Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587763
Bhagyesh Desai, Er. Nitika Kapoor
Software product refers to the software which is developed for a specific requirement. Simultaneously, engineering deals with the development of product using explicit technical fundamentals and methods. The software defect can be predicted in diverse stages in which data is utilized as input and pre-processed, attributes are extracted, and classification is performed. This research work makes the implementation of several classifiers in order to predict the software defect. These classifiers are GNB (gaussian naive bayes), Bernoulli NB, RF (random forest) and MLP (multilayer perceptron) which are employed with the objective of forecasting the software defect. The performance of the software defect is enhanced by developing an ensemble classifier. In the introduced ensemble classifier, the PCA (Principal Component Analysis) algorithm is integrated with class balancing. Python is executed to implement the introduced model. Diverse metrics are considered to analyze the results concerning accuracy, precision and recall.
{"title":"Hybrid Classification Approach for Software Defect Prediction with Feature Reduction and Clustering","authors":"Bhagyesh Desai, Er. Nitika Kapoor","doi":"10.1109/GCAT52182.2021.9587763","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587763","url":null,"abstract":"Software product refers to the software which is developed for a specific requirement. Simultaneously, engineering deals with the development of product using explicit technical fundamentals and methods. The software defect can be predicted in diverse stages in which data is utilized as input and pre-processed, attributes are extracted, and classification is performed. This research work makes the implementation of several classifiers in order to predict the software defect. These classifiers are GNB (gaussian naive bayes), Bernoulli NB, RF (random forest) and MLP (multilayer perceptron) which are employed with the objective of forecasting the software defect. The performance of the software defect is enhanced by developing an ensemble classifier. In the introduced ensemble classifier, the PCA (Principal Component Analysis) algorithm is integrated with class balancing. Python is executed to implement the introduced model. Diverse metrics are considered to analyze the results concerning accuracy, precision and recall.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"53 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127988694","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587868
Y. Narayan
Brain-Computer Interfacing (BCI) is the latest research trend for developing the rehabilitation robotic system based on electroencephalogram (EEG) signals to make human life more comfortable. In this context, a framework was suggested to critically compare the performance of two different classification methods so that the performance of EEG signals could be improved in conjunction with Common Spatial Pattern (CSP), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) approach. Further, the performance of Multilayer Perceptron Classifier (MLP) and Distinction Sensitive Learning Vector Quantization (DSLVQ) was compared with each other on a single feature accuracy scale. EEG dataset was recorded from ten healthy human subjects followed by band-pass Butterworth filtering for de-noising and ocular artifact rejection by ICA. The CSP was utilized for generating the discriminating features followed by PCA dimension reduction. After performing the all desired preprocessing steps, eight features were extracted to form the feature vector and classified by MLP and DSLVQ classifiers. The best classification accuracy of 98.75% was achieved with ten healthy subjects’ EEG datasets by exploiting the MLP method followed by the DSLVQ classifier. This study reveals that MLP classifier with PCA, CSP and ICA methods produced the best performance and able to enhance the practical implementation of various assistive robotic devices.
{"title":"Analysis of MLP and DSLVQ Classifiers for EEG Signals Based Movements Identification","authors":"Y. Narayan","doi":"10.1109/GCAT52182.2021.9587868","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587868","url":null,"abstract":"Brain-Computer Interfacing (BCI) is the latest research trend for developing the rehabilitation robotic system based on electroencephalogram (EEG) signals to make human life more comfortable. In this context, a framework was suggested to critically compare the performance of two different classification methods so that the performance of EEG signals could be improved in conjunction with Common Spatial Pattern (CSP), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) approach. Further, the performance of Multilayer Perceptron Classifier (MLP) and Distinction Sensitive Learning Vector Quantization (DSLVQ) was compared with each other on a single feature accuracy scale. EEG dataset was recorded from ten healthy human subjects followed by band-pass Butterworth filtering for de-noising and ocular artifact rejection by ICA. The CSP was utilized for generating the discriminating features followed by PCA dimension reduction. After performing the all desired preprocessing steps, eight features were extracted to form the feature vector and classified by MLP and DSLVQ classifiers. The best classification accuracy of 98.75% was achieved with ten healthy subjects’ EEG datasets by exploiting the MLP method followed by the DSLVQ classifier. This study reveals that MLP classifier with PCA, CSP and ICA methods produced the best performance and able to enhance the practical implementation of various assistive robotic devices.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128735604","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587461
Supriya Katwe, N. Iyer, Moin Khan, Mathew Peters, Mahesh S. Mahale
The fundamental task in an autonomous vehicle navigation system is localization from the available sensor measurements. GPS in the vehicles locates it with error of 1 to 10 meters so localization process should be performed to avoid fatal accidents. The realization of algorithms to estimate our vehicle’s position precisely is Localization. Odometry, Kalman Filter, Particle Filter and SLAM(Simultaneous Localization And Mapping) are the techniques used in an autonomous vehicle to localize itself in the map. Among these the particle filter is widely employed in the localization of autonomous vehicles as it provides accurate position of the vehicle in the environment. This paper aims at a localization technique for autonomous vehicles or robots using Particle Filter algorithm. The position estimator is implemented using the GPS and IMU sensor measurements. The map contains specific landmarks identified such as buildings and poles which assist the vehicle to know its position accurately by matching the distance between them in the particle filtering process. The results show that this algorithm can deliver accurate vehicle positioning even in erroneous GPS data.
{"title":"Particle Filter Based Localization of Autonomous Vehicle","authors":"Supriya Katwe, N. Iyer, Moin Khan, Mathew Peters, Mahesh S. Mahale","doi":"10.1109/GCAT52182.2021.9587461","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587461","url":null,"abstract":"The fundamental task in an autonomous vehicle navigation system is localization from the available sensor measurements. GPS in the vehicles locates it with error of 1 to 10 meters so localization process should be performed to avoid fatal accidents. The realization of algorithms to estimate our vehicle’s position precisely is Localization. Odometry, Kalman Filter, Particle Filter and SLAM(Simultaneous Localization And Mapping) are the techniques used in an autonomous vehicle to localize itself in the map. Among these the particle filter is widely employed in the localization of autonomous vehicles as it provides accurate position of the vehicle in the environment. This paper aims at a localization technique for autonomous vehicles or robots using Particle Filter algorithm. The position estimator is implemented using the GPS and IMU sensor measurements. The map contains specific landmarks identified such as buildings and poles which assist the vehicle to know its position accurately by matching the distance between them in the particle filtering process. The results show that this algorithm can deliver accurate vehicle positioning even in erroneous GPS data.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122368965","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587638
Parth Mahajan, Aniket Gupta
Recognition of human activity plays an important role in computer-human interaction, surveillance, reconnaissance, robotics for humans, and understanding interpersonal behaviour relationships. These activities can be recorded as a sequence of still images but only using vision to solve the HAR poses a major task due to problems like scale variation, wide change, in contrast, lighting, viewpoint and occlusions. Thus to address this our work is concentrated on developing and training two deep learning pipelines one Spatiotemporal based and the other being skeletal based on publicly available human activity classification datasets. Moreover, we merge the two pipelines using late fusion and provide a comparison between the three with the existing state of the art algorithms for various activities in the dataset. Finally, we present the future work for the same problem.
{"title":"Depth And Skeleton Based View-invariant Human Action Recognition","authors":"Parth Mahajan, Aniket Gupta","doi":"10.1109/GCAT52182.2021.9587638","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587638","url":null,"abstract":"Recognition of human activity plays an important role in computer-human interaction, surveillance, reconnaissance, robotics for humans, and understanding interpersonal behaviour relationships. These activities can be recorded as a sequence of still images but only using vision to solve the HAR poses a major task due to problems like scale variation, wide change, in contrast, lighting, viewpoint and occlusions. Thus to address this our work is concentrated on developing and training two deep learning pipelines one Spatiotemporal based and the other being skeletal based on publicly available human activity classification datasets. Moreover, we merge the two pipelines using late fusion and provide a comparison between the three with the existing state of the art algorithms for various activities in the dataset. Finally, we present the future work for the same problem.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133892978","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587764
Eeda Srinavya, Maddula Bhaswitha, S. Vineeth, B. K. Priya
Every year lot of children are passing away due to hyperthermia and coronary heart strokes. This is happening because the children are left inside the car unknowingly. Many incidents of such cases are increasing rapidly in the past few decades. These incidents are recognized as the automobile injuries and for this a research has been done to know more about the fat situations of the surroundings of such instances. By the research it is known that there are two elements which made the kids more liable to hyperthermia when compared to adults. A systematic rationalization about how this can be appeared that the children are left unknowingly by their parents in the vehicle can be identified with working memory, it builds up the pressure obstruction and impends to a particular interest. In past two years, 16 children of these cases in Italy and 53 children of these cases in US of infant hyperthermia because of abandonment in vehicles were perceived. These discoveries propose that instructive bundles and writing for guardians concerning auto insurance should incorporate such data about these threats of the heart stress, in fact such actions are unknowingly happened and not intentionally done. In triumph over these issues a prototype has been proposed by means of the child safety alert system.
{"title":"Implementation of Child Safety Alert System in Automobiles","authors":"Eeda Srinavya, Maddula Bhaswitha, S. Vineeth, B. K. Priya","doi":"10.1109/GCAT52182.2021.9587764","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587764","url":null,"abstract":"Every year lot of children are passing away due to hyperthermia and coronary heart strokes. This is happening because the children are left inside the car unknowingly. Many incidents of such cases are increasing rapidly in the past few decades. These incidents are recognized as the automobile injuries and for this a research has been done to know more about the fat situations of the surroundings of such instances. By the research it is known that there are two elements which made the kids more liable to hyperthermia when compared to adults. A systematic rationalization about how this can be appeared that the children are left unknowingly by their parents in the vehicle can be identified with working memory, it builds up the pressure obstruction and impends to a particular interest. In past two years, 16 children of these cases in Italy and 53 children of these cases in US of infant hyperthermia because of abandonment in vehicles were perceived. These discoveries propose that instructive bundles and writing for guardians concerning auto insurance should incorporate such data about these threats of the heart stress, in fact such actions are unknowingly happened and not intentionally done. In triumph over these issues a prototype has been proposed by means of the child safety alert system.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131000283","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587850
Alankar Uniyal, Ayush Patel, Ritesh Dhanare
with the increasing integration of automobiles in our daily lives, the number of four-wheelers on the road has seen a substantial jump in the tally. Furthermore, the number of drivers has also increased. Moreover, people nowadays have a slightly higher chance to opt for a taxi for daily commute. With this statistic, a coinciding fact that the number of Parkinson’s Disease cases have also increased cannot be overlooked. Also, the advancement in the technology of machine learning has enabled us to accurately detect Parkinson’s Disease with unorthodox testing techniques like voice analysis. With these things in mind, we have attempted to use machine learning to predict whether a person has Parkinson’s disease or not using their voice samples whilst designing the model to assign higher weights to features that help accurately classify the voice sample. For Example, pitch being a critical factor to determine if the person is showing an excited emotional state. Once the model reaches the desired generalization ability, it can be integrated into the recruiting process of organizations like uber.
{"title":"Parkinson’s Disease Predictor via Voice Analysis","authors":"Alankar Uniyal, Ayush Patel, Ritesh Dhanare","doi":"10.1109/GCAT52182.2021.9587850","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587850","url":null,"abstract":"with the increasing integration of automobiles in our daily lives, the number of four-wheelers on the road has seen a substantial jump in the tally. Furthermore, the number of drivers has also increased. Moreover, people nowadays have a slightly higher chance to opt for a taxi for daily commute. With this statistic, a coinciding fact that the number of Parkinson’s Disease cases have also increased cannot be overlooked. Also, the advancement in the technology of machine learning has enabled us to accurately detect Parkinson’s Disease with unorthodox testing techniques like voice analysis. With these things in mind, we have attempted to use machine learning to predict whether a person has Parkinson’s disease or not using their voice samples whilst designing the model to assign higher weights to features that help accurately classify the voice sample. For Example, pitch being a critical factor to determine if the person is showing an excited emotional state. Once the model reaches the desired generalization ability, it can be integrated into the recruiting process of organizations like uber.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134288480","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587801
I.T Shruthi, Shreelekha Panchal, Sarita Uniyal, Dr. Shashidhar Tantry
The schematic of class-AB yield stage with BJT, CMOS, BiCMOS is carried out in cadence virtuoso simulator. Every transistor size in the operational amp is designed, validated and BiCMOS operated at supply voltage of 3.3V. The proposed amplifier circuit utilizes a class-AB output stage comprising of PMOS and NMOS transistors along with NPN an PNP push pull circuit is made use. The BiCMOS circuit is made use to achieve advantage of CMOS as well as bipolar. Then, at that point Cascode amplifier stage-based op amp using CMOS Class-AB output and Cascode amplifier stage-based op amp using BiCMOS Class-AB output are compared.
{"title":"A High Gain, Low Power Operational Amplifier utilizing BiCMOS Class AB Output Stage","authors":"I.T Shruthi, Shreelekha Panchal, Sarita Uniyal, Dr. Shashidhar Tantry","doi":"10.1109/GCAT52182.2021.9587801","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587801","url":null,"abstract":"The schematic of class-AB yield stage with BJT, CMOS, BiCMOS is carried out in cadence virtuoso simulator. Every transistor size in the operational amp is designed, validated and BiCMOS operated at supply voltage of 3.3V. The proposed amplifier circuit utilizes a class-AB output stage comprising of PMOS and NMOS transistors along with NPN an PNP push pull circuit is made use. The BiCMOS circuit is made use to achieve advantage of CMOS as well as bipolar. Then, at that point Cascode amplifier stage-based op amp using CMOS Class-AB output and Cascode amplifier stage-based op amp using BiCMOS Class-AB output are compared.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134553035","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587498
N. Raj, M. Suri, S. K.
Electric Vehicles (EV) are gaining popularity from the transportation sector, as it causes less harm to the environment. The battery inside the EV can be refilled using battery charging or battery swapping. As battery swapping method is found to be advantageous over battery charging, Battery Swapping Stations (BSS) is presently the hot topic of research. Forecasting of EV arrivals helps in optimal planning of BSS. Back Propagation Neural Network (BPNN) is frequently used in forecasting. BPNN trained with traditional algorithms such as Levenberg Marquardt (LM) gets stuck at the local optima. This problem can be overcomed using metaheuristic algorithms such as Genetic Algorithm (GA). Thus, in this present work a comparative study on forecasting the EV arrivals at BSS is carried out using LM-BPNN and GA-BPNN. The two models have been simulated using MATLAB/Simulink environment and their performance is analysed using metrics such as Mean Square Error (MSE) and simulation time.
{"title":"Forecasting of EV Arrivals at Battery Swapping Station using GA-BPNN","authors":"N. Raj, M. Suri, S. K.","doi":"10.1109/GCAT52182.2021.9587498","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587498","url":null,"abstract":"Electric Vehicles (EV) are gaining popularity from the transportation sector, as it causes less harm to the environment. The battery inside the EV can be refilled using battery charging or battery swapping. As battery swapping method is found to be advantageous over battery charging, Battery Swapping Stations (BSS) is presently the hot topic of research. Forecasting of EV arrivals helps in optimal planning of BSS. Back Propagation Neural Network (BPNN) is frequently used in forecasting. BPNN trained with traditional algorithms such as Levenberg Marquardt (LM) gets stuck at the local optima. This problem can be overcomed using metaheuristic algorithms such as Genetic Algorithm (GA). Thus, in this present work a comparative study on forecasting the EV arrivals at BSS is carried out using LM-BPNN and GA-BPNN. The two models have been simulated using MATLAB/Simulink environment and their performance is analysed using metrics such as Mean Square Error (MSE) and simulation time.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133224745","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587844
N. Suguna, S. Revathi
A Compact miniaturized monopole super wide band (SWB) antenna has been originated and simulated using electromagnetic computational HFSS simulation tool. The Designed antenna is printed on Rogers RT / Duroid 5880 (tm) dielectric material having a dielectric permittivity of 2.2 & its thickness is 0.5mm. Proposed antenna composed of a radiating patch having space filling slots and a 50Ω triangle tapered microstrip feedline. Impedance bandwidth ranges from 18.81 to 64.09GHz at reflection coefficient < -10dB and fractional bandwidth of 171.51%. Simulated gain varies up to 6dBi and its radiation efficiency over the operating band is 88 – 99%. The designed SWB antenna has wide bandwidth, proper impedance matching, good gain, smaller in size and high radiation efficiency compared to earlier reported models. The presented antenna can be employed for K –band (18 – 27GHz), Ka – band (27 – 40GHz) and some of the applications adopted from V – band (40 – 75GHz) applications.
{"title":"Bandwidth Enhancement of Compact Printed Super Wide Band Antenna with Space Filling Slots for Microwave Applications","authors":"N. Suguna, S. Revathi","doi":"10.1109/GCAT52182.2021.9587844","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587844","url":null,"abstract":"A Compact miniaturized monopole super wide band (SWB) antenna has been originated and simulated using electromagnetic computational HFSS simulation tool. The Designed antenna is printed on Rogers RT / Duroid 5880 (tm) dielectric material having a dielectric permittivity of 2.2 & its thickness is 0.5mm. Proposed antenna composed of a radiating patch having space filling slots and a 50Ω triangle tapered microstrip feedline. Impedance bandwidth ranges from 18.81 to 64.09GHz at reflection coefficient < -10dB and fractional bandwidth of 171.51%. Simulated gain varies up to 6dBi and its radiation efficiency over the operating band is 88 – 99%. The designed SWB antenna has wide bandwidth, proper impedance matching, good gain, smaller in size and high radiation efficiency compared to earlier reported models. The presented antenna can be employed for K –band (18 – 27GHz), Ka – band (27 – 40GHz) and some of the applications adopted from V – band (40 – 75GHz) applications.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127844333","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 : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587880
Naman Aneja, Sandeep Suri, Sachin Papneja, Nikhil Khurana
The world is seeing a rapid growth in mobile malware applications. Traditional computer malware programmers are shifting to android malware applications. Consequently, mobile security specialists are also working very hard to obtain a robust explication to this current problem. Many anti malware applications have also been launched to tackle this problem. In this paper we have tried to propose a system for detection of malware application based on Blockchain with help of machine learning. We use one internal permissioned blockchain with feature extractor model and one external permissioned blockchain feedback to another machine learning model to accomplish this task. We use dedicated internal blockchain for each application to make our system error free and more accurate.
{"title":"Malware Mobile Application Detection Using Blockchain and Machine Learning","authors":"Naman Aneja, Sandeep Suri, Sachin Papneja, Nikhil Khurana","doi":"10.1109/GCAT52182.2021.9587880","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587880","url":null,"abstract":"The world is seeing a rapid growth in mobile malware applications. Traditional computer malware programmers are shifting to android malware applications. Consequently, mobile security specialists are also working very hard to obtain a robust explication to this current problem. Many anti malware applications have also been launched to tackle this problem. In this paper we have tried to propose a system for detection of malware application based on Blockchain with help of machine learning. We use one internal permissioned blockchain with feature extractor model and one external permissioned blockchain feedback to another machine learning model to accomplish this task. We use dedicated internal blockchain for each application to make our system error free and more accurate.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127970976","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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