2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT)最新文献_第3页

Flow Based Anomaly Detection in Software Defined Networking: A Deep Learning Approach With Feature Selection Method 基于流的软件定义网络异常检测:一种基于特征选择方法的深度学习方法

2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) Pub Date : 2018-09-01 DOI: 10.1109/CEEICT.2018.8628069

Samrat Kumar Dey, M. Rahman

{"title":"Flow Based Anomaly Detection in Software Defined Networking: A Deep Learning Approach With Feature Selection Method","authors":"Samrat Kumar Dey, M. Rahman","doi":"10.1109/CEEICT.2018.8628069","DOIUrl":"https://doi.org/10.1109/CEEICT.2018.8628069","url":null,"abstract":"Software Defined Networking (SDN) has come to prominence in recent years and demonstrates an enormous potential in shaping the future of networking by separating control plane from data plane. OpenFlow is the first and most widely used protocol that makes this separation possible in the first place. As a newly emerged technology, SDN has its inherent security threats that can be eliminated or at least mitigated by securing the OpenFlow controller that manages flow control in SDN. A flow based anomaly detection method in OpenFlow controller using Deep Neural Network (DNN) have been approached in this research. Hence, in this exploration, we propose a combined Gated Recurrent Unit Long Short Term Memory (GRU-LSTM) Network intrusion detection system. In order to improve the classifier performance, an appropriate ANOVA F-Test and Recursive feature Elimination (RFE) (ANOVA F-RFE) feature selection method also have been applied. The proposed approach is tested using the benchmark dataset NSL-KDD. A subset of complete dataset with convenient feature selection ensures the highest accuracy of 87% with GRU-LSTM Model. Experimental results show that deep-learning approach with feature selection method offers high potential for flow-based anomaly detection in OpenFlow controller.","PeriodicalId":417359,"journal":{"name":"2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127647852","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: 27

eExpense: A Smart Approach to Track Everyday Expense eExpense:一个跟踪日常开支的聪明方法

2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) Pub Date : 2018-09-01 DOI: 10.1109/CEEICT.2018.8628070

S. Sabab, Sadman Saumik Islam, Md. Jewel Rana, Monir Hossain

引用次数: 5

Player’s Performance Prediction in ODI Cricket Using Machine Learning Algorithms 使用机器学习算法预测ODI板球运动员的表现

2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) Pub Date : 2018-09-01 DOI: 10.1109/CEEICT.2018.8628118

Aminul Islam Anik, Sakif Yeaser, A. Hossain, Amitabha Chakrabarty

{"title":"Player’s Performance Prediction in ODI Cricket Using Machine Learning Algorithms","authors":"Aminul Islam Anik, Sakif Yeaser, A. Hossain, Amitabha Chakrabarty","doi":"10.1109/CEEICT.2018.8628118","DOIUrl":"https://doi.org/10.1109/CEEICT.2018.8628118","url":null,"abstract":"This paper presents a method that is aimed towards predicting a cricket player’s upcoming match performance by implementing machine learning algorithms. The proposed model consists of statistical data of players of Bangladesh national cricket team which has been collected from trusted sports websites, feature selection algorithms such as recursive feature elimination and univariate selection and machine learning algorithms such as linear regression, support vector machine with linear and polynomial kernel. To implement the proposed model, the accumulated statistical data is processed into numerical value in order to implement those in the algorithms. Furthermore, aforementioned feature selection algorithms are applied for extracting the attributes that are more related to the output feature. Additionally, the machine learning algorithms are used to predict runs scored by a batsman and runs considered by a bowler in the upcoming match. The experimental setup demonstrates that the model gives up to 91.5% accuracy for batsman Tamim and up to 75.3% accuracy for bowler Mahmudullah whereas prediction accuracy for other players are also up to the mark. Therefore, this will help in calculating player’s future performance and thus will ensure better team selection for forthcoming cricket matches.","PeriodicalId":417359,"journal":{"name":"2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126228322","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: 23

Extremely Low Loss, Dispersion Flattened Polarization Maintaining Fiber in THz Regime 极低损耗，色散平坦偏振维持光纤在太赫兹区

2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) Pub Date : 2018-09-01 DOI: 10.1109/CEEICT.2018.8628169

Md. Ahasan Habib, M. Anower, Arnob Kumar Bairagi

引用次数: 2

Performance Analysis of Peak to Average Power Ratio (PAPR) Reduction Techniques in OFDM System for Different Modulation Schemes 不同调制方式下OFDM系统峰值平均功率比(PAPR)降低技术的性能分析

2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) Pub Date : 2018-09-01 DOI: 10.1109/CEEICT.2018.8628163

Tasnova Nasrin Munni, Mohammad Hossam-E-Haider

引用次数: 0

Fractional Order Robust PID Controller Design for Voltage Control of Islanded Microgrid 孤岛微电网电压控制的分数阶鲁棒PID控制器设计

2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) Pub Date : 2018-09-01 DOI: 10.1109/CEEICT.2018.8628040

S. Sikder, Md. Mukidur Rahman, S. K. Sarkar, S. Das

引用次数: 8

Auto-Encoder Based Nonlinear Dimensionality Reduction of ECG data and Classification of Cardiac Arrhythmia Groups Using Deep Neural Network 基于自编码器的心电数据非线性降维及基于深度神经网络的心律失常分类

2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) Pub Date : 2018-09-01 DOI: 10.1109/CEEICT.2018.8628044

Tanoy Debnath, Tanwi Biswas, Mahmudul Hassan Ashik, Shovon Dash

{"title":"Auto-Encoder Based Nonlinear Dimensionality Reduction of ECG data and Classification of Cardiac Arrhythmia Groups Using Deep Neural Network","authors":"Tanoy Debnath, Tanwi Biswas, Mahmudul Hassan Ashik, Shovon Dash","doi":"10.1109/CEEICT.2018.8628044","DOIUrl":"https://doi.org/10.1109/CEEICT.2018.8628044","url":null,"abstract":"In this paper, we propose a neural network based dimensionality reduction approach to classify the four different categories of cardiac arrhythmias such as 'Normal', 'Bradycardia', 'Tachycardia' and 'Block' using MIT-BIH Arrhythmia database. We designed a nonlinear auto-encoder with three hidden layers and applied it to a dataset of ECG signal. It was found that our method is able to compress the data with less reconstruction error than that of linear transformation such as Principal component analysis. Using a deep neural network, we get the best accuracy (92.1%) for classifying the cardiac arrhythmia groups into four categories compared to Ensemble of Binary Support Vector Machine Decision Tree and Multilayer Perceptron (MLP) feed-forward Neural Network with back-propagation. The classifier performance is evaluated using Positive predictive Value (Precision), False discovery Rate, True Positive Rate (Recall) and False Negative Rate. This method has a great importance for researcher to predict the potential cardiac disease before it is too late.","PeriodicalId":417359,"journal":{"name":"2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121261747","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

A Facial Region Segmentation Based Approach to Recognize Human Emotion Using Fusion of HOG & LBP Features and Artificial Neural Network 基于HOG和LBP特征融合和人工神经网络的人脸区域分割人脸情感识别方法

2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) Pub Date : 2018-09-01 DOI: 10.1109/CEEICT.2018.8628140

Bayezid Islam, F. Mahmud, A. Hossain, Pushpen Bikash Goala, Md. Sumon Mia

引用次数: 11

An Investigation of Spectroscopic Characterization on Biological Tissue 生物组织的光谱表征研究

2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) Pub Date : 2018-09-01 DOI: 10.1109/CEEICT.2018.8628081

M. M. Nishat, Fahim Faisal

{"title":"An Investigation of Spectroscopic Characterization on Biological Tissue","authors":"M. M. Nishat, Fahim Faisal","doi":"10.1109/CEEICT.2018.8628081","DOIUrl":"https://doi.org/10.1109/CEEICT.2018.8628081","url":null,"abstract":"This paper deals with a novel approach of investigating spectroscopic characterization of different biological tissues (leaf and liver) by colorimetric study where images are taken by a digital camera. Colorimetric study refers to a technique used to evaluate an unknown color in reference to known colors. The method is deployed in this case on the hypothesis that the color spectrums and relative intensities vary for directly transmitted light and light scattered at an angle which provide the tissue characteristics. It carries a huge importance in various practical spheres which includes medical diagnosis too. A collimated beam of light from a white LED is passed through a slide containing the tissue. The values for intensities of the primary colors Red, Green and Blue in the region of the tissues are extracted from the image for both positions. An analysis of these color and intensity values provides information on the morphological structure of the tissue and their optical characteristics. Then different biological tissues (e.g., mango leaf and guava leaf) are analyzed and age of different leaves are characterized according to their light intensities ratio. The ratio is high in matured aged than a tender aged leaf. In case of animal tissue (cow's liver) it is evident that scattering ratio of RGB spectrum is higher of a diseased liver.","PeriodicalId":417359,"journal":{"name":"2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134350837","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

Bone Cancer Detection Classification Using Fuzzy Clustering Neuro Fuzzy Classifier 基于模糊聚类神经模糊分类器的骨癌检测分类

2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT) Pub Date : 2018-09-01 DOI: 10.1109/CEEICT.2018.8628164

E. Hossain, Mohammad Anisur Rahaman

引用次数: 13