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A Bibliometric Overview of the Research Impact of Lotfi A. Zadeh Lotfi A. Zadeh研究影响的文献计量学综述
2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628761
J. Merigó, E. Herrera-Viedma, R. Yager, J. Kacprzyk
{"title":"A Bibliometric Overview of the Research Impact of Lotfi A. Zadeh","authors":"J. Merigó, E. Herrera-Viedma, R. Yager, J. Kacprzyk","doi":"10.1109/SSCI.2018.8628761","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628761","url":null,"abstract":"Lotfi A. Zadeh is the founder of fuzzy logic. He is one of the most prominent computer scientists of all-time. On the 6th of September of 2017 he passed away. In order to commemorate and provide a complete overview of his research impact in the scientific community, this study presents a bibliometric overview of his publications according to the results available in the Web of Science Core Collection. The article also uses the VOS viewer in order to map graphically the leading trends connected to Zadeh in terms of journals, papers, authors and countries. Obviously, the bibliometric sources used concern more recent works of Zadeh and one should bear in mind that his brilliant and prominent works on signal analysis, Z-transform, state space approach, optimal control, etc., are not included in our analyses.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"286 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125740048","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}
引用次数: 0
Highway Cluster Density and Average Speed Prediction in Vehicular Ad Hoc Networks (VANETs) 基于车辆自组织网络(VANETs)的公路集群密度和平均速度预测
2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628749
Hamzah Al Najada, I. Mahgoub, Imran Mohammed
{"title":"Highway Cluster Density and Average Speed Prediction in Vehicular Ad Hoc Networks (VANETs)","authors":"Hamzah Al Najada, I. Mahgoub, Imran Mohammed","doi":"10.1109/SSCI.2018.8628749","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628749","url":null,"abstract":"With huge amounts of data being generated from almost everywhere, our universe has become data-driven. Decision making, risk prevention and mitigation, and systems assessment will not be as effective as desired without having the right data. The projected impacts and benefits of Vehicular Ad Hoc Networks (VANETs) are the driving forces for researchers to develop and further enhance VANET technology. One of the challenging and imperative issues in VANETs research is the unavailability of data. To the best of our knowledge, in this research, we are the first to create a VANET traffic dataset by using real-life traffic data. We massage the data by applying VANET human behavioral model. We experiment and validate our dataset by focusing on traffic congestion prediction. Traffic congestion can be determined by traffic density and average speed at any given point. Highly dense roads are the basic definition of congestion resulting in lower speeds of moving vehicles. We develop three time-series models ARIMA, BATS, TBATS, and a neural network model and apply them to our created VANET data to analyze and predict the total number of nodes in a cluster (density) and the average speed of the nodes. We have validated these time series prediction models by comparing the four developed models in terms of MSE, MAE, MAPE, and MASE. The created dataset and developed models can assist in predicting cluster density and average node speed to detect congestion, which will enhance route navigation.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120905463","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}
引用次数: 4
Insights in evolutionary exploration of robot morphology spaces 机器人形态空间的进化探索
2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628662
Karine Miras, Arwin Gansekoele, K. Glette, A. Eiben
{"title":"Insights in evolutionary exploration of robot morphology spaces","authors":"Karine Miras, Arwin Gansekoele, K. Glette, A. Eiben","doi":"10.1109/SSCI.2018.8628662","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628662","url":null,"abstract":"In a recent study we have encountered an unexpected result regarding the evolutionary exploration of robot morphology spaces. Specifically, we found that an algorithm driven by selection based on morphological novelty exploredfewerspots in the space of morphologies than another algorithm based on a combination of morphological novelty and some behavioral criterion (speed of movement). Here we revisit these results, perform new analyses, and obtain new insights. These insights clarify the exploration behavior of these algorithms and provide guidelines for designing selection mechanisms for evolutionary robotics.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"33 14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116640950","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}
引用次数: 4
Agent-Based Model Exploration of Latency Arbitrage in Fragmented Financial Markets 碎片化金融市场中基于agent的延迟套利模型探索
2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628638
M. Duffin, J. Cartlidge
{"title":"Agent-Based Model Exploration of Latency Arbitrage in Fragmented Financial Markets","authors":"M. Duffin, J. Cartlidge","doi":"10.1109/SSCI.2018.8628638","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628638","url":null,"abstract":"Computerisation of the financial markets has precipitated an arms-race for ever-faster trading. In combination, regulatory reform to encourage competition has resulted in market fragmentation, such that a single financial instrument can now be traded across multiple venues. This has led to the proliferation of high-frequency trading (HFT), and the ability to engage in latency arbitrage (taking advantage of accessing and acting upon price information before it is received by others). The impact of HFT and the consequences of latency arbitrage is a contentious issue. In 2013, Wah and Wellman used an agent-based model to study latency arbitrage in a fragmented market. They showed: (a) market efficiency is negatively affected by the actions of a latency arbitrageur; and (b) introducing a discrete-time call auction (DCA) eliminates latency arbitrage opportunities and improves efficiency. Here, we explore and extend Wah and Wellman's model, and demonstrate that results are sensitive to the bid-shading parameter used for zero-intelligence (ZIC) trading agents. To overcome this, we introduce the more realistic, minimally intelligent trading algorithm, ZIP. Using ZIP, we reach contrary conclusions: (a) fragmented markets benefit from latency arbitrage; and (b) DCAs do not improve efficiency. We present these results as evidence that the debate on latency arbitrage in financial markets is far from definitively settled, and suggest that ABM simulation-a form of decentralised collective computational intelligence-is a productive method for understanding and engineering financial systems.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124934779","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}
引用次数: 14
On Ordered Weighted Logarithmic Averaging Operators and Distance Measures 关于有序加权对数平均算子和距离测度
2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628916
V. Alfaro-Garcia, J. Merigó, Leobardo Plata-Perez, Gerardo G. Alfaro Calderón
{"title":"On Ordered Weighted Logarithmic Averaging Operators and Distance Measures","authors":"V. Alfaro-Garcia, J. Merigó, Leobardo Plata-Perez, Gerardo G. Alfaro Calderón","doi":"10.1109/SSCI.2018.8628916","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628916","url":null,"abstract":"In this paper we perform an in-depth description of the main properties and families of the introduced ordered weighted logarithmic averaging distance (OWLAD) operator, the generalized ordered weighted averaging distance (GWLAD) operator, and the generalized ordered weighted logarithmic averaging distance (GOWLAD) operator. These operators have as foundation the well-known Hamming distance measure and the generalized ordered weighted logarithmic averaging (GOWLA) operator. Furthermore, we analyze multiple classical measures to characterize the operators’ weighting vectors and we present alternative formulations of the operators based on the ordering of the arguments.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125232282","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}
引用次数: 1
A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its Fast Learning Algorithm for Time Series Forecasting 基于记忆神经元的元认知递归模糊推理系统及其时间序列预测快速学习算法
2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628936
Subhrajit Samanta, Shubhangi Ghosh, S. Sundaram
{"title":"A Meta-cognitive Recurrent Fuzzy Inference System with Memory Neurons (McRFIS-MN) and its Fast Learning Algorithm for Time Series Forecasting","authors":"Subhrajit Samanta, Shubhangi Ghosh, S. Sundaram","doi":"10.1109/SSCI.2018.8628936","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628936","url":null,"abstract":"In this paper, a Meta-cognitive Recurrent Fuzzy Inference System is proposed where recurrence is brought using Memory type Neurons (McRFIS-MN) to retain the effect of all past instances, while the meta-cognition component is employed to control the learning process, by deciding what-to-learn, when-to-learn and how-to-learn from the training data. The McRFIS-MN model has five layers, and Memory Neurons (MN) are employed only in the layers handling crisp values. The antecedent parameters are set randomly while only the consequent weights of the network are updated using a one-shot type projection based learning algorithm through time (PBLT) which makes the learning very fast. The performance evaluation of McRFIS-MN has been carried out using benchmark problems in the areas of nonlinear system identification and time-series forecasting. The results are evaluated against some of the most popular neural fuzzy methods and the obtained results indicate that McRFIS-MN performs better in terms of speed while achieving better or similar accuracy.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127794974","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}
引用次数: 7
Stock Price Manipulation Detection using Generative Adversarial Networks 基于生成对抗网络的股票价格操纵检测
2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628777
Teema Leangarun, P. Tangamchit, S. Thajchayapong
{"title":"Stock Price Manipulation Detection using Generative Adversarial Networks","authors":"Teema Leangarun, P. Tangamchit, S. Thajchayapong","doi":"10.1109/SSCI.2018.8628777","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628777","url":null,"abstract":"We implemented Generative Adversarial Networks (GANs) for detecting abnormal trading behaviors caused by stock price manipulations. Long short-term memory (LSTM) was used as a base structure of our GANs, which learned normal market behaviors in an unsupervised way. After the training, the discriminator network of GANs was used as a detector to discriminate between normal and manipulative trading. Our work is different from the previous work in that we did not use manipulation cases to train the neural networks. Instead, we used normal data to train them, and simulated manipulation cases were only used for testing purposes. The detection system was tested with the trading data from the Stock Exchange of Thailand (SET). It can achieve 68.1% accuracy in detecting pump-and-dump manipulations in unseen market data.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132273103","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}
引用次数: 22
Heuristic Based Learning of Parameters for Dictionaries in Sparse Representations 基于启发式的稀疏表示字典参数学习
2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628661
Rajesh K, A. Negi
{"title":"Heuristic Based Learning of Parameters for Dictionaries in Sparse Representations","authors":"Rajesh K, A. Negi","doi":"10.1109/SSCI.2018.8628661","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628661","url":null,"abstract":"Sparse representation has attracted attention recently by successful applications in the computer vision domain. The success of these methods depends on the learned dictionary as it represents the latent feature space of the data. Different parameters affect the dictionary learning process like the number of atoms and sparsity limit. Generally, these parameters are learned through trial and error experimentation which requires a lot of time. In the literature, no approach is seen that attempts to relate these dictionary parameters to the data. In this paper, we propose heuristics for this problem. These heuristics use statistical properties of the data to estimate dictionary parameters. The proposed heuristics are applied to several datasets.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134528415","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}
引用次数: 5
Intermediate quantifiers in presence of partial fuzzy sets 存在部分模糊集的中间量词
2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628922
V. Novák
{"title":"Intermediate quantifiers in presence of partial fuzzy sets","authors":"V. Novák","doi":"10.1109/SSCI.2018.8628922","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628922","url":null,"abstract":"This paper is a contribution to the formal theory ofintermediate quantifiers(linguistic expressions such as most, few, almost all, a lot of, many, a great deal of, a large part of, a small part of). This concept informally introduced by Peterson was formalized in the frame of higher-order fuzzy logic by the author of this paper. The formal theory of intermediate quantifiers is in this paper modified to situations when fuzzy sets characterizing properties of elements can be only partially defined. The presentation in this paper is purely semantic and provides also algorithm how intermediate quantifiers can be computed in a finite model.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133405592","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}
引用次数: 1
Study on Remote Sensing Image Vegetation Classification Method Based on Decision Tree Classifier 基于决策树分类器的遥感影像植被分类方法研究
2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628721
Wei Wei, Dawid Połap, Xiaohua Li, M. Woźniak, Junzhe Liu
{"title":"Study on Remote Sensing Image Vegetation Classification Method Based on Decision Tree Classifier","authors":"Wei Wei, Dawid Połap, Xiaohua Li, M. Woźniak, Junzhe Liu","doi":"10.1109/SSCI.2018.8628721","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628721","url":null,"abstract":"Aiming at the problem of inaccurate classification of forest vegetation, this paper presents a study on possible method for remote sensing from images. As classifiers we have used decision tree methods based on the idea of Boost Tree, Ada Tree and C5 approaches. For the experiments we have used single decision tree generation method for which training tuples are generated by sampling. In experiments we tried to evaluate how classifications work for agriculture images.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134061504","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}
引用次数: 14
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