Pub Date : 2023-05-26DOI: 10.1109/INCET57972.2023.10170198
Prathiba Jonnala, G. Reddy
Stroke is one of the most important causes of death in recent days. The accumulation of plaque in the carotid artery helps in identifying the possibility of cardiovascular disease and long-term disabilities. Atherosclerosis is a disease caused by the accumulation of plaque in the carotid artery of a person. B-mode ultrasound imaging is the imaging modality that is used for early prediction of this disease. Ultrasound images are affected by speckle noise, which degrades the quality of the image. The purpose of this article is to give a widespread review and implementation of various de-noising methods to de-noise the images for further processing which aids in identifying stroke, atherosclerosis and related cardiovascular diseases. Gaussian, Anisotropic, Bilateral, Wavelet, Non-Local Mean, Total Variation, Block matching 3D filtering techniques were used to remove the speckle noise in the ultrasound B-mode carotid artery images. Therefore, work is required to reduce noise without losing main image features. Various strategies for reducing the noise in the US B-mode images have been suggested in the existing body of knowledge. Each technique has pros and cons of its own. In this article, we presented some significant image de-noising studies. First, we give the formulation of the image de-noising problem, and then outlined several image de-noising techniques. Also, we go over the features of these techniques. The effectiveness of different preprocessing approaches is compared based on performance criteria like Peak Signal to Noise Ratio (PSNR) and Speckle Suppression Index (SSI). Finally, we compared the performance of conventional de-noising filters and the results are presented.
{"title":"Image de-noising of Ultrasound Carotid artery images using various filters","authors":"Prathiba Jonnala, G. Reddy","doi":"10.1109/INCET57972.2023.10170198","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170198","url":null,"abstract":"Stroke is one of the most important causes of death in recent days. The accumulation of plaque in the carotid artery helps in identifying the possibility of cardiovascular disease and long-term disabilities. Atherosclerosis is a disease caused by the accumulation of plaque in the carotid artery of a person. B-mode ultrasound imaging is the imaging modality that is used for early prediction of this disease. Ultrasound images are affected by speckle noise, which degrades the quality of the image. The purpose of this article is to give a widespread review and implementation of various de-noising methods to de-noise the images for further processing which aids in identifying stroke, atherosclerosis and related cardiovascular diseases. Gaussian, Anisotropic, Bilateral, Wavelet, Non-Local Mean, Total Variation, Block matching 3D filtering techniques were used to remove the speckle noise in the ultrasound B-mode carotid artery images. Therefore, work is required to reduce noise without losing main image features. Various strategies for reducing the noise in the US B-mode images have been suggested in the existing body of knowledge. Each technique has pros and cons of its own. In this article, we presented some significant image de-noising studies. First, we give the formulation of the image de-noising problem, and then outlined several image de-noising techniques. Also, we go over the features of these techniques. The effectiveness of different preprocessing approaches is compared based on performance criteria like Peak Signal to Noise Ratio (PSNR) and Speckle Suppression Index (SSI). Finally, we compared the performance of conventional de-noising filters and the results are presented.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133641761","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170696
Deepak Kommana, G. Kumar, R. Vidyadhar, Mahalakshmi Bellamkonda, Saikethan Goundla
Sense amplifiers contribute to the effectiveness, practicality, and durability of memory devices. In the following paper, two new sense amplifier schematics seemed to enhance the performance of memory circuits. The suggested circuits, as opposed to traditional circuits, utilize a PMOS biasing strategy that allows for high output impedance while minimizing sensing delay and power consumption. The circuits function similarly to conventional sense amplifiers, however these circuits have been found to exhibit higher efficiency of their absorbed power and sensing delay. Presented sense amplifiers’ performance was evaluated through simulations leveraging Tanner EDA software and a 180nm technology. In accordance with simulations, the presented circuits functioned consistently with the theoretical analysis, demonstrating their sound design. The results of this study suggest that the sense amplifiers that have been suggested might improve the performance and usefulness of memory circuits found in a number of electronic devices. The reduced power consumption and sensor latency may lead to longer battery life and faster processing times, which may enhance user experience.
{"title":"Implementation of PMOS Biased Sense Amplifier Using Tanner EDA tool","authors":"Deepak Kommana, G. Kumar, R. Vidyadhar, Mahalakshmi Bellamkonda, Saikethan Goundla","doi":"10.1109/INCET57972.2023.10170696","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170696","url":null,"abstract":"Sense amplifiers contribute to the effectiveness, practicality, and durability of memory devices. In the following paper, two new sense amplifier schematics seemed to enhance the performance of memory circuits. The suggested circuits, as opposed to traditional circuits, utilize a PMOS biasing strategy that allows for high output impedance while minimizing sensing delay and power consumption. The circuits function similarly to conventional sense amplifiers, however these circuits have been found to exhibit higher efficiency of their absorbed power and sensing delay. Presented sense amplifiers’ performance was evaluated through simulations leveraging Tanner EDA software and a 180nm technology. In accordance with simulations, the presented circuits functioned consistently with the theoretical analysis, demonstrating their sound design. The results of this study suggest that the sense amplifiers that have been suggested might improve the performance and usefulness of memory circuits found in a number of electronic devices. The reduced power consumption and sensor latency may lead to longer battery life and faster processing times, which may enhance user experience.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133930579","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170344
Pulkit Kumar, Manjeet Singh, Amandeep Gill
The development of vehicle-to-grid technologies has been aided by an increase in electric vehicle mobility. Technology that connects vehicles to the grid allows the power flow from the electric vehicle battery to the grid and from the grid to the electric vehicle and vice-versa. That helps in the reduction of peak load, balancing of the load, regulating voltage, and enhancing power system stability. In this study, the dedicated electric car battery charger that enables power flow in both directions that are between the power grid and the electric vehicle battery is implemented. The charging station’s architecture ensures that the grid’s injected current suffers from the least amount of harmonic distortion, and the controller provides stable dc bus voltage under dynamic conditions. The battery system of an electric car is being given a revolutionary bidirectional battery charger control approach which allows charging and discharging in both slow and fast modes. The MATLAB-Simulink environment is utilized to validate the approach that has been suggested.
{"title":"ANFIS Based Bidirectional Electric Vehicle Charger for Grid-to-Vehicle and Vehicle-to-Grid Connectivity","authors":"Pulkit Kumar, Manjeet Singh, Amandeep Gill","doi":"10.1109/INCET57972.2023.10170344","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170344","url":null,"abstract":"The development of vehicle-to-grid technologies has been aided by an increase in electric vehicle mobility. Technology that connects vehicles to the grid allows the power flow from the electric vehicle battery to the grid and from the grid to the electric vehicle and vice-versa. That helps in the reduction of peak load, balancing of the load, regulating voltage, and enhancing power system stability. In this study, the dedicated electric car battery charger that enables power flow in both directions that are between the power grid and the electric vehicle battery is implemented. The charging station’s architecture ensures that the grid’s injected current suffers from the least amount of harmonic distortion, and the controller provides stable dc bus voltage under dynamic conditions. The battery system of an electric car is being given a revolutionary bidirectional battery charger control approach which allows charging and discharging in both slow and fast modes. The MATLAB-Simulink environment is utilized to validate the approach that has been suggested.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116629937","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170088
Vishnu P Parandhaman
The Artificial intelligence Ubiquitous power internet of things (UPIoT) for the Energy Internet not only advances its digital and intelligent level, but also introduces unpredictable social variables, creating a new setting for the emergence and spread of its cyber risks. Proper comprehension and assessment of the UPIoT's cyber security risk is a crucial assurance for Energy Internet Construction. To assess the possible risk, a technique to assess and quantify the cyber security risk of UPIoT is proposed. The network's cyber security risk assessment is assessed along with the significance of the network unit using the AHP method and security status to estimate risk. According to experimental findings, this method is capable of identifying the main network hazards as well as the security state of each network unit. The increased connectivity will also provide threat hackers access to a larger attack surface. Cyber attacks on electricity networks have in the past resulted in widespread blackouts. In this study, we examined the network infrastructure underlying energy grids in order to identify the fundamental factors driving power grid computer networks. A Digital Substation's Communication and Information Security Assessment.
{"title":"Analysis Techniques Artificial intelligence for Detection of Cyber Security Risks in a Communication and Information Security","authors":"Vishnu P Parandhaman","doi":"10.1109/INCET57972.2023.10170088","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170088","url":null,"abstract":"The Artificial intelligence Ubiquitous power internet of things (UPIoT) for the Energy Internet not only advances its digital and intelligent level, but also introduces unpredictable social variables, creating a new setting for the emergence and spread of its cyber risks. Proper comprehension and assessment of the UPIoT's cyber security risk is a crucial assurance for Energy Internet Construction. To assess the possible risk, a technique to assess and quantify the cyber security risk of UPIoT is proposed. The network's cyber security risk assessment is assessed along with the significance of the network unit using the AHP method and security status to estimate risk. According to experimental findings, this method is capable of identifying the main network hazards as well as the security state of each network unit. The increased connectivity will also provide threat hackers access to a larger attack surface. Cyber attacks on electricity networks have in the past resulted in widespread blackouts. In this study, we examined the network infrastructure underlying energy grids in order to identify the fundamental factors driving power grid computer networks. A Digital Substation's Communication and Information Security Assessment.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116991520","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170098
Anand Mohan, Abhilash Sharma Somayajula
Most ships that carry cargo from one place to another are underactuated. Therefore, controlling such vessels is challenging, and even a few major accidents in the industry can be attributed to ineffective control by a human operator. Automated control can significantly aid in the prevention of such incidents. This paper demonstrates the practical implementation of a path-following algorithm for an underactuated scaled model of a container ship. In this research, two different control strategies have been compared against each other: Proportional Derivative Control (PD) and Sliding Mode Control (SMC). The underactuated vessel is made to track a given set of waypoints, and the performance of the controllers is measured. For this study, a 1:75.5 scaled free-running model of the KRISO Container Ship (KCS) is chosen as the test ship. Simulations were done using a numerical model of the vessel’s dynamics and the controllers are compared for their accuracy and robustness.
{"title":"Analyzing Robustness and Accuracy of Different Controllers for Underactuated Ships","authors":"Anand Mohan, Abhilash Sharma Somayajula","doi":"10.1109/INCET57972.2023.10170098","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170098","url":null,"abstract":"Most ships that carry cargo from one place to another are underactuated. Therefore, controlling such vessels is challenging, and even a few major accidents in the industry can be attributed to ineffective control by a human operator. Automated control can significantly aid in the prevention of such incidents. This paper demonstrates the practical implementation of a path-following algorithm for an underactuated scaled model of a container ship. In this research, two different control strategies have been compared against each other: Proportional Derivative Control (PD) and Sliding Mode Control (SMC). The underactuated vessel is made to track a given set of waypoints, and the performance of the controllers is measured. For this study, a 1:75.5 scaled free-running model of the KRISO Container Ship (KCS) is chosen as the test ship. Simulations were done using a numerical model of the vessel’s dynamics and the controllers are compared for their accuracy and robustness.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115028001","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170036
R. Raut, Siddharth Shelke, Atharva Nanavate, Dhruv Notwani
The creation of a real-time face mask detection system based on machine learning is the main goal of this research. With the global COVID-19 pandemic, face mask use has become essential for safety and adherence to regulations. The need to identify individuals not complying with the safety measures in public transit, retail settings, and healthcare facilities inspired this project. The goal is to develop a reliable face mask identification algorithm that performs well regardless of the user's mask type, facial angle, or lighting conditions. This research proposes the use of a Convolutional Neural Network based on deep learning, which is trained on a dataset of images of people wearing/not wearing face masks. The TensorFlow framework and Keras API are used to create the model. Transfer learning is also employed by adapting the MobileNetV2 architecture to improve the model's accuracy with less training data. The effectiveness of the proposed model is assessed on two datasets, one containing real-world photos and the other containing artificially generated images. The model performs well, achieving an accuracy rate of 97.5 per cent and 96.8 per cent, respectively, in identifying face masks in real-time. The proposed system has real-world applicability in settings such as hospitals, airports, and other public spaces, where adherence to safety measures is critical. The model can be further improved to detect other types of Personal Protective Equipment such as gloves and face shields. The project concludes with a CNN-based face mask detection algorithm capable of determining in real-time if a person has a mask on or not. The goal is to develop a reliable face mask identification algorithm that performs well regardless of the user's mask type, facial angle, or lighting conditions.
{"title":"Face Mask Detection Using Convolutional Neural Network","authors":"R. Raut, Siddharth Shelke, Atharva Nanavate, Dhruv Notwani","doi":"10.1109/INCET57972.2023.10170036","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170036","url":null,"abstract":"The creation of a real-time face mask detection system based on machine learning is the main goal of this research. With the global COVID-19 pandemic, face mask use has become essential for safety and adherence to regulations. The need to identify individuals not complying with the safety measures in public transit, retail settings, and healthcare facilities inspired this project. The goal is to develop a reliable face mask identification algorithm that performs well regardless of the user's mask type, facial angle, or lighting conditions. This research proposes the use of a Convolutional Neural Network based on deep learning, which is trained on a dataset of images of people wearing/not wearing face masks. The TensorFlow framework and Keras API are used to create the model. Transfer learning is also employed by adapting the MobileNetV2 architecture to improve the model's accuracy with less training data. The effectiveness of the proposed model is assessed on two datasets, one containing real-world photos and the other containing artificially generated images. The model performs well, achieving an accuracy rate of 97.5 per cent and 96.8 per cent, respectively, in identifying face masks in real-time. The proposed system has real-world applicability in settings such as hospitals, airports, and other public spaces, where adherence to safety measures is critical. The model can be further improved to detect other types of Personal Protective Equipment such as gloves and face shields. The project concludes with a CNN-based face mask detection algorithm capable of determining in real-time if a person has a mask on or not. The goal is to develop a reliable face mask identification algorithm that performs well regardless of the user's mask type, facial angle, or lighting conditions.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117003279","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}
Employee attrition is a significant concern for businesses as it can negatively impact productivity, profitability, and overall success. Employee turnover can be costly and time-consuming, and it can also result in the loss of valuable talent and knowledge. It is important for businesses to predict and understand employee attrition so that they can take proactive measures to retain employees. During Covid, the organisations faced issues due to employees leaving uncertainly, making it difficult for the HR Department to hire new people quickly and the companies had to spend a huge fortune to fill the void. It is important for businesses to monitor and evaluate the effectiveness of retention strategies over time to ensure that they are achieving the desired outcomes. Artificial Intelligence has proved to be of great use in predicting how likely an employee is to leave the organization. By using predictive analytics to identify employees who are at risk of leaving and developing targeted retention strategies, businesses can reduce the negative impact of employee attrition and create a more stable and productive workforce. Our system assists with foreseeing the rate at which employees are stopping position in light of getting logical information available and utilize different Artificial Neural Networks to diminish prediction error. The main objective of this model is to study employee attrition in an organization, to find out the issues of the representatives in the enterprise, and to distinguish how maintenance procedure lessens worker turnover.
{"title":"Employee Attrition Prediction using Artificial Neural Networks","authors":"Akansha Chaurasia, Shreyas Kadam, Kalyani Bhagat, Shreenath Gauda, Priyanka Shingane","doi":"10.1109/INCET57972.2023.10170676","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170676","url":null,"abstract":"Employee attrition is a significant concern for businesses as it can negatively impact productivity, profitability, and overall success. Employee turnover can be costly and time-consuming, and it can also result in the loss of valuable talent and knowledge. It is important for businesses to predict and understand employee attrition so that they can take proactive measures to retain employees. During Covid, the organisations faced issues due to employees leaving uncertainly, making it difficult for the HR Department to hire new people quickly and the companies had to spend a huge fortune to fill the void. It is important for businesses to monitor and evaluate the effectiveness of retention strategies over time to ensure that they are achieving the desired outcomes. Artificial Intelligence has proved to be of great use in predicting how likely an employee is to leave the organization. By using predictive analytics to identify employees who are at risk of leaving and developing targeted retention strategies, businesses can reduce the negative impact of employee attrition and create a more stable and productive workforce. Our system assists with foreseeing the rate at which employees are stopping position in light of getting logical information available and utilize different Artificial Neural Networks to diminish prediction error. The main objective of this model is to study employee attrition in an organization, to find out the issues of the representatives in the enterprise, and to distinguish how maintenance procedure lessens worker turnover.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114900083","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170375
N. A, Shoney Sebastian
Determining patterns in monolithic systems to help improve the overall system development and maintenance has become quite commonplace. However, recognizing the patterns that have emerged (or are emerging) in cloud computing - especially with respect to microservices, is challenging. Although numerous patterns have been proposed through extensive research and implementation, the quality assessment tools that are currently available fall short when it comes to accurately recognizing patterns in microservices. It has been identified that a completely autonomous tool for the identification and classification of patterns in microservices has not been developed so far. Moreover, classification of services is an approach that has not been considered by researchers that are working in this field. This paper aims to perform a detailed systematic literature review that can help to explore the various possibilities of identifying and classifying the patterns in microservices. The article also briefly lists out a set of tools that is used in the industry for the implementation of patterns in microservices.
{"title":"A Systematic Review on the Identification and Classification of Patterns in Microservices","authors":"N. A, Shoney Sebastian","doi":"10.1109/INCET57972.2023.10170375","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170375","url":null,"abstract":"Determining patterns in monolithic systems to help improve the overall system development and maintenance has become quite commonplace. However, recognizing the patterns that have emerged (or are emerging) in cloud computing - especially with respect to microservices, is challenging. Although numerous patterns have been proposed through extensive research and implementation, the quality assessment tools that are currently available fall short when it comes to accurately recognizing patterns in microservices. It has been identified that a completely autonomous tool for the identification and classification of patterns in microservices has not been developed so far. Moreover, classification of services is an approach that has not been considered by researchers that are working in this field. This paper aims to perform a detailed systematic literature review that can help to explore the various possibilities of identifying and classifying the patterns in microservices. The article also briefly lists out a set of tools that is used in the industry for the implementation of patterns in microservices.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122138217","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170399
Kaustav Sen, Bindu Verma
Heart disease is associated with a high mortality rate because it affects a significant number of people around the world. There is a pressing need for improved diagnostic methods that are both effective and accurate. Techniques from the field of machine learning have been put to extensive use on tabular data from the healthcare sector, where they have proven to be effective in prediction and analysis. To address the issue of the traditional machine learning model’s low accuracy, precision, and recall value, we propose a soft voting meta classifier composed of Catboost, Light-Gradient Boosting Machine, Gaussian Naive Bayes , Random Forest, and XGBoost. The proposed soft voting ensemble outperformed the other models used in this experiment, which was conducted on a fused UCI heart disease and Statlog dataset. The proposed soft voting ensemble model achieved 91.85% accuracy and a 0.9344 Area Under The Curve Score.
{"title":"Heart Disease Prediction Using a Soft Voting Ensemble of Gradient Boosting Models, RandomForest, and Gaussian Naive Bayes","authors":"Kaustav Sen, Bindu Verma","doi":"10.1109/INCET57972.2023.10170399","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170399","url":null,"abstract":"Heart disease is associated with a high mortality rate because it affects a significant number of people around the world. There is a pressing need for improved diagnostic methods that are both effective and accurate. Techniques from the field of machine learning have been put to extensive use on tabular data from the healthcare sector, where they have proven to be effective in prediction and analysis. To address the issue of the traditional machine learning model’s low accuracy, precision, and recall value, we propose a soft voting meta classifier composed of Catboost, Light-Gradient Boosting Machine, Gaussian Naive Bayes , Random Forest, and XGBoost. The proposed soft voting ensemble outperformed the other models used in this experiment, which was conducted on a fused UCI heart disease and Statlog dataset. The proposed soft voting ensemble model achieved 91.85% accuracy and a 0.9344 Area Under The Curve Score.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123876955","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 : 2023-05-26DOI: 10.1109/INCET57972.2023.10170648
S. Rajendra Prasad, Namani Kavya Sree, Kondra Omkumar, Kothapalli Srujana
The main purpose of the Digital to Analog converter (DAC) is to act as an interface between the digital device and the analog device. Which converts the binary digital values(0,1) into a series of analog voltages. Each type of DAC has its own set of advantages and disadvantages, It is not possible to attain all positive aspects in one circuit. By considering different parameters like power, resolution etc., we have designed a 4-bit CMOS based R-2R DAC in 45nm technology. In this paper, we are using two stage operational amplifier(opamp) in order to enhance the performance of the R-2R DAC. A differential amplifier stage and a gain stage form the two stage opamp, and two values of resistors R and 2R are invoked to form the R-2R ladder network. This two stage opamp and 4-bit R-2R ladder network are used together to design a 4-bit R-2R DAC. Then This DAC is simulated using the Synopsys H-spice tool and based on the simulation results, analysis is performed by considering various parameters like accuracy-Integral nonlinearity (INL) and Differential nonlinearity(DNL) errors, resolution, average, static, and dynamic powers, and settling time. The proposed R-2R DAC has low power and less INL, and DNL errors which are efficient when compared with the related work.
{"title":"An Efficient and Low Power 45nm CMOS Based R-2R DAC","authors":"S. Rajendra Prasad, Namani Kavya Sree, Kondra Omkumar, Kothapalli Srujana","doi":"10.1109/INCET57972.2023.10170648","DOIUrl":"https://doi.org/10.1109/INCET57972.2023.10170648","url":null,"abstract":"The main purpose of the Digital to Analog converter (DAC) is to act as an interface between the digital device and the analog device. Which converts the binary digital values(0,1) into a series of analog voltages. Each type of DAC has its own set of advantages and disadvantages, It is not possible to attain all positive aspects in one circuit. By considering different parameters like power, resolution etc., we have designed a 4-bit CMOS based R-2R DAC in 45nm technology. In this paper, we are using two stage operational amplifier(opamp) in order to enhance the performance of the R-2R DAC. A differential amplifier stage and a gain stage form the two stage opamp, and two values of resistors R and 2R are invoked to form the R-2R ladder network. This two stage opamp and 4-bit R-2R ladder network are used together to design a 4-bit R-2R DAC. Then This DAC is simulated using the Synopsys H-spice tool and based on the simulation results, analysis is performed by considering various parameters like accuracy-Integral nonlinearity (INL) and Differential nonlinearity(DNL) errors, resolution, average, static, and dynamic powers, and settling time. The proposed R-2R DAC has low power and less INL, and DNL errors which are efficient when compared with the related work.","PeriodicalId":403008,"journal":{"name":"2023 4th International Conference for Emerging Technology (INCET)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121571611","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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