Pub Date : 2018-12-01DOI: 10.1109/ICSCCC.2018.8703202
A. Kamboj, Rajneesh Rani, Jiten Chaudhary
Brain tumor has been a cause of concern for the medical fraternity. The manual segmentation of brain tumor by medical expert is a time-consuming process and this needs to be automated. The Computer-aided diagnosis (CAD) system, help to improve the diagnosis and reduces the overall time required to identify the tumor. Researchers have proposed methods that can diagnose brain tumor based on machine learning and deep learning techniques. But the methods based on deep learning have proven much better than the traditional machine learning methods. In this paper we have discussed the state-of-the-art methods for brain tumor segmentation based on deep learning.
{"title":"Deep Leaming Approaches for Brain Tumor Segmentation: A Review","authors":"A. Kamboj, Rajneesh Rani, Jiten Chaudhary","doi":"10.1109/ICSCCC.2018.8703202","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703202","url":null,"abstract":"Brain tumor has been a cause of concern for the medical fraternity. The manual segmentation of brain tumor by medical expert is a time-consuming process and this needs to be automated. The Computer-aided diagnosis (CAD) system, help to improve the diagnosis and reduces the overall time required to identify the tumor. Researchers have proposed methods that can diagnose brain tumor based on machine learning and deep learning techniques. But the methods based on deep learning have proven much better than the traditional machine learning methods. In this paper we have discussed the state-of-the-art methods for brain tumor segmentation based on deep learning.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"45 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":"114536019","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.2018.8703367
{"title":"ICSCCC 2018 Committee and Message","authors":"","doi":"10.1109/icsccc.2018.8703367","DOIUrl":"https://doi.org/10.1109/icsccc.2018.8703367","url":null,"abstract":"","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"5 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":"129502865","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.2018.8703349
Ritu Singh, N. Rajpal, R. Mehta
Biomedical signals like Electrocardiogram (ECG) contains essential information related to the functionality of heart. The pre analysis of ECG disturbances, aided by computer designed algorithms can prove to be efficient support in reducing cardiac emergencies. In this present method, dual tree complex wavelet transform (DTCWT) with linear discriminate analysis (LDA) also known as hybrid feature extraction are employed for denoising and dimensionally reduced non linear feature extraction respectively. The classification and analysis of ECG dataset into normal and abnormal beats is done by independently deploying five classifiers like support vector machine (SVM), decision tree (DT), back propagation neural network (BPNN), feed forward neural network (FNNN) and K nearest neighbour (KNN). The outcomes of proposed work are compared with pre existing methods. The highest percentage accuracy of 99.7% is achieved using BPNN, SVM and KNN. The simulation results show that the shift invariance nature of DTCWT provides a robust technique for non linear and non stationary ECG signals.
{"title":"Abnormality detection in ECG using hybrid feature extraction approach","authors":"Ritu Singh, N. Rajpal, R. Mehta","doi":"10.1109/ICSCCC.2018.8703349","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703349","url":null,"abstract":"Biomedical signals like Electrocardiogram (ECG) contains essential information related to the functionality of heart. The pre analysis of ECG disturbances, aided by computer designed algorithms can prove to be efficient support in reducing cardiac emergencies. In this present method, dual tree complex wavelet transform (DTCWT) with linear discriminate analysis (LDA) also known as hybrid feature extraction are employed for denoising and dimensionally reduced non linear feature extraction respectively. The classification and analysis of ECG dataset into normal and abnormal beats is done by independently deploying five classifiers like support vector machine (SVM), decision tree (DT), back propagation neural network (BPNN), feed forward neural network (FNNN) and K nearest neighbour (KNN). The outcomes of proposed work are compared with pre existing methods. The highest percentage accuracy of 99.7% is achieved using BPNN, SVM and KNN. The simulation results show that the shift invariance nature of DTCWT provides a robust technique for non linear and non stationary ECG signals.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"9 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":"122203919","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.2018.8703285
Seema Sharma, D. Mehrotra
Multi-label classification has generated enthusiasm in many fields over the last few years. It allows the classifications of dataset where each instance can be associated with one or more label. It has successfully ended up being superiorstrategy as compared to Single labelclassification. In this paper, we provide an overview of multi-label classification approaches. We also discussed the various tools thatutilizes MLC approaches. Lastly, we have presented an experimental study to compare different algorithms of multi-label classification. After applying and studying the accuracies of various multilabel classification techniques, we have found that performance of Random Forest is better than the rest of the other compared multilabelclassification algorithms with 96% accuracy.
{"title":"Comparative Analysis of Multi-label Classification Algorithms","authors":"Seema Sharma, D. Mehrotra","doi":"10.1109/ICSCCC.2018.8703285","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703285","url":null,"abstract":"Multi-label classification has generated enthusiasm in many fields over the last few years. It allows the classifications of dataset where each instance can be associated with one or more label. It has successfully ended up being superiorstrategy as compared to Single labelclassification. In this paper, we provide an overview of multi-label classification approaches. We also discussed the various tools thatutilizes MLC approaches. Lastly, we have presented an experimental study to compare different algorithms of multi-label classification. After applying and studying the accuracies of various multilabel classification techniques, we have found that performance of Random Forest is better than the rest of the other compared multilabelclassification algorithms with 96% accuracy.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"46 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":"124794227","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.2018.8703267
Akanksha Sharma, N. Jindal, A. Thakur
Various new deep learning models have been invented, among which generative adversarial networks have gained exceptional prominence in last four years due to its property of image synthesis. GANs have been utilized in diverse fields ranging from conventional areas like image processing, biomedical signal processing, remote sensing, video generation to even off beat areas like sound and music generation. In this paper, we provide an overview of GANs along with its comparison with other networks, as well as different versions of Generative Adversarial Networks.
{"title":"Comparison on Generative Adversarial Networks –A Study","authors":"Akanksha Sharma, N. Jindal, A. Thakur","doi":"10.1109/ICSCCC.2018.8703267","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703267","url":null,"abstract":"Various new deep learning models have been invented, among which generative adversarial networks have gained exceptional prominence in last four years due to its property of image synthesis. GANs have been utilized in diverse fields ranging from conventional areas like image processing, biomedical signal processing, remote sensing, video generation to even off beat areas like sound and music generation. In this paper, we provide an overview of GANs along with its comparison with other networks, as well as different versions of Generative Adversarial Networks.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"10 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":"124189781","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.2018.8703344
Pooja Singh, Praveen Pawar, A. Trivedi
Rapid growth in the fifth generation (5G) wireless network applications demands new requirements on the data storage, computation, and networking. Thus, it will introduce new threats to the integrity, availability, and confidentiality. 5G is advantageous concerning high data rate, low latency, energy and spectrum efficient, higher capacity, and reliable connectivity. Currently, safeguarding information in the 5G wireless networks is the pivotal issue for research. In this paper, the importance of Physical Layer Security (PLS) for secure transmission of information in wireless networks are discussed. Some popular 5G technologies are studied in the context of security during transmission. With all this, significant issues and challenges are identified in the implementation of new technologies into reality. These technologies are mobile-health (m-health), cognitive radio networks (CRNs), constructive interference, massive multiple input multiple output (massive MIMO), non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT). Future challenges and direction for the further study of such technologies are also given. Moreover, one numerical result is presented for the spectral efficiency in MIMO communication system.
{"title":"Physical Layer Security Approaches in 5G Wireless Communication Networks","authors":"Pooja Singh, Praveen Pawar, A. Trivedi","doi":"10.1109/ICSCCC.2018.8703344","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703344","url":null,"abstract":"Rapid growth in the fifth generation (5G) wireless network applications demands new requirements on the data storage, computation, and networking. Thus, it will introduce new threats to the integrity, availability, and confidentiality. 5G is advantageous concerning high data rate, low latency, energy and spectrum efficient, higher capacity, and reliable connectivity. Currently, safeguarding information in the 5G wireless networks is the pivotal issue for research. In this paper, the importance of Physical Layer Security (PLS) for secure transmission of information in wireless networks are discussed. Some popular 5G technologies are studied in the context of security during transmission. With all this, significant issues and challenges are identified in the implementation of new technologies into reality. These technologies are mobile-health (m-health), cognitive radio networks (CRNs), constructive interference, massive multiple input multiple output (massive MIMO), non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT). Future challenges and direction for the further study of such technologies are also given. Moreover, one numerical result is presented for the spectral efficiency in MIMO communication system.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"175 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":"129145338","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.2018.8703214
Pankaj Kumar, Lokesh Chouhan
In wireless sensor network (WSN) nodes relay data, which maintains consistency in the sensor network. If cut vertex nodes failed it leads to partitioning in network, which disrupts the communication. It requires an autonomous technique that finds the fail node and provides recovery to them. In this paper the proposed approach provides recovery from network partitioning in distribute WSN. The proposed approach increases the lifetime of network.
{"title":"Distributed Recovery in Moveable Wireless Sensor Networks","authors":"Pankaj Kumar, Lokesh Chouhan","doi":"10.1109/ICSCCC.2018.8703214","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703214","url":null,"abstract":"In wireless sensor network (WSN) nodes relay data, which maintains consistency in the sensor network. If cut vertex nodes failed it leads to partitioning in network, which disrupts the communication. It requires an autonomous technique that finds the fail node and provides recovery to them. In this paper the proposed approach provides recovery from network partitioning in distribute WSN. The proposed approach increases the lifetime of network.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"2 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":"132689745","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.2018.8703364
D. K. Gupta, R. Singh, Vikalp Ravi Jain
Financial forecasting is one of the trending research going on in the current market. Researchers are mainly focused on the parameters that are affecting share market. Presently researchers are able to discover the relation between macroeconomic variables and different indices. We are driving our research by discovering the relationship between macroeconomic variables and individual share prices, but before proceeding for the prediction, we firstly analyze each individual company and their dependence on raw products for the improvement in predicting the individual share prices. Each individual companies produce different units used by consumers. To compose those elements companies necessitate raw materials and because of those materials their share prices are being affected. We did an analysis that how strongly they need those raw materials in their production and how these companies are related. From that relation, we are able to parameterize our neural network for the prediction of different individual companies share prices.
{"title":"Analysis and Forecasting of Individual Stock Prices of Various Constituents in NIFTY 50","authors":"D. K. Gupta, R. Singh, Vikalp Ravi Jain","doi":"10.1109/ICSCCC.2018.8703364","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703364","url":null,"abstract":"Financial forecasting is one of the trending research going on in the current market. Researchers are mainly focused on the parameters that are affecting share market. Presently researchers are able to discover the relation between macroeconomic variables and different indices. We are driving our research by discovering the relationship between macroeconomic variables and individual share prices, but before proceeding for the prediction, we firstly analyze each individual company and their dependence on raw products for the improvement in predicting the individual share prices. Each individual companies produce different units used by consumers. To compose those elements companies necessitate raw materials and because of those materials their share prices are being affected. We did an analysis that how strongly they need those raw materials in their production and how these companies are related. From that relation, we are able to parameterize our neural network for the prediction of different individual companies share prices.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"2 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":"133052798","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.2018.8703204
Simarjot Kaur Randhawa, R. K. Sunkaria, Anterpreet Kaur Bedi
Liver diseases are one of the significant public health issues in present scenario. Liver cirrhosis is one of the leading causes of deaths due to liver related diseases. In this work, classification of normal and cirrhotic liver is done using texture analysis. Various texture features are extracted for classification. Out of all the extracted features, seven best features are identified using fisher discriminant ratio. Further, weighted fisher discriminant ratio algorithm is designed using the selected features, such that maximum accuracy and sensitivity is achieved.
{"title":"Prediction of Liver Cirrhosis Using Weighted Fisher Discriminant Ratio Algorithm","authors":"Simarjot Kaur Randhawa, R. K. Sunkaria, Anterpreet Kaur Bedi","doi":"10.1109/ICSCCC.2018.8703204","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703204","url":null,"abstract":"Liver diseases are one of the significant public health issues in present scenario. Liver cirrhosis is one of the leading causes of deaths due to liver related diseases. In this work, classification of normal and cirrhotic liver is done using texture analysis. Various texture features are extracted for classification. Out of all the extracted features, seven best features are identified using fisher discriminant ratio. Further, weighted fisher discriminant ratio algorithm is designed using the selected features, such that maximum accuracy and sensitivity is achieved.","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"70 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":"134269171","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.2018.8703299
I. Shah
Cloud computing has shifted the paradigm when it comes to handling our data. We are no more restricted to our standalone systems and can now access our data on the go. All the big companies like Google, Amazon et al have shifted their business on the cloud and are now offering their customers a new lease of life. However, with this ease come several challenges which have drawn the attention of people be it cryptographers, scientists or from business fraternity round the globe. Two main issues that one can relate to cloud computing is security and energy consumption. The aim of this paper is to address the energy concern in cloud data center. It would not be an aberration to say that cloud computing is at odds with the interests of the company when it comes to the financial aspect. If we go by stats, nearly 5% of the world’s total energy is being consumed by these data centers as their energy requirements are huge owing to services like PaaS, Iaas, SaaS.In this paper, an algorithm is proposed for minimizing the energy consumption of a typical data center catering to the need of cloud users. By modeling the incoming workload of a data center based on this approach, it can be proved that considerable savings in energy can be achieved
{"title":"An Algorithm for Computing Efficiently in Cloud Based Data Centers","authors":"I. Shah","doi":"10.1109/ICSCCC.2018.8703299","DOIUrl":"https://doi.org/10.1109/ICSCCC.2018.8703299","url":null,"abstract":"Cloud computing has shifted the paradigm when it comes to handling our data. We are no more restricted to our standalone systems and can now access our data on the go. All the big companies like Google, Amazon et al have shifted their business on the cloud and are now offering their customers a new lease of life. However, with this ease come several challenges which have drawn the attention of people be it cryptographers, scientists or from business fraternity round the globe. Two main issues that one can relate to cloud computing is security and energy consumption. The aim of this paper is to address the energy concern in cloud data center. It would not be an aberration to say that cloud computing is at odds with the interests of the company when it comes to the financial aspect. If we go by stats, nearly 5% of the world’s total energy is being consumed by these data centers as their energy requirements are huge owing to services like PaaS, Iaas, SaaS.In this paper, an algorithm is proposed for minimizing the energy consumption of a typical data center catering to the need of cloud users. By modeling the incoming workload of a data center based on this approach, it can be proved that considerable savings in energy can be achieved","PeriodicalId":148491,"journal":{"name":"2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":"16 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":"133746425","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}
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