Pub Date : 2020-12-18DOI: 10.5121/csit.2020.101807
D. Guel, Boureima Zerbo, J. Palicot, Oumarou Sié
In recent past years, PAPR (Peak-to-Average Power Ratio) of OFDM (Orthogonal FrequencyDivision Multiplexing) system has been intensively investigated. Published works mainly focus on how to reduce PAPR. Since high PAPR will lead to clipping of the signal when passed through a nonlinear amplifier. This paper proposes to extend the work related to "Gaussian Tone Reservation Clipping and Filtering for PAPR Mitigation" which has been previously published. So, in this paper, we deeply investigate the statistical correlation between PAPR reduction, and the distortion generated by three (3) adding signal techniques for PAPR reduction. Thereby, we first propose a generic function for PAPR reduction. Then, we analyse the PAPR reduction capabilities of each PAPR reduction technique versus the distortion generated. The signal-to-noise-and-distortion ratio (SNDR) metric is used to evaluate the distortion generated within each technique by assuming that OFDM baseband signals are modelled by complex Gaussian processes with Rayleigh envelope distribution for a large number of subcarriers. The results related to one of the techniques is proposed in the first time in this paper, unlike those related to the other two PAPR reduction techniques where the studies were already published. Comparisons of the proposed approximations of SNDR with those obtained by computer simulations show good agreement. An interesting result highlighted in this paper is the strong correlation existing between PAPR reduction performance and distortion signal power. Indeed, the results show that PAPR reduction gain increases as the distortion signal power increases. Through these 3 examples of PAPR reduction techniques; we could derive the following conclusion: in an adding signal context, the adding signal for PAPR reduction is closely linked to the distortion generated, and a trade-off between PAPR-reduction and distortion must be definitely found.
近年来，OFDM（Orthogonal Frequency Division Multiplexing，正交频分复用）系统的峰均功率比（Peak to Average Power Ratio，PAPR）得到了深入的研究。已发表的工作主要集中在如何降低PAPR。因为当信号通过非线性放大器时，高PAPR将导致信号的削波。本文提出对先前发表的“用于PAPR缓解的高斯音调保留剪裁和滤波”的相关工作进行扩展。因此，在本文中，我们深入研究了PAPR降低与三（3）种信号相加技术产生的失真之间的统计相关性。因此，我们首先提出了一种用于降低PAPR的通用函数。然后，我们分析了每种PAPR降低技术的PAPR降低能力与所产生的失真的关系。通过假设OFDM基带信号是通过具有大量子载波的瑞利包络分布的复高斯过程建模的，使用信噪比和失真比（SNDR）度量来评估在每种技术中产生的失真。本文首次提出了与其中一种技术相关的结果，与其他两种PAPR降低技术相关的研究不同，在其他两种技术中，研究已经发表。将所提出的SNDR近似值与计算机模拟获得的近似值进行比较，结果显示出良好的一致性。本文中强调的一个有趣的结果是PAPR降低性能和失真信号功率之间存在强烈的相关性。事实上，结果表明，PAPR降低增益随着失真信号功率的增加而增加。通过这3个PAPR降低技术的例子；我们可以得出以下结论：在加法信号的背景下，用于降低PAPR的加法信号与所产生的失真密切相关，并且必须在降低PAPR和失真之间找到折衷。
{"title":"The Statistical Correlation between Distortion and Adding Signal for PAPR Reduction in OFDM based Communication Systems","authors":"D. Guel, Boureima Zerbo, J. Palicot, Oumarou Sié","doi":"10.5121/csit.2020.101807","DOIUrl":"https://doi.org/10.5121/csit.2020.101807","url":null,"abstract":"In recent past years, PAPR (Peak-to-Average Power Ratio) of OFDM (Orthogonal FrequencyDivision Multiplexing) system has been intensively investigated. Published works mainly focus on how to reduce PAPR. Since high PAPR will lead to clipping of the signal when passed through a nonlinear amplifier. This paper proposes to extend the work related to \"Gaussian Tone Reservation Clipping and Filtering for PAPR Mitigation\" which has been previously published. So, in this paper, we deeply investigate the statistical correlation between PAPR reduction, and the distortion generated by three (3) adding signal techniques for PAPR reduction. Thereby, we first propose a generic function for PAPR reduction. Then, we analyse the PAPR reduction capabilities of each PAPR reduction technique versus the distortion generated. The signal-to-noise-and-distortion ratio (SNDR) metric is used to evaluate the distortion generated within each technique by assuming that OFDM baseband signals are modelled by complex Gaussian processes with Rayleigh envelope distribution for a large number of subcarriers. The results related to one of the techniques is proposed in the first time in this paper, unlike those related to the other two PAPR reduction techniques where the studies were already published. Comparisons of the proposed approximations of SNDR with those obtained by computer simulations show good agreement. An interesting result highlighted in this paper is the strong correlation existing between PAPR reduction performance and distortion signal power. Indeed, the results show that PAPR reduction gain increases as the distortion signal power increases. Through these 3 examples of PAPR reduction techniques; we could derive the following conclusion: in an adding signal context, the adding signal for PAPR reduction is closely linked to the distortion generated, and a trade-off between PAPR-reduction and distortion must be definitely found.","PeriodicalId":72673,"journal":{"name":"Computer science & information technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47118200","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 : 2020-12-12DOI: 10.5121/csit.2020.101703
Shuxing Zhang, Qinneng Xu
The purpose of this study is to investigate the relationship between career maturity and a branch of factors among senior school students. The sample data were collected from a total of 189 students. The linear relationship between career maturity and 72 factors were tested by using feature selection methods. LASSO and forward stepwise were compared based on crossvalidation. The results showed that LASSO was a feasible method to select the significant factors, and 12 of the total 72 factors were found to be important in predicting career maturity.
{"title":"An Examination of Relationship between Career Maturity and Multiple Factors by Feature Selection","authors":"Shuxing Zhang, Qinneng Xu","doi":"10.5121/csit.2020.101703","DOIUrl":"https://doi.org/10.5121/csit.2020.101703","url":null,"abstract":"The purpose of this study is to investigate the relationship between career maturity and a branch of factors among senior school students. The sample data were collected from a total of 189 students. The linear relationship between career maturity and 72 factors were tested by using feature selection methods. LASSO and forward stepwise were compared based on crossvalidation. The results showed that LASSO was a feasible method to select the significant factors, and 12 of the total 72 factors were found to be important in predicting career maturity.","PeriodicalId":72673,"journal":{"name":"Computer science & information technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46075474","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 : 2020-12-12DOI: 10.5121/csit.2020.101704
Stefanie Scholz, Christian G. Winkler
In this article we show how fashion brands communicate with their follower on Instagram. We use a continuously update dataset of 68 brands, more than 300,000 posts and more than 40,000,000 comments. Starting with descriptive statistics, we uncover different behavior and success of the various brands. It turns out that there are patterns specific to luxury, mass-market and sportswear brands. Posting volume is extremely brand dependent as is the number of comments and the engagement of the community. Having understood the statistics, we turn to machine learning techniques to measure the response of the community via comments. Topic models help us understand the structure of their respective community and uncover insights regarding the response to campaigns. Having up-to-date content is essential for this kind of analysis, as the market is highly volatile. Furthermore, automatic data analysis is crucial to measure the success of campaigns and adjust them accordingly for maximum effect.
{"title":"How to Engage Followers: Classifying Fashion Brands According to Their Instagram Profiles, Posts and Comments","authors":"Stefanie Scholz, Christian G. Winkler","doi":"10.5121/csit.2020.101704","DOIUrl":"https://doi.org/10.5121/csit.2020.101704","url":null,"abstract":"In this article we show how fashion brands communicate with their follower on Instagram. We use a continuously update dataset of 68 brands, more than 300,000 posts and more than 40,000,000 comments. Starting with descriptive statistics, we uncover different behavior and success of the various brands. It turns out that there are patterns specific to luxury, mass-market and sportswear brands. Posting volume is extremely brand dependent as is the number of comments and the engagement of the community. Having understood the statistics, we turn to machine learning techniques to measure the response of the community via comments. Topic models help us understand the structure of their respective community and uncover insights regarding the response to campaigns. Having up-to-date content is essential for this kind of analysis, as the market is highly volatile. Furthermore, automatic data analysis is crucial to measure the success of campaigns and adjust them accordingly for maximum effect.","PeriodicalId":72673,"journal":{"name":"Computer science & information technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43141330","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 : 2020-12-12DOI: 10.5121/csit.2020.101705
Yu Guan
Belief rule-based inference system introduces a belief distribution structure into the conventional rule-based system, which can effectively synthesize incomplete and fuzzy information. In order to optimize reasoning efficiency and reduce redundant rules, this paper proposes a rule reduction method based on regularization. This method controls the distribution of rules by setting corresponding regularization penalties in different learning steps and reduces redundant rules. This paper first proposes the use of the Gaussian membership function to optimize the structure and activation process of the belief rule base, and the corresponding regularization penalty construction method. Then, a step-by-step training method is used to set a different objective function for each step to control the distribution of belief rules, and a reduction threshold is set according to the distribution information of the belief rule base to perform rule reduction. Two experiments will be conducted based on the synthetic classification data set and the benchmark classification data set to verify the performance of the reduced belief rule base.
{"title":"Regularization Method for Rule Reduction in Belief Rule-based SystemRegularization Method for Rule Reduction in Belief Rule-based System","authors":"Yu Guan","doi":"10.5121/csit.2020.101705","DOIUrl":"https://doi.org/10.5121/csit.2020.101705","url":null,"abstract":"Belief rule-based inference system introduces a belief distribution structure into the conventional rule-based system, which can effectively synthesize incomplete and fuzzy information. In order to optimize reasoning efficiency and reduce redundant rules, this paper proposes a rule reduction method based on regularization. This method controls the distribution of rules by setting corresponding regularization penalties in different learning steps and reduces redundant rules. This paper first proposes the use of the Gaussian membership function to optimize the structure and activation process of the belief rule base, and the corresponding regularization penalty construction method. Then, a step-by-step training method is used to set a different objective function for each step to control the distribution of belief rules, and a reduction threshold is set according to the distribution information of the belief rule base to perform rule reduction. Two experiments will be conducted based on the synthetic classification data set and the benchmark classification data set to verify the performance of the reduced belief rule base.","PeriodicalId":72673,"journal":{"name":"Computer science & information technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47770063","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 : 2020-12-12DOI: 10.5121/csit.2020.101702
Fatimah Alshamari, Abdou Youssef
Document classification is a fundamental task for many applications, including document annotation, document understanding, and knowledge discovery. This is especially true in STEM fields where the growth rate of scientific publications is exponential, and where the need for document processing and understanding is essential to technological advancement. Classifying a new publication into a specific domain based on the content of the document is an expensive process in terms of cost and time. Therefore, there is a high demand for a reliable document classification system. In this paper, we focus on classification of mathematics documents, which consist of English text and mathematics formulas and symbols. The paper addresses two key questions. The first question is whether math-document classification performance is impacted by math expressions and symbols, either alone or in conjunction with the text contents of documents. Our investigations show that Text-Only embedding produces better classification results. The second question we address is the optimization of a deep learning (DL) model, the LSTM combined with one dimension CNN, for math document classification. We examine the model with several input representations, key design parameters and decision choices, and choices of the best input representation for math documents classification.
{"title":"A Study into Math Document Classification using Deep Learning","authors":"Fatimah Alshamari, Abdou Youssef","doi":"10.5121/csit.2020.101702","DOIUrl":"https://doi.org/10.5121/csit.2020.101702","url":null,"abstract":"Document classification is a fundamental task for many applications, including document annotation, document understanding, and knowledge discovery. This is especially true in STEM fields where the growth rate of scientific publications is exponential, and where the need for document processing and understanding is essential to technological advancement. Classifying a new publication into a specific domain based on the content of the document is an expensive process in terms of cost and time. Therefore, there is a high demand for a reliable document classification system. In this paper, we focus on classification of mathematics documents, which consist of English text and mathematics formulas and symbols. The paper addresses two key questions. The first question is whether math-document classification performance is impacted by math expressions and symbols, either alone or in conjunction with the text contents of documents. Our investigations show that Text-Only embedding produces better classification results. The second question we address is the optimization of a deep learning (DL) model, the LSTM combined with one dimension CNN, for math document classification. We examine the model with several input representations, key design parameters and decision choices, and choices of the best input representation for math documents classification.","PeriodicalId":72673,"journal":{"name":"Computer science & information technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43993600","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 : 2020-12-12DOI: 10.5121/csit.2020.101701
Jiawei Li, T. Gonsalves
This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.
{"title":"Genetic Algorithm for Exam Timetabling Problem - A Specific Case for Japanese University Final Presentation Timetabling","authors":"Jiawei Li, T. Gonsalves","doi":"10.5121/csit.2020.101701","DOIUrl":"https://doi.org/10.5121/csit.2020.101701","url":null,"abstract":"This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.","PeriodicalId":72673,"journal":{"name":"Computer science & information technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41769663","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 : 2020-11-28DOI: 10.5121/csit.2020.101517
Chie Ikeda, K. Ouazzane, Qicheng Yu
Financial fraud activities have soared despite the advancement of fraud detection models empowered by machine learning (ML). To address this issue, we propose a new framework of feature engineering for ML models. The framework consists of feature creation that combines feature aggregation and feature transformation, and feature selection that accommodates a variety of ML algorithms. To illustrate the effectiveness of the framework, we conduct an experiment using an actual financial transaction dataset and show that the framework significantly improves the performance of ML fraud detection models. Specifically, all the ML models complemented by a feature set generated from our framework surpass the same models without such a feature set by nearly 40% on the F1-measure and 20% on the Area Under the Curve (AUC) value.
{"title":"A New Framework of Feature Engineering for Machine Learning in Financial Fraud Detection","authors":"Chie Ikeda, K. Ouazzane, Qicheng Yu","doi":"10.5121/csit.2020.101517","DOIUrl":"https://doi.org/10.5121/csit.2020.101517","url":null,"abstract":"Financial fraud activities have soared despite the advancement of fraud detection models empowered by machine learning (ML). To address this issue, we propose a new framework of feature engineering for ML models. The framework consists of feature creation that combines feature aggregation and feature transformation, and feature selection that accommodates a variety of ML algorithms. To illustrate the effectiveness of the framework, we conduct an experiment using an actual financial transaction dataset and show that the framework significantly improves the performance of ML fraud detection models. Specifically, all the ML models complemented by a feature set generated from our framework surpass the same models without such a feature set by nearly 40% on the F1-measure and 20% on the Area Under the Curve (AUC) value.","PeriodicalId":72673,"journal":{"name":"Computer science & information technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46678748","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 : 2020-11-28DOI: 10.5121/csit.2020.101514
Michael Dorin, T. Le, Rajkumar Kolakaluri, Sergio Montenegro
It is commonly understood that code reviews are a cost-effective way of finding faults early in the development cycle. However, many modern software developers are too busy to do them. Skipping code reviews means a loss of opportunity to detect expensive faults prior to software release. Software engineers can be pushed in many directions and reviewing code is very often considered an undesirable task, especially when time is wasted reviewing programs that are not ready. In this study, we wish to ascertain the potential for using machine learning and image recognition to detect immature software source code prior to a review. We show that it is possible to use machine learning to detect software problems visually and allow code reviews to focus on application details. The results are promising and are an indication that further research could be valuable.
{"title":"Using Machine Learning Image Recognition for Code Reviews","authors":"Michael Dorin, T. Le, Rajkumar Kolakaluri, Sergio Montenegro","doi":"10.5121/csit.2020.101514","DOIUrl":"https://doi.org/10.5121/csit.2020.101514","url":null,"abstract":"It is commonly understood that code reviews are a cost-effective way of finding faults early in the development cycle. However, many modern software developers are too busy to do them. Skipping code reviews means a loss of opportunity to detect expensive faults prior to software release. Software engineers can be pushed in many directions and reviewing code is very often considered an undesirable task, especially when time is wasted reviewing programs that are not ready. In this study, we wish to ascertain the potential for using machine learning and image recognition to detect immature software source code prior to a review. We show that it is possible to use machine learning to detect software problems visually and allow code reviews to focus on application details. The results are promising and are an indication that further research could be valuable.","PeriodicalId":72673,"journal":{"name":"Computer science & information technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43880795","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 : 2020-11-21DOI: 10.5121/csit.2020.101417
Cyprien Gottstein, Philippe Raipin Parvédy, M. Hurfin, Thomas Hassan, T. Coupaye
The most recent developments in graph partitioning research often consider scale-free graphs. Instead we focus on partitioning geometric graphs using a less usual strategy: Inverse Spacefilling Partitioning (ISP). ISP relies on a space filling curve to partition a graph and was previously applied to graphs essentially generated from Meshes. We extend ISP to apply it to a new context where the targets are now Wide Area Graphs. We provide an extended comparison with two state-of-the-art graph partitioning streaming strategies, namely LDG and FENNEL. We also propose customized metrics to better understand and identify the use cases for which the ISP partitioning solution is best suited. Experimentations show that in favourable contexts, edge-cuts can be drastically reduced, going from more 34% using FENNEL to less than 1% using ISP.
{"title":"Inverse Space Filling Curve Partitioning Applied to Wide Area Graphs","authors":"Cyprien Gottstein, Philippe Raipin Parvédy, M. Hurfin, Thomas Hassan, T. Coupaye","doi":"10.5121/csit.2020.101417","DOIUrl":"https://doi.org/10.5121/csit.2020.101417","url":null,"abstract":"The most recent developments in graph partitioning research often consider scale-free graphs. Instead we focus on partitioning geometric graphs using a less usual strategy: Inverse Spacefilling Partitioning (ISP). ISP relies on a space filling curve to partition a graph and was previously applied to graphs essentially generated from Meshes. We extend ISP to apply it to a new context where the targets are now Wide Area Graphs. We provide an extended comparison with two state-of-the-art graph partitioning streaming strategies, namely LDG and FENNEL. We also propose customized metrics to better understand and identify the use cases for which the ISP partitioning solution is best suited. Experimentations show that in favourable contexts, edge-cuts can be drastically reduced, going from more 34% using FENNEL to less than 1% using ISP.","PeriodicalId":72673,"journal":{"name":"Computer science & information technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46453649","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 : 2020-10-24DOI: 10.5121/csit.2020.101304
Shaosheng Xu, Jinde Cao, Xiangnan Liu
{"title":"Data Prediction of Deflection Basin Evolution of Asphalt Pavement Structure Based on Multi-Level Neural Network","authors":"Shaosheng Xu, Jinde Cao, Xiangnan Liu","doi":"10.5121/csit.2020.101304","DOIUrl":"https://doi.org/10.5121/csit.2020.101304","url":null,"abstract":"","PeriodicalId":72673,"journal":{"name":"Computer science & information technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41543413","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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