Pub Date : 2020-11-26DOI: 10.1109/ICRCICN50933.2020.9296179
Yusra Amir, S. Deb, M. S. Alam, Y. Rafat, S. Hameed
The COVID-19 is the current widespread health disaster. It has speedily spread all over the world causing a massive impact on the health, environmental, social and economic condition of the total world’s population. Enormous actions undertaken worldwide to minimize the expansion of this deadly contagion by testing at a large scale, quarantining the suspected people, upholding lockdowns and restricting social gatherings. The transportation sector has been one amid the leading sufferers of Coronavirus. Airlines, railways, and the public transport sector are badly hit due to this coronavirus outbreak. In this paper, we studied the effect of Coronavirus on the various transport sectors all over the world, taking into consideration the worldwide scenario and India’s condition as well. Further, this paper analyses the possible ways and measures regarding how the transport services are dealing with this pandemic.
{"title":"Real World Solutions for Smart Cities Transportation to be Pandemic Ready","authors":"Yusra Amir, S. Deb, M. S. Alam, Y. Rafat, S. Hameed","doi":"10.1109/ICRCICN50933.2020.9296179","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296179","url":null,"abstract":"The COVID-19 is the current widespread health disaster. It has speedily spread all over the world causing a massive impact on the health, environmental, social and economic condition of the total world’s population. Enormous actions undertaken worldwide to minimize the expansion of this deadly contagion by testing at a large scale, quarantining the suspected people, upholding lockdowns and restricting social gatherings. The transportation sector has been one amid the leading sufferers of Coronavirus. Airlines, railways, and the public transport sector are badly hit due to this coronavirus outbreak. In this paper, we studied the effect of Coronavirus on the various transport sectors all over the world, taking into consideration the worldwide scenario and India’s condition as well. Further, this paper analyses the possible ways and measures regarding how the transport services are dealing with this pandemic.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116881168","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 : 2020-11-26DOI: 10.1109/ICRCICN50933.2020.9296197
Susmita Das, Swanirbhar Majumder
Deep learning is an emergent and influential method which is used for feature learning and pattern recognition. We provide a comparison between Computer Aided Diagnosis scheme using Deep Learning Technique and traditional Computer Aided Diagnosis scheme in our paper. In this paper, we have compared several deep neural networks for recognition of pulmonary cancer. In our study, we find that Convolutional neural networks are used for pulmonary cancer detection in most of the cases, as compared to other algorithms in deep learning techniques. In conclusion, we address the few difficulties in the implementation of the systems for pulmonary cancer, then we summarise the advantages and disadvantages of the existing algorithms for diagnosis of pulmonary cancer.
{"title":"Lung Cancer Detection Using Deep Learning Network: A Comparative Analysis","authors":"Susmita Das, Swanirbhar Majumder","doi":"10.1109/ICRCICN50933.2020.9296197","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296197","url":null,"abstract":"Deep learning is an emergent and influential method which is used for feature learning and pattern recognition. We provide a comparison between Computer Aided Diagnosis scheme using Deep Learning Technique and traditional Computer Aided Diagnosis scheme in our paper. In this paper, we have compared several deep neural networks for recognition of pulmonary cancer. In our study, we find that Convolutional neural networks are used for pulmonary cancer detection in most of the cases, as compared to other algorithms in deep learning techniques. In conclusion, we address the few difficulties in the implementation of the systems for pulmonary cancer, then we summarise the advantages and disadvantages of the existing algorithms for diagnosis of pulmonary cancer.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116086854","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 : 2020-11-26DOI: 10.1109/icrcicn50933.2020.9296151
{"title":"ICRCICN 2020 Cover Page","authors":"","doi":"10.1109/icrcicn50933.2020.9296151","DOIUrl":"https://doi.org/10.1109/icrcicn50933.2020.9296151","url":null,"abstract":"","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125581831","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 : 2020-11-26DOI: 10.1109/ICRCICN50933.2020.9296163
Subhankar Ghosh, D. De
Device-to-Device (D2D) communication have paved the way for resource allocation among a large number of wireless devices. A cooperative game theory based resource allocation in D2D communication for 5G wireless network is discussed in this article. Nearby devices combine to form a cluster and exchange data between themselves. Due to the short distance between the devices into the cluster, the amount of data loss between them is much less. In CG-D2D, a utility function and two algorithms are proposed through which spectrum manager allocate the resources between devices in the cluster and allocate the resources among the clusters. In this article, the power consumption, Signal to Interference plus Noise Ratio (SINR) and spectral efficiency (SE) of proposed CG-D2D are calculated and compared it with the existing networks. The proposed CG-D2D reduces the power consumption by approximately 10% – 30% and enhances the SINR and spectral efficiency (SE) by approximately 2% – 8% and 2% – 4% respectively than the existing approaches.
{"title":"CG-D2D: Cooperative Game Theory based Resource optimization for D2D Communication in 5G Wireless Network","authors":"Subhankar Ghosh, D. De","doi":"10.1109/ICRCICN50933.2020.9296163","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296163","url":null,"abstract":"Device-to-Device (D2D) communication have paved the way for resource allocation among a large number of wireless devices. A cooperative game theory based resource allocation in D2D communication for 5G wireless network is discussed in this article. Nearby devices combine to form a cluster and exchange data between themselves. Due to the short distance between the devices into the cluster, the amount of data loss between them is much less. In CG-D2D, a utility function and two algorithms are proposed through which spectrum manager allocate the resources between devices in the cluster and allocate the resources among the clusters. In this article, the power consumption, Signal to Interference plus Noise Ratio (SINR) and spectral efficiency (SE) of proposed CG-D2D are calculated and compared it with the existing networks. The proposed CG-D2D reduces the power consumption by approximately 10% – 30% and enhances the SINR and spectral efficiency (SE) by approximately 2% – 8% and 2% – 4% respectively than the existing approaches.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122932056","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 : 2020-11-26DOI: 10.1109/ICRCICN50933.2020.9296174
G. Taroon, Ayushi Tomar, Chinthakunta Manjunath, M. Balamurugan, Bikramaditya Ghosh, Addapalli V. N. Krishna
Accurate stock price prediction is a significant benefit to the Stock investors. The future Stock value of any company is determined by Stock market prediction. A successful prediction of the stock’s future price could result in a significant profit; Hence investors prefer a precise Stock price prediction. Although there are many different approaches to helps in forecasting stock prices, this paper will briefly look into the deep learning models and compare LSTM model and its variants. The key intention of this study is to propose a model that is best suitable and can be implemented to forecasting trend of stock prices. This paper focuses on binary classification problem, predicting the next-minute price movement of SPDR S&P 500 index The testing experiments performed on the SPDR S&P 500 index reveals that the variants of LSTM models, Slim LSTM1, slim LSTM2, and Slim LSTM3 with less parameters, provide better performance when compared to the Standard LSTM Model.
{"title":"Employing Deep Learning In Intraday Stock Trading","authors":"G. Taroon, Ayushi Tomar, Chinthakunta Manjunath, M. Balamurugan, Bikramaditya Ghosh, Addapalli V. N. Krishna","doi":"10.1109/ICRCICN50933.2020.9296174","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296174","url":null,"abstract":"Accurate stock price prediction is a significant benefit to the Stock investors. The future Stock value of any company is determined by Stock market prediction. A successful prediction of the stock’s future price could result in a significant profit; Hence investors prefer a precise Stock price prediction. Although there are many different approaches to helps in forecasting stock prices, this paper will briefly look into the deep learning models and compare LSTM model and its variants. The key intention of this study is to propose a model that is best suitable and can be implemented to forecasting trend of stock prices. This paper focuses on binary classification problem, predicting the next-minute price movement of SPDR S&P 500 index The testing experiments performed on the SPDR S&P 500 index reveals that the variants of LSTM models, Slim LSTM1, slim LSTM2, and Slim LSTM3 with less parameters, provide better performance when compared to the Standard LSTM Model.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128972693","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 : 2020-11-26DOI: 10.1109/ICRCICN50933.2020.9296177
S. Nagendra Prasad, Subhash Kulkarni, Prasanth Venkatareddy
heterogeneous multicore computational environment are increasingly being used for executing scientific workload. Heterogeneous computational framework aid is reducing energy dissipation for executing real-time data intensive workload by employing Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS). However, reducing energy and improving performance is becoming major constraint in modelling workload scheduling model in heterogeneous computational environment. For building tradeoffs model this work assume that different task will have different execution path, I/O access, memory, active processing, and cache requirement. Considering such assumption this paper present cache aware workload scheduling (CATS) algorithm by minimizing energy dissipation and utilizing cache resource more efficiently. The CATS model achieves much lesser execution time and energy consumption when compared with existing multiobjective based and DVFS-based workload scheduling algorithm.
{"title":"Cache Aware Task Scheduling Algorithm for Heterogeneous Cloud Computing Environment","authors":"S. Nagendra Prasad, Subhash Kulkarni, Prasanth Venkatareddy","doi":"10.1109/ICRCICN50933.2020.9296177","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296177","url":null,"abstract":"heterogeneous multicore computational environment are increasingly being used for executing scientific workload. Heterogeneous computational framework aid is reducing energy dissipation for executing real-time data intensive workload by employing Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS). However, reducing energy and improving performance is becoming major constraint in modelling workload scheduling model in heterogeneous computational environment. For building tradeoffs model this work assume that different task will have different execution path, I/O access, memory, active processing, and cache requirement. Considering such assumption this paper present cache aware workload scheduling (CATS) algorithm by minimizing energy dissipation and utilizing cache resource more efficiently. The CATS model achieves much lesser execution time and energy consumption when compared with existing multiobjective based and DVFS-based workload scheduling algorithm.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121075818","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 : 2020-11-26DOI: 10.1109/ICRCICN50933.2020.9296172
Ali Raya, K. Mariyappn
key exchange primitive is a fundamental pillar in public key cryptography. Diffie-Hellman is an example of a keyexchange protocol, which enables the establishment of shared secret key over an insecure public channel between two parties. Different instantiations of Diffie-Hellman can be found based on the underlying group/set where the computations required for establishing the shared key are carried out. In this paper, three different implementations of Diffie-Hellman protocol will be reviewed and compared: the core Diffie-Hellman (DH) protocol, Elliptic Curve Diffie-Hellman (ECDH) as examples of secure protocols in a pre-quantum world, and Supersingular Isogeny Diffie-Hellman (SIDH) as an example of a quantum-safe protocol for key establishment. The steps of establishing the shared key in each instantiation will be shown, security analysis will be discussed for the different implementations in both pre- and post-quantum world, and finally, a brief comparison of the three instantiations will be drawn.
{"title":"Diffie-Hellman Instantiations in Pre- and Post- Quantum World: A Review Paper","authors":"Ali Raya, K. Mariyappn","doi":"10.1109/ICRCICN50933.2020.9296172","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296172","url":null,"abstract":"key exchange primitive is a fundamental pillar in public key cryptography. Diffie-Hellman is an example of a keyexchange protocol, which enables the establishment of shared secret key over an insecure public channel between two parties. Different instantiations of Diffie-Hellman can be found based on the underlying group/set where the computations required for establishing the shared key are carried out. In this paper, three different implementations of Diffie-Hellman protocol will be reviewed and compared: the core Diffie-Hellman (DH) protocol, Elliptic Curve Diffie-Hellman (ECDH) as examples of secure protocols in a pre-quantum world, and Supersingular Isogeny Diffie-Hellman (SIDH) as an example of a quantum-safe protocol for key establishment. The steps of establishing the shared key in each instantiation will be shown, security analysis will be discussed for the different implementations in both pre- and post-quantum world, and finally, a brief comparison of the three instantiations will be drawn.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130062777","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 : 2020-11-26DOI: 10.1109/ICRCICN50933.2020.9296181
Loreen Mahmoud, R. Praveen
Nowadays, the networks attacks became very sophisticated and hard to be recognized, The traditional types of intrusion detection systems became inefficient in predicting new types of attacks. As the IDS is an important factor in securing the network in the real time, many new effective IDS approaches have been proposed. In this paper, we intend to discuss different Artificial Neural Networks based IDS approaches, also we are going to categorize them in four categories (normal ANN, DNN, CNN, RNN) and make a comparison between them depending on different performance parameters (accuracy, FNR, FPR, training time, epochs and the learning rate) and other factors like the network structure, the classification type, the used dataset. At the end of the survey, we will mention the merits and demerits of each approach and suggest some enhancements to avoid the noticed drawbacks.
如今，网络攻击变得越来越复杂和难以识别，传统的入侵检测系统在预测新的攻击类型方面变得低效。由于入侵检测是保证网络实时安全的重要因素，人们提出了许多新的有效的入侵检测方法。在本文中，我们打算讨论不同的基于人工神经网络的IDS方法，并将它们分为四类(normal ANN, DNN, CNN, RNN)，并根据不同的性能参数(准确率，FNR, FPR，训练时间，epoch和学习率)以及网络结构，分类类型，使用的数据集等其他因素对它们进行比较。在调查的最后，我们将提到每种方法的优点和缺点，并提出一些改进建议，以避免注意到的缺点。
{"title":"Artificial Neural Networks for detecting Intrusions: A survey","authors":"Loreen Mahmoud, R. Praveen","doi":"10.1109/ICRCICN50933.2020.9296181","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296181","url":null,"abstract":"Nowadays, the networks attacks became very sophisticated and hard to be recognized, The traditional types of intrusion detection systems became inefficient in predicting new types of attacks. As the IDS is an important factor in securing the network in the real time, many new effective IDS approaches have been proposed. In this paper, we intend to discuss different Artificial Neural Networks based IDS approaches, also we are going to categorize them in four categories (normal ANN, DNN, CNN, RNN) and make a comparison between them depending on different performance parameters (accuracy, FNR, FPR, training time, epochs and the learning rate) and other factors like the network structure, the classification type, the used dataset. At the end of the survey, we will mention the merits and demerits of each approach and suggest some enhancements to avoid the noticed drawbacks.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125550987","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 : 2020-11-26DOI: 10.1109/icrcicn50933.2020.9296149
{"title":"ICRCICN 2020 Index","authors":"","doi":"10.1109/icrcicn50933.2020.9296149","DOIUrl":"https://doi.org/10.1109/icrcicn50933.2020.9296149","url":null,"abstract":"","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133965824","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 : 2020-11-26DOI: 10.1109/ICRCICN50933.2020.9296178
K. Sudar, P. Deepalakshmi, P. Nagaraj, V. Muneeswaran
With the help of Internet, there are tremendous innovations and developments in technologies. Most of the business organizations are forced to use the flexible and modern network technologies for business processing. This opens the door for cyber criminals to initiate cyberattacks to disrupt the business process. There are lot of reasons behind these cyberattacks like stealing login credentials, financial information and confidential information, disrupting the services available to legitimate users and to gain unauthorized access. To defend against cyberattacks, several mechanisms were proposed by researchers. In this paper, we analyzed about different types of cyberattacks and detection mechanisms to defend against those attacks. We also analyzed about different datasets and evaluation metrics used to evaluate the performance of every detection mechanism.
{"title":"Analysis of Cyberattacks and its Detection Mechanisms","authors":"K. Sudar, P. Deepalakshmi, P. Nagaraj, V. Muneeswaran","doi":"10.1109/ICRCICN50933.2020.9296178","DOIUrl":"https://doi.org/10.1109/ICRCICN50933.2020.9296178","url":null,"abstract":"With the help of Internet, there are tremendous innovations and developments in technologies. Most of the business organizations are forced to use the flexible and modern network technologies for business processing. This opens the door for cyber criminals to initiate cyberattacks to disrupt the business process. There are lot of reasons behind these cyberattacks like stealing login credentials, financial information and confidential information, disrupting the services available to legitimate users and to gain unauthorized access. To defend against cyberattacks, several mechanisms were proposed by researchers. In this paper, we analyzed about different types of cyberattacks and detection mechanisms to defend against those attacks. We also analyzed about different datasets and evaluation metrics used to evaluate the performance of every detection mechanism.","PeriodicalId":138966,"journal":{"name":"2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132685227","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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