Pub Date : 2019-07-31DOI: 10.5121/IJAIA.2019.10403
Saeid Soheily-Khah, Yiming Wu
Digital advertising is growing massively all over the world, and, nowadays, is the best way to reach potential customers, where they spend the vast majority of their time on the Internet. While an advertisement is an announcement online about something such as a product or service, predicting the probability that a user do any action on the ads, is critical to many web applications. Due to over billions daily active users, and millions daily active advertisers, a typical model should provide predictions on billions events per day. So, the main challenge lies in the large design space to address issues of scale, where we need to rely on a subset of well-designed features. In this paper, we propose a novel feature engineering framework, specialized in feature selection using the efficient statistical approaches, which significantly outperform the state-of-the-art ones. To justify our claim, a large dataset of a running marketing campaign is used to evaluate the efficiency of the proposed approaches, where the results illustrate their benefits.
{"title":"A Novel Feature Engineering Framework in Digital Advertising Platform","authors":"Saeid Soheily-Khah, Yiming Wu","doi":"10.5121/IJAIA.2019.10403","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10403","url":null,"abstract":"Digital advertising is growing massively all over the world, and, nowadays, is the best way to reach potential customers, where they spend the vast majority of their time on the Internet. While an advertisement is an announcement online about something such as a product or service, predicting the probability that a user do any action on the ads, is critical to many web applications. Due to over billions daily active users, and millions daily active advertisers, a typical model should provide predictions on billions events per day. So, the main challenge lies in the large design space to address issues of scale, where we need to rely on a subset of well-designed features. In this paper, we propose a novel feature engineering framework, specialized in feature selection using the efficient statistical approaches, which significantly outperform the state-of-the-art ones. To justify our claim, a large dataset of a running marketing campaign is used to evaluate the efficiency of the proposed approaches, where the results illustrate their benefits.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44965750","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 : 2019-05-30DOI: 10.5121/IJAIA.2019.10305
Abdullah A. Al-Shaher
In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We proceed then, by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.
{"title":"MIXTURES OF TRAINED REGRESSION CURVESMODELS FOR HANDRITTEN ARABIC CHARACTER RECOGNITION","authors":"Abdullah A. Al-Shaher","doi":"10.5121/IJAIA.2019.10305","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10305","url":null,"abstract":"In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We proceed then, by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46770855","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 : 2019-05-30DOI: 10.5121/IJAIA.2019.10302
Yingzhi Chen, Tianqi Yang
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets to help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
{"title":"MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATION","authors":"Yingzhi Chen, Tianqi Yang","doi":"10.5121/IJAIA.2019.10302","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10302","url":null,"abstract":"Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets to help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42815244","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 : 2019-05-30DOI: 10.5121/IJAIA.2019.10301
Rama Devi Ravipati, Munther Abualkibash
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So, these result in model not able to efficiently learn the characteristics of rare categories and this will result in poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
{"title":"A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ON KDD AND NSL-KDD DATASETS","authors":"Rama Devi Ravipati, Munther Abualkibash","doi":"10.5121/IJAIA.2019.10301","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10301","url":null,"abstract":"Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So, these result in model not able to efficiently learn the characteristics of rare categories and this will result in poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2019.10301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44046268","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 : 2019-05-30DOI: 10.5121/IJAIA.2019.10304
H. Musa, Bala Modi, Ismail Abdulkarim Adamu, Ali Ahmad Aminu, H. Adamu, Yahaya Ajiya
Reducing the risk pose by phishers and other cybercriminals in the cyber space requires a robust and automatic means of detecting phishing websites, since the culprits are constantly coming up with new techniques of achieving their goals almost on daily basis. Phishers are constantly evolving the methods they used for luring user to revealing their sensitive information. Many methods have been proposed in past for phishing detection. But the quest for better solution is still on. This research covers the development of phishing website model based on different algorithms with different set of features in order to investigate the most significant features in the dataset.
{"title":"A COMPARATIVE ANALYSIS OF DIFFERENT FEATURE SET ON THE PERFORMANCE OF DIFFERENT ALGORITHMS IN PHISHING WEBSITE DETECTION","authors":"H. Musa, Bala Modi, Ismail Abdulkarim Adamu, Ali Ahmad Aminu, H. Adamu, Yahaya Ajiya","doi":"10.5121/IJAIA.2019.10304","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10304","url":null,"abstract":"Reducing the risk pose by phishers and other cybercriminals in the cyber space requires a robust and automatic means of detecting phishing websites, since the culprits are constantly coming up with new techniques of achieving their goals almost on daily basis. Phishers are constantly evolving the methods they used for luring user to revealing their sensitive information. Many methods have been proposed in past for phishing detection. But the quest for better solution is still on. This research covers the development of phishing website model based on different algorithms with different set of features in order to investigate the most significant features in the dataset.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2019.10304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43614098","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 : 2019-03-31DOI: 10.5121/IJAIA.2019.10205
Fernando Zacaŕias, Rosalba Cuapa, Luna Jimenez, N. Vazquez
{"title":"Modelling of Intelligent Agents Using A–Prolog","authors":"Fernando Zacaŕias, Rosalba Cuapa, Luna Jimenez, N. Vazquez","doi":"10.5121/IJAIA.2019.10205","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10205","url":null,"abstract":"","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2019.10205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49251500","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 : 2019-03-31DOI: 10.5121/IJAIA.2019.10203
Yang Gu, Yanke Hu
Recent development of generative pretrained language models has been proven very successful on a wide range of NLP tasks, such as text classification, question answering, textual entailment and so on.In this work, we present a two-phase encoder decoder architecture based on Bidirectional Encoding Representation from Transformers(BERT) for extractive summarization task. We evaluated our model by both automatic metrics and human annotators, and demonstrated that the architecture achieves the stateof-the-art comparable result on large scale corpus - CNN/Daily Mail . As the best of our knowledge, this is the first work that applies BERT based architecture to a text summarization task and achieved the state-of-the-art comparable result.
{"title":"Extractive Summarization with Very Deep Pretrained Language Model","authors":"Yang Gu, Yanke Hu","doi":"10.5121/IJAIA.2019.10203","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10203","url":null,"abstract":"Recent development of generative pretrained language models has been proven very successful on a wide range of NLP tasks, such as text classification, question answering, textual entailment and so on.In this work, we present a two-phase encoder decoder architecture based on Bidirectional Encoding Representation from Transformers(BERT) for extractive summarization task. We evaluated our model by both automatic metrics and human annotators, and demonstrated that the architecture achieves the stateof-the-art comparable result on large scale corpus - CNN/Daily Mail . As the best of our knowledge, this is the first work that applies BERT based architecture to a text summarization task and achieved the state-of-the-art comparable result.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2019.10203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47582146","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 : 2019-03-31DOI: 10.5121/IJAIA.2019.10202
Atindra Dahal
Artificial intelligence is the highest form of human development and sound outcome of human conscience till the date. But the very development seems to be devastating to human future ahead and has been heavily projected accordingly. More than it may be to decay and destroy the world, the negative and chilling views on the prospective damages of AI that scholars are percolating to public are costing many times on humans; and that is plunging human mindset into irreparable pessimism and negativity. This article explores the way that AI is being depressingly explored and investigated to browbeat public. In addition, this write-up highlights the serious lacuna, which the advanced academic engagement has still grossly failed to fill up, of a great deal in course of mainstreaming views and discussions for noble cause of human development and societal well-belling . Further, it unmasks the dire need in making constructive, encouraging and optimistic mind-set building academic pursuits and writings then makes an alarming call to the all prominent scholars to engage with due compliance of it Method and Hypothesis As a doctrinal qualitative research based on extensive survey of secondary data and literature, methodologically, with adoption of paradigm of descriptive interpretation, this research hypothesizes that the discussions and discourses over AI are biased, hold a serious lacuna thus need to be reconstructed to make it balanced and build better world than to browbeat people.
{"title":"An Obnoxious Lacuna on Discourses and Counter Discourses Over Artificial Intelligence","authors":"Atindra Dahal","doi":"10.5121/IJAIA.2019.10202","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10202","url":null,"abstract":"Artificial intelligence is the highest form of human development and sound outcome of human conscience till the date. But the very development seems to be devastating to human future ahead and has been heavily projected accordingly. More than it may be to decay and destroy the world, the negative and chilling views on the prospective damages of AI that scholars are percolating to public are costing many times on humans; and that is plunging human mindset into irreparable pessimism and negativity. This article explores the way that AI is being depressingly explored and investigated to browbeat public. In addition, this write-up highlights the serious lacuna, which the advanced academic engagement has still grossly failed to fill up, of a great deal in course of mainstreaming views and discussions for noble cause of human development and societal well-belling . Further, it unmasks the dire need in making constructive, encouraging and optimistic mind-set building academic pursuits and writings then makes an alarming call to the all prominent scholars to engage with due compliance of it\u0000\u0000Method and Hypothesis\u0000As a doctrinal qualitative research based on extensive survey of secondary data and literature, methodologically, with adoption of paradigm of descriptive interpretation, this research hypothesizes that the discussions and discourses over AI are biased, hold a serious lacuna thus need to be reconstructed to make it balanced and build better world than to browbeat people.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2019.10202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42160018","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 : 2019-01-31DOI: 10.5121/IJAIA.2019.10107
M. Ahmed, Bala Modi, S. Oladejo
One of the problems faced by the Electricity Power consumers is the issue of charging higher than their consumption. The extended framework presented in this work provides a lasting solution by developing a Multi-agent Based System, which allows a Meter Agent to detect a case of bypassing a prepaid meter and report the case to the distribution company. Similarly, the system should be able monitor the amount in which the customer is being charged based on their power consumption. This will solve the problem of overcharging power consumers. Multi-Agent System Engineering (MaSE) methodology was used to establish how agents are able to accomplish the stated challenge encountered in the Nigerian Electricity Distribution System. The proposed platform shows that Multi-Agent Systems can play a vital role in addressing the challenges facing the power distribution sector.
{"title":"Multi-Agent Based Smart Metering and Monitoring of Power Distribution System: An Extended Framework","authors":"M. Ahmed, Bala Modi, S. Oladejo","doi":"10.5121/IJAIA.2019.10107","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10107","url":null,"abstract":"One of the problems faced by the Electricity Power consumers is the issue of charging higher than their consumption. The extended framework presented in this work provides a lasting solution by developing a Multi-agent Based System, which allows a Meter Agent to detect a case of bypassing a prepaid meter and report the case to the distribution company. Similarly, the system should be able monitor the amount in which the customer is being charged based on their power consumption. This will solve the problem of overcharging power consumers. Multi-Agent System Engineering (MaSE) methodology was used to establish how agents are able to accomplish the stated challenge encountered in the Nigerian Electricity Distribution System. The proposed platform shows that Multi-Agent Systems can play a vital role in addressing the challenges facing the power distribution sector.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44389273","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 : 2019-01-31DOI: 10.5121/IJAIA.2019.10103
A. Massaro, Palo Lisco, A. Lombardi, A. Galiano, Nicola Savino
In this paper is analyzed a case study of an upgrade of an industry communication system developed by following ‘Frascati’ research guidelines. The goal of the proposed model is to enhance the industry knowledge Base –KB- by acting directly on information communication system improvements and data system integration, enabling automated process and data processing. The paper follow all the steps performed during the project development: the preliminary data infrastructure design, the information infrastructure improvements, and data processing. Data processing is performed by a calculus engine embedding data mining association rules and Artificial Neural Network –ANN- predictive algorithms thus improving the research. The calculus engine has been implemented by a multiple variables model where the contract data are preliminary processed in order to define functions classifying the operation processes and activating automatically the service process management. The business intelligence –BI- operations are performed mainly by the calculus engine optimizing industry performances. The goal of the paper is to show how research and development –R&D- can be applied by gaining and optimizing the knowledge and processes of an Italian industry working in car services. The project has been developed with the collaboration of the industry ACI Global working in roadside assistance services. By means of a research project resources, the information technology –IT- infrastructure has been improved by new solutions of the communication system and of the data transfer. The proposed case of study provides a model and a guideline to follow in order to apply research in industry acting directly on data and information network.
{"title":"A Case Study of Research Improvements in an Service Industry Upgrading the Knowledge Base of the Information System and the Process Management: Data Flow Automation, Association Rules and Data Mining","authors":"A. Massaro, Palo Lisco, A. Lombardi, A. Galiano, Nicola Savino","doi":"10.5121/IJAIA.2019.10103","DOIUrl":"https://doi.org/10.5121/IJAIA.2019.10103","url":null,"abstract":"In this paper is analyzed a case study of an upgrade of an industry communication system developed by following ‘Frascati’ research guidelines. The goal of the proposed model is to enhance the industry knowledge Base –KB- by acting directly on information communication system improvements and data system integration, enabling automated process and data processing. The paper follow all the steps performed during the project development: the preliminary data infrastructure design, the information infrastructure improvements, and data processing. Data processing is performed by a calculus engine embedding data mining association rules and Artificial Neural Network –ANN- predictive algorithms thus improving the research. The calculus engine has been implemented by a multiple variables model where the contract data are preliminary processed in order to define functions classifying the operation processes and activating automatically the service process management. The business intelligence –BI- operations are performed mainly by the calculus engine optimizing industry performances. The goal of the paper is to show how research and development –R&D- can be applied by gaining and optimizing the knowledge and processes of an Italian industry working in car services. The project has been developed with the collaboration of the industry ACI Global working in roadside assistance services. By means of a research project resources, the information technology –IT- infrastructure has been improved by new solutions of the communication system and of the data transfer. The proposed case of study provides a model and a guideline to follow in order to apply research in industry acting directly on data and information network.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/IJAIA.2019.10103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47076834","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：