Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072616
Aishwarya Awasthi, Vaishali Gupta
Data points are grouped together during clustering. The data points may be grouped according to comparable attributes using clustering methods. Data points are grouped using fuzzy clustering, which groups data points into one or even more clusters. Density Peak (DP) grouping may identify clusters, however as the sum of clusters is raised, memory overflow occurs because a normal-sized picture with more pixels is utilized for image segmentation, leading to a high level of similarity matrix. Automated Fuzzy Clustering Frame (AFCF) for picture segmentation might be used to prevent this. This framework offers three contributions. In order to lower the length of the similarity measure and hence increase the computational efficiency of the DP algorithm, the Density Peak approach is first employed for the idea of Super Pixel. A stable choice graph is produced by using the Density Balance approach, which also allows the DP algorithm to perform completely independent clustering. Last but not least, the system uses a Fuzzy c-means grouping based on previous entropy to enhance the results of picture segmentation. This allows for better segmentation outcomes by taking into account the data of pixels from spatial neighbors. The goal of the current study is to create and describe an Automated Fuzzy Clustering Framework for segmenting photos.
{"title":"Fully Automated Clustering based Blueprint for Image Analysis","authors":"Aishwarya Awasthi, Vaishali Gupta","doi":"10.1109/IC3I56241.2022.10072616","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072616","url":null,"abstract":"Data points are grouped together during clustering. The data points may be grouped according to comparable attributes using clustering methods. Data points are grouped using fuzzy clustering, which groups data points into one or even more clusters. Density Peak (DP) grouping may identify clusters, however as the sum of clusters is raised, memory overflow occurs because a normal-sized picture with more pixels is utilized for image segmentation, leading to a high level of similarity matrix. Automated Fuzzy Clustering Frame (AFCF) for picture segmentation might be used to prevent this. This framework offers three contributions. In order to lower the length of the similarity measure and hence increase the computational efficiency of the DP algorithm, the Density Peak approach is first employed for the idea of Super Pixel. A stable choice graph is produced by using the Density Balance approach, which also allows the DP algorithm to perform completely independent clustering. Last but not least, the system uses a Fuzzy c-means grouping based on previous entropy to enhance the results of picture segmentation. This allows for better segmentation outcomes by taking into account the data of pixels from spatial neighbors. The goal of the current study is to create and describe an Automated Fuzzy Clustering Framework for segmenting photos.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"15 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114117593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073274
Lalit Singh Parmar, S. Singh
Background: In recent years, environmental and energy challenges have gained popularity, especially the speed of climate change (CC) and the steady decline of natural systems. In actuality, the transition to energy generation, mostly based on renewable technologies such as wind, hydro, and solar, will positively impact the environment and the economy. Global concern has been raised regarding solar thermal electricity systems (STES), commonly called concentrated solar power (CSP). It functions by focusing sunlight and transforming it into high-energy heat, which may then be utilized to operate conventional steam turbines, which generate power, or directly for industrial activities.Aim and Objectives: This research aims to conduct research and offer a critical assessment of the efficiency of CSP renewable energy plants built on solar towers.Methods: To collect the required information for this study, the researchers used a methodology that comprised of performing an in-depth evaluation of the relevant scientific literature. This research was able to find important ideas and theories which could be the basis for this new paradigm by doing a comprehensive study of more than 35 published research papers.Research Findings: The study results show that solar tower technology has the potential to be more efficient. The most expensive part of a CSP system is usually the cost of putting in a solar field. This research reported that the novel CSP systems are inclined to be revolutionary technology because they could give the whole world a clean, cheap energy source.
{"title":"A Study of Performance and Analysis CSP Renewable based on Solar Tower Power Plant","authors":"Lalit Singh Parmar, S. Singh","doi":"10.1109/IC3I56241.2022.10073274","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073274","url":null,"abstract":"Background: In recent years, environmental and energy challenges have gained popularity, especially the speed of climate change (CC) and the steady decline of natural systems. In actuality, the transition to energy generation, mostly based on renewable technologies such as wind, hydro, and solar, will positively impact the environment and the economy. Global concern has been raised regarding solar thermal electricity systems (STES), commonly called concentrated solar power (CSP). It functions by focusing sunlight and transforming it into high-energy heat, which may then be utilized to operate conventional steam turbines, which generate power, or directly for industrial activities.Aim and Objectives: This research aims to conduct research and offer a critical assessment of the efficiency of CSP renewable energy plants built on solar towers.Methods: To collect the required information for this study, the researchers used a methodology that comprised of performing an in-depth evaluation of the relevant scientific literature. This research was able to find important ideas and theories which could be the basis for this new paradigm by doing a comprehensive study of more than 35 published research papers.Research Findings: The study results show that solar tower technology has the potential to be more efficient. The most expensive part of a CSP system is usually the cost of putting in a solar field. This research reported that the novel CSP systems are inclined to be revolutionary technology because they could give the whole world a clean, cheap energy source.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114208329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072860
G. Krishna, Rajesh Singh, A. Gehlot
The battery is the most crucial part of a car. Therefore, for best operation, each battery must be restored to its full potential. Lead Acid batteries are typically used in automobile batteries, and they need to undergo meticulous inspection to perform well under all circumstances. Consequently, a more organized battery control system is required to permit continuous monitoring of the battery's functioning. When it comes to batteries, the SoH (State of Health), SoC (State of Charging), and SoD (State of Discharging) are the most important features. Such parameters can be calculated using a number of cogent ways. However, as the battery's components, surroundings, and load will all have an impact on the parameters, such methods cannot produce precise results. A battery that has been overcharged releases gases like oxygen and hydrogen. In addition to attempting to detect the escape of various gases from the battery under overload situations, the Battery Management System (BMS) uses sensors and an STM controller to display the voltage, current, and temperature of the battery. Through the use of IOT and cloud technologies, this study focused on the detection of hydrogen gas released by batteries.
{"title":"Cloud-based Monitoring of the Health of Battery using IoT","authors":"G. Krishna, Rajesh Singh, A. Gehlot","doi":"10.1109/IC3I56241.2022.10072860","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072860","url":null,"abstract":"The battery is the most crucial part of a car. Therefore, for best operation, each battery must be restored to its full potential. Lead Acid batteries are typically used in automobile batteries, and they need to undergo meticulous inspection to perform well under all circumstances. Consequently, a more organized battery control system is required to permit continuous monitoring of the battery's functioning. When it comes to batteries, the SoH (State of Health), SoC (State of Charging), and SoD (State of Discharging) are the most important features. Such parameters can be calculated using a number of cogent ways. However, as the battery's components, surroundings, and load will all have an impact on the parameters, such methods cannot produce precise results. A battery that has been overcharged releases gases like oxygen and hydrogen. In addition to attempting to detect the escape of various gases from the battery under overload situations, the Battery Management System (BMS) uses sensors and an STM controller to display the voltage, current, and temperature of the battery. Through the use of IOT and cloud technologies, this study focused on the detection of hydrogen gas released by batteries.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124449586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072615
Sanjay S. Tippannavar, R. Shashidhar, H. R. Sathvik, S. Varun, G. V. Punith, H. G. Nikshep
The method of automatically identifying the speaker using the speaker-specific data included in voice waves is known as speaker recognition. For speaker recognition, a variety of uses have been investigated. Monitoring, speech-activated secure access control, voice-activated customization of services or information for certain users, instances include using recorded voice samples in forensic and criminal investigations. The application that is now mentioned most often is access control, which also includes voice dialing, banking, telephone shopping, and database access services. Thus, it is projected that speaker recognition technology would provide new services in smart environments and enhance the comfort of daily life. Research has been done on the phenomenon known as “speaker idolization,” which occurs when speakers are automatically added to an input audio channel. It makes speech recognition easier, makes it easier to search and index audio archives, and gives machine transcriptions more depth and intelligibility. An important additional application for voice recognition technology is as a forensics tool. The speaker’s short-time spectral coefficients are described using vector quantization using a codebook. The success of these techniques is assessed from the perspective of robustness against utterance variation, such as variances in content, temporal variation, and changes in utterance pace. The voice of each individual is recorded three times. The experiment’s double distance measurement result is 96.97%, whereas the KNN technique’s single data center result is 84.85% The outcome shows that the twofold distance method increases the precision of voice recognition.
{"title":"Text Independent Speaker Recognition and Classification using KNN Algorithm","authors":"Sanjay S. Tippannavar, R. Shashidhar, H. R. Sathvik, S. Varun, G. V. Punith, H. G. Nikshep","doi":"10.1109/IC3I56241.2022.10072615","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072615","url":null,"abstract":"The method of automatically identifying the speaker using the speaker-specific data included in voice waves is known as speaker recognition. For speaker recognition, a variety of uses have been investigated. Monitoring, speech-activated secure access control, voice-activated customization of services or information for certain users, instances include using recorded voice samples in forensic and criminal investigations. The application that is now mentioned most often is access control, which also includes voice dialing, banking, telephone shopping, and database access services. Thus, it is projected that speaker recognition technology would provide new services in smart environments and enhance the comfort of daily life. Research has been done on the phenomenon known as “speaker idolization,” which occurs when speakers are automatically added to an input audio channel. It makes speech recognition easier, makes it easier to search and index audio archives, and gives machine transcriptions more depth and intelligibility. An important additional application for voice recognition technology is as a forensics tool. The speaker’s short-time spectral coefficients are described using vector quantization using a codebook. The success of these techniques is assessed from the perspective of robustness against utterance variation, such as variances in content, temporal variation, and changes in utterance pace. The voice of each individual is recorded three times. The experiment’s double distance measurement result is 96.97%, whereas the KNN technique’s single data center result is 84.85% The outcome shows that the twofold distance method increases the precision of voice recognition.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"75 24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124470850","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}
The experienced environment of vehicular ad hoc networks (VANETs), like signal-to-noise ratio (SNR), speed, and traffic movement, regularly changes as automobiles proceed along a roadway. The systems make use of VANETs, and they comprise remote services like traffic warnings and weather reporting, vehicle-to-vehicle as well as vehicle-to-roadside facility communications. Nevertheless, since the device nodes in these networks frequently have limited resources, they make use of edge computing, wireless technology, as well as data analytics to enhance the total driving expertise by impacting facets like security, dependability, solace, and financial effectiveness. The goal of this research paper is to discover and emphasize unresolved issues that must be resolved in order to safeguard and effectively combine limited IoT devices with powerful cloud services. In this article, we provide a situation for context-aware content sharing for VANETs and list the precise conditions needed to make it happen. Researchers employ FogNetSim++to simulate various VANET conditions in terms of lag and data rate, revealing issues and openings for further study.
{"title":"Applications for Vehicle Ad HOC Networks and Associated Technical Issues","authors":"R. Raman, Rajesh Singh, Soumyashree Sabat, Shivaji Bothe, Shalini Singh, Lalit Thakur","doi":"10.1109/IC3I56241.2022.10072662","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072662","url":null,"abstract":"The experienced environment of vehicular ad hoc networks (VANETs), like signal-to-noise ratio (SNR), speed, and traffic movement, regularly changes as automobiles proceed along a roadway. The systems make use of VANETs, and they comprise remote services like traffic warnings and weather reporting, vehicle-to-vehicle as well as vehicle-to-roadside facility communications. Nevertheless, since the device nodes in these networks frequently have limited resources, they make use of edge computing, wireless technology, as well as data analytics to enhance the total driving expertise by impacting facets like security, dependability, solace, and financial effectiveness. The goal of this research paper is to discover and emphasize unresolved issues that must be resolved in order to safeguard and effectively combine limited IoT devices with powerful cloud services. In this article, we provide a situation for context-aware content sharing for VANETs and list the precise conditions needed to make it happen. Researchers employ FogNetSim++to simulate various VANET conditions in terms of lag and data rate, revealing issues and openings for further study.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127599325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072741
Fred Torres-Cruz, Swati Tyagi, Manoj Sathe, S. Mary, K. Joshi, Surendra Kumar Shukla
An effective speech signal chat bot’s concept and evolution are discussed in this study. A technological demonstration is presented in the study to test a suggested framework needed to enable such a fake account (a online service). All types of clients can communicate with the public server from any location thanks to web solutions. device, even if a black box method is utilised by regulating the framework between and to the webserver. The service is made available via a created interface that enables easy XML reading, and the flexibility increases the service’s longevity. The screen bot creates personalised user replies that are linked to the intended character by incorporating an artificial heart. Ununderstood questions sent to the bot are analysed further utilising a second classification model (a scientist doing online cognitive study), and the outcome is retained, improving the skills of the cybernetic organisms for the production of answers in the later.
{"title":"Evaluation of Performance of Artificial Intelligence System during Voice Recognition in Social Conversation","authors":"Fred Torres-Cruz, Swati Tyagi, Manoj Sathe, S. Mary, K. Joshi, Surendra Kumar Shukla","doi":"10.1109/IC3I56241.2022.10072741","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072741","url":null,"abstract":"An effective speech signal chat bot’s concept and evolution are discussed in this study. A technological demonstration is presented in the study to test a suggested framework needed to enable such a fake account (a online service). All types of clients can communicate with the public server from any location thanks to web solutions. device, even if a black box method is utilised by regulating the framework between and to the webserver. The service is made available via a created interface that enables easy XML reading, and the flexibility increases the service’s longevity. The screen bot creates personalised user replies that are linked to the intended character by incorporating an artificial heart. Ununderstood questions sent to the bot are analysed further utilising a second classification model (a scientist doing online cognitive study), and the outcome is retained, improving the skills of the cybernetic organisms for the production of answers in the later.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126322424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072781
Neha Shukla, Anand Pandey, A. P. Shukla
We are aware that cardiovascular diseases are very lethal, patients do not get enough time for treatment and the treatment is also expensive for most people. The goal of this study is to predict the likelihood of an acute heart attack using a variety of machine learning approaches, including K closest neighbour, logistic regression, random forest classifier, support vector machine, and XGB classifier. The accuracy score obtained by all the machine learning algorithms has been demonstrated with the help of a table.
{"title":"Heart Anomalies Prediction Utilizing a Variety of Machine Learning Algorithms","authors":"Neha Shukla, Anand Pandey, A. P. Shukla","doi":"10.1109/IC3I56241.2022.10072781","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072781","url":null,"abstract":"We are aware that cardiovascular diseases are very lethal, patients do not get enough time for treatment and the treatment is also expensive for most people. The goal of this study is to predict the likelihood of an acute heart attack using a variety of machine learning approaches, including K closest neighbour, logistic regression, random forest classifier, support vector machine, and XGB classifier. The accuracy score obtained by all the machine learning algorithms has been demonstrated with the help of a table.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128007524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072429
R. Mittal, Varun Malik, A. Rana
Identifying a person’s feelings and sentiments is known as emotion recognition and analysis. The emotion analysis approach correctly recognizes normal people’s facial emotions in the first attempt. Children with Autism Spectrum Disorder (ASD) who have trouble talking or expressing themselves can struggle emotionally to understand. To predict ASD and No ASD in children aged 1-10 using dynamic analysis, this work presents a robust deep learning model with multi-label categorization. We proposed a DL-ASD framework for identifying autism spectrum disorder. The proposed model has used the Kaggle dataset as an image dataset. The datasets are trained with an Improved Convolutional Neural Network (I-CNN), and the images are used to classify individuals as having autism spectrum disorder or not having ASD. Feature-based calculations of internal and exterior distances are used to identify the emotion. Optimization procedures such as dropout, batch normalization, and parameter update are used to optimize the Improved Convolutional Neural Network’s (I-CNN) processing of the returning facial landmarks. The proposed method correctly predicts six emotions in addition to four general emotions. According to the experimental results, the classification accuracy of the approach proposed in this study can reach 98%.
{"title":"DL-ASD: A Deep Learning Approach for Autism Spectrum Disorder","authors":"R. Mittal, Varun Malik, A. Rana","doi":"10.1109/IC3I56241.2022.10072429","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072429","url":null,"abstract":"Identifying a person’s feelings and sentiments is known as emotion recognition and analysis. The emotion analysis approach correctly recognizes normal people’s facial emotions in the first attempt. Children with Autism Spectrum Disorder (ASD) who have trouble talking or expressing themselves can struggle emotionally to understand. To predict ASD and No ASD in children aged 1-10 using dynamic analysis, this work presents a robust deep learning model with multi-label categorization. We proposed a DL-ASD framework for identifying autism spectrum disorder. The proposed model has used the Kaggle dataset as an image dataset. The datasets are trained with an Improved Convolutional Neural Network (I-CNN), and the images are used to classify individuals as having autism spectrum disorder or not having ASD. Feature-based calculations of internal and exterior distances are used to identify the emotion. Optimization procedures such as dropout, batch normalization, and parameter update are used to optimize the Improved Convolutional Neural Network’s (I-CNN) processing of the returning facial landmarks. The proposed method correctly predicts six emotions in addition to four general emotions. According to the experimental results, the classification accuracy of the approach proposed in this study can reach 98%.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121628089","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}
Image processing plays a vital role during the analysis of the data, whenever the image is taken from the device it is not possible that the quality of the image is poor or found a lot of noise. This paper is working on the GAN’s subpart of SRGAN, which helps in processing of the image to get the HR of the image. By using the SRGAN, we just need to input the image’s low resolution, and after processing the data it will convert into a high-resolution image. Here we are reviewing all the related SRGAN papers.
{"title":"An Overview: Super-Image Resolution using Generative Adversarial Network for Image Enhancement","authors":"Ravindra Singh Kushwaha, Manik Rakhra, Dalwinder Singh, Ashutosh Kumar Singh","doi":"10.1109/IC3I56241.2022.10072862","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072862","url":null,"abstract":"Image processing plays a vital role during the analysis of the data, whenever the image is taken from the device it is not possible that the quality of the image is poor or found a lot of noise. This paper is working on the GAN’s subpart of SRGAN, which helps in processing of the image to get the HR of the image. By using the SRGAN, we just need to input the image’s low resolution, and after processing the data it will convert into a high-resolution image. Here we are reviewing all the related SRGAN papers.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131957440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072848
M. Kathikeyan, A. Roy, S. S. Hameed, P. R. Gedamkar, G. Manikandan, Vinita Kale
In India right now, there is a rapid increase in the number of businesses experiencing financial difficulties, and businesses’ overall resilience to risks is low. As a result of advances and changes throughout time, traditional financial accounting has developed into management accounting. Accountants will need to improve their skills and knowledge to add more value to their clients’ businesses in the age of computational intelligence. To establish a corporate financial crisis early warning system, this paper selects the two-year data of five companies from 2019 to 2021 for training samples and the data of five companies for prediction samples, with the goal of detecting the early warning signs of a corporate financial crisis and alerting managers in advance so that they can take swift, decisive action to eliminate any potential threats. Based on the results of the tests, the 6 index variables that best capture the energy industry’s financial woes have been chosen as the starting point for the modeling. Using In order to better the early-warning effect of enterprise financial crisis management and reduce the occurrence of enterprise financial crises, a financial crisis early-warning indicator system was developed from the five aspects of profitability: debt-paying ability, development ability, operation ability, and cash flow ability, using listed companies as examples.crises. We analyse and evaluate data from 2019 to 2021 using operational and Bayesian neural network models, to foresee fiscal risk in 2021. When comparing the two models, neural network for BP model does better than the logical model in terms of how well it fits the data and how well it predicts the future.
{"title":"Optimization System for Financial Early Warning Model Based on the Computational Intelligence and Neural Network Method","authors":"M. Kathikeyan, A. Roy, S. S. Hameed, P. R. Gedamkar, G. Manikandan, Vinita Kale","doi":"10.1109/IC3I56241.2022.10072848","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072848","url":null,"abstract":"In India right now, there is a rapid increase in the number of businesses experiencing financial difficulties, and businesses’ overall resilience to risks is low. As a result of advances and changes throughout time, traditional financial accounting has developed into management accounting. Accountants will need to improve their skills and knowledge to add more value to their clients’ businesses in the age of computational intelligence. To establish a corporate financial crisis early warning system, this paper selects the two-year data of five companies from 2019 to 2021 for training samples and the data of five companies for prediction samples, with the goal of detecting the early warning signs of a corporate financial crisis and alerting managers in advance so that they can take swift, decisive action to eliminate any potential threats. Based on the results of the tests, the 6 index variables that best capture the energy industry’s financial woes have been chosen as the starting point for the modeling. Using In order to better the early-warning effect of enterprise financial crisis management and reduce the occurrence of enterprise financial crises, a financial crisis early-warning indicator system was developed from the five aspects of profitability: debt-paying ability, development ability, operation ability, and cash flow ability, using listed companies as examples.crises. We analyse and evaluate data from 2019 to 2021 using operational and Bayesian neural network models, to foresee fiscal risk in 2021. When comparing the two models, neural network for BP model does better than the logical model in terms of how well it fits the data and how well it predicts the future.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132495137","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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