2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)最新文献_第3页

Facial Expression recognition using higher order moments on facial patches 基于面部斑块高阶矩的面部表情识别

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944802

Tobin T Kaleekal, Jyotsna Singh

{"title":"Facial Expression recognition using higher order moments on facial patches","authors":"Tobin T Kaleekal, Jyotsna Singh","doi":"10.1109/icccnt45670.2019.8944802","DOIUrl":"https://doi.org/10.1109/icccnt45670.2019.8944802","url":null,"abstract":"The discriminative features between different facial expressions are mostly constrained to some regions of a face when an expression occurs on a face. Extracting these discriminating features is one of the most important aspect of a facial expression recognition (FER) system. Though significant amount of work has been done in identifying optimum features for expression recognition, it is still a challenging part of FER systems. Identifying the most significant regions and selectively applying higher order moments on these patches of the face image is found to give good feature vectors that have high discriminative ability between the classes (expressions). Point distribution model (PDM) has been used in the proposed method for locating the facial landmarks required to extract facial patches. Higher order Zernike moment (ZM) invariants were evaluated for these facial patches which form the features to be used for classification. ZMs are rotation invariant orthogonal moments having a high degree of image representation capability, hence they can satisfactorily extract significant amounts of both local and global information contents from the facial patch. The final feature vector consists of higher order Zernike moment invariants from all facial patches. Finally, support vector machines (SVM) classifiers are trained which are used to predict the facial expressions. Experiments on the extended CK+ dataset and JAFFE dataset show the effectiveness of the proposed FER system.","PeriodicalId":438405,"journal":{"name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129041998","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

Spectrum Assignment using Prediction in Elastic Optical Networks 弹性光网络中基于预测的频谱分配

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944683

Neha Mahala, Jaisingh Thangaraj

引用次数: 2

Anonymity and Performance Analysis of Stream Isolation in Tor Network Tor网络中流隔离的匿名性及性能分析

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944443

K. Kiran, S. ChalkeSaurabh, Mohammad Usman, P. D. Shenoy, K. Venugopal

{"title":"Anonymity and Performance Analysis of Stream Isolation in Tor Network","authors":"K. Kiran, S. ChalkeSaurabh, Mohammad Usman, P. D. Shenoy, K. Venugopal","doi":"10.1109/icccnt45670.2019.8944443","DOIUrl":"https://doi.org/10.1109/icccnt45670.2019.8944443","url":null,"abstract":"Tor is an overlay network software which enables users to communicate anonymously over the web. There have been numerous improvements to the original Tor design mainly to upgrade its performance and overall user anonymity. This paper explores the impact of a proposal to separate client based streams over the circuits, based on the metadata provided by the connection. Prior to the implementation of stream isolation, all the streams from the client end selected the circuit based on the exit node and the lifetime of the circuit. This could result in unrelated traffic being combined and let the adversary at the exit node perform probabilistic timing attacks, thereby compromising the overall anonymity of the user. Stream isolation obfuscates these attacks by creating a new circuit and isolating the streams as much as possible. We have analyzed and examined the effects of stream isolation in the Shadow and Tor Path simulator(TorPS) by creating up to ten parallel transfers from the client end which connects to several non-identical servers. These parallel transfers create distinct circuits for every request in the case of the configuration where the stream isolation is enabled. The impact on the anonymity has been investigated using TorPS, which clearly indicates a diminution in the fraction of compromised streams after isolating disparate traffic across discrete paths. The performance insights attained after the Shadow simulations show that the stream isolation when enabled in the configuration, results in an upsurge in the memory usage, increase in the latency for finishing all the downloads, and an overall rise in the network throughput. We can, therefore, conclude that this increased overall anonymity of the network comes at the cost of an increase in the network overhead.","PeriodicalId":438405,"journal":{"name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129140272","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: 2

EEG based automatic emotion recognition using EMD and Random forest classifier 基于EMD和随机森林分类器的脑电自动情绪识别

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944903

Gnana Keerthi Priya Veeramallu, Yamuna Anupalli, Sravan kumar Jilumudi, Abhijit Bhattacharyya

{"title":"EEG based automatic emotion recognition using EMD and Random forest classifier","authors":"Gnana Keerthi Priya Veeramallu, Yamuna Anupalli, Sravan kumar Jilumudi, Abhijit Bhattacharyya","doi":"10.1109/icccnt45670.2019.8944903","DOIUrl":"https://doi.org/10.1109/icccnt45670.2019.8944903","url":null,"abstract":"Electroencephalogram (EEG) signal based automatic emotion recognition is an inter-disciplinary field which has been studied for a long time. In this study, empirical mode decomposition (EMD) is used for EEG-based automatic emotion classification. The EMD is used for decomposition of non-stationary and nonlinear EEG signals into different modes called intrinsic mode functions (IMFs). From the resulting IMFs different nonlinear features namely Hjorth parameters, Shannon entropy, collision entropy, differential entropy, and Higuchis fractal dimension are extracted in order to determine the characteristics associated with emotional states. The obtained features are smoothed using moving average filter with a window size of 5 samples for removing unwanted rapid fluctuations in the extracted features. Finally, the processed features are fed to random forest (RF) classifier and the accuracies are noted for different profiles of 4, 6 and 12 channels. The proposed method is applied to SJTU emotion EEG dataset (SEED). We have obtained highest classification accuracies of 89.59%, 91.45%, 93.87% using activity feature for the above 3 channel profiles in classifying positive, neutral and negative emotions respectively. Finally a comparative analysis is performed in terms of computed classification accuracies with other above mentioned features. As compared to existing methods, the proposed method has shown better performance and can be considered for recognition of human emotional states.","PeriodicalId":438405,"journal":{"name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129222739","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: 15

Energy Efficient Real Time Reliable MAC Protocol for Wireless Body Area Network 无线体域网络节能实时可靠的MAC协议

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944431

T. Samal, M. R. Kabat

{"title":"Energy Efficient Real Time Reliable MAC Protocol for Wireless Body Area Network","authors":"T. Samal, M. R. Kabat","doi":"10.1109/icccnt45670.2019.8944431","DOIUrl":"https://doi.org/10.1109/icccnt45670.2019.8944431","url":null,"abstract":"Wireless Body Area Network (WBAN) has attracted the researchers during the last decade due to its wide range of applications. However, QoS requirement, prolong network lifetime and supporting different types of traffics produced by various sensors are the prominent barriers for deploying the network. A number of Medium Access Control (MAC) protocols have been developed to meet the above requirements. However, stringent Qos requirement, energy efficiency and delivery of emergency data have become the prime objectives of the researchers. In this paper, we propose an energy efficient real-time reliable media access control (ER-MAC) protocol for QoS-aware data transmission over WBAN with prolonged network life time. This protocol is based on IEEE 802.15.4 model which is very popular in healthcare application due to its low power and low cost features. Our proposed ER-MAC protocol modifies the super frame of IEEE 802.15.4 model with an intention to reducing the loss rate and delay of the emergency data without degrading the QoS of non emergent data. The proposed protocol is compared with existing protocol through the simulation carried out in Castalia 3.2. The simulation results reveal that ER-MAC outperforms the existing counterparts.","PeriodicalId":438405,"journal":{"name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129340085","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

Fuzzy Vector Quantization for Classification of Olfactory Stimuli from the Acquired Brain Signals 模糊向量量化在嗅觉刺激分类中的应用

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944662

Lidia Ghosh, Dipanjan Konar, A. Konar

{"title":"Fuzzy Vector Quantization for Classification of Olfactory Stimuli from the Acquired Brain Signals","authors":"Lidia Ghosh, Dipanjan Konar, A. Konar","doi":"10.1109/icccnt45670.2019.8944662","DOIUrl":"https://doi.org/10.1109/icccnt45670.2019.8944662","url":null,"abstract":"Quantization offers a simple means to reduce unwanted data variations into quantized levels of fixed amplitudes. A simple Normalization of data in the scale [0, 1] without attributing special importance to specific sub-intervals in [0, 1] results in significant loss in information, which cannot be retrieved otherwise to identify the proposer class of a given data point. This paper attempts to represent $boldsymbol{n}$-dimensional data point patterns into quantized fuzzy vectors of $3n$ dimensions. As the class information for all the data points of the training instances are known, a majority voting over (the individual quantized fuzzy) components of the data points in a class effectively returns an approximate centroidal measure of the data points lying in the same class. The concatenation of such majority-voted components of the data points in a class represents the class centroid of the respective class. The class centroids thus obtained are saved to determine the class of an unknown data point. The classification of an unknown data point here is performed in two steps. First the unknown data point is quantized in fuzzy space similarly as for the training data, and the resulting quantized fuzzy vector is projected along all the unit class centroid vectors of all known classes. In case the projection along the $k$-th unit class centroid vector is the largest, then the class of the unknown data point is regarded as $boldsymbol{k}$. The proposed principle has successfully been applied in classification of olfactory stimuli from the acquired EEG signals of the subjects and the classification performance is found to be superior with respect to the existing state-of-the art algorithms.","PeriodicalId":438405,"journal":{"name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124728338","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: 2

Data Security Approach in IoT Environment 物联网环境下的数据安全方法

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944831

Shruti Sawardekar, R. Pawar

引用次数: 4

A Data Mining Based Approach to Predict Autism Spectrum Disorder Considering Behavioral Attributes 基于数据挖掘的自闭症谱系障碍行为属性预测方法

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944905

Shaon Bhatta Shuvo, Joyoshree Ghosh, Atia Sujana Oyshi

{"title":"A Data Mining Based Approach to Predict Autism Spectrum Disorder Considering Behavioral Attributes","authors":"Shaon Bhatta Shuvo, Joyoshree Ghosh, Atia Sujana Oyshi","doi":"10.1109/icccnt45670.2019.8944905","DOIUrl":"https://doi.org/10.1109/icccnt45670.2019.8944905","url":null,"abstract":"Autism Spectrum Disorder (ASD) is a disease connected to the growth of brain. It affects a person's way of communication and behavior. It impacts a person's way of understanding and social attachments. Moreover, people with ASD experience various types of symptoms, for examples, difficulty while interacting with others, repetitive behaviors, and face difficulty to function properly in other areas of day-to-day life. These symptoms generally occur in early childhood. Most of the people are not well aware of the syndrome, therefore, they do not know whether a person is suffering from the disorder or not. As a result, instead of helping which very often leads the victim to become isolated from society. However, the prediction of the disorder at an early stage should help to get a quick recovery. In this paper, the Random Forest classifier was used for the prediction of ASD based on behavioral attributes. We are satisfied with the results with the overall accuracy of 0.96%. The dataset used for prediction had 10 behavioral attributes and 10 more individual attributes.","PeriodicalId":438405,"journal":{"name":"2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130325146","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: 10

Deep Learning Model for Text Recognition in Images 图像中文本识别的深度学习模型

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944593

A. Shrivastava, J. Amudha, Deepa Gupta, Kshitij Sharma

引用次数: 15

Real Time object detection and multilingual speech synthesis 实时目标检测和多语言语音合成

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Pub Date : 2019-07-01 DOI: 10.1109/icccnt45670.2019.8944591

Okeke Stephen, Deepanjali Mishra, M. Sain

引用次数: 4