Pub Date : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936448
Kanak Mahor, A. Manjhvar
Worldwide, the (COVID-19) pandemic had also affected people's daily routines. In general also during lockdown periods, people around the world use social media to express their thoughts and feelings about the epidemic which has interrupted their daily lives. There has been a huge spike in tweets about coronavirus on Twitter in a short period of time, including both positive and negative messages. As a result of the wide range of content in the tweets, the researchers have turned to sentiment analysis in order to gauge how the general public feels about COVID-19. According to the findings of this study, the best way to examine COVID-19 is to look at how people use Twitter to share their thoughts and opinions. Sentiment categorization can be accomplished by utilising a variety of feature sets as well as classifiers in combination with the suggested approach. Tweets collected from people with COVID-19 perceptions can be used to better understand and manage the epidemic. Positive, negative, as well as neutral emotion classifications are being used to classify tweets. In this study, Tweets containing specific information about the Coronavirus epidemic are used as sentiment analysis packages. Bidirectional Encoder Representations from Transformers (BERT) are used to identify sentiment categories, whereas the TF-IDF (term frequency-inverse document frequency) prototype is used to summarise the topics of postings. Trend analysis and qualitative methods are being used to identify negative sentiment traits. In general, when it comes to sentiment classification, the fine-tuned BERT is very accurate. In addition, the COVID-19-related post features of TF-IDF themes are accurately conveyed. Coronavirus tweet sentiments are analysed using a BERT and TF-IDF hybrid classifier. Single-sentence classification is transformed into pair-sentence classification, which solves BERT's performance issue in text classification problems. Our evaluation measures (accuracy= 0.70; precision= 0.67; recall= 0.64; and F1-score= 0.65) are used to evaluate the effectiveness of the classifier.
{"title":"Public Sentiment Assessment of Coronavirus-Specific Tweets using a Transformer-based BERT Classifier","authors":"Kanak Mahor, A. Manjhvar","doi":"10.1109/ICECAA55415.2022.9936448","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936448","url":null,"abstract":"Worldwide, the (COVID-19) pandemic had also affected people's daily routines. In general also during lockdown periods, people around the world use social media to express their thoughts and feelings about the epidemic which has interrupted their daily lives. There has been a huge spike in tweets about coronavirus on Twitter in a short period of time, including both positive and negative messages. As a result of the wide range of content in the tweets, the researchers have turned to sentiment analysis in order to gauge how the general public feels about COVID-19. According to the findings of this study, the best way to examine COVID-19 is to look at how people use Twitter to share their thoughts and opinions. Sentiment categorization can be accomplished by utilising a variety of feature sets as well as classifiers in combination with the suggested approach. Tweets collected from people with COVID-19 perceptions can be used to better understand and manage the epidemic. Positive, negative, as well as neutral emotion classifications are being used to classify tweets. In this study, Tweets containing specific information about the Coronavirus epidemic are used as sentiment analysis packages. Bidirectional Encoder Representations from Transformers (BERT) are used to identify sentiment categories, whereas the TF-IDF (term frequency-inverse document frequency) prototype is used to summarise the topics of postings. Trend analysis and qualitative methods are being used to identify negative sentiment traits. In general, when it comes to sentiment classification, the fine-tuned BERT is very accurate. In addition, the COVID-19-related post features of TF-IDF themes are accurately conveyed. Coronavirus tweet sentiments are analysed using a BERT and TF-IDF hybrid classifier. Single-sentence classification is transformed into pair-sentence classification, which solves BERT's performance issue in text classification problems. Our evaluation measures (accuracy= 0.70; precision= 0.67; recall= 0.64; and F1-score= 0.65) are used to evaluate the effectiveness of the classifier.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128896452","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936224
Yuanyuan Lv
Laser scanning and infrared feature analysis technology are used to collect images of fake art works, and superpixel fusion method is used to fuse the collected information to extract the boundary feature information of images. The article describes in detail how teachers organize and carry out activities in art areas Effective strategies, through the development of colorful art area activities, make art area activities interesting, playable, studious, and develop children's creative thinking ability. The segmentation accuracy rate is stable. Object segmentation technology provides accurate positioning for further contour extraction and removes useless image background.
{"title":"Stability Test of Traditional Art Image Recognition Algorithm Integrated into Children’s Art Works Information Display Platform","authors":"Yuanyuan Lv","doi":"10.1109/ICECAA55415.2022.9936224","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936224","url":null,"abstract":"Laser scanning and infrared feature analysis technology are used to collect images of fake art works, and superpixel fusion method is used to fuse the collected information to extract the boundary feature information of images. The article describes in detail how teachers organize and carry out activities in art areas Effective strategies, through the development of colorful art area activities, make art area activities interesting, playable, studious, and develop children's creative thinking ability. The segmentation accuracy rate is stable. Object segmentation technology provides accurate positioning for further contour extraction and removes useless image background.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679742","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936239
S. Sivakumar, D. Jayaram, S. V, V. Avasthi, R. Dhanalakshmi, S. S. Kumar
More than 500,000 humans go to emergency rooms every year for kidney stone problems. One out of each ten humans will broaden a kidney stone sooner or later in their lives. In India, kidney stones are one of the most common diseases which can be fatal if not treated properly. It can be caused by various parameters making it even more difficult to treat. When kidney stones are discovered in their early stages, they are much easier to treat than when they are discovered later on. To help this purpose, this study aims the development a website that is capable of predicting the presence of kidney stones using an image that was uploaded by the user itself. This website serves as a preliminary screening tool for the detection of kidney stones. This website is backed up by the algorithm which is proven to be the best in the prediction of kidney stones after a comparison between two different algorithms. These algorithms are trained and tested using the dataset which was obtained from Kaggle. This dataset is preprocessed to ensure the best performance of the classifier models. The performance of both the models is then compared and it is found that theSupport Vector Machine (SVM) algorithm is better than the Logistic Regression (LR) algorithm. The website is also integrated with the cloud using the AWS platform. This ensures the presence of an eternal space that supports the website when the number of users of the website increases.
{"title":"Deployment of Disease Prediction Model in AWS Cloud","authors":"S. Sivakumar, D. Jayaram, S. V, V. Avasthi, R. Dhanalakshmi, S. S. Kumar","doi":"10.1109/ICECAA55415.2022.9936239","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936239","url":null,"abstract":"More than 500,000 humans go to emergency rooms every year for kidney stone problems. One out of each ten humans will broaden a kidney stone sooner or later in their lives. In India, kidney stones are one of the most common diseases which can be fatal if not treated properly. It can be caused by various parameters making it even more difficult to treat. When kidney stones are discovered in their early stages, they are much easier to treat than when they are discovered later on. To help this purpose, this study aims the development a website that is capable of predicting the presence of kidney stones using an image that was uploaded by the user itself. This website serves as a preliminary screening tool for the detection of kidney stones. This website is backed up by the algorithm which is proven to be the best in the prediction of kidney stones after a comparison between two different algorithms. These algorithms are trained and tested using the dataset which was obtained from Kaggle. This dataset is preprocessed to ensure the best performance of the classifier models. The performance of both the models is then compared and it is found that theSupport Vector Machine (SVM) algorithm is better than the Logistic Regression (LR) algorithm. The website is also integrated with the cloud using the AWS platform. This ensures the presence of an eternal space that supports the website when the number of users of the website increases.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121951350","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936511
T. Varshini, Badgu Samatha
Arboviral disease-dengue infections are viral diseases that are transmitted via the bite of infected insects such as mosquitoes. Some of the well-known vector-borne diseases are chikungunya, zika, yellow fever, etc. According to the national centre for vector-borne disease control, the growing number of dengue infections in India has reached 1,23,106 cases in September 2021. This unprecedented increase in the infection has resulted in developing new and automated technologies to detect and recognize the platelets. Aside from the symptoms, this condition can be identified via a blood smear. The proposed technology is based on the images retrieved from blood smears. The image processing and segmentation has been performed by incorporating a deep learning algorithm to detect and determine whether the image is dengue infected or not infected by counting the platelets in the blood cells.
{"title":"Deep Learning Technology to Identify Arboviral Disease-Dengue Prediction","authors":"T. Varshini, Badgu Samatha","doi":"10.1109/ICECAA55415.2022.9936511","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936511","url":null,"abstract":"Arboviral disease-dengue infections are viral diseases that are transmitted via the bite of infected insects such as mosquitoes. Some of the well-known vector-borne diseases are chikungunya, zika, yellow fever, etc. According to the national centre for vector-borne disease control, the growing number of dengue infections in India has reached 1,23,106 cases in September 2021. This unprecedented increase in the infection has resulted in developing new and automated technologies to detect and recognize the platelets. Aside from the symptoms, this condition can be identified via a blood smear. The proposed technology is based on the images retrieved from blood smears. The image processing and segmentation has been performed by incorporating a deep learning algorithm to detect and determine whether the image is dengue infected or not infected by counting the platelets in the blood cells.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122418170","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936278
Arpit Arora, Mohana Mohana
In the product development and management area, .NET is critical. The sequential development of versions of .NET describes the importance and continuous feedback of customers about their experience. There are several architectural and functional differences of .NET evolution to its cross-platform version i.e., .NET core and above. Prominence of .NET in the improvement of development sector is evident. Quantum of open-source projects all over the globe and place of C# among the five most well-known programming languages are two pointers. Its ubiquity is simply going to develop, particularly now that the most recent emphasis (.NET 5) has changed business by presenting the idea of general programming advancement. .NET help for programming improvement isn’t restricted to the numerous programming dialects can utilize. .NET likewise advances utilization of a few prescribed procedures while allowing to utilize the methodology like to construct our application. .NET framework was the underlying kind of .NET. It gives engineer a bunch of APIs for most widely recognized programming needs and connects with basic working framework. It runs just on Windows, and its lifecycle is reaching a conclusion at this moment, after the arrival of .NET 5. Numerous executions emerged from that point forward, so the .NET name made ambiguities. .NET 5 means to make concrete the underlying vision of a widespread improvement stage.
在产品开发和管理领域，. net至关重要。. net版本的连续开发描述了客户对其体验的重要性和持续反馈。从。net演进到跨平台版本，即。net核心及以上版本，在架构和功能上存在一些差异。. net在改进开发领域中的突出作用是显而易见的。全球开源项目的数量和c#在五大最知名编程语言中的地位是两个指针。它的无处不在只会继续发展，特别是现在，最近的重点是。.NET通过提出通用编程改进的概念改变了业务。.NET对编程改进的帮助不仅限于可以使用的众多编程方言。.NET还促进了一些指定过程的使用，同时允许使用类似于构建应用程序的方法。NET框架是。NET的基础。它为工程师提供了一堆api，以满足最广泛认可的编程需求，并与基本的工作框架相连接。它只在Windows上运行，在。net 5到来之后，它的生命周期在这一刻即将结束。从那以后出现了大量的执行，所以。net的名字变得模棱两可，. net 5意味着使广泛改进阶段的潜在愿景具体化。
{"title":"Architectural and Functional Differences in Dot Net Solutions","authors":"Arpit Arora, Mohana Mohana","doi":"10.1109/ICECAA55415.2022.9936278","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936278","url":null,"abstract":"In the product development and management area, .NET is critical. The sequential development of versions of .NET describes the importance and continuous feedback of customers about their experience. There are several architectural and functional differences of .NET evolution to its cross-platform version i.e., .NET core and above. Prominence of .NET in the improvement of development sector is evident. Quantum of open-source projects all over the globe and place of C# among the five most well-known programming languages are two pointers. Its ubiquity is simply going to develop, particularly now that the most recent emphasis (.NET 5) has changed business by presenting the idea of general programming advancement. .NET help for programming improvement isn’t restricted to the numerous programming dialects can utilize. .NET likewise advances utilization of a few prescribed procedures while allowing to utilize the methodology like to construct our application. .NET framework was the underlying kind of .NET. It gives engineer a bunch of APIs for most widely recognized programming needs and connects with basic working framework. It runs just on Windows, and its lifecycle is reaching a conclusion at this moment, after the arrival of .NET 5. Numerous executions emerged from that point forward, so the .NET name made ambiguities. .NET 5 means to make concrete the underlying vision of a widespread improvement stage.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121090705","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936567
G. Murthy, V. Iswarya, K. R. Sri, K. Harshitha, Ch. Prasanth
Spasmodic dysphonia, a rare voice disorder is detected in the current work using Random Forest frame work. Voice pathology is related to the vocal tract area affecting the quality of speech. Numerous voice pathologies have been over the years of them are unnoticed as the symptoms are not significant. Even the symptoms are known the nature of the disorder is difficult to identify due to the over lapping nature of the symptoms. The existing algorithms for voice pathology detection are capable of classifying between normal and affected subjects, while the nature of the disorder has been considered in the proposed algorithm. Computational complexity has been reduced due to the incorporation of finite significant energy features estimated over non overlapping frames. Classification of accuracy of 93.5 has been seen with a population of 100 trees.
{"title":"A Novel Algorithm for Detecting Spasmodic Dysphonia Voice Pathology using Random Forest Frame Work","authors":"G. Murthy, V. Iswarya, K. R. Sri, K. Harshitha, Ch. Prasanth","doi":"10.1109/ICECAA55415.2022.9936567","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936567","url":null,"abstract":"Spasmodic dysphonia, a rare voice disorder is detected in the current work using Random Forest frame work. Voice pathology is related to the vocal tract area affecting the quality of speech. Numerous voice pathologies have been over the years of them are unnoticed as the symptoms are not significant. Even the symptoms are known the nature of the disorder is difficult to identify due to the over lapping nature of the symptoms. The existing algorithms for voice pathology detection are capable of classifying between normal and affected subjects, while the nature of the disorder has been considered in the proposed algorithm. Computational complexity has been reduced due to the incorporation of finite significant energy features estimated over non overlapping frames. Classification of accuracy of 93.5 has been seen with a population of 100 trees.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121107984","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936486
Bhavya Dhiman, Rubin Bose S
KYC or Know Your Customer is the procedure to verify the individuality of its consumers & evaluating the possible dangers of illegitimate trade relations. A few problems with the existing KYC manual process are that it is less secure, time-consuming and expensive. With the advent of Blockchain technology, its structures such as consistency, security, and geographical diversity make them an ideal solution to such problems. Although marketing solutions such as KYC-chain.co, K-Y-C. The legal right to enable blockchain-based KYC authentication provides a way for documents to be verified by a trusted network participant. This project uses an ETHereum based Optimised KYC Block-chain system with uniform A-E-S encryption and compression built on the LZ method. The system publicly verifies a distributed encryption, is protected by cryptography, operates by pressing the algorithm and is all well-designed blockchain features. The suggested scheme is a novel explanation based on Distributed Ledger Technology or Blockchain technology that would cut KYC authentication process expenses of organisations & decrease the regular schedule for completion of the procedure whilst becoming easier for clients. The largest difference in the system in traditional methods is the full authentication procedure is performed in just no time for every client, regardless of the number of institutions you desire to be linked to. Furthermore, since DLT is employed, validation findings may be securely distributed to consumers, enhancing transparency. Based on this method, a Proof of Concept (POC) is produced with Ethereum's API, websites as endpoints and the android app as the front office, recognising the viability and efficacy of this technique. Ultimately, this strategy enhances consumer satisfaction, lowers budget overrun & promotes transparency in the customer transport network.
{"title":"A Reliable, Secure and Efficient Decentralised Conditional of KYC Verification System: A Blockchain Approach","authors":"Bhavya Dhiman, Rubin Bose S","doi":"10.1109/ICECAA55415.2022.9936486","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936486","url":null,"abstract":"KYC or Know Your Customer is the procedure to verify the individuality of its consumers & evaluating the possible dangers of illegitimate trade relations. A few problems with the existing KYC manual process are that it is less secure, time-consuming and expensive. With the advent of Blockchain technology, its structures such as consistency, security, and geographical diversity make them an ideal solution to such problems. Although marketing solutions such as KYC-chain.co, K-Y-C. The legal right to enable blockchain-based KYC authentication provides a way for documents to be verified by a trusted network participant. This project uses an ETHereum based Optimised KYC Block-chain system with uniform A-E-S encryption and compression built on the LZ method. The system publicly verifies a distributed encryption, is protected by cryptography, operates by pressing the algorithm and is all well-designed blockchain features. The suggested scheme is a novel explanation based on Distributed Ledger Technology or Blockchain technology that would cut KYC authentication process expenses of organisations & decrease the regular schedule for completion of the procedure whilst becoming easier for clients. The largest difference in the system in traditional methods is the full authentication procedure is performed in just no time for every client, regardless of the number of institutions you desire to be linked to. Furthermore, since DLT is employed, validation findings may be securely distributed to consumers, enhancing transparency. Based on this method, a Proof of Concept (POC) is produced with Ethereum's API, websites as endpoints and the android app as the front office, recognising the viability and efficacy of this technique. Ultimately, this strategy enhances consumer satisfaction, lowers budget overrun & promotes transparency in the customer transport network.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"118 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126417048","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936335
Cuddapah Anitha, K. Komala Devi, D. Jayasutha, B. Gomathi, R. Mahaveerakannan, Chamandeep Kaur
Internet of Things (IoT) developments in biomedical and health care technology have opened up exciting new avenues for innovation. A wide range of principles and fascinating examples are explored in this chapter, including theoretical, methodological, conceptual, and empirical aspects of the subject. This research study is initiated with a description on how IoT and big data are being used to analyze a massive image database created daily from diverse sources using big data, machine learning, and other kinds of artificial intelligence to produce structured data for remote diagnosis. Health care providers may rely on the heterogeneous IoT platform to manage their data reliably, thanks to dedicated computing equipment. It is critical to healthcare service reliability that varied data streams are effectively managed owing to variations and errors. To make sense of the gathered data, a Chi-square-based term feature extraction method was employed. Outliers in sensor data are filtered out and unwanted features are removed with the use of density-based spatial clustering (DBSCAN) and random forest (RF)-backward feature elimination (BFE) as RF-BFE. The pre-trained model of Convolutional Neural Network (CNN) is used to make predictions based on these features. Finally, experiments are run to determine the effectiveness of the suggested model based on a number of different criteria.
{"title":"Development of Medical Internet of Things with Big Data using RF-BFA and DL in Healthcare System","authors":"Cuddapah Anitha, K. Komala Devi, D. Jayasutha, B. Gomathi, R. Mahaveerakannan, Chamandeep Kaur","doi":"10.1109/ICECAA55415.2022.9936335","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936335","url":null,"abstract":"Internet of Things (IoT) developments in biomedical and health care technology have opened up exciting new avenues for innovation. A wide range of principles and fascinating examples are explored in this chapter, including theoretical, methodological, conceptual, and empirical aspects of the subject. This research study is initiated with a description on how IoT and big data are being used to analyze a massive image database created daily from diverse sources using big data, machine learning, and other kinds of artificial intelligence to produce structured data for remote diagnosis. Health care providers may rely on the heterogeneous IoT platform to manage their data reliably, thanks to dedicated computing equipment. It is critical to healthcare service reliability that varied data streams are effectively managed owing to variations and errors. To make sense of the gathered data, a Chi-square-based term feature extraction method was employed. Outliers in sensor data are filtered out and unwanted features are removed with the use of density-based spatial clustering (DBSCAN) and random forest (RF)-backward feature elimination (BFE) as RF-BFE. The pre-trained model of Convolutional Neural Network (CNN) is used to make predictions based on these features. Finally, experiments are run to determine the effectiveness of the suggested model based on a number of different criteria.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"26 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127654466","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936341
Gogineni Hima Bindu, Thalakola Syamsundararao, Vuyyuru Lakshmareddy, P. R., Dasari Koteswara Rao, B. Samatha
The steady increase in the death tolls due to road accidents has gained a significant research attention from both academia and industries. The main reason behind road accidents is vehicle collision. In particular, to model the effect of accidents, the rear-end collisions can be analyzed by using vehicle location and speed. Moreover, the speed, direction, distance between cars, and relative speed of each vehicle simulator in various accident/collision scenarios in automobile networks must be investigated and analyzed. A safety system has been designed to reduce the probability of accidents. The proposed technique estimates the impact of a vehicular collision by considering: pedestrian crossings, interval between collisions, and accident avoidance at intersections. The proposed method is dependent on a novel criterion to determine accidents with 92.6% accuracy. Cases with a 7.4% chance of occurrence allow the passive safety system to help people survive and prevent injury in the case of an emergency.
{"title":"Road Safety Approach to Mitigating the Accidents in Vehicular Networks","authors":"Gogineni Hima Bindu, Thalakola Syamsundararao, Vuyyuru Lakshmareddy, P. R., Dasari Koteswara Rao, B. Samatha","doi":"10.1109/ICECAA55415.2022.9936341","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936341","url":null,"abstract":"The steady increase in the death tolls due to road accidents has gained a significant research attention from both academia and industries. The main reason behind road accidents is vehicle collision. In particular, to model the effect of accidents, the rear-end collisions can be analyzed by using vehicle location and speed. Moreover, the speed, direction, distance between cars, and relative speed of each vehicle simulator in various accident/collision scenarios in automobile networks must be investigated and analyzed. A safety system has been designed to reduce the probability of accidents. The proposed technique estimates the impact of a vehicular collision by considering: pedestrian crossings, interval between collisions, and accident avoidance at intersections. The proposed method is dependent on a novel criterion to determine accidents with 92.6% accuracy. Cases with a 7.4% chance of occurrence allow the passive safety system to help people survive and prevent injury in the case of an emergency.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128782447","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936159
Lijuan Cui
A new swarm intelligence algorithm, the Wolf Pack Algorithm has been proposed in this paper, and the convergence of the algorithm is proved based on the Markov chain theory. It reduces the risk of the algorithm falling into local optimum due to the excessively large penalty parameter. Inspired by the reproduction mode of wol ves, a big data environment analysis for the stability of the QoS system for abnormal data is proposed based on the binary wolf pack algorithm. Moreover, the Convolutional Neural Network with 4 hidden layers is used to classify and evaluate the constructed time series financial data. Data testing and analysis are performed using actual financial data. It is believed that the supervision system and relevant laws and regulations need to be improved first; secondly, the big data is used to collect personal credit records so as to establish a sound credit system as soon as possible; finally, through big data and computer technology, risk control methods are innovated to enhance the stability of Internet finance.
{"title":"Analysis of Stability Big Data Environment of Intelligent Financial Data Abnormal QoS System based on Wolf Pack Algorithm","authors":"Lijuan Cui","doi":"10.1109/ICECAA55415.2022.9936159","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936159","url":null,"abstract":"A new swarm intelligence algorithm, the Wolf Pack Algorithm has been proposed in this paper, and the convergence of the algorithm is proved based on the Markov chain theory. It reduces the risk of the algorithm falling into local optimum due to the excessively large penalty parameter. Inspired by the reproduction mode of wol ves, a big data environment analysis for the stability of the QoS system for abnormal data is proposed based on the binary wolf pack algorithm. Moreover, the Convolutional Neural Network with 4 hidden layers is used to classify and evaluate the constructed time series financial data. Data testing and analysis are performed using actual financial data. It is believed that the supervision system and relevant laws and regulations need to be improved first; secondly, the big data is used to collect personal credit records so as to establish a sound credit system as soon as possible; finally, through big data and computer technology, risk control methods are innovated to enhance the stability of Internet finance.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128966747","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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