Pub Date : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358382
Kamlesh Lakhwani, S. Bhargava, D. Somwanshi, Ruchi Doshi, K. Hiran
Numerous coronaviruses are capable of transmitting interspecies. In recent years, transmission of coronavirus created a panic situation in the whole world. Therefore it is very important to infer the potential host of coro- navirus. In this research work nineteen parameters computed from the spike genes of coronavirus has been analysed to infer the potential host of coron- avirus. An enhanced multilayer neural network approach is proposed to analyse the data. The proposed model is compared with the other exiting statistical predictors like decision tree predictor, Support vector machine predictor and PNN predictor. All the model shown the higher accuracy such as 82.051 % by SVM predictor, 85.256% by PNN predictor,94.872% by decision tree predictor, and the highest accuracy 95.% is shown by proposed Multilayer Perceptron Predictor.
{"title":"An Enhanced Approach to Infer Potential Host of Coronavirus by Analyzing Its Spike Genes Using Multilayer Artificial Neural Network","authors":"Kamlesh Lakhwani, S. Bhargava, D. Somwanshi, Ruchi Doshi, K. Hiran","doi":"10.1109/ICRAIE51050.2020.9358382","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358382","url":null,"abstract":"Numerous coronaviruses are capable of transmitting interspecies. In recent years, transmission of coronavirus created a panic situation in the whole world. Therefore it is very important to infer the potential host of coro- navirus. In this research work nineteen parameters computed from the spike genes of coronavirus has been analysed to infer the potential host of coron- avirus. An enhanced multilayer neural network approach is proposed to analyse the data. The proposed model is compared with the other exiting statistical predictors like decision tree predictor, Support vector machine predictor and PNN predictor. All the model shown the higher accuracy such as 82.051 % by SVM predictor, 85.256% by PNN predictor,94.872% by decision tree predictor, and the highest accuracy 95.% is shown by proposed Multilayer Perceptron Predictor.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127851835","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358304
A. Parihar, Shivam Singhal, Srishti Nanduri, Y. Raghav
Images clicked under low and non-uniform light conditions are visually unpleasant and lose details. Low-light images also impact the performance and thus reduce the effectiveness of various computer vision tasks. Thus numerous methods have been put forward in the past to upgrade the quality of low-light images. The innovations in the field of deep learning have paved the way for the application of neural networks to the task of enhancing low-light images. In this paper, we offer a comparative analysis of various approaches using deep learning for enhancing low-light images. We explore retinex based methods including KinD and RDGAN, and other non-retinex based methods including LLNet, GLAD Net, and Zero-DCE. We measure the effectiveness of these methods on various datasets and provide their advantages and disadvantages.
{"title":"A Comparative Analysis of Deep Learning based Approaches for Low-Light Image Enhancement","authors":"A. Parihar, Shivam Singhal, Srishti Nanduri, Y. Raghav","doi":"10.1109/ICRAIE51050.2020.9358304","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358304","url":null,"abstract":"Images clicked under low and non-uniform light conditions are visually unpleasant and lose details. Low-light images also impact the performance and thus reduce the effectiveness of various computer vision tasks. Thus numerous methods have been put forward in the past to upgrade the quality of low-light images. The innovations in the field of deep learning have paved the way for the application of neural networks to the task of enhancing low-light images. In this paper, we offer a comparative analysis of various approaches using deep learning for enhancing low-light images. We explore retinex based methods including KinD and RDGAN, and other non-retinex based methods including LLNet, GLAD Net, and Zero-DCE. We measure the effectiveness of these methods on various datasets and provide their advantages and disadvantages.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677526","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358371
B. Bukhari, G. M. Rather
With the change in the size of ground, the current distribution on the ground varies which in turn affects the impedance and radiation properties of the patch antenna. This paper presents the parametric study of the ground plane size on a small coaxial probe fed rectangular microstrip antenna at 2.4 GHz. The design and simulation of the microstrip antenna has been done using CST Microwave Studio simulator. The effects of ground plane size on directivity, return loss, radiation pattern, gain and radiation efficiency of patch antenna were investigated for the optimal antenna performance and same are presented here.
{"title":"Ground Plane Effects on the Performance of a Rectangular Microstrip Patch Antenna: A Study","authors":"B. Bukhari, G. M. Rather","doi":"10.1109/ICRAIE51050.2020.9358371","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358371","url":null,"abstract":"With the change in the size of ground, the current distribution on the ground varies which in turn affects the impedance and radiation properties of the patch antenna. This paper presents the parametric study of the ground plane size on a small coaxial probe fed rectangular microstrip antenna at 2.4 GHz. The design and simulation of the microstrip antenna has been done using CST Microwave Studio simulator. The effects of ground plane size on directivity, return loss, radiation pattern, gain and radiation efficiency of patch antenna were investigated for the optimal antenna performance and same are presented here.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125422135","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358323
Maged M. Eljazzar, E. Hemayed
The electric Load is affected by various factors such as economic, social, and meteorological factors. This classification simplifies the studying of the correlation between these factors. It also provides a useful reference for researchers to pick up the best elements for their case according to the forecasting period; short, medium, or long term forecasting. This work introduces a comprehensive study of the factors that affect load forecasting in short, medium, and long-term load forecasting. Correlational model is applied to assess the relationship among parameters in different time horizons. The result provides two critical things to notes. Firstly, there are direct and indirect effects for some parameters based on the timeframe, and secondly, there is a significant accumulative effect of some parameters.
{"title":"Impact of Economic, Social and Meteorological Factors on Load Forecasting in Different Timeframes-A Survey","authors":"Maged M. Eljazzar, E. Hemayed","doi":"10.1109/ICRAIE51050.2020.9358323","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358323","url":null,"abstract":"The electric Load is affected by various factors such as economic, social, and meteorological factors. This classification simplifies the studying of the correlation between these factors. It also provides a useful reference for researchers to pick up the best elements for their case according to the forecasting period; short, medium, or long term forecasting. This work introduces a comprehensive study of the factors that affect load forecasting in short, medium, and long-term load forecasting. Correlational model is applied to assess the relationship among parameters in different time horizons. The result provides two critical things to notes. Firstly, there are direct and indirect effects for some parameters based on the timeframe, and secondly, there is a significant accumulative effect of some parameters.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127475907","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358365
L. Chandrashekar, A. Sreedevi, Chandan M Shekar, M. Raj, N. Kumar, R. Vinay
Image registration in field of medical images is highly recommended for detection brain tumor related diseases. With Deep Learning, features are learnt automatically and it allows the system to quickly iterate complex functions. The paper proposes an image registration methodology for Magnetic Resonance Imaging and Computed Tomography using Deep learning architecture - Convolutional Neural Network. This can identify the orientation of the images. The paper highlights the choice of activation functions for the classifier, trained with 4000 CT and MRI images grouped in 10 classes with angle orientation of 0 - 20 degrees. Experiments indicate the highest accuracy of 95.4 % with clipped Relu activation function, for the proposed architecture trained with 55 epochs. ADAM optimizer provides the highest validation accuracy of 91.28%. A confusion matrix is generated to indicate the classified and misclassified images along with precision and recall values.
{"title":"Angle Classifier for Registration of MRI and CT Brain Images using Deep Learning","authors":"L. Chandrashekar, A. Sreedevi, Chandan M Shekar, M. Raj, N. Kumar, R. Vinay","doi":"10.1109/ICRAIE51050.2020.9358365","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358365","url":null,"abstract":"Image registration in field of medical images is highly recommended for detection brain tumor related diseases. With Deep Learning, features are learnt automatically and it allows the system to quickly iterate complex functions. The paper proposes an image registration methodology for Magnetic Resonance Imaging and Computed Tomography using Deep learning architecture - Convolutional Neural Network. This can identify the orientation of the images. The paper highlights the choice of activation functions for the classifier, trained with 4000 CT and MRI images grouped in 10 classes with angle orientation of 0 - 20 degrees. Experiments indicate the highest accuracy of 95.4 % with clipped Relu activation function, for the proposed architecture trained with 55 epochs. ADAM optimizer provides the highest validation accuracy of 91.28%. A confusion matrix is generated to indicate the classified and misclassified images along with precision and recall values.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123658875","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358305
K. Kaur, Inderjit Singh Dhanoa, P. Bhambri
The expansion of cloud infrastructure follows with increase in number of data centers hosting number of computing nodes and then, it becomes the reason for huge amount of energy consumption across the world. However, benefits of cloud computing industry with its low-price and high productivity keep diverting the attention of organizations from environmental mess and high energy cost incurred by the data centers. Therefore, it becomes very urgent to curtail the increase in requirement of energy for cloud service providers with the provision of sufficient quality of service to end users. The best way to achieve the balance between energy usage and quality of service is workload aware energy efficient Virtual Machine (VM) consolidation. The various parameters are managed to strike the trade-off between energy consumption and cloud services. This paper presents the optimized PSO-EFA algorithm for energy efficiency with workload management in terms of number of migrations and number of systems shut down during migration process of consolidation. This study paved the way forward for energy efficient cloud environment during migration process. The simulation conducted in constrained environment indicated that workload variation has significant impact on different energy consumption allied parameters. The PSO-EFA algorithm outperformed existing base algorithm for energy consumption and other parameters. The proposed algorithm worked in sync with sustainability efforts.
{"title":"Optimized PSO-EFA Algorithm for Energy Efficient Virtual Machine Migrations","authors":"K. Kaur, Inderjit Singh Dhanoa, P. Bhambri","doi":"10.1109/ICRAIE51050.2020.9358305","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358305","url":null,"abstract":"The expansion of cloud infrastructure follows with increase in number of data centers hosting number of computing nodes and then, it becomes the reason for huge amount of energy consumption across the world. However, benefits of cloud computing industry with its low-price and high productivity keep diverting the attention of organizations from environmental mess and high energy cost incurred by the data centers. Therefore, it becomes very urgent to curtail the increase in requirement of energy for cloud service providers with the provision of sufficient quality of service to end users. The best way to achieve the balance between energy usage and quality of service is workload aware energy efficient Virtual Machine (VM) consolidation. The various parameters are managed to strike the trade-off between energy consumption and cloud services. This paper presents the optimized PSO-EFA algorithm for energy efficiency with workload management in terms of number of migrations and number of systems shut down during migration process of consolidation. This study paved the way forward for energy efficient cloud environment during migration process. The simulation conducted in constrained environment indicated that workload variation has significant impact on different energy consumption allied parameters. The PSO-EFA algorithm outperformed existing base algorithm for energy consumption and other parameters. The proposed algorithm worked in sync with sustainability efforts.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125660239","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358338
Zainab H. Al-Araji, N. Swaikat, Hassan Souikat, V. Korneeva, A. Samofalova
Modern electronic units during operation are subjected to various types of stress, such as vibration and shock. Vibration damages the printed circuit board due to stress. We proposed a methodology that differs from the traditional methods that did not address the relationship between stress and fixation methods. Previous studies on predicting the stress and fatigue life of the structure have been improved using the theory of linear cumulative damage and the three syllables proposed by Steinberg which did not take into account the type of installation and its effects on stress distribution on PCB. We have shown a close relationship between stress and fixing methods that It was not previously searched for. The methodology of reverse engineering in the design using modelling by Creo parametric program has tested four limit conditions to determine which method of fixation of the PCB with the least stress and determine the fatigue life through mathematical equations, this is before the installation process, thus reducing the cost and time.
{"title":"The New Way of Estimating the PCB's Lifetime of Fatigue using the Principle of Linear Accumulated Damage in Various Boundary Condition","authors":"Zainab H. Al-Araji, N. Swaikat, Hassan Souikat, V. Korneeva, A. Samofalova","doi":"10.1109/ICRAIE51050.2020.9358338","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358338","url":null,"abstract":"Modern electronic units during operation are subjected to various types of stress, such as vibration and shock. Vibration damages the printed circuit board due to stress. We proposed a methodology that differs from the traditional methods that did not address the relationship between stress and fixation methods. Previous studies on predicting the stress and fatigue life of the structure have been improved using the theory of linear cumulative damage and the three syllables proposed by Steinberg which did not take into account the type of installation and its effects on stress distribution on PCB. We have shown a close relationship between stress and fixing methods that It was not previously searched for. The methodology of reverse engineering in the design using modelling by Creo parametric program has tested four limit conditions to determine which method of fixation of the PCB with the least stress and determine the fatigue life through mathematical equations, this is before the installation process, thus reducing the cost and time.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128235628","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358291
Poonam Dhabai, N. Tiwari
In an integrated power system with solar generation, congestion in transmission lines and its management is a pivotal task. To deal and manage the congestion, one can adopt technical or commercial aspect. Computation of Locational Marginal Pricing (LMP) gives commercial perspective and can be commercial solution for congestion management. This work presents a strategy for assessment of LMP in the presence of solar generation in a restructured power system. To analyze the effect of uncertainty on marginal pricing, IEEE 30-bus framework is thought of. Real time based solar insolation from 1stJanuary 2014 to 31st December 2018 data points are investigated, the distribution followed by data is normal. Equivalent generator output values, over a period of 4 years are considered. The data is actual historical records obtained from IMD department, Pune district, India.
{"title":"Computation of Locational Marginal Pricing in the Presence of Uncertainty of Solar Generation","authors":"Poonam Dhabai, N. Tiwari","doi":"10.1109/ICRAIE51050.2020.9358291","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358291","url":null,"abstract":"In an integrated power system with solar generation, congestion in transmission lines and its management is a pivotal task. To deal and manage the congestion, one can adopt technical or commercial aspect. Computation of Locational Marginal Pricing (LMP) gives commercial perspective and can be commercial solution for congestion management. This work presents a strategy for assessment of LMP in the presence of solar generation in a restructured power system. To analyze the effect of uncertainty on marginal pricing, IEEE 30-bus framework is thought of. Real time based solar insolation from 1stJanuary 2014 to 31st December 2018 data points are investigated, the distribution followed by data is normal. Equivalent generator output values, over a period of 4 years are considered. The data is actual historical records obtained from IMD department, Pune district, India.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134007809","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358310
Sujeendran Menon, Pawel Zarzycki, M. Ganzha, M. Paprzycki
Machine learning frameworks, like Tensorflow and PyTorch, use GPU hardware acceleration to deliver the needed performance. Since GPUs require a lot of power (and space) to operate, typical use cases involve high-performance servers, with the final deployment available as a cloud service. To address limitations of this approach, AI Accelerators have been proposed. In this context, we have designed and implemented a library of neural network algorithms, to efficiently run on “edge devices”, with AI Accelerators. Moreover, a unified interface has been provided, to allow easy experimentation with various neural networks applied to the same dataset. Here, let us stress that we do not propose new algorithms, but port known ones to, resource restricted, edge devices. The context is provided by a speech synthesis application for edge devices that is deployed on an NVIDIA Jetson Nano. This application is to be used by social robots for real-time off-cloud text-to-speech processing.
{"title":"Development of a Neural Network Library for Resource Constrained Speech Synthesis","authors":"Sujeendran Menon, Pawel Zarzycki, M. Ganzha, M. Paprzycki","doi":"10.1109/ICRAIE51050.2020.9358310","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358310","url":null,"abstract":"Machine learning frameworks, like Tensorflow and PyTorch, use GPU hardware acceleration to deliver the needed performance. Since GPUs require a lot of power (and space) to operate, typical use cases involve high-performance servers, with the final deployment available as a cloud service. To address limitations of this approach, AI Accelerators have been proposed. In this context, we have designed and implemented a library of neural network algorithms, to efficiently run on “edge devices”, with AI Accelerators. Moreover, a unified interface has been provided, to allow easy experimentation with various neural networks applied to the same dataset. Here, let us stress that we do not propose new algorithms, but port known ones to, resource restricted, edge devices. The context is provided by a speech synthesis application for edge devices that is deployed on an NVIDIA Jetson Nano. This application is to be used by social robots for real-time off-cloud text-to-speech processing.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134412373","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 : 2020-12-01DOI: 10.1109/ICRAIE51050.2020.9358289
P. Deshmukh, S. Mohod
A new privilege of biometrics help to reduce the stress of user, Which comes along with the traditional access methods of passwords and token. Using the biometrics limitations and weaknesses can be knocked out. However, biometrics has raise privacy risks and new security since they cannot be easily revoked. Due to the spoofing attack on biometrics. Thus, to protect biometric traits against spoofing attack a multimodal biometric jammer scheme for the security enhancement have been developed and suggested in this paper. Firstly, we analyze why the multimodal biometric system have attracted attention for high security-demanding schemes. Secondly, security of biometric system is increasing and prevented it from spoofing attack developing a machine learning system model. We show that these machine learning algorithms perform pre-processing of biometric traits images. Further we analyze user identification with the increase precision and reliability using biometric features. Where feature extraction of each one trait of biometric is done and then all features are concatenation to get a single feature. With the aid of machine learning classifier using extracted features the algorithm predict the result of the system.
{"title":"Biometric Jammer: A Security Enhancement Scheme using SVM Classifier","authors":"P. Deshmukh, S. Mohod","doi":"10.1109/ICRAIE51050.2020.9358289","DOIUrl":"https://doi.org/10.1109/ICRAIE51050.2020.9358289","url":null,"abstract":"A new privilege of biometrics help to reduce the stress of user, Which comes along with the traditional access methods of passwords and token. Using the biometrics limitations and weaknesses can be knocked out. However, biometrics has raise privacy risks and new security since they cannot be easily revoked. Due to the spoofing attack on biometrics. Thus, to protect biometric traits against spoofing attack a multimodal biometric jammer scheme for the security enhancement have been developed and suggested in this paper. Firstly, we analyze why the multimodal biometric system have attracted attention for high security-demanding schemes. Secondly, security of biometric system is increasing and prevented it from spoofing attack developing a machine learning system model. We show that these machine learning algorithms perform pre-processing of biometric traits images. Further we analyze user identification with the increase precision and reliability using biometric features. Where feature extraction of each one trait of biometric is done and then all features are concatenation to get a single feature. With the aid of machine learning classifier using extracted features the algorithm predict the result of the system.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134149792","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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