Pub Date : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972221
Emmanuel Muragijimana, T. Shankar, Dr. Naween Kumar, Basant Sah, Sasmita Padhy
In the internet era cloud via blockchain performs a vital role in the domain of Information Technology. Cyber forensics faces several challenges in the cloud such as AWS, IBM, Google, Microsoft, etc. in terms of investigations to resolve various cybercrimes. In current trends, cloud computing is ubiquitous and popular for availing inexpensive services with good speed. The systems of this computing are distinct from the conventional internet technology by employing advanced computations. The availability of the information and its ascertainment case of need is an important factor to monitor the counterfeit attack by the cyber forensic study is one of the investigations processes to prove the authentication. To overcome such situations the forensics method via blockchain is proposed with the hands-on experimental output.
{"title":"Digital Crimes in Cloud Environment and the Analysis via Blockchain","authors":"Emmanuel Muragijimana, T. Shankar, Dr. Naween Kumar, Basant Sah, Sasmita Padhy","doi":"10.1109/GCAT55367.2022.9972221","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972221","url":null,"abstract":"In the internet era cloud via blockchain performs a vital role in the domain of Information Technology. Cyber forensics faces several challenges in the cloud such as AWS, IBM, Google, Microsoft, etc. in terms of investigations to resolve various cybercrimes. In current trends, cloud computing is ubiquitous and popular for availing inexpensive services with good speed. The systems of this computing are distinct from the conventional internet technology by employing advanced computations. The availability of the information and its ascertainment case of need is an important factor to monitor the counterfeit attack by the cyber forensic study is one of the investigations processes to prove the authentication. To overcome such situations the forensics method via blockchain is proposed with the hands-on experimental output.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133244864","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9971908
P. Seema, M. P. Suresh, M. Nair
The world is becoming smarter. Technology is spreading even in the grid side, nowadays the energy meter is being converted to smart meter and more home energy management systems are being installed. This paper proposes the combination of Smart Meter (SM) and Home Energy Management System (HEMS) to form a new Smart Energy Management System, which have the characteristics of both SM and HEMS, in turn help the grid and the consumer at the same time. The consumer can allocate the loads either at his wish or the SEMS can automatically decide and by doing this the grid will have the information regarding the load patterns and can have the demand response accordingly.
{"title":"Smart Energy Management System for the Optimal Control of Loads to Reduce the Burden on The Grid","authors":"P. Seema, M. P. Suresh, M. Nair","doi":"10.1109/GCAT55367.2022.9971908","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9971908","url":null,"abstract":"The world is becoming smarter. Technology is spreading even in the grid side, nowadays the energy meter is being converted to smart meter and more home energy management systems are being installed. This paper proposes the combination of Smart Meter (SM) and Home Energy Management System (HEMS) to form a new Smart Energy Management System, which have the characteristics of both SM and HEMS, in turn help the grid and the consumer at the same time. The consumer can allocate the loads either at his wish or the SEMS can automatically decide and by doing this the grid will have the information regarding the load patterns and can have the demand response accordingly.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132961750","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9971833
Srikumar Manghat
The founding principles of the decoupled power flow method together with the fact that active/reactive power losses are miniscule compared to the generation and load in a typical power system, are utilized to develop a new non-iterative power flow method for a radial network in the present paper. In the paper, first a scheme to find the sensitivities of injected power flows to changes in voltage magnitude and angles at the sending end and also to find the values of the power injected at the sending end for a particular voltage there, is developed for the branches of the network. Based on this scheme, fully utilizing its radial nature, a non-iterative algorithm is developed to find the power flow solution of the network. The highlight of the method is that since it is non-iterative, the problem of convergence of the solution encountered while using other methods to find the power flow solution of radial networks is overcome immediately. The method is used to find the power flow solutions of various networks for varying loading conditions and it is shown that the results obtained are at par with the results obtained by the direct application of Newton-Raphson method with minimum errors.
{"title":"A New Non-Iterative Power Flow Method for Radial Networks","authors":"Srikumar Manghat","doi":"10.1109/GCAT55367.2022.9971833","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9971833","url":null,"abstract":"The founding principles of the decoupled power flow method together with the fact that active/reactive power losses are miniscule compared to the generation and load in a typical power system, are utilized to develop a new non-iterative power flow method for a radial network in the present paper. In the paper, first a scheme to find the sensitivities of injected power flows to changes in voltage magnitude and angles at the sending end and also to find the values of the power injected at the sending end for a particular voltage there, is developed for the branches of the network. Based on this scheme, fully utilizing its radial nature, a non-iterative algorithm is developed to find the power flow solution of the network. The highlight of the method is that since it is non-iterative, the problem of convergence of the solution encountered while using other methods to find the power flow solution of radial networks is overcome immediately. The method is used to find the power flow solutions of various networks for varying loading conditions and it is shown that the results obtained are at par with the results obtained by the direct application of Newton-Raphson method with minimum errors.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132864015","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972159
K. Kannadasan, Md Tanjirul Islam Miraj, Kritant Sao Bheekharry, B. S. Begum
Electroencephalogram (EEG) based affective brain-computer interface (a-BCI) systems are gaining interest among researchers in recent decades. a-BCI systems interpret/recognize human emotions using features extracted from the EEG signals. Hence, features play a crucial role in building EEG based emotion recognition models. As a result, analysis of feature extraction models becomes inevitable. In this work, we have proposed an analysis model for analyzing the feature extraction models for EEG based emotion recognition with the help of the DEAP dataset. Features were extracted using manual feature extraction techniques and the convolutional neural network. Several combinations of feature sets were given as input to classifiers and the results obtained were analyzed with various evaluation metrics. The proposed analysis model will help the researchers to choose the feature extraction model for emotion recognition.
{"title":"Analysis of Feature Extraction Models for Emotion Recognition using EEG Signals","authors":"K. Kannadasan, Md Tanjirul Islam Miraj, Kritant Sao Bheekharry, B. S. Begum","doi":"10.1109/GCAT55367.2022.9972159","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972159","url":null,"abstract":"Electroencephalogram (EEG) based affective brain-computer interface (a-BCI) systems are gaining interest among researchers in recent decades. a-BCI systems interpret/recognize human emotions using features extracted from the EEG signals. Hence, features play a crucial role in building EEG based emotion recognition models. As a result, analysis of feature extraction models becomes inevitable. In this work, we have proposed an analysis model for analyzing the feature extraction models for EEG based emotion recognition with the help of the DEAP dataset. Features were extracted using manual feature extraction techniques and the convolutional neural network. Several combinations of feature sets were given as input to classifiers and the results obtained were analyzed with various evaluation metrics. The proposed analysis model will help the researchers to choose the feature extraction model for emotion recognition.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133491736","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972158
Urmil Bharti, A. Goel, S. C. Gupta
Serverless development is challenging as applications are composed of stateless and short-lived functions. Many workflows require time-bound functions to transfer their state to other function before termination. The serverless Function-as-a-Service offerings lack state management support; therefore, it must be handled at application-level. In this paper, we propose a scalable design approach that simplifies development of workflows that require sharing of ephemeral intermediate data. Our design uses object serialization/deserialization with cloud object storage to share state across functions. It provides a mechanism for fine-grained support for state propagation and synchronization in a serverless workflow. This solution is cost-effective and efficient as it does not depend on any external database or cache for state management. The design has been validated by implementing ‘Word Count’- a classic MapReduce use case. Our results show that the proposed scalable design can process input of any size and can handle state propagation in complex serverless workflow.
{"title":"A Scalable Design Approach for State Propagation in Serverless Workflow","authors":"Urmil Bharti, A. Goel, S. C. Gupta","doi":"10.1109/GCAT55367.2022.9972158","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972158","url":null,"abstract":"Serverless development is challenging as applications are composed of stateless and short-lived functions. Many workflows require time-bound functions to transfer their state to other function before termination. The serverless Function-as-a-Service offerings lack state management support; therefore, it must be handled at application-level. In this paper, we propose a scalable design approach that simplifies development of workflows that require sharing of ephemeral intermediate data. Our design uses object serialization/deserialization with cloud object storage to share state across functions. It provides a mechanism for fine-grained support for state propagation and synchronization in a serverless workflow. This solution is cost-effective and efficient as it does not depend on any external database or cache for state management. The design has been validated by implementing ‘Word Count’- a classic MapReduce use case. Our results show that the proposed scalable design can process input of any size and can handle state propagation in complex serverless workflow.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133598224","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9971951
A. Mukhopadhyay, B. S. Limitha, R. Anusha, V. Gowthami
A vehicular ad hoc network (VANET) can be used to share data and provide services to moving vehicles on the road. Vehicles can transmit a variety of messages, including both safety and non-safety-related or entertainment-related messages. Certain vehicles may need to transmit emergency messages and those messages are crucial to the network. Prioritizing the messages will help them be transmitted more quickly. The priority system used in this study places Emergency messages, General Messages, and Entertainment messages in order of importance. Entertainment is given low priority, general message is given medium priority, and emergency communication is given high priority. To assign flags to the messages, we use the TCP protocol.
{"title":"TCP and Priority Queue based Emergency Data Transmission in VANETs","authors":"A. Mukhopadhyay, B. S. Limitha, R. Anusha, V. Gowthami","doi":"10.1109/GCAT55367.2022.9971951","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9971951","url":null,"abstract":"A vehicular ad hoc network (VANET) can be used to share data and provide services to moving vehicles on the road. Vehicles can transmit a variety of messages, including both safety and non-safety-related or entertainment-related messages. Certain vehicles may need to transmit emergency messages and those messages are crucial to the network. Prioritizing the messages will help them be transmitted more quickly. The priority system used in this study places Emergency messages, General Messages, and Entertainment messages in order of importance. Entertainment is given low priority, general message is given medium priority, and emergency communication is given high priority. To assign flags to the messages, we use the TCP protocol.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116133795","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9971993
S. Kanagaraj, M. Hema, M. Guptha, V. Namitha
The non-curable neurological disorder that affects the motor system is known as Parkinson disease. When Parkinson disease is detected earlier, then it can diagnose, and we can get a quick relief but not permanent. The neurons segregate a chemical called dopamine. That helps for transmitting the signs to the other neurons in the brain. When the dopamine flow starts to fall, then the PD occurs. This makes the patients to, resting tremors, bradykinesia and rigidity problems. Here machine-learning dramatizations position in patterns tag in biomedical sciences. The PD mainly attack the motor system so that can be analysed by the Magnetic Resonance Imaging (MRI) scan, one can detect and predict the disease. In this paper, with MRI scan the Parkinson's disease is detected by using CNN, VGG-16 model and ResNET-50. The VGG-16 and ResNet-50 are compared and find the best model based on the accuracy.
{"title":"Detecting Parkinson's Disease with Image Classification","authors":"S. Kanagaraj, M. Hema, M. Guptha, V. Namitha","doi":"10.1109/GCAT55367.2022.9971993","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9971993","url":null,"abstract":"The non-curable neurological disorder that affects the motor system is known as Parkinson disease. When Parkinson disease is detected earlier, then it can diagnose, and we can get a quick relief but not permanent. The neurons segregate a chemical called dopamine. That helps for transmitting the signs to the other neurons in the brain. When the dopamine flow starts to fall, then the PD occurs. This makes the patients to, resting tremors, bradykinesia and rigidity problems. Here machine-learning dramatizations position in patterns tag in biomedical sciences. The PD mainly attack the motor system so that can be analysed by the Magnetic Resonance Imaging (MRI) scan, one can detect and predict the disease. In this paper, with MRI scan the Parkinson's disease is detected by using CNN, VGG-16 model and ResNET-50. The VGG-16 and ResNet-50 are compared and find the best model based on the accuracy.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115482203","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972077
Vipin Kumar, R. Sivakumar, S. K. Kiran
This paper present unique design technique of 4 channel downconverter receiver for monopulse AESA based target tracking radar. Sum and difference channels are designed along with Guard channel which possess extra gain requirement and stringent noise figure as per system requirements. Supportive calibration mechanism via innovative switch circuitry is incorporated along with Guard channel thus avoids need of dedicated additional channel. In line with system needs design methodology is chosen, block diagram is developed, circuit and EM simulations are carried out in ADS, CST and Cascade software and critical aspects of designing such receiver are also disclosed.
{"title":"4 Channel Downconverter Receiver with Integrated Guard Channel Supportive Calibration","authors":"Vipin Kumar, R. Sivakumar, S. K. Kiran","doi":"10.1109/GCAT55367.2022.9972077","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972077","url":null,"abstract":"This paper present unique design technique of 4 channel downconverter receiver for monopulse AESA based target tracking radar. Sum and difference channels are designed along with Guard channel which possess extra gain requirement and stringent noise figure as per system requirements. Supportive calibration mechanism via innovative switch circuitry is incorporated along with Guard channel thus avoids need of dedicated additional channel. In line with system needs design methodology is chosen, block diagram is developed, circuit and EM simulations are carried out in ADS, CST and Cascade software and critical aspects of designing such receiver are also disclosed.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115551572","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972053
N. Nimbarte, Aniket Nagpure, Badal Sanodiya, Harshal Sevatkar, S. Balamwar
Pattern Recognition is quickly becoming a popular topic of image processing. It is a branch of remote sensing, and it can be useful where it is difficult to visit and analyze geographical locations such as forestry or islands, and it can also be difficult to visit areas affected by natural disasters. To do this, a system to distinguish areas such as buildings, greenery, cultivated land, land, water, and so on must be devised. Previously, research on these themes had been conducted, but it was confined to one or two remote sensor items. This work introduces a method for identifying items such as buildings, greenery, water, and land. Because the knowledge basis for this recognition is based on analysis, it is also unbound to specific types of locations. This method is useful for determining the area under civilization as well as the percentage area of a given pattern. The Image classification technique uses supervised and unsupervised classification methods. The supervised classification uses a maximum likelihood classifier. The unsupervised classification uses the ISO Cluster classifier to classify images. ArcGIS PRO and ERDAS IMAGINE software are used for algorithm analysis.
{"title":"Knowledge Based Classifier and Pattern Recognition Technique for Satellite Image Analysis","authors":"N. Nimbarte, Aniket Nagpure, Badal Sanodiya, Harshal Sevatkar, S. Balamwar","doi":"10.1109/GCAT55367.2022.9972053","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972053","url":null,"abstract":"Pattern Recognition is quickly becoming a popular topic of image processing. It is a branch of remote sensing, and it can be useful where it is difficult to visit and analyze geographical locations such as forestry or islands, and it can also be difficult to visit areas affected by natural disasters. To do this, a system to distinguish areas such as buildings, greenery, cultivated land, land, water, and so on must be devised. Previously, research on these themes had been conducted, but it was confined to one or two remote sensor items. This work introduces a method for identifying items such as buildings, greenery, water, and land. Because the knowledge basis for this recognition is based on analysis, it is also unbound to specific types of locations. This method is useful for determining the area under civilization as well as the percentage area of a given pattern. The Image classification technique uses supervised and unsupervised classification methods. The supervised classification uses a maximum likelihood classifier. The unsupervised classification uses the ISO Cluster classifier to classify images. ArcGIS PRO and ERDAS IMAGINE software are used for algorithm analysis.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"13 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124688462","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 : 2022-10-07DOI: 10.1109/GCAT55367.2022.9972100
Shubhangi S. Mohite, V. Attar, Shrida Kalamkar
Twitter has essential and often unpleasant consequences in everyday life. Users have turned major social networking sites into a platform for disseminating much unnecessary and undesired material. Twitter has become one of the best and most popular little blogging services for sharing random thoughts. The majority of the participants who make comments on a specific occurrence are inclined to disgrace the victim. In this paper, to identify the shameful tweets or comments on twitter are done. Specifically, after identifying shameful tweets, it categorized into five categories: ill-treat, social comparison, Bad judgment, blasphemy, and unpleasant jokes, with each shaming tweet falling into one of these categories. After categorization the shaming user automatically blocked after giving the one message to that shamer. To detect these tweets, the system recommends utilizing machine learning classifiers like Random Forest, Naive Bayes, KNN. The classifier analysis aids in determining the accuracy of each for spotting shaming tweets. These classifiers are for better analysis of tweets.
{"title":"Shaming tweets detection on Twitter using Machine learning Algorithms","authors":"Shubhangi S. Mohite, V. Attar, Shrida Kalamkar","doi":"10.1109/GCAT55367.2022.9972100","DOIUrl":"https://doi.org/10.1109/GCAT55367.2022.9972100","url":null,"abstract":"Twitter has essential and often unpleasant consequences in everyday life. Users have turned major social networking sites into a platform for disseminating much unnecessary and undesired material. Twitter has become one of the best and most popular little blogging services for sharing random thoughts. The majority of the participants who make comments on a specific occurrence are inclined to disgrace the victim. In this paper, to identify the shameful tweets or comments on twitter are done. Specifically, after identifying shameful tweets, it categorized into five categories: ill-treat, social comparison, Bad judgment, blasphemy, and unpleasant jokes, with each shaming tweet falling into one of these categories. After categorization the shaming user automatically blocked after giving the one message to that shamer. To detect these tweets, the system recommends utilizing machine learning classifiers like Random Forest, Naive Bayes, KNN. The classifier analysis aids in determining the accuracy of each for spotting shaming tweets. These classifiers are for better analysis of tweets.","PeriodicalId":133597,"journal":{"name":"2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121374576","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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