance. In this article, we propose an adaptive lion-based neural network (ALNN) to detect the intrusion behaviour. Initially, the cloud network has generated the clusters using a WLI fuzzy clustering mechanism. This mechanism obtains the different numbers of clusters in which the data objects are grouped together. Then, the clustered data is fed into the newly designed adaptive lion-based neural network. The proposed method is developed by the combination of Levenberg-Marquardt algorithm of neural network and adaptive lion algorithm where female lions are used to update the weight adaptively using lion optimization algorithm. Then, the proposed method is used to detect the malicious activity through training process. Thus, the different clustered data is given to the proposed ALNN model. Once the data is trained, then it needs to be aggregated. Subsequently, the aggregated data is fed into the proposed ALNN method where the intrusion behaviour is detected. Finally, the simulation results of the proposed method and performance is analysed through accuracy, false positive rate, and true positive rate. Thus, the proposed ALNN algorithm attains 96.46% accuracy which ensures better detection performance.
{"title":"WLI Fuzzy Clustering and Adaptive Lion Neural Network (ALNN) for Cloud Intrusion Detection","authors":"Pinki Sharma, J. Sengupta, P. K. Suri","doi":"10.4018/ijdai.2019010101","DOIUrl":"https://doi.org/10.4018/ijdai.2019010101","url":null,"abstract":"Cloud computing is the internet-based technique where the users utilize the online resources for computing services. The attacks or intrusion into the cloud service is the major issue in the cloud environment since it degrades performance. In this article, we propose an adaptive lion-based neural network (ALNN) to detect the intrusion behaviour. Initially, the cloud network has generated the clusters using a WLI fuzzy clustering mechanism. This mechanism obtains the different numbers of clusters in which the data objects are grouped together. Then, the clustered data is fed into the newly designed adaptive lion-based neural network. The proposed method is developed by the combination of Levenberg-Marquardt algorithm of neural network and adaptive lion algorithm where female lions are used to update the weight adaptively using lion optimization algorithm. Then, the proposed method is used to detect the malicious activity through training process. Thus, the different clustered data is given to the proposed ALNN model. Once the data is trained, then it needs to be aggregated. Subsequently, the aggregated data is fed into the proposed ALNN method where the intrusion behaviour is detected. Finally, the simulation results of the proposed method and performance is analysed through accuracy, false positive rate, and true positive rate. Thus, the proposed ALNN algorithm attains 96.46% accuracy which ensures better detection performance.","PeriodicalId":176325,"journal":{"name":"International Journal of Distributed Artificial Intelligence","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116191346","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 : 1900-01-01DOI: 10.4018/ijdai.2019010104
Alankrita Aggarwal, D. Chatha
An artificial neural network (ANN) is used to resolve problems related to complex scenarios and logical thinking. Nowadays, a cause for concern is the mortality rate among women due to cancer. Generally, women to around 45 years old are the most vulnerable to this disease. Early detection is the only hope for the patient to survive, otherwise it may reach an unrecoverable stage. Currently, there are numerous techniques available for the diagnosis of such diseases out of which mammography is the most trustworthy method for detecting early stage cancer. The analysis of these mammogram images is always difficult to analyze due to low contrast and non-uniform background. The mammogram images are scanned, digitized for processing, nut that further reduces the contrast between region of interest (ROI) and the background. Furthermore, presence of noise, glands, and muscles leads to background contrast variations. The boundaries of the suspected tumor area are always fuzzy and improper. The aim of this article is to develop a robust edge detection technique which works optimally on mammogram images to segment a tumor area.
{"title":"To Design a Mammogram Edge Detection Algorithm Using an Artificial Neural Network (ANN)","authors":"Alankrita Aggarwal, D. Chatha","doi":"10.4018/ijdai.2019010104","DOIUrl":"https://doi.org/10.4018/ijdai.2019010104","url":null,"abstract":"An artificial neural network (ANN) is used to resolve problems related to complex scenarios and logical thinking. Nowadays, a cause for concern is the mortality rate among women due to cancer. Generally, women to around 45 years old are the most vulnerable to this disease. Early detection is the only hope for the patient to survive, otherwise it may reach an unrecoverable stage. Currently, there are numerous techniques available for the diagnosis of such diseases out of which mammography is the most trustworthy method for detecting early stage cancer. The analysis of these mammogram images is always difficult to analyze due to low contrast and non-uniform background. The mammogram images are scanned, digitized for processing, nut that further reduces the contrast between region of interest (ROI) and the background. Furthermore, presence of noise, glands, and muscles leads to background contrast variations. The boundaries of the suspected tumor area are always fuzzy and improper. The aim of this article is to develop a robust edge detection technique which works optimally on mammogram images to segment a tumor area.","PeriodicalId":176325,"journal":{"name":"International Journal of Distributed Artificial Intelligence","volume":"14 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132479007","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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