2018 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第8页

Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market 深度学习可以在限购金融市场中复制自适应交易者

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628854

A. L. Calvez, D. Cliff

{"title":"Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market","authors":"A. L. Calvez, D. Cliff","doi":"10.1109/SSCI.2018.8628854","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628854","url":null,"abstract":"We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.","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":"123957781","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: 18

Traffic Convergence Detection in IoT LLNs: A Multilayer Perceptron based Mechanism 物联网LLNs中的流量收敛检测:基于多层感知机的机制

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628921

R. Sahay, G. Geethakumari, Koushik Modugu, Barsha Mitra

{"title":"Traffic Convergence Detection in IoT LLNs: A Multilayer Perceptron based Mechanism","authors":"R. Sahay, G. Geethakumari, Koushik Modugu, Barsha Mitra","doi":"10.1109/SSCI.2018.8628921","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628921","url":null,"abstract":"The Low power and Lossy Networks (LLNs) form an important segment of the Internet of Things (IoT). LLNs comprise of sensors and RFIDs which are constrained and not IP-enabled. IPv6 over Low power Personal Area Networks (6LoWPAN) enables connectivity of constrained non IP-enabled devices to the Internet. The routing protocol used in 6LoWPAN is IPv6 Routing Protocol over Low power and lossy networks (RPL). Though RPL meets all the routing requirements of LLNs, it is prone to several attacks. Among the several RPL attacks, misappropriation attacks are those which disrupt the legitimate path of traffic flow in the lossy network and causes convergence of a large section of traffic towards a particular malicious node. Also, misappropriation attacks can make the LLNs vulnerable to several other security attacks. Hence, it is important to timely detect misappropriation attacks. In this paper, we propose a mechanism to detect misappropriation attacks in IoT LLNs. Our approach makes use of Multilayer Perceptron (MLP) neural network as a classification tool. The MLP classifies the network data as normal or as under attack. Our proposed mechanism also identifies the nodes affected by the attack and identifies the attacker node.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"23 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":"125274107","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

Monocular Vision based Topological Map Generation in Real-time 基于单目视觉的拓扑地图实时生成

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628883

Soumabha Bhowmick, A. K. Deb, J. Mukhopadhyay

引用次数: 1

Analysing Vibrotactually Stimulated EEG Signals to Comprehend Object Shapes 分析振动刺激的脑电图信号以理解物体形状

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628759

A. Khasnobish, Dwaipayan Sardar, Monalisa Pal, A. Nagar

{"title":"Analysing Vibrotactually Stimulated EEG Signals to Comprehend Object Shapes","authors":"A. Khasnobish, Dwaipayan Sardar, Monalisa Pal, A. Nagar","doi":"10.1109/SSCI.2018.8628759","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628759","url":null,"abstract":"Tactile feedback has the capability of reducing the workload on the visual channel, during visual feedback in brain-computer interfaces (BCIs). It is requisite to analyse the brain signals corresponding to the tactile stimulations. This work is aimed at analysing the brain signals while the users are vibrotactually stimulated. The brain signals are acquired non-invasively by electroencephalography (EEG), while brushless coin-type vibration motors are actuated in particular patterns to convey the object shape information on subjects’ skin surface in form of vibrations. The acquired EEG signals are pre-processed to eliminate the effect of various types of noises and to extract the EEG signals corresponding to relevant frequency bands. Adaptive autoregressive (AAR) parameters are extracted from the pre-processed EEG signals and are finally classified by Naive Bayesian $(NB)$ approach, in order to recognize the vibratotactually stimulated object shapes from brain signals. In addition to the classifier output, subjects’ verbal responses about the object shape they perceived are also noted for validation. Three successive sessions of shape recognition from vibrotactile pattern show an improvement in EEG classification accuracy from 63.75% to 74.37%, and also depicted learning of the stimulus from subjects’ psychological response which is observed to increase from 75% to 95%. This observation substantiates the learning of vibrotactile stimulation in user over the sessions which in turn increases the system efficacy.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"59 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":"125781032","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

NEAT Algorithm for Testsuite generation in Automated Software Testing 自动化软件测试中用于生成测试套件的NEAT算法

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628668

H. Raj, K. Chandrasekaran

引用次数: 0

Cascade Gaussian Process Regression Framework for Biomass Prediction in a Fed-batch Reactor 加料间歇反应器中生物质预测的级联高斯过程回归框架

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628937

V. Masampally, A. Pareek, V. Runkana

{"title":"Cascade Gaussian Process Regression Framework for Biomass Prediction in a Fed-batch Reactor","authors":"V. Masampally, A. Pareek, V. Runkana","doi":"10.1109/SSCI.2018.8628937","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628937","url":null,"abstract":"Model-based control of a fed-batch bioreactor requires an accurate dynamic model of the bioprocess. Process dynamics in a bioreactor can be highly non-linear making it difficult to identify phenomenological models with large numbers of model parameters, especially in real time. In the present work, the Gaussian process regression (GPR) algorithm is used to build a fed-batch bioreactor model using a cascade structure. This model predicts the biomass concentration in response to a given substrate feed-rate profile using three cascaded GPR sub-models, each predicting hold-up, dissolved oxygen (DO) and biomass respectively. A mathematical model of an industry fed-batch fermentation process is used to depict the kinetics in a bioreactor. Firstly, open-loop sub-models are trained and tested with data generated using the mathematical model. Later, these fine-tuned open-loop sub-models are integrated sequentially into a closed-loop cascaded GPR structure. The cascaded GPR model is validated in a closed-loop environment with the solution obtained using a mathematical model. Various model performance metrics such as RMSE, MAE and MAPE are calculated to determine the accuracy of each sub-model and final cascaded GPR fed-batch bioreactor model.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"53 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":"128484353","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: 8

Learning TWSVM using Privilege Information 利用特权信息学习TWSVM

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628645

Aman Pal, R. Rastogi

{"title":"Learning TWSVM using Privilege Information","authors":"Aman Pal, R. Rastogi","doi":"10.1109/SSCI.2018.8628645","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628645","url":null,"abstract":"Expert’s knowledge can be used to improve classification performance of the algorithm or to reduce the requirement of data for training the algorithm. However, in the field of machine learning, the knowledge offered by the expert is rarely used. Recently, Qi et al. [1] proposed a fast learning model for TWSVM using privilege information termed as FTWSVMPI where privilege information is acquired by Oracle function. Oracle function needs to solve two additional TWSVM based Quadratic Programming Problems (QPPs) which leads to higher computational cost. Therefore, to avoid to solve two additional TWSVM based QPPs, in this paper, we propose a novel method to extract privilege information from the dataset itself. Using this privilege information, we further introduce an improved version of Twin Support Vector Machine termed as I-TWSVMPI. The proposed I-TWSVMPI incorporates privilege information using correcting function so as to obtain two nonparallel hyperplanes. We also perform experiments for pedestrian detection as an application to proposed I-TWSVMPI. The experimental results on several benchmark UCI datasets and pedestrian detection prove the efficacy of our proposed formulation to that of other state-of-the-art classification algorithms with comparatively lesser computational time.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"58 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":"130549783","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

Enhanced Correlation Matrix Based Visualization for Multi- and Many-objective optimization 基于增强关联矩阵的多目标、多目标优化可视化

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628739

Amin Ibrahim, S. Rahnamayan, Miguel Vargas Martin, K. Deb

{"title":"Enhanced Correlation Matrix Based Visualization for Multi- and Many-objective optimization","authors":"Amin Ibrahim, S. Rahnamayan, Miguel Vargas Martin, K. Deb","doi":"10.1109/SSCI.2018.8628739","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628739","url":null,"abstract":"Visualization of an approximate solution set in multi- and many-objective optimization is a crucial component of the optimization process. To date, the focus of many of the visualization techniques is illustration of the distribution and convergence of solutions set without making any visual connection between the decision variables and solutions set. This paper proposes simple correlation-based visualization scheme called Enhanced Correlation Matrix plot (ECM) capable of showing the relationship among decision variables and objective values. The ECM plot can provide visual correlation information between each decision variable and objective functions as well as objective-wise relationship for different regions of the approximated solution set. Moreover, it can provide visual distribution of solutions along each objective. The efficiency of the proposed method is demonstrated on three widely used two-to eight objective-benchmark problems and two real-world problems with 6 and 17 decision variables. The experimental results show that the proposed ECM plot can provide essential information pertaining to relationships among objective functions and objective-to-decision variables.","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":"129903570","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

Breast Cancer Diagnosis using Simultaneous Feature Selection and Classification: A Genetic Programming Approach 同时使用特征选择和分类的乳腺癌诊断:一种遗传规划方法

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628935

Harshit Bhardwaj, Aditi Sakalle, Arpit Bhardwaj, Aruna Tiwari, M. Verma

{"title":"Breast Cancer Diagnosis using Simultaneous Feature Selection and Classification: A Genetic Programming Approach","authors":"Harshit Bhardwaj, Aditi Sakalle, Arpit Bhardwaj, Aruna Tiwari, M. Verma","doi":"10.1109/SSCI.2018.8628935","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628935","url":null,"abstract":"Breast cancer is the most prevalent type of cancer found in women worldwide. It is becoming a leading cause of death among women in the whole world. Early detection and effective treatment of this disease is the only rescue to reduce breast cancer mortality. Because of the effective classification and high diagnostic capability expert systems are gaining popularity in this field. But the problem with machine learning algorithms is that if redundant and irrelevant features are available in the dataset then they are not being able to achieve desired performance. Therefore, in this paper, a simultaneous feature selection and classification technique using Genetic Programming (GPsfsc) is proposed for breast cancer diagnosis. To demonstrate our results, we had taken the Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) databases from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, and Mann Whitney test results of GONN with classical multi-tree GP algorithm for feature selection (GPmtfs). The experimental results on WBC and WDBC datasets show that the proposed method produces better classification accuracy with reduced features. Therefore, our proposed method is of great significance and can serve as first-rate clinical tool for the detection of breast cancer.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"33 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":"130084646","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

Day-ahead forecasting approach for energy consumption of an office building using support vector machines 基于支持向量机的办公楼能耗日前预测方法

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628734

A. Jozi, T. Pinto, Isabel Praça, Z. Vale

引用次数: 5