Pub Date : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201766
Abhisek Gantayat, S. Behera, J. K. Pradhan, A. Naik
This paper addresses issues arise due to the integration of wind solar photovoltaic hybrid generation (WSPVHG) system with the grid. WSPVHG system minimizes the power storing requirements and improves the system efficiency. Here, power from solar system is controlled by integer proportional integral (PI) controller where as a fractional order proportional integral controller (FOPI) is designed for wind generation system due to its highly stochastic in nature. To achieve faster response, the inner loop is designed with FOPI controller and outer loop by integer PI controller. The FOPI controller is designed by using pole placement technique by utilizing the performance specification. The performance of the proposed system is tested with the real-time simulator (OPAL-RT).
{"title":"Design and Implementation of Fractional Proportional-Integral Control For Hybrid Solar and Wind System","authors":"Abhisek Gantayat, S. Behera, J. K. Pradhan, A. Naik","doi":"10.1109/APSIT58554.2023.10201766","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201766","url":null,"abstract":"This paper addresses issues arise due to the integration of wind solar photovoltaic hybrid generation (WSPVHG) system with the grid. WSPVHG system minimizes the power storing requirements and improves the system efficiency. Here, power from solar system is controlled by integer proportional integral (PI) controller where as a fractional order proportional integral controller (FOPI) is designed for wind generation system due to its highly stochastic in nature. To achieve faster response, the inner loop is designed with FOPI controller and outer loop by integer PI controller. The FOPI controller is designed by using pole placement technique by utilizing the performance specification. The performance of the proposed system is tested with the real-time simulator (OPAL-RT).","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114666252","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201662
A. Mishra, P. K. Nanda, Debiprasanna Das, A. Patra, Narayan Nahak, Lalit M. Sathapathy
This work focuses on the process of designing and the utilization of DC to DC fly back converter in closed loop system. Form six decagons conversion of DC to DC has found to be one of the major research areas in the field of power electronics engineering. For getting different levels of output voltage with single input, the advance systems like computer, telecommunication use single input single output dc to dc converter. This system has certain disadvantages like it is less efficient, low power density and the whole system becomes costly. In order to obtained the characteristics of being highly efficient and high power density the power converter with multiple output features are gaining attention. By using this multiple output converter the output is to be regulated for any type of load and source side disturbances. A Multiple Output Flyback Converter (MOFC) is designed, modeled and simulated in Simulink for controlling the output voltage as the desired value. Self Tuned Proportional Integral Controller (STPIC) or Conventional Proportional Integral Controller (PIC) are the two different methods which are utilized for getting the reference signal to obtain the switching pulse of the converter. Different parameters such as Settling Time (Ts), Rise Time (Tr) and Overshoot (OS) are obtained to analyze the performances. The responses of the used method are depicted for comparison of the outputs.
{"title":"Output Voltage Regulated Multiple Output Flyback Converter using PIC and STPIC","authors":"A. Mishra, P. K. Nanda, Debiprasanna Das, A. Patra, Narayan Nahak, Lalit M. Sathapathy","doi":"10.1109/APSIT58554.2023.10201662","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201662","url":null,"abstract":"This work focuses on the process of designing and the utilization of DC to DC fly back converter in closed loop system. Form six decagons conversion of DC to DC has found to be one of the major research areas in the field of power electronics engineering. For getting different levels of output voltage with single input, the advance systems like computer, telecommunication use single input single output dc to dc converter. This system has certain disadvantages like it is less efficient, low power density and the whole system becomes costly. In order to obtained the characteristics of being highly efficient and high power density the power converter with multiple output features are gaining attention. By using this multiple output converter the output is to be regulated for any type of load and source side disturbances. A Multiple Output Flyback Converter (MOFC) is designed, modeled and simulated in Simulink for controlling the output voltage as the desired value. Self Tuned Proportional Integral Controller (STPIC) or Conventional Proportional Integral Controller (PIC) are the two different methods which are utilized for getting the reference signal to obtain the switching pulse of the converter. Different parameters such as Settling Time (Ts), Rise Time (Tr) and Overshoot (OS) are obtained to analyze the performances. The responses of the used method are depicted for comparison of the outputs.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116928052","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201799
Abha Pragati, D. A. Gadanayak, S. Hasan, Manohar Mishra
Over the last few decades, the High Voltage Direct Current (HVDC) technology has experienced significant growth. HVDC grid technologies are increasingly being employed for strengthening transmission systems and improving connectivity. In cases of long-range and bulk power transmission, HVDC systems have proven to be an attractive option compared to HVAC systems. HVDC grids exhibit reduced power loss and almost negligible lines reactive power. Faults must be fixed promptly, regardless of any challenges. This study presents a fault detection and classification method based on Bayesian optimized decision tree classifiers for an MT-VSC-HVDC transmission system. The primary objective of this research is to extract the DC voltage and current signal through the relays installed in the HVDC network. Afterward, fourteen features are formulated using these signals for the experimentation. Based on these features, Bayesian-optimized decision tree classifier is used to identify and differentiate the faults events. The proposed approach enables rapid identification, faster detection, and fixation of both internal and external faults. The proposed approach is rigorously assessed for various probable fault circumstances simulated with varying transmission system operating parameters. This experimental approach considerably reduces the complexity and time required to identify faults at various points on the HVDC transmission grids with high precision.
{"title":"Bayesian Optimized Ensemble Decision Tree models for MT-VSC-HVDC Transmission Line Protection","authors":"Abha Pragati, D. A. Gadanayak, S. Hasan, Manohar Mishra","doi":"10.1109/APSIT58554.2023.10201799","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201799","url":null,"abstract":"Over the last few decades, the High Voltage Direct Current (HVDC) technology has experienced significant growth. HVDC grid technologies are increasingly being employed for strengthening transmission systems and improving connectivity. In cases of long-range and bulk power transmission, HVDC systems have proven to be an attractive option compared to HVAC systems. HVDC grids exhibit reduced power loss and almost negligible lines reactive power. Faults must be fixed promptly, regardless of any challenges. This study presents a fault detection and classification method based on Bayesian optimized decision tree classifiers for an MT-VSC-HVDC transmission system. The primary objective of this research is to extract the DC voltage and current signal through the relays installed in the HVDC network. Afterward, fourteen features are formulated using these signals for the experimentation. Based on these features, Bayesian-optimized decision tree classifier is used to identify and differentiate the faults events. The proposed approach enables rapid identification, faster detection, and fixation of both internal and external faults. The proposed approach is rigorously assessed for various probable fault circumstances simulated with varying transmission system operating parameters. This experimental approach considerably reduces the complexity and time required to identify faults at various points on the HVDC transmission grids with high precision.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117169641","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201735
Devee Dutta Mishra, Pratiti Padhi, Ankit Aniket Tripathy, S. Patnaik, P. Sahoo
This paper is about the study of tuning of Fractional Order PID (proportional, derivative and integral) controller using the Nelder-Mead algorithm for a separately excited DC motor's speed control. The parameters of the fractional order PID controller were determined via N elder-Mead algorithm by using the Integral Time Square Error (ITSE) as the objective function. To demonstrate the superior execution of the proposed approach, it has been compared with the Grey Wolf Optimization (GWO) based FOPID controller with the same DC speed control parameters. It has been noticed that when compared with the GWO based FOPID controller, the suggested technique with the ITSE as the objective function offers a settling time reduction of 64.99%, a rise time reduction of 61.22%, and a little overshoot. A sturdiness analysis of the Nelder-Mead Fractional Order PID technique was also performed by varying the DC motor parameters.
{"title":"Optimal Tuning of Fractional Order PID controller using Nelder-Mead Algorithm for DC Motor Speed Control","authors":"Devee Dutta Mishra, Pratiti Padhi, Ankit Aniket Tripathy, S. Patnaik, P. Sahoo","doi":"10.1109/APSIT58554.2023.10201735","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201735","url":null,"abstract":"This paper is about the study of tuning of Fractional Order PID (proportional, derivative and integral) controller using the Nelder-Mead algorithm for a separately excited DC motor's speed control. The parameters of the fractional order PID controller were determined via N elder-Mead algorithm by using the Integral Time Square Error (ITSE) as the objective function. To demonstrate the superior execution of the proposed approach, it has been compared with the Grey Wolf Optimization (GWO) based FOPID controller with the same DC speed control parameters. It has been noticed that when compared with the GWO based FOPID controller, the suggested technique with the ITSE as the objective function offers a settling time reduction of 64.99%, a rise time reduction of 61.22%, and a little overshoot. A sturdiness analysis of the Nelder-Mead Fractional Order PID technique was also performed by varying the DC motor parameters.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127094238","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201679
Sonal Wadhwa, R. Tiwari
Accurate weather forecasting is essential in many industries, including agriculture, transportation, and disaster management, making it a prime use case for machine learning algorithms. In this study, we investigate how to forecast several types of weather, including rain, sunshine, clouds, fog, drizzle, and snow, using a variety of fundamental machine learning methods and boosting algorithms. To train and evaluate the various algorithms, we utilized a dataset made up of historical meteorological data, including characteristics like temperature, humidity, wind speed, and pressure. We performed tests on many machine learning methods, some of which you may be familiar with: decision trees, random forests, naive bayes, k-nearest neighbors, and support vector machines. We also used boosting techniques like XGBoost and AdaBoost to further enhance the precision of our forecasts. Our results indicated that XGBoost and AdaBoost, two popular boosting algorithms, achieved the highest levels of accuracy (87.86% and 87.33%) compared to the other algorithms we tested. The findings were verified using ROC Curve Analysis and Lift Curve Analysis, which demonstrated that the XGBoost and AdaBoost models performed better in terms of true positive rate, false positive rate, and lift.
{"title":"Machine Learning-based Weather Prediction: A Comparative Study of Regression and Classification Algorithms","authors":"Sonal Wadhwa, R. Tiwari","doi":"10.1109/APSIT58554.2023.10201679","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201679","url":null,"abstract":"Accurate weather forecasting is essential in many industries, including agriculture, transportation, and disaster management, making it a prime use case for machine learning algorithms. In this study, we investigate how to forecast several types of weather, including rain, sunshine, clouds, fog, drizzle, and snow, using a variety of fundamental machine learning methods and boosting algorithms. To train and evaluate the various algorithms, we utilized a dataset made up of historical meteorological data, including characteristics like temperature, humidity, wind speed, and pressure. We performed tests on many machine learning methods, some of which you may be familiar with: decision trees, random forests, naive bayes, k-nearest neighbors, and support vector machines. We also used boosting techniques like XGBoost and AdaBoost to further enhance the precision of our forecasts. Our results indicated that XGBoost and AdaBoost, two popular boosting algorithms, achieved the highest levels of accuracy (87.86% and 87.33%) compared to the other algorithms we tested. The findings were verified using ROC Curve Analysis and Lift Curve Analysis, which demonstrated that the XGBoost and AdaBoost models performed better in terms of true positive rate, false positive rate, and lift.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125280193","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201764
C. Nayak, A. Tripathy, Manoranjan Parhi, S. Barisal
The most dangerous cancer that affects women is ovarian cancer and the early-stage diagnosis is difficult. To overcome this issue, machine learning techniques being used to predict the early stage of ovarian cancer in women. This paper discusses the different features that link with the prediction of cancer through clinical data. The different machine learning algorithms, like logistic regression, support vector machines (SVM), and decision trees are the primary focus of the paper to predict cancer at the early stage. This paper focuses on the accuracy of different models like logistic regression, support vector machine, decision tree used to predict the early stage cancer. The paper discusses an integrated approach that uses random forest feature selection method and a random forest classifier to give more accurate results. The proposed model has accuracy of 91% as compared to the other models with accuracy 81%,84%,83% respectively.
{"title":"Early Stage Ovarian Cancer Prediction using Machine Learning","authors":"C. Nayak, A. Tripathy, Manoranjan Parhi, S. Barisal","doi":"10.1109/APSIT58554.2023.10201764","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201764","url":null,"abstract":"The most dangerous cancer that affects women is ovarian cancer and the early-stage diagnosis is difficult. To overcome this issue, machine learning techniques being used to predict the early stage of ovarian cancer in women. This paper discusses the different features that link with the prediction of cancer through clinical data. The different machine learning algorithms, like logistic regression, support vector machines (SVM), and decision trees are the primary focus of the paper to predict cancer at the early stage. This paper focuses on the accuracy of different models like logistic regression, support vector machine, decision tree used to predict the early stage cancer. The paper discusses an integrated approach that uses random forest feature selection method and a random forest classifier to give more accurate results. The proposed model has accuracy of 91% as compared to the other models with accuracy 81%,84%,83% respectively.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114486094","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201796
Rasmi Rani Dhala, A.V.S Pavan Kumar, S. Panda
Natural Language Processing (NLP) is an emerging field that aims to enable machines to understand and interpret human language. Keyword extraction and synonym generation are essential tasks in natural language processing. They play a significant role in information retrieval, text classification, and sentiment analysis. In this paper, we explore three different approaches to keyword extraction and synonym generation: rule-based model, statistical model, and extreme learning machine (ELM) model. We compare the performance of each method on a corpus of text and analyze the strengths and weaknesses of each approach. Our results show that the ELM model outperforms the other two methods in terms of accuracy and efficiency.
{"title":"A Comparative Study on Keyword Extraction and Generation of Synonyms in Natural Language Processing","authors":"Rasmi Rani Dhala, A.V.S Pavan Kumar, S. Panda","doi":"10.1109/APSIT58554.2023.10201796","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201796","url":null,"abstract":"Natural Language Processing (NLP) is an emerging field that aims to enable machines to understand and interpret human language. Keyword extraction and synonym generation are essential tasks in natural language processing. They play a significant role in information retrieval, text classification, and sentiment analysis. In this paper, we explore three different approaches to keyword extraction and synonym generation: rule-based model, statistical model, and extreme learning machine (ELM) model. We compare the performance of each method on a corpus of text and analyze the strengths and weaknesses of each approach. Our results show that the ELM model outperforms the other two methods in terms of accuracy and efficiency.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121881852","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201693
Princess Elmalyn B. Malik, Wen James P. Bulasa, Gernel S. Lumacad, Lester Dave T. Dagtay, Cookie J. Fajardo
Investment fraud/scam is defined as the intentional misinterpretation, concealment, or omission of facts regarding promised goods, services, or other expectations by putting funds into investments that are not real, unnecessary, never intended to be fulfilled, or intentionally distorted for the purpose of monetary gain. We present in this paper, an analysis of individuals' features/characteristics of those who are highly susceptible to retail investment scamming using machine learning (ML) methods. Purposive sampling is applied in data collection, asking only those who've at least experienced being scammed in a retail investment. Participants' demographic profile, emotional intelligence scores, personality traits scores and financial literacy levels are collected as parameters for the analysis. The data (N = 177) is first submitted to a Boruta algorithm for feature selection and out of nineteen (19) input features, only seven (7) features are confirmed to be important in determining low or high likelihood of susceptibility in retail investment scamming. Afterwards, a 2 - cluster solution is revealed using the $k$ - means clustering. Cluster 1 is composed of individuals having higher number of times being scammed - characterized by higher social class, higher income, higher emotional intelligence scores, higher levels of agreeableness, openness and extraversion, and lower financial knowledge. Cluster 2 is composed of individuals having lesser number of times being scammed - characterized by lower social class, lower income, lower emotional intelligence scores, lower levels of agreeableness, openness and extraversion and higher financial knowledge. Findings of this study may serve as basis for prevention, protection and enforcement against retail investment frauds.
{"title":"Features of Low and Highly Susceptible Individuals in Retail Investment Fraud: A Machine Learning – Based Analysis","authors":"Princess Elmalyn B. Malik, Wen James P. Bulasa, Gernel S. Lumacad, Lester Dave T. Dagtay, Cookie J. Fajardo","doi":"10.1109/APSIT58554.2023.10201693","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201693","url":null,"abstract":"Investment fraud/scam is defined as the intentional misinterpretation, concealment, or omission of facts regarding promised goods, services, or other expectations by putting funds into investments that are not real, unnecessary, never intended to be fulfilled, or intentionally distorted for the purpose of monetary gain. We present in this paper, an analysis of individuals' features/characteristics of those who are highly susceptible to retail investment scamming using machine learning (ML) methods. Purposive sampling is applied in data collection, asking only those who've at least experienced being scammed in a retail investment. Participants' demographic profile, emotional intelligence scores, personality traits scores and financial literacy levels are collected as parameters for the analysis. The data (N = 177) is first submitted to a Boruta algorithm for feature selection and out of nineteen (19) input features, only seven (7) features are confirmed to be important in determining low or high likelihood of susceptibility in retail investment scamming. Afterwards, a 2 - cluster solution is revealed using the $k$ - means clustering. Cluster 1 is composed of individuals having higher number of times being scammed - characterized by higher social class, higher income, higher emotional intelligence scores, higher levels of agreeableness, openness and extraversion, and lower financial knowledge. Cluster 2 is composed of individuals having lesser number of times being scammed - characterized by lower social class, lower income, lower emotional intelligence scores, lower levels of agreeableness, openness and extraversion and higher financial knowledge. Findings of this study may serve as basis for prevention, protection and enforcement against retail investment frauds.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"43 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129611737","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201772
Prabhjot Kaur, N. Kumar
The Biometric cryptosystem uses a variety of methods to protect templates. In this work, a deep learning-based approach to improve the robustness of the fuzzy vault scheme in biometric cryptosystems. Our approach uses a CNN to extract distinctive features from biometric data and generate the polynomial equation that unlocks the vault. We evaluate our approach on a dataset of fingerprint images and demonstrate that it achieves higher accuracy of 89.9% than traditional methods. The relation between original and decrypted image is computed based on various parameters such as Cr., MSE, MAE etc. and overall fair performance is achieved on four fingerprint databases.
{"title":"Biometric Cryptosystem with Deep Learning: A New Frontier in Security","authors":"Prabhjot Kaur, N. Kumar","doi":"10.1109/APSIT58554.2023.10201772","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201772","url":null,"abstract":"The Biometric cryptosystem uses a variety of methods to protect templates. In this work, a deep learning-based approach to improve the robustness of the fuzzy vault scheme in biometric cryptosystems. Our approach uses a CNN to extract distinctive features from biometric data and generate the polynomial equation that unlocks the vault. We evaluate our approach on a dataset of fingerprint images and demonstrate that it achieves higher accuracy of 89.9% than traditional methods. The relation between original and decrypted image is computed based on various parameters such as Cr., MSE, MAE etc. and overall fair performance is achieved on four fingerprint databases.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130614614","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 : 2023-06-09DOI: 10.1109/APSIT58554.2023.10201795
S. Kar, D. Dash, Renu Sharma
The Position Based Mutation with Whale Optimization Algorithm (PBMWOA) is suggested in this article as a method for solving Environmental Economic Dispatch (EED) issues that affect solar, wind, and thermal power systems together. Also, there are restrictions, and test cases are used to verify and assess the efficacy of the suggested approach. After that, the test results are matched to the results already received from SPEA 2 and PBMWOA. It has been determined from the comparative analysis that the submitted PBMWOA can offer a better answer.
{"title":"Environmental Economic Dispatch of Hybrid Renewable Energy using PBMWOA","authors":"S. Kar, D. Dash, Renu Sharma","doi":"10.1109/APSIT58554.2023.10201795","DOIUrl":"https://doi.org/10.1109/APSIT58554.2023.10201795","url":null,"abstract":"The Position Based Mutation with Whale Optimization Algorithm (PBMWOA) is suggested in this article as a method for solving Environmental Economic Dispatch (EED) issues that affect solar, wind, and thermal power systems together. Also, there are restrictions, and test cases are used to verify and assess the efficacy of the suggested approach. After that, the test results are matched to the results already received from SPEA 2 and PBMWOA. It has been determined from the comparative analysis that the submitted PBMWOA can offer a better answer.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114860213","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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