Pub Date : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703215
Himansu Sekhar Pattanayak, H. Verma, A. L. Sangal
Community detection is one of the key areas of social network analysis. There are various community detection algorithms available in the literature. Numerous community metrics are also available to evaluate the detected communities. In our study, by using synthetic networks, we compare between four well known community metrics, namely; modularity, conductance, coverage and performance. We also compare seven different community detection algorithms based on above mentioned parameters.
{"title":"Community Detection Metrics and Algorithms in Social Networks","authors":"Himansu Sekhar Pattanayak, H. Verma, A. L. Sangal","doi":"10.1109/ICSCCC.2018.8703215","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703215","url":null,"abstract":"Community detection is one of the key areas of social network analysis. There are various community detection algorithms available in the literature. Numerous community metrics are also available to evaluate the detected communities. In our study, by using synthetic networks, we compare between four well known community metrics, namely; modularity, conductance, coverage and performance. We also compare seven different community detection algorithms based on above mentioned parameters.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121280030","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703218
Shobhit Verma, N. Sharma
In Recent times, Chikungunya is considered as one of the most severe disease in India. It is caused by mosquitoes bite (CHIKV). But till now around the globe, scientists are unable to find the exact cure of this disease. Hence as a precautionary measure, there is an imperative need to predict the future possibilities of Chikungunya cases. Therefore, in this manuscript, machine learning based forecasting models are used for prediction of chikungunya cases in India for year 2018-2024. Analysis is conducted on the data of past years (2007-2017) Chikungunya cases in India. Box Cox, Mean Forecast, Seasonal Naive, and Neural Network are techniques are used for analysis and forecasting. The surpassing model is adopted based on the accuracy factor. Accuracy of the models are compared with respect to Root Mean Square Error and Auto Correlation Function. Result analysis reveal that the neural network model produces least error and hence is the best prediction model for our dataset in terms of accuracy.
最近，基孔肯雅热被认为是印度最严重的疾病之一。它是由蚊子叮咬(CHIKV)引起的。但到目前为止，全球的科学家们还无法找到治疗这种疾病的确切方法。因此，作为一项预防措施，迫切需要预测基孔肯雅热病例未来的可能性。因此，在本文中，基于机器学习的预测模型用于预测2018-2024年印度基孔肯雅病例。对印度过去几年(2007-2017年)基孔肯雅病例的数据进行了分析。Box Cox, Mean Forecast, Seasonal Naive和Neural Network是用于分析和预测的技术。采用基于精度因子的超越模型。从均方根误差和自相关函数两方面比较了模型的精度。结果分析表明，神经网络模型产生的误差最小，因此在精度方面是我们数据集的最佳预测模型。
{"title":"Statistical Models for Predicting Chikungunya Incidences in India","authors":"Shobhit Verma, N. Sharma","doi":"10.1109/ICSCCC.2018.8703218","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703218","url":null,"abstract":"In Recent times, Chikungunya is considered as one of the most severe disease in India. It is caused by mosquitoes bite (CHIKV). But till now around the globe, scientists are unable to find the exact cure of this disease. Hence as a precautionary measure, there is an imperative need to predict the future possibilities of Chikungunya cases. Therefore, in this manuscript, machine learning based forecasting models are used for prediction of chikungunya cases in India for year 2018-2024. Analysis is conducted on the data of past years (2007-2017) Chikungunya cases in India. Box Cox, Mean Forecast, Seasonal Naive, and Neural Network are techniques are used for analysis and forecasting. The surpassing model is adopted based on the accuracy factor. Accuracy of the models are compared with respect to Root Mean Square Error and Auto Correlation Function. Result analysis reveal that the neural network model produces least error and hence is the best prediction model for our dataset in terms of accuracy.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124863419","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703303
Simarjot Kaur Randhawa, R. K. Sunkaria
Speckle noise is inherent nature of ultrasound images which makes interpretation of the images difficult and hence is undesirable. In this work, Savitzky-Golay filter is investigated for speckle reduction in ultrasound images. This filter is tested for different order and frame lengths. Optimal order and frame length is chosen heuristically by analyzing performance of the filter for a range of filter order and frame length. The method is tested on synthetic images as well as clinical ultrasound images and promising results are achieved.
{"title":"Investigation of Performance of Savitzky-Golay Filter for Speckle Reduction in Ultrasound Images","authors":"Simarjot Kaur Randhawa, R. K. Sunkaria","doi":"10.1109/ICSCCC.2018.8703303","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703303","url":null,"abstract":"Speckle noise is inherent nature of ultrasound images which makes interpretation of the images difficult and hence is undesirable. In this work, Savitzky-Golay filter is investigated for speckle reduction in ultrasound images. This filter is tested for different order and frame lengths. Optimal order and frame length is chosen heuristically by analyzing performance of the filter for a range of filter order and frame length. The method is tested on synthetic images as well as clinical ultrasound images and promising results are achieved.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123740655","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703316
Rahul Chauhan, K. Ghanshala, R. Joshi
Deep Learning algorithms are designed in such a way that they mimic the function of the human cerebral cortex. These algorithms are representations of deep neural networks i.e. neural networks with many hidden layers. Convolutional neural networks are deep learning algorithms that can train large datasets with millions of parameters, in form of 2D images as input and convolve it with filters to produce the desired outputs. In this article, CNN models are built to evaluate its performance on image recognition and detection datasets. The algorithm is implemented on MNIST and CIFAR-10 dataset and its performance are evaluated. The accuracy of models on MNIST is 99.6 %, CIFAR-10 is using real-time data augmentation and dropout on CPU unit.
{"title":"Convolutional Neural Network (CNN) for Image Detection and Recognition","authors":"Rahul Chauhan, K. Ghanshala, R. Joshi","doi":"10.1109/ICSCCC.2018.8703316","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703316","url":null,"abstract":"Deep Learning algorithms are designed in such a way that they mimic the function of the human cerebral cortex. These algorithms are representations of deep neural networks i.e. neural networks with many hidden layers. Convolutional neural networks are deep learning algorithms that can train large datasets with millions of parameters, in form of 2D images as input and convolve it with filters to produce the desired outputs. In this article, CNN models are built to evaluate its performance on image recognition and detection datasets. The algorithm is implemented on MNIST and CIFAR-10 dataset and its performance are evaluated. The accuracy of models on MNIST is 99.6 %, CIFAR-10 is using real-time data augmentation and dropout on CPU unit.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"767 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123006077","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703346
R. Mahajan, Devanand Padha
In today’s modern era security is one of the major concern. The security surveillance cameras are installed everywhere in order to detect any kind of concealed object which may pose a threat to the security. The concealed object can be any kind of firearms or any weapon including knife, scissors, etc. The researchers have focused on techniques to track and detect concealed objects. The process involves the feature extraction of arms, segmentation of images with any concealed object and detection of the weapon. This paper deals with the various techniques used for detection of concealed objects and the respective pitfalls in the current security system.
{"title":"Detection Of Concealed Weapons Using Image Processing Techniques: A Review","authors":"R. Mahajan, Devanand Padha","doi":"10.1109/ICSCCC.2018.8703346","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703346","url":null,"abstract":"In today’s modern era security is one of the major concern. The security surveillance cameras are installed everywhere in order to detect any kind of concealed object which may pose a threat to the security. The concealed object can be any kind of firearms or any weapon including knife, scissors, etc. The researchers have focused on techniques to track and detect concealed objects. The process involves the feature extraction of arms, segmentation of images with any concealed object and detection of the weapon. This paper deals with the various techniques used for detection of concealed objects and the respective pitfalls in the current security system.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116851678","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703369
Shilpa Mahajan, Rajneesh Rani
Natural scenic images usually contains textual data which may provide valuable information about the scene. To make this textual data useful, processing of scenic images involves various phases such as detection, localization, segmentation and recognition of text. First phase i.e. extraction of textual data plays an important role in further processing. Text extraction becomes difficult owing to variation in text font, size, skewness and noise in the captured image and is a challenge for the researchers. Over the years a lot of research has been dedicated to overcome these challenges and, this work presents extraction methods used for text in different Indian and non-Indian scripts. This article will provide academicians and practitioners with state of the art and future directions in this phase.
{"title":"Text Extraction from Indian and Non-Indian Natural Scene Images: A Review","authors":"Shilpa Mahajan, Rajneesh Rani","doi":"10.1109/ICSCCC.2018.8703369","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703369","url":null,"abstract":"Natural scenic images usually contains textual data which may provide valuable information about the scene. To make this textual data useful, processing of scenic images involves various phases such as detection, localization, segmentation and recognition of text. First phase i.e. extraction of textual data plays an important role in further processing. Text extraction becomes difficult owing to variation in text font, size, skewness and noise in the captured image and is a challenge for the researchers. Over the years a lot of research has been dedicated to overcome these challenges and, this work presents extraction methods used for text in different Indian and non-Indian scripts. This article will provide academicians and practitioners with state of the art and future directions in this phase.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124563269","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703284
S. Khade, P. Zade, S. Badjate
A compact Fractal Elliptical Monopole antenna system with dimensions $40 times 45 mm^{2}$ is proposed. Four small circular defects are placed in the antenna to improve isolation. The ring resonator is introduced to increase the number of bands i.e. multiband. The reflection coefficient of antenna is well below to -10 dB at 2.4 GHz, 3.51 GHz and 4.45 GHz. The antenna performance is evaluated by its radiation pattern, peak gain, VSWR and directivity. The highest gain achieved by antenna is 4.47 dB.
{"title":"Fractal Elliptical Monopole Antenna for Wi-Fi, WiMax and WLAN","authors":"S. Khade, P. Zade, S. Badjate","doi":"10.1109/ICSCCC.2018.8703284","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703284","url":null,"abstract":"A compact Fractal Elliptical Monopole antenna system with dimensions $40 times 45 mm^{2}$ is proposed. Four small circular defects are placed in the antenna to improve isolation. The ring resonator is introduced to increase the number of bands i.e. multiband. The reflection coefficient of antenna is well below to -10 dB at 2.4 GHz, 3.51 GHz and 4.45 GHz. The antenna performance is evaluated by its radiation pattern, peak gain, VSWR and directivity. The highest gain achieved by antenna is 4.47 dB.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"399 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124575570","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 : 2018-12-01DOI: 10.1109/icsccc.2018.8703361
{"title":"ICSCCC 2018 Title Page","authors":"","doi":"10.1109/icsccc.2018.8703361","DOIUrl":"https://doi.org/10.1109/icsccc.2018.8703361","url":null,"abstract":"","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121305843","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703351
Jaspreet Singh, Gurvinder Singh
The exponential growth of content on social media raised the need for evaluation of user reviews to recognize the underlying sentiments. The traditional Natural Language Processing (NLP) techniques necessitate novel Quality of Service (QoS) parameters from the aspect based reviews. The classical methods espouse QoS parameters acquired from feedback system where, a predefined range of questions affects the authenticity of sentiments. This paper proposes the method of evaluation that assimilates aspect related QoS parameters obtained from user reviews. The pre-processing phase of our proposed model involves steps like review cleaning followed by word tokenization, stemming, and stop-word removal. Pre-processed set of word tokens go through Parts Of Speech (POS) tagging using Stanford POS tagger. Post-processing phase leverages standard NLP and Machine Learning (ML) techniques to identify the prominent QoS features. However, the task of sentiment classification exploits Natural Language Toolkit (NLTK) but, the impact of relevant terms in a review is learned using Logistic Regression (LR). The efficacy of proposed model is evaluated using a real world dataset and the results confirm the effectiveness of introduced QoS features.
{"title":"Sentiment Analysis of Social Media Reviews using QOS Parameterization","authors":"Jaspreet Singh, Gurvinder Singh","doi":"10.1109/ICSCCC.2018.8703351","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703351","url":null,"abstract":"The exponential growth of content on social media raised the need for evaluation of user reviews to recognize the underlying sentiments. The traditional Natural Language Processing (NLP) techniques necessitate novel Quality of Service (QoS) parameters from the aspect based reviews. The classical methods espouse QoS parameters acquired from feedback system where, a predefined range of questions affects the authenticity of sentiments. This paper proposes the method of evaluation that assimilates aspect related QoS parameters obtained from user reviews. The pre-processing phase of our proposed model involves steps like review cleaning followed by word tokenization, stemming, and stop-word removal. Pre-processed set of word tokens go through Parts Of Speech (POS) tagging using Stanford POS tagger. Post-processing phase leverages standard NLP and Machine Learning (ML) techniques to identify the prominent QoS features. However, the task of sentiment classification exploits Natural Language Toolkit (NLTK) but, the impact of relevant terms in a review is learned using Logistic Regression (LR). The efficacy of proposed model is evaluated using a real world dataset and the results confirm the effectiveness of introduced QoS features.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115904825","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 : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703313
Zeenia, Jagmeet Singh Aidan, Urvashi Garg
Nowadays, security of the networks is one of the major concern. Attack paths in an attack graph give a way to get a view of the big network, illustrating all the possible vulnerabilities in a network, from a security point of view. This paper proposes a new methodology for finding all the possible attack paths in a graph. It helps us in identifying most desirable and least desirable attack paths by the attacker, which will give network administrators a view for securing their network. Some researchers have used a genetic algorithm (GA) for finding the attack paths as GA helps us in providing a fast way to generate the possible list of solutions in very less time. We have also used this genetic algorithm but in a different, better and modified way for our approach by introducing a new scheme of backward mutation, with 100 percent GA operators(crossover, mutation) rate and by also modifying the phases of GA for generating fast results. By performing experiments, our new modified approach for GA is producing 7 percent (approx.) more solutions by keeping same parameters as that of existing GA. Other algorithms may also tell us about all attack paths but they will either be slow or may miss out some attack paths in a network.
{"title":"Analyzing Complex Non-Trivial Network using Attack Set Generation by Genetic Algorithm","authors":"Zeenia, Jagmeet Singh Aidan, Urvashi Garg","doi":"10.1109/ICSCCC.2018.8703313","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703313","url":null,"abstract":"Nowadays, security of the networks is one of the major concern. Attack paths in an attack graph give a way to get a view of the big network, illustrating all the possible vulnerabilities in a network, from a security point of view. This paper proposes a new methodology for finding all the possible attack paths in a graph. It helps us in identifying most desirable and least desirable attack paths by the attacker, which will give network administrators a view for securing their network. Some researchers have used a genetic algorithm (GA) for finding the attack paths as GA helps us in providing a fast way to generate the possible list of solutions in very less time. We have also used this genetic algorithm but in a different, better and modified way for our approach by introducing a new scheme of backward mutation, with 100 percent GA operators(crossover, mutation) rate and by also modifying the phases of GA for generating fast results. By performing experiments, our new modified approach for GA is producing 7 percent (approx.) more solutions by keeping same parameters as that of existing GA. Other algorithms may also tell us about all attack paths but they will either be slow or may miss out some attack paths in a network.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131975612","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：