Pub Date : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986358
S. Khan, Areena Nisar, Asma Arshad, Abid Ali Khan, Omar Farooq
The myoelectric prosthetic devices advancement has been essential to redevelop the grasping capabilities of amputees. Despite these advancements, myoelectric prosthetic devices need improvements to replicate the grasping performed by the human hand. The grasping performed by the human hand needs to be firm and avoid slippage of the objects. To avoid slippage, the information related to grasping force is important. In this study, the EMG signals are acquired while grasping a cylindrical object at different weight levels with two precision prismatic gestures. Using these EMG signals, force-based classification is performed based on the different weight levels for each gesture. The result shows that the highest mean classification accuracy was obtained using Support Vector Machines (SVM), followed by the k-Nearest Neighbors (k-NN) for each gesture. The mean classification accuracy obtained using SVM were 94.05% and 96.8% for 1st and 2nd gesture respectively. It is concluded that better outcomes are obtained using more complex classifiers as compared to simple classifiers such as Naïve Bayes and Linear Discriminant Analysis. In the future, a more descriptive and detailed analysis is expected to be performed using the outcomes obtained.
{"title":"Selection of Machine Learning Algorithm for Pattern Recognition Based Bionic Devices: *Note: Sub-titles are not captured in Xplore and should not be used","authors":"S. Khan, Areena Nisar, Asma Arshad, Abid Ali Khan, Omar Farooq","doi":"10.1109/UPCON56432.2022.9986358","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986358","url":null,"abstract":"The myoelectric prosthetic devices advancement has been essential to redevelop the grasping capabilities of amputees. Despite these advancements, myoelectric prosthetic devices need improvements to replicate the grasping performed by the human hand. The grasping performed by the human hand needs to be firm and avoid slippage of the objects. To avoid slippage, the information related to grasping force is important. In this study, the EMG signals are acquired while grasping a cylindrical object at different weight levels with two precision prismatic gestures. Using these EMG signals, force-based classification is performed based on the different weight levels for each gesture. The result shows that the highest mean classification accuracy was obtained using Support Vector Machines (SVM), followed by the k-Nearest Neighbors (k-NN) for each gesture. The mean classification accuracy obtained using SVM were 94.05% and 96.8% for 1st and 2nd gesture respectively. It is concluded that better outcomes are obtained using more complex classifiers as compared to simple classifiers such as Naïve Bayes and Linear Discriminant Analysis. In the future, a more descriptive and detailed analysis is expected to be performed using the outcomes obtained.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125736876","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986418
Ramesh Kumar Bhukya, Aditya Raj
Automatic Speaker Verification (ASV) is an emerging biometric authentication technique with the process of accepting/rejecting the users' claimed identity based on his/her speech samples. Robust countermeasures for spoofing attack detections are required to secure biometric systems from intruders. Anti-spoofing is also called replay detection in which voice is recorded, stored and replayed to deceive ASV systems. The ASVspoof series of challenge provides a shared anti-spoofing attack, ASVspoof 2019 focused on both synthetic and replay speech that are referred to as physical and logical access attacks, respectively. To build the robust system, we considered separate data for bonafide and spoofed voice data and implemented separate models for both classes. We addressed our system based on Gaussian Mixture Model, which is performed on ASVspoof 2019 Database. Finally, the experiments focused on both MFCC features and machine learned features have a comparable results with an equal error rate (EER) of 5.64% and 7.56 %.
{"title":"Automatic Speaker Verification Spoof Detection and Countermeasures Using Gaussian Mixture Model","authors":"Ramesh Kumar Bhukya, Aditya Raj","doi":"10.1109/UPCON56432.2022.9986418","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986418","url":null,"abstract":"Automatic Speaker Verification (ASV) is an emerging biometric authentication technique with the process of accepting/rejecting the users' claimed identity based on his/her speech samples. Robust countermeasures for spoofing attack detections are required to secure biometric systems from intruders. Anti-spoofing is also called replay detection in which voice is recorded, stored and replayed to deceive ASV systems. The ASVspoof series of challenge provides a shared anti-spoofing attack, ASVspoof 2019 focused on both synthetic and replay speech that are referred to as physical and logical access attacks, respectively. To build the robust system, we considered separate data for bonafide and spoofed voice data and implemented separate models for both classes. We addressed our system based on Gaussian Mixture Model, which is performed on ASVspoof 2019 Database. Finally, the experiments focused on both MFCC features and machine learned features have a comparable results with an equal error rate (EER) of 5.64% and 7.56 %.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126164725","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986390
C. Mutta, A. Prajapati
The performance of Cuk converter is heavily dependent on circuit parameter ratings, which assist in optimizing capacitor values, inductor values, duty cycles, etc. for optimum conversion efficiency. To perform this task, various researchers have proposed multiple models that allow for dynamic selection of these ratings under real-time use-cases. But most of these models have higher complexity, and their values cannot be used for general-purpose circuit deployments. To overcome this limitation, a novel Elephant Herding Optimization (EHO) based model for the selection of Cuk converter parameters for improving conversion efficiency levels is proposed and discussed in this paper. The proposed model uses an EHO method for estimating circuit ratings under ON and OFF states. These values are validated for different converter configurations and are incrementally tuned by the EHO model for general-purpose applicability. These values include ratings of the parallel diode, switching duty cycle, and ratings for series inductors and capacitors under different conditions. Due to the optimum selection of these ratings, the underlying model is capable of low power, and high-efficiency operations. The proposed model is evaluated under different real-time applications including solar power conversion, battery power conversion, wind power conversion, etc., and its power efficiency and total harmonic distortion (THD) levels are compared with various state-of-the-art models. Based on this comparison it is observed that the proposed model showcased 8.5% lower THD, with 4.9% better power efficiency when compared with these models, which makes the proposed model highly useful for large-scale conversion applications.
{"title":"Performance of Cuk Converter Based on EHO Model for Improving the Conversion Efficiency Levels","authors":"C. Mutta, A. Prajapati","doi":"10.1109/UPCON56432.2022.9986390","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986390","url":null,"abstract":"The performance of Cuk converter is heavily dependent on circuit parameter ratings, which assist in optimizing capacitor values, inductor values, duty cycles, etc. for optimum conversion efficiency. To perform this task, various researchers have proposed multiple models that allow for dynamic selection of these ratings under real-time use-cases. But most of these models have higher complexity, and their values cannot be used for general-purpose circuit deployments. To overcome this limitation, a novel Elephant Herding Optimization (EHO) based model for the selection of Cuk converter parameters for improving conversion efficiency levels is proposed and discussed in this paper. The proposed model uses an EHO method for estimating circuit ratings under ON and OFF states. These values are validated for different converter configurations and are incrementally tuned by the EHO model for general-purpose applicability. These values include ratings of the parallel diode, switching duty cycle, and ratings for series inductors and capacitors under different conditions. Due to the optimum selection of these ratings, the underlying model is capable of low power, and high-efficiency operations. The proposed model is evaluated under different real-time applications including solar power conversion, battery power conversion, wind power conversion, etc., and its power efficiency and total harmonic distortion (THD) levels are compared with various state-of-the-art models. Based on this comparison it is observed that the proposed model showcased 8.5% lower THD, with 4.9% better power efficiency when compared with these models, which makes the proposed model highly useful for large-scale conversion applications.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130429852","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986447
Nidhish Dubey, Nanduri Mahathi Sai Nithin, S. Tripathi
This paper presents Unmanned Aerial Vehicles (UAVs) detection and classification with the help of different image-based machine learning modalities. The field of UAVs attracted researchers in recent years in response to the exponential rise in the number of UAVs available in the market with applications ranging from entertainment to defense operations and the risk associated risk by the same. Presently, visual, radar, radio frequency, and acoustic sensing systems are the prevailing technologies in the field of detection and identification of UAVs. The general results of this study show that UAV machine learning-based classifications are propitious and that there are many successful individual contributions. In this research, UAVs were detected and classified using classification methods like Support Vector Machines (SVM), k-nearest neighbor (KNN), and Convolutional Neural Networks (CNN). The results demonstrated that CNN, SVM, and KNN had an accuracy of 91%, 87%, and 78%, respectively. The classifier CNN outperformed other classifiers under the same empirical circumstances.
{"title":"Analysis and Comparison of Image-Based UAV Detection and Identification","authors":"Nidhish Dubey, Nanduri Mahathi Sai Nithin, S. Tripathi","doi":"10.1109/UPCON56432.2022.9986447","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986447","url":null,"abstract":"This paper presents Unmanned Aerial Vehicles (UAVs) detection and classification with the help of different image-based machine learning modalities. The field of UAVs attracted researchers in recent years in response to the exponential rise in the number of UAVs available in the market with applications ranging from entertainment to defense operations and the risk associated risk by the same. Presently, visual, radar, radio frequency, and acoustic sensing systems are the prevailing technologies in the field of detection and identification of UAVs. The general results of this study show that UAV machine learning-based classifications are propitious and that there are many successful individual contributions. In this research, UAVs were detected and classified using classification methods like Support Vector Machines (SVM), k-nearest neighbor (KNN), and Convolutional Neural Networks (CNN). The results demonstrated that CNN, SVM, and KNN had an accuracy of 91%, 87%, and 78%, respectively. The classifier CNN outperformed other classifiers under the same empirical circumstances.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128946786","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986360
Swati Jain, Shaili Patel, Avi Mehta, J. P. Verma
Drones, or unmanned aerial vehicles, have a wide range of uses in a variety of sectors. Surveillance, photography, surveying physically difficult locations, and traffic patrols are some of the applications. License plate detection and identification by utilizing UAV s based video streaming is one such scenario on which we have engaged. Recognizing license plates in drone photographs is a difficult task since the images may be distorted, blurred, or contain background noise such as other cars, banners, or people. The automatic license plate detecting system is a well-established system in which the majority of research has been conducted. These approaches, on the other hand, concentrate on photographs taken from the front. The suggested study is limited to a subset of drone photos. We examined multiple state-of-the-art methods like Wpodnet and yolov5 for detecting license plates in subsets of drone images in this paper.
{"title":"Number Plate Detection Using Drone Surveillance","authors":"Swati Jain, Shaili Patel, Avi Mehta, J. P. Verma","doi":"10.1109/UPCON56432.2022.9986360","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986360","url":null,"abstract":"Drones, or unmanned aerial vehicles, have a wide range of uses in a variety of sectors. Surveillance, photography, surveying physically difficult locations, and traffic patrols are some of the applications. License plate detection and identification by utilizing UAV s based video streaming is one such scenario on which we have engaged. Recognizing license plates in drone photographs is a difficult task since the images may be distorted, blurred, or contain background noise such as other cars, banners, or people. The automatic license plate detecting system is a well-established system in which the majority of research has been conducted. These approaches, on the other hand, concentrate on photographs taken from the front. The suggested study is limited to a subset of drone photos. We examined multiple state-of-the-art methods like Wpodnet and yolov5 for detecting license plates in subsets of drone images in this paper.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115414777","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986433
D. K. Sah, Nabajyoti Mazumdar, Pankaj Pal, Tarachand Amgoth
Wireless Sensor Networks (WSNs) incorporate sensor nodes with minimal power consumption. Sensor devices are in high demand in many areas, including smart cities, environmental monitoring, the Internet of Things (IoT), health monitoring, and the like. As nodes are frequently located in remote places, and an ordinary node battery life is too short, energy depletion is a significant problem for the sensor network. But it's not practical to change or regularly maintain the sensor node's battery. This could cause the network to disconnect. Consequently, a recharging sensor node battery has been identified using energy harvesting (EH). It has several environmental forms, including solar, wind, mechanical, etc. The solar system provides unlimited energy resources to nodes. This paper examines a comprehensive case study of solar harvesting systems and their most recent applications. In solar harvesting nodes, the following primary components are utilised: solar panels, energy storage classes, a $DC-DC$ converter, maximum power point tracking (MPPT), an energy predictor, and a sensing module. Furthermore, we have discussed some recent applications and future work of sensor networks, for example, green street lights, agriculture 4.0, outdoor environment-based monitoring, IIoT, hybrid storage class and new communication technologies.
{"title":"A Comprehensive Study of Solar Energy Harvesting System in Wireless Sensor Networks","authors":"D. K. Sah, Nabajyoti Mazumdar, Pankaj Pal, Tarachand Amgoth","doi":"10.1109/UPCON56432.2022.9986433","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986433","url":null,"abstract":"Wireless Sensor Networks (WSNs) incorporate sensor nodes with minimal power consumption. Sensor devices are in high demand in many areas, including smart cities, environmental monitoring, the Internet of Things (IoT), health monitoring, and the like. As nodes are frequently located in remote places, and an ordinary node battery life is too short, energy depletion is a significant problem for the sensor network. But it's not practical to change or regularly maintain the sensor node's battery. This could cause the network to disconnect. Consequently, a recharging sensor node battery has been identified using energy harvesting (EH). It has several environmental forms, including solar, wind, mechanical, etc. The solar system provides unlimited energy resources to nodes. This paper examines a comprehensive case study of solar harvesting systems and their most recent applications. In solar harvesting nodes, the following primary components are utilised: solar panels, energy storage classes, a $DC-DC$ converter, maximum power point tracking (MPPT), an energy predictor, and a sensing module. Furthermore, we have discussed some recent applications and future work of sensor networks, for example, green street lights, agriculture 4.0, outdoor environment-based monitoring, IIoT, hybrid storage class and new communication technologies.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115998725","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986458
Sagar Lakhani, Ashok Yadav, Vrijendra Singh
In today's digital era, online attacks are increasing in number and are becoming severe day by day, especially those related to web applications. The data accessible over the web persuades the attackers to dispatch new kinds of attacks. Serious exploration on web security has shown that the most hazardous attack that affects web security is the Structured Query Language Injection(SQLI). This attack addresses a genuine threat to web application security and a few examination works have been directed to defend against this attack by detecting it when it happens. Traditional methods like input validation and filtering, use of parameterized queries, etc. are not sufficient to counter these attacks as they rely solely on the implementation of the code hence factoring in the developer's skill-set which in turn gave rise to Machine Learning based solutions. In this study, we have proposed a novel approach that takes the help of Natural Language Processing(NLP) and uses BERT for feature extraction that is capable to adapt to SQLI variants and provides an accuracy of 97% with a false positive rate of 0.8% and a false negative rate of 5.8%.
{"title":"Detecting SQL Injection Attack using Natural Language Processing","authors":"Sagar Lakhani, Ashok Yadav, Vrijendra Singh","doi":"10.1109/UPCON56432.2022.9986458","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986458","url":null,"abstract":"In today's digital era, online attacks are increasing in number and are becoming severe day by day, especially those related to web applications. The data accessible over the web persuades the attackers to dispatch new kinds of attacks. Serious exploration on web security has shown that the most hazardous attack that affects web security is the Structured Query Language Injection(SQLI). This attack addresses a genuine threat to web application security and a few examination works have been directed to defend against this attack by detecting it when it happens. Traditional methods like input validation and filtering, use of parameterized queries, etc. are not sufficient to counter these attacks as they rely solely on the implementation of the code hence factoring in the developer's skill-set which in turn gave rise to Machine Learning based solutions. In this study, we have proposed a novel approach that takes the help of Natural Language Processing(NLP) and uses BERT for feature extraction that is capable to adapt to SQLI variants and provides an accuracy of 97% with a false positive rate of 0.8% and a false negative rate of 5.8%.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131082645","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986410
Damini Rathi, Aashvi Gajjar, Hardik Joshi
Nowadays, FSO is the best choice for last-mile communication due to its high data rate and low complexity. There are many challenges in FSO, like atmospheric turbulence, scintillation, background noise, beam divergence loss, etc. However, to overcome these, we have several mitigation techniques like modulation and coding, diversity, adaptive optics, aperture averaging, and hybrid RF/FSO. The FSO link needs to be compared for different modulation and diversity schemes to check its effectiveness against atmospheric turbulence. This article compares the BER performance of different modulation schemes like BPSK, DBPSK, QPSK, 8-QAM, 8-PSK, binary and quaternary Optical Space Shift Keying (OSSK) in different strengths of atmospheric turbulence considering the Gamma-Gamma channel model. We have also compared diversity and combining schemes like Maximum Ratio Combining (MRC) and Equal Gain Combining (EGC) to overcome the effect of atmospheric turbulence in weak, strong, and moderate turbulence.
{"title":"BER Performance Comparison of Gamma-Gamma FSO link for Different Modulations and Diversity Techniques","authors":"Damini Rathi, Aashvi Gajjar, Hardik Joshi","doi":"10.1109/UPCON56432.2022.9986410","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986410","url":null,"abstract":"Nowadays, FSO is the best choice for last-mile communication due to its high data rate and low complexity. There are many challenges in FSO, like atmospheric turbulence, scintillation, background noise, beam divergence loss, etc. However, to overcome these, we have several mitigation techniques like modulation and coding, diversity, adaptive optics, aperture averaging, and hybrid RF/FSO. The FSO link needs to be compared for different modulation and diversity schemes to check its effectiveness against atmospheric turbulence. This article compares the BER performance of different modulation schemes like BPSK, DBPSK, QPSK, 8-QAM, 8-PSK, binary and quaternary Optical Space Shift Keying (OSSK) in different strengths of atmospheric turbulence considering the Gamma-Gamma channel model. We have also compared diversity and combining schemes like Maximum Ratio Combining (MRC) and Equal Gain Combining (EGC) to overcome the effect of atmospheric turbulence in weak, strong, and moderate turbulence.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126793933","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 : 2022-12-02DOI: 10.1109/UPCON56432.2022.9986419
Puneeth B R, R. K. R., B. K., Surendra Shetty, K. N. S., Radhakrishna Dodmane, Ramya, Sarda M N Islam
In today's world, the most vital feature is the security of shared information. To achieve this, various algorithms are offered in cryptosystems, with RSA being one among them. RSA is a robust encryption method, however it can be cracked using a factorization attack. As a result, this work presents an improved RSA algorithm that emphasises RSA's security feature of giving immunity to factorization attacks. The algorithm provides a third variable, which is used as the public key in the network, replacing the common modulus n. The experimental end results, such as differential analysis, performance analysis, and statistical analysis, clearly demonstrated the efficacy of the proposed methodology for secure communication. In addition, the article examines various assaults that could be made against the proposed system and reflects on its effectiveness.
{"title":"Preserving Confidentiality against Factorization Attacks using Fake Modulus ($zeta$) Approach in RSA and its Security Analysis","authors":"Puneeth B R, R. K. R., B. K., Surendra Shetty, K. N. S., Radhakrishna Dodmane, Ramya, Sarda M N Islam","doi":"10.1109/UPCON56432.2022.9986419","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986419","url":null,"abstract":"In today's world, the most vital feature is the security of shared information. To achieve this, various algorithms are offered in cryptosystems, with RSA being one among them. RSA is a robust encryption method, however it can be cracked using a factorization attack. As a result, this work presents an improved RSA algorithm that emphasises RSA's security feature of giving immunity to factorization attacks. The algorithm provides a third variable, which is used as the public key in the network, replacing the common modulus n. The experimental end results, such as differential analysis, performance analysis, and statistical analysis, clearly demonstrated the efficacy of the proposed methodology for secure communication. In addition, the article examines various assaults that could be made against the proposed system and reflects on its effectiveness.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508120","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}
In the present-day time, there has been a gain in interest in video summarization and highlights generation in football, cricket, basketball, and baseball. Some pose recognition methods for recognizing the pose of an umpire in cricket have been proposed, but none of them leverage the potential of pose estimation and neural networks, which are two of the most powerful tools in Deep learning. In this paper, we work on the dataset termed SNOW, for the detection of umpire pose in the game of cricket. This dataset has been assessed as an introductory aid for pose recognition of the umpire in cricket. The umpire in cricket has the power to give decisions, and these decisions are conveyed using hand signals. On the basis of identifying the umpire's pose from the frames of a cricket video, we try to identify five such signals: NO BALL, SIX, WIDE, OUT, and NO ACTION. This paper discusses a technique for recognition of the gestures and poses of the umpire using keypoints generated using pose estimation. The experimental results show that the accuracy of our proposed technique is 87%, and the evaluation metrics of our technique are quite promising compared to existing state-of-the-art works.
{"title":"Pose Recognition in Cricket using Keypoints","authors":"Rahul Mili, Nayana Das, Arjun Tandon, Saquelain Mokhtar, Imon Mukherjee, Goutam Paul","doi":"10.1109/UPCON56432.2022.9986481","DOIUrl":"https://doi.org/10.1109/UPCON56432.2022.9986481","url":null,"abstract":"In the present-day time, there has been a gain in interest in video summarization and highlights generation in football, cricket, basketball, and baseball. Some pose recognition methods for recognizing the pose of an umpire in cricket have been proposed, but none of them leverage the potential of pose estimation and neural networks, which are two of the most powerful tools in Deep learning. In this paper, we work on the dataset termed SNOW, for the detection of umpire pose in the game of cricket. This dataset has been assessed as an introductory aid for pose recognition of the umpire in cricket. The umpire in cricket has the power to give decisions, and these decisions are conveyed using hand signals. On the basis of identifying the umpire's pose from the frames of a cricket video, we try to identify five such signals: NO BALL, SIX, WIDE, OUT, and NO ACTION. This paper discusses a technique for recognition of the gestures and poses of the umpire using keypoints generated using pose estimation. The experimental results show that the accuracy of our proposed technique is 87%, and the evaluation metrics of our technique are quite promising compared to existing state-of-the-art works.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116590768","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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