Pub Date : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085187
M. Shunmathi, S. Fusic, V. Jayshree, K.J.V. Aishwarya, G. Sharanya
A high gain Z-Source converter including an active switching capacitor is used in the solar system's front-end power conversion stage to produce the required DC bus voltage. A Z-source DC-DC converter with a high step-up capabilities and few device voltage stress is shown in the study. The converter is operating in discontinuous conduction mode (DCM). In comparison to a conventional converter, the model that was designed can increase voltage gain at the same duty ratio while decreasing voltage stress on the switch and diode at the same output condition. The structure of the converter is simple and the voltage stress on the extra capacitor and diode are lesser. The simulation findings validate the study and the converter's boost capacity.
{"title":"Solar PV based High Gain Converter for Microgrid Applications","authors":"M. Shunmathi, S. Fusic, V. Jayshree, K.J.V. Aishwarya, G. Sharanya","doi":"10.1109/ICEARS56392.2023.10085187","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085187","url":null,"abstract":"A high gain Z-Source converter including an active switching capacitor is used in the solar system's front-end power conversion stage to produce the required DC bus voltage. A Z-source DC-DC converter with a high step-up capabilities and few device voltage stress is shown in the study. The converter is operating in discontinuous conduction mode (DCM). In comparison to a conventional converter, the model that was designed can increase voltage gain at the same duty ratio while decreasing voltage stress on the switch and diode at the same output condition. The structure of the converter is simple and the voltage stress on the extra capacitor and diode are lesser. The simulation findings validate the study and the converter's boost capacity.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128874633","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085237
N. Kalpana
Artificial Bee Colony (ABC) method is explored in this paper to solve optimum power flow (OPF) problems in power systems by using a static VAR compensator (SVC). With the usage of ABC the system can reduces the overall generating cost of a power system by employing SVC devices; in addition to that it also maintains Voltage stability. The ABC is developed which is influenced by honey bees' browsing behavior in the discovery of the appropriate nectars. It is a newly developed optimization algorithm in power systems. The suggested ABC method was compared to existing optimization algorithms on IEEE 11-bus & IEEE 30-bus systems to examine how effectively it functioned. Result shows that to handle nonlinear problems in power systems, ABC can be strongly accepted it is widely used in power systems.
{"title":"A Novel Technique for Evaluating Optimal Power Flow and SVC Performance using the ABC Algorithm","authors":"N. Kalpana","doi":"10.1109/ICEARS56392.2023.10085237","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085237","url":null,"abstract":"Artificial Bee Colony (ABC) method is explored in this paper to solve optimum power flow (OPF) problems in power systems by using a static VAR compensator (SVC). With the usage of ABC the system can reduces the overall generating cost of a power system by employing SVC devices; in addition to that it also maintains Voltage stability. The ABC is developed which is influenced by honey bees' browsing behavior in the discovery of the appropriate nectars. It is a newly developed optimization algorithm in power systems. The suggested ABC method was compared to existing optimization algorithms on IEEE 11-bus & IEEE 30-bus systems to examine how effectively it functioned. Result shows that to handle nonlinear problems in power systems, ABC can be strongly accepted it is widely used in power systems.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121116061","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085218
D. Diana, R. Hema, G. N. Kumar, R. Rohith Kumar
Support vector machine, a newly developed machine learning technology, is suggested as a tool for carrying out nonlinear equalization in communication networks. Support vector machine has the benefit of allowing the discovery of fewer model parameters while requiring less previous information and heuristic assumptions than some earlier systems. A support vector machine's optimization process also uses quadratic programming, a well-researched and well-understood mathematical programming paradigm.On nonlinear topics that have already been researched by other researchers utilizing neural networks, support vector machine simulations are run. This makes it possible to compare the suggested approach for nonlinear detection first to other methods in order to assess its viability. Results demonstrate that support vector machines outperform neural networks on the nonlinear issues studied.
{"title":"Support Vector based classification for Adaptive Channel Equalization","authors":"D. Diana, R. Hema, G. N. Kumar, R. Rohith Kumar","doi":"10.1109/ICEARS56392.2023.10085218","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085218","url":null,"abstract":"Support vector machine, a newly developed machine learning technology, is suggested as a tool for carrying out nonlinear equalization in communication networks. Support vector machine has the benefit of allowing the discovery of fewer model parameters while requiring less previous information and heuristic assumptions than some earlier systems. A support vector machine's optimization process also uses quadratic programming, a well-researched and well-understood mathematical programming paradigm.On nonlinear topics that have already been researched by other researchers utilizing neural networks, support vector machine simulations are run. This makes it possible to compare the suggested approach for nonlinear detection first to other methods in order to assess its viability. Results demonstrate that support vector machines outperform neural networks on the nonlinear issues studied.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122254608","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085310
Vishwas Dehankar, Pranjali M. Jumle, S. Tadse
The goal of this study is to demonstrate a non- invasive method for assessing driver tiredness and yawning utilising behavioural and vehicle-based methodologies. Today's traffic accidents occur as the result of driver negligence. The drivers gross recklessness and intoxicated behaviour were on display. On this problem many research works was going on to overcome such accidents, which depends on abnormal behaviour of drivers, drunken driver detections, and many more. The driver tiredness and yawning detection system is one of the research work on the same domain which employs a Raspberry Pi microcontroller to focus on the driver's unusual behaviour. The suggested method uses computer vision techniques to provide a non- intrusive driver drowsiness and yawning monitoring system. The system can detect driver fatigue in two to three seconds, irrespective of whether driver is wearing spectacles or the inside of the vehicle is dark.
{"title":"Design of Drowsiness and Yawning Detection System","authors":"Vishwas Dehankar, Pranjali M. Jumle, S. Tadse","doi":"10.1109/ICEARS56392.2023.10085310","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085310","url":null,"abstract":"The goal of this study is to demonstrate a non- invasive method for assessing driver tiredness and yawning utilising behavioural and vehicle-based methodologies. Today's traffic accidents occur as the result of driver negligence. The drivers gross recklessness and intoxicated behaviour were on display. On this problem many research works was going on to overcome such accidents, which depends on abnormal behaviour of drivers, drunken driver detections, and many more. The driver tiredness and yawning detection system is one of the research work on the same domain which employs a Raspberry Pi microcontroller to focus on the driver's unusual behaviour. The suggested method uses computer vision techniques to provide a non- intrusive driver drowsiness and yawning monitoring system. The system can detect driver fatigue in two to three seconds, irrespective of whether driver is wearing spectacles or the inside of the vehicle is dark.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122346160","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10084967
Juby Abraham, George Joseph Cherian, N. Jayapandian
In this era, Machine Learning is transforming human lives in a very different way. The need to give machines the power to make decisions or giving the moral compass is a big dilemma when humanity is more divided than it has ever been. There are two main ways in which law and AI interact. AI may be subject to legal restrictions and be employed in courtroom procedures. The world around us is being significantly and swiftly changed by AI in all of its manifestations. Public law includes important facets such as nondiscrimination law and labor law. In a manner similar to this when artificial intelligence (AI) is applied to tangible technology like robots. In certain cases, artificial intelligence (AI) might be hardly noticeable to customers but evident to those who built and are using it. The behavior research offers suggestions for how to build enduring and beneficial interactions between intelligent robots and people. The human improvement is main obstacles in the development and implementation of artificial intelligence. Best practices in this area are not governed by any one strategy that is generally acknowledged. Machine learning is about to revolutionize society as it is know it. It is crucial to give intelligent computers a moral compass now more than ever before because of how divided mankind is. Although machine learning has limitless potential, inappropriate usage might have detrimental long-term implications. It will think about how, for instance, earlier cultures built trust and improved social interactions via creative answers to many of the ethical issues that machine learning is posing now.
{"title":"Systematic Review on Humanizing Machine Intelligence and Artificial Intelligence","authors":"Juby Abraham, George Joseph Cherian, N. Jayapandian","doi":"10.1109/ICEARS56392.2023.10084967","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10084967","url":null,"abstract":"In this era, Machine Learning is transforming human lives in a very different way. The need to give machines the power to make decisions or giving the moral compass is a big dilemma when humanity is more divided than it has ever been. There are two main ways in which law and AI interact. AI may be subject to legal restrictions and be employed in courtroom procedures. The world around us is being significantly and swiftly changed by AI in all of its manifestations. Public law includes important facets such as nondiscrimination law and labor law. In a manner similar to this when artificial intelligence (AI) is applied to tangible technology like robots. In certain cases, artificial intelligence (AI) might be hardly noticeable to customers but evident to those who built and are using it. The behavior research offers suggestions for how to build enduring and beneficial interactions between intelligent robots and people. The human improvement is main obstacles in the development and implementation of artificial intelligence. Best practices in this area are not governed by any one strategy that is generally acknowledged. Machine learning is about to revolutionize society as it is know it. It is crucial to give intelligent computers a moral compass now more than ever before because of how divided mankind is. Although machine learning has limitless potential, inappropriate usage might have detrimental long-term implications. It will think about how, for instance, earlier cultures built trust and improved social interactions via creative answers to many of the ethical issues that machine learning is posing now.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126913665","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085032
Ravi Mohan, S. Chalasani, S. Suma Christal Mary, Amit Chauhan, S. Parte, S. Anusuya
In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved.
{"title":"Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine","authors":"Ravi Mohan, S. Chalasani, S. Suma Christal Mary, Amit Chauhan, S. Parte, S. Anusuya","doi":"10.1109/ICEARS56392.2023.10085032","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085032","url":null,"abstract":"In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125689108","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085297
C. Mythili, M. Yazhini Nivethitha
This paper makes a fundamental advancement in the field of Very Large Scale Integration by proposing an autonomous and evolutionary method for building diverse LOD and LOPD circuits (VLSI). Furthermore, there are a few efficient methods for constructing higher-order LODs and LOPDs from the evolved lower-order circuits. As a result, performance has been proven to increase with gate-level rise in LOD and LOPD circuits. The synthesis findings also show that, as a result of the optimized architecture, our system has the lowest latency. In future, the power consumption and the number of transistor will be further reduced to reduce the area.
{"title":"Design and Methodology of LOD and LOPD using Evolutionary Algorithm","authors":"C. Mythili, M. Yazhini Nivethitha","doi":"10.1109/ICEARS56392.2023.10085297","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085297","url":null,"abstract":"This paper makes a fundamental advancement in the field of Very Large Scale Integration by proposing an autonomous and evolutionary method for building diverse LOD and LOPD circuits (VLSI). Furthermore, there are a few efficient methods for constructing higher-order LODs and LOPDs from the evolved lower-order circuits. As a result, performance has been proven to increase with gate-level rise in LOD and LOPD circuits. The synthesis findings also show that, as a result of the optimized architecture, our system has the lowest latency. In future, the power consumption and the number of transistor will be further reduced to reduce the area.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123772762","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085056
Haritha J, B. T, B. S, Darshan S, A. A., Mooses Pradeep A
The predominant genotype in Indian native breeds of cows and buffaloes is A2A2, meaning they produce A2 milk. Cow milk has calcium, phosphorus, rich fats, potassium which helps to maintain blood pressure. This work focuses on the capacitive sensor method to identify the native breed cow milk from different varieties of milk. The milk variety taken for study are Holstein Friesian(HF), Girr, Jersey and Native breed. This study is carried out using parallel plate capacitor and cylindrical type capacitor. The parameters taken for study are capacitance, magnitude and phase value for both the type of capacitors. The results show that parallel plate capacitor characteristics provides differentiation of milk samples in capacitance at 1kHz frequency and in magnitude values of impedance at 100Hz and 120Hz frequencies. The characteristics of the cylindrical capacitor shows clear identification of native breed in magnitude of impedance at all the frequency from 100Hz to 100kHz but in capacitance value the difference is very low at all the frequency range. Hence, by considering capacitance as a parameter, parallel plate capacitor characteristics provide better results at 1kHz frequency compared to the cylindrical capacitor.
{"title":"Characteristics Study of Capacitive Sensor to Identify Native Breed Cow Milk","authors":"Haritha J, B. T, B. S, Darshan S, A. A., Mooses Pradeep A","doi":"10.1109/ICEARS56392.2023.10085056","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085056","url":null,"abstract":"The predominant genotype in Indian native breeds of cows and buffaloes is A2A2, meaning they produce A2 milk. Cow milk has calcium, phosphorus, rich fats, potassium which helps to maintain blood pressure. This work focuses on the capacitive sensor method to identify the native breed cow milk from different varieties of milk. The milk variety taken for study are Holstein Friesian(HF), Girr, Jersey and Native breed. This study is carried out using parallel plate capacitor and cylindrical type capacitor. The parameters taken for study are capacitance, magnitude and phase value for both the type of capacitors. The results show that parallel plate capacitor characteristics provides differentiation of milk samples in capacitance at 1kHz frequency and in magnitude values of impedance at 100Hz and 120Hz frequencies. The characteristics of the cylindrical capacitor shows clear identification of native breed in magnitude of impedance at all the frequency from 100Hz to 100kHz but in capacitance value the difference is very low at all the frequency range. Hence, by considering capacitance as a parameter, parallel plate capacitor characteristics provide better results at 1kHz frequency compared to the cylindrical capacitor.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117341310","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085157
Yuhes Raajha. M. R, K. A, Rajkumar. D, R. Reshma, Dr. R. Santhosh, N. Mekala
Technology and the revolution in communication have increased the popularity of digital money usage. Most of the monetary transactions currently take place digitally. It is more convenient and increases the ease for the user. But one major problem in digital money and credit card usage is security. With the increase in credit card usage, security issues increase correspondingly. Many studies and research work are going on to avoid and prevent such practices from taking place. Moreover, various studies on real-international credit scorecard statistics are attributable to confidentiality issues. This paper focuses on current credit card fraud practices and fraud detection methods implemented in real time. Different ML algorithms like fuzzy-based SVM (FSVM), random forest (RF), logistic regression (LR), and support vector machine (SVM) for fraudulent transaction detection on the dataset collected from credit card users have been used to classify legitimate and fraudulent transactions. The comparative analysis of the credit card fraud detection scheme using these classification models was performed with precision, accuracy, sensitivity, and specificity. The comparative analysis outcomes showed that the highest performance was given by the FS VM over other algorithms with an accuracy of 98.61%.
{"title":"An Analytical Approach to Fraudulent Credit Card Transaction Detection using Various Machine Learning Algorithms","authors":"Yuhes Raajha. M. R, K. A, Rajkumar. D, R. Reshma, Dr. R. Santhosh, N. Mekala","doi":"10.1109/ICEARS56392.2023.10085157","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085157","url":null,"abstract":"Technology and the revolution in communication have increased the popularity of digital money usage. Most of the monetary transactions currently take place digitally. It is more convenient and increases the ease for the user. But one major problem in digital money and credit card usage is security. With the increase in credit card usage, security issues increase correspondingly. Many studies and research work are going on to avoid and prevent such practices from taking place. Moreover, various studies on real-international credit scorecard statistics are attributable to confidentiality issues. This paper focuses on current credit card fraud practices and fraud detection methods implemented in real time. Different ML algorithms like fuzzy-based SVM (FSVM), random forest (RF), logistic regression (LR), and support vector machine (SVM) for fraudulent transaction detection on the dataset collected from credit card users have been used to classify legitimate and fraudulent transactions. The comparative analysis of the credit card fraud detection scheme using these classification models was performed with precision, accuracy, sensitivity, and specificity. The comparative analysis outcomes showed that the highest performance was given by the FS VM over other algorithms with an accuracy of 98.61%.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133994543","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 : 2023-03-02DOI: 10.1109/ICEARS56392.2023.10085334
E. K, S. K
The proposed RCNN-based classification system for vitamin deficiency in skin surface microscopy images involves several important steps. The first step is to extract relevant features from the images, which in this case will be border/edge information obtained through the use of Blur Trace (BT) techniques. The BT analysis is a powerful tool for extracting meaningful information from images, and it has been shown to be effective in pattern recognition tasks similar to the one being proposed here. The next step in the process is to perform preprocessing on the images to remove unwanted elements such as hair and noise. This is achieved through the use of nonlinear filtering, specifically median filtering, which has been chosen for its superior performance compared to linear filtering methods. The filtered images are then analyzed to extract energy characteristics that are used to accurately categorize the patterns of vitamin deficiency present in the images. The final stage of the system is the classification of the dermoscopy image into one of the predefined categories, such as Normal, Benign, or Malignant. This is accomplished through the use of the RCNN, which has been trained on the features extracted from the images. The RCNN is a highly advanced machine learning algorithm that has been shown to perform well in a wide range of pattern recognition tasks, making it an ideal choice for this application. The ultimate goal of this research is to contribute to the field of dermatology by improving the accuracy of diagnosing vitamin deficiency and enhancing therapy efficacy through the use of cutting-edge imaging technology. By combining the power of the RCNN with the capabilities of the BT analysis, it is expected that a highly accurate and effective classification system will be developed, which will benefit patients and healthcare practitioners alike.
{"title":"Diagnosis of Vitamin Deficiency in Human Beings using DNN Algorithm","authors":"E. K, S. K","doi":"10.1109/ICEARS56392.2023.10085334","DOIUrl":"https://doi.org/10.1109/ICEARS56392.2023.10085334","url":null,"abstract":"The proposed RCNN-based classification system for vitamin deficiency in skin surface microscopy images involves several important steps. The first step is to extract relevant features from the images, which in this case will be border/edge information obtained through the use of Blur Trace (BT) techniques. The BT analysis is a powerful tool for extracting meaningful information from images, and it has been shown to be effective in pattern recognition tasks similar to the one being proposed here. The next step in the process is to perform preprocessing on the images to remove unwanted elements such as hair and noise. This is achieved through the use of nonlinear filtering, specifically median filtering, which has been chosen for its superior performance compared to linear filtering methods. The filtered images are then analyzed to extract energy characteristics that are used to accurately categorize the patterns of vitamin deficiency present in the images. The final stage of the system is the classification of the dermoscopy image into one of the predefined categories, such as Normal, Benign, or Malignant. This is accomplished through the use of the RCNN, which has been trained on the features extracted from the images. The RCNN is a highly advanced machine learning algorithm that has been shown to perform well in a wide range of pattern recognition tasks, making it an ideal choice for this application. The ultimate goal of this research is to contribute to the field of dermatology by improving the accuracy of diagnosing vitamin deficiency and enhancing therapy efficacy through the use of cutting-edge imaging technology. By combining the power of the RCNN with the capabilities of the BT analysis, it is expected that a highly accurate and effective classification system will be developed, which will benefit patients and healthcare practitioners alike.","PeriodicalId":338611,"journal":{"name":"2023 Second International Conference on Electronics and Renewable Systems (ICEARS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131719743","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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