Pub Date : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936118
Li Xu
This paper proposes a relatively complete multidimensional dynamic data mining system theoretical framework, constructs a multi-dimensional dynamic information representation model, establishes a time series mining model based on support vector regression machine, and a continuous input and output process neural network mining model. The new information teaching system with "multi-dimensional information processing and application in professional research" as the main structure has shown its advantages and characteristics in the current innovative education platform of colleges and universities in my country. This new information teaching system has played a positive supporting role in the development of relevant courses, innovative education platforms and students' innovative ability in colleges and universities.
{"title":"Online Platform Innovation of Targeted Training in College Education based on Multi-Dimensional Information Data Mining","authors":"Li Xu","doi":"10.1109/ICECAA55415.2022.9936118","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936118","url":null,"abstract":"This paper proposes a relatively complete multidimensional dynamic data mining system theoretical framework, constructs a multi-dimensional dynamic information representation model, establishes a time series mining model based on support vector regression machine, and a continuous input and output process neural network mining model. The new information teaching system with \"multi-dimensional information processing and application in professional research\" as the main structure has shown its advantages and characteristics in the current innovative education platform of colleges and universities in my country. This new information teaching system has played a positive supporting role in the development of relevant courses, innovative education platforms and students' innovative ability in colleges and universities.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"208 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":"115047395","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/ICECAA55415.2022.9936560
Junaid Ahmed Mohammed Abdul, Santhosh Kumar Dhatrika, P. Kumar
A Wireless Sensor Network is an infrastructure-free wireless network that uses an ad-hoc deployment of a large number of wireless sensors to monitor system, physical, and environmental factors. Sensor node energy consumption is a major determinant of wireless sensor network longevity. The Distributed Energy-aware Fuzzy Logic-based routing algorithm (DEFL) proposed in this paper aims to strike a compromise between energy efficiency measures balance. For the shortest path calculation, our architecture captures the network state using relevant energy measurements and maps them to cost values. I also added a Redundant Packet Monitoring Algorithm to each sensor node as a recommended technique, which attaches temporary memory to each sensor node and checks it anytime the sensor node senses any data.
{"title":"A Technique to Improve the Lifetime of Heterogeneous Wireless Sensor Networks by Removing Redundant Packets","authors":"Junaid Ahmed Mohammed Abdul, Santhosh Kumar Dhatrika, P. Kumar","doi":"10.1109/ICECAA55415.2022.9936560","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936560","url":null,"abstract":"A Wireless Sensor Network is an infrastructure-free wireless network that uses an ad-hoc deployment of a large number of wireless sensors to monitor system, physical, and environmental factors. Sensor node energy consumption is a major determinant of wireless sensor network longevity. The Distributed Energy-aware Fuzzy Logic-based routing algorithm (DEFL) proposed in this paper aims to strike a compromise between energy efficiency measures balance. For the shortest path calculation, our architecture captures the network state using relevant energy measurements and maps them to cost values. I also added a Redundant Packet Monitoring Algorithm to each sensor node as a recommended technique, which attaches temporary memory to each sensor node and checks it anytime the sensor node senses any data.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"3 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":"121904674","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/ICECAA55415.2022.9936547
Baboo Barik, D. Srinivasan, K. Arulvendhan, Suresh N
A solar cell turns photon energy into electrical potential in a P-N junction (P-Type and N-Type), which are both equivalent circuits. While synchronizing with various grid and non-linear loads, the PV Photovoltaic input source comprises oscillations distorting, voltage sags/swell, and dc voltage of power quality concerns. The proposed technique for resolving the problem is Grid-connected output-based Photovoltaic (P.V.) System Power Quality Improvement. Proportional Integral (PI) Controllers are used in this method to control parameters like sampling rate and Improved Disrupt and Observe values, which have a substantial impact on the inter oscillatory form property of PV systems. The High gain (Step-Up) DC-DC Converter coupled based capacitor is recovered by the passive clamped circuit, which also limits the switch. Maximum power point tracking is a controller technique that provides inter harmonic emission, which is one of the most significant pieces of enhancing source voltage and current. The end result is improved power quality and gain without even any distortion in the Renewable Energy System's output.
{"title":"High step-up DC-DC Converter based Renewable Energy System for Improving Power Quality and Low Voltage Stress using PI Controller Technique","authors":"Baboo Barik, D. Srinivasan, K. Arulvendhan, Suresh N","doi":"10.1109/ICECAA55415.2022.9936547","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936547","url":null,"abstract":"A solar cell turns photon energy into electrical potential in a P-N junction (P-Type and N-Type), which are both equivalent circuits. While synchronizing with various grid and non-linear loads, the PV Photovoltaic input source comprises oscillations distorting, voltage sags/swell, and dc voltage of power quality concerns. The proposed technique for resolving the problem is Grid-connected output-based Photovoltaic (P.V.) System Power Quality Improvement. Proportional Integral (PI) Controllers are used in this method to control parameters like sampling rate and Improved Disrupt and Observe values, which have a substantial impact on the inter oscillatory form property of PV systems. The High gain (Step-Up) DC-DC Converter coupled based capacitor is recovered by the passive clamped circuit, which also limits the switch. Maximum power point tracking is a controller technique that provides inter harmonic emission, which is one of the most significant pieces of enhancing source voltage and current. The end result is improved power quality and gain without even any distortion in the Renewable Energy System's output.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"30 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":"123403893","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/ICECAA55415.2022.9936409
D. Mahalakshmi, S. Appavu alias Balamurugan, M. Chinnadurai, D. Vaishnavi
Data processing and analytics are wide spread study with profound applications. Data analytics deals with deriving or applying an algorithm to an application that work with dataset. The proposed work analyses the image data with optimization algorithm by using novel method of Fire-Fly (FF) algorithm, which is named as Densely Search Fire-Fly (DSFF) optimization algorithm. The Neural Network (NN) is applied to classify the optimized data. In this process, the optimized data refers to selective attributes from the raw data of image features. To test the performance of proposed optimization, the Gabor feature extraction method is used to fetch the features from raw image data. The Gabor method retrieves the pattern in various angle of projections. This produces 5 × 8 number of patterns to represent the image feature. From this feature attributes of whole image dataset, the optimization search for the best attributes by the reference of weight value is calculated from the particles of Fire-Fly. According to the best selection of attributes from the objective function, the neurons in a network that can segregate the different classes in the training dataset. The performance of the proposed FF algorithm are compared with the traditional optimization methods in the image classification application.
{"title":"A Novel Densely Search based Fire-Fly (DSFF) Optimization Algorithm for Image Classification Application","authors":"D. Mahalakshmi, S. Appavu alias Balamurugan, M. Chinnadurai, D. Vaishnavi","doi":"10.1109/ICECAA55415.2022.9936409","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936409","url":null,"abstract":"Data processing and analytics are wide spread study with profound applications. Data analytics deals with deriving or applying an algorithm to an application that work with dataset. The proposed work analyses the image data with optimization algorithm by using novel method of Fire-Fly (FF) algorithm, which is named as Densely Search Fire-Fly (DSFF) optimization algorithm. The Neural Network (NN) is applied to classify the optimized data. In this process, the optimized data refers to selective attributes from the raw data of image features. To test the performance of proposed optimization, the Gabor feature extraction method is used to fetch the features from raw image data. The Gabor method retrieves the pattern in various angle of projections. This produces 5 × 8 number of patterns to represent the image feature. From this feature attributes of whole image dataset, the optimization search for the best attributes by the reference of weight value is calculated from the particles of Fire-Fly. According to the best selection of attributes from the objective function, the neurons in a network that can segregate the different classes in the training dataset. The performance of the proposed FF algorithm are compared with the traditional optimization methods in the image classification application.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"77 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":"125234714","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/ICECAA55415.2022.9936206
A. P, Avinash Sharma, S. R. Kawale, S. P. Diwan, Dankan Gowda V
Unlike the healthy cells in the breast tissue, cancerous breast cells are unwelcome and have strange properties. In both sexes, this will quickly expand and infiltrate adjacent tissue, leading to the formation of a tumour. Using the Intelligent-Breast Abnormality Detection (I-BAD) framework, many breast cancer parameters are evaluated in this article. It has already been shown that some indicators may be used for early detection of breast cancer. There is also discussion of the instruments and strategies that facilitate the monitoring of the selected breast health metrics. Classification methods that use machine learning to store and analyse data are also discussed. The suggested I-BAD framework’s process is then visually shown in clean drawings.
{"title":"Intelligent Breast Abnormality Framework for Detection and Evaluation of Breast Abnormal Parameters","authors":"A. P, Avinash Sharma, S. R. Kawale, S. P. Diwan, Dankan Gowda V","doi":"10.1109/ICECAA55415.2022.9936206","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936206","url":null,"abstract":"Unlike the healthy cells in the breast tissue, cancerous breast cells are unwelcome and have strange properties. In both sexes, this will quickly expand and infiltrate adjacent tissue, leading to the formation of a tumour. Using the Intelligent-Breast Abnormality Detection (I-BAD) framework, many breast cancer parameters are evaluated in this article. It has already been shown that some indicators may be used for early detection of breast cancer. There is also discussion of the instruments and strategies that facilitate the monitoring of the selected breast health metrics. Classification methods that use machine learning to store and analyse data are also discussed. The suggested I-BAD framework’s process is then visually shown in clean drawings.","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":"123278040","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/ICECAA55415.2022.9936568
D. Ganesh, K. Suresh, M. S. Kumar, K. Balaji, Sreedhar Burada
As a result of this new computer design, edge computing can process data rapidly and effectively near to the source, avoiding network resource and latency constraints. By shifting computing power to the network edge, edge computing decreases the load on cloud services centers while also reducing the time required for users to input data. Edge computing advantages for data-intensive services, in particular, could be obscured if access latency becomes a bottleneck. Edge computing raises a number of challenges, such as security concerns, data incompleteness, and a hefty up-front and ongoing expense. There is now a shift in the worldwide mobile communications sector toward 5G technology. This unprecedented attention to edge computing has come about because 5G is one of the primary entry technologies for large-scale deployment. Edge computing privacy has been a major concern since the technology’s inception, limiting its adoption and advancement. As the capabilities of edge computing have evolved, so have the security issues that have arisen as a result of these developments, as well as the increasing public demand for privacy protection. The lack of trust amongst IoT devices is exacerbated by the inherent security concerns and assaults that plague IoT edge devices. A cognitive trust management system is proposed to reduce this malicious activity by maintaining the confidence of an appliance & managing the service level belief & Quality of Service (QoS). Improved packet delivery ratio and jitter in cognitive trust management systems based on QoS parameters show promise for spotting potentially harmful edge nodes in computing networks at the edge.
{"title":"Improving Security in Edge Computing by using Cognitive Trust Management Model","authors":"D. Ganesh, K. Suresh, M. S. Kumar, K. Balaji, Sreedhar Burada","doi":"10.1109/ICECAA55415.2022.9936568","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936568","url":null,"abstract":"As a result of this new computer design, edge computing can process data rapidly and effectively near to the source, avoiding network resource and latency constraints. By shifting computing power to the network edge, edge computing decreases the load on cloud services centers while also reducing the time required for users to input data. Edge computing advantages for data-intensive services, in particular, could be obscured if access latency becomes a bottleneck. Edge computing raises a number of challenges, such as security concerns, data incompleteness, and a hefty up-front and ongoing expense. There is now a shift in the worldwide mobile communications sector toward 5G technology. This unprecedented attention to edge computing has come about because 5G is one of the primary entry technologies for large-scale deployment. Edge computing privacy has been a major concern since the technology’s inception, limiting its adoption and advancement. As the capabilities of edge computing have evolved, so have the security issues that have arisen as a result of these developments, as well as the increasing public demand for privacy protection. The lack of trust amongst IoT devices is exacerbated by the inherent security concerns and assaults that plague IoT edge devices. A cognitive trust management system is proposed to reduce this malicious activity by maintaining the confidence of an appliance & managing the service level belief & Quality of Service (QoS). Improved packet delivery ratio and jitter in cognitive trust management systems based on QoS parameters show promise for spotting potentially harmful edge nodes in computing networks at the edge.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"270 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":"122467909","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/ICECAA55415.2022.9936341
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/ICECAA55415.2022.9936448
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/ICECAA55415.2022.9936159
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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