Pub Date : 2022-12-26DOI: 10.1109/ICERECT56837.2022.10059628
Shalabh Mehrotra, Ashwini Mathur
In dentistry as well as medication, tissue engineering is being considered to make structures like veneer, the dentin-mash mind boggling, the periodontium, or even whole teeth. The goal of tissue engineering is to use designed or regenerated products to replace missing, harmed, ill, or dysfunctional tissues in the human body. Tissue engineering's development has created a great possibility for improved clinical dentistry, particularly in the areas of endodontics, bone and periodontal tissue, and complete tooth regeneration. We quickly summarise the potential selection criteria for scaffolds in this review in light of future dental applications for tissue engineering. In this review, we assess the ordinary platform creation methods and break down the perspectives expected to work on the plan and creation of scaffolds for use in tissue engineering with regards to materials, structure, and mechanical properties. The benefits and drawbacks of these conventional techniques are also explored.
{"title":"Analysis of Tissue Engineering for the Scaffolds in Dentistry","authors":"Shalabh Mehrotra, Ashwini Mathur","doi":"10.1109/ICERECT56837.2022.10059628","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10059628","url":null,"abstract":"In dentistry as well as medication, tissue engineering is being considered to make structures like veneer, the dentin-mash mind boggling, the periodontium, or even whole teeth. The goal of tissue engineering is to use designed or regenerated products to replace missing, harmed, ill, or dysfunctional tissues in the human body. Tissue engineering's development has created a great possibility for improved clinical dentistry, particularly in the areas of endodontics, bone and periodontal tissue, and complete tooth regeneration. We quickly summarise the potential selection criteria for scaffolds in this review in light of future dental applications for tissue engineering. In this review, we assess the ordinary platform creation methods and break down the perspectives expected to work on the plan and creation of scaffolds for use in tissue engineering with regards to materials, structure, and mechanical properties. The benefits and drawbacks of these conventional techniques are also explored.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122791364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-26DOI: 10.1109/ICERECT56837.2022.10060696
T. Kumar, M. Jayaram
Effort estimation is the process of predicting a software's likely cost and development time. Estimating software development effort is still a complex problem that receives a lot of research attention. The accuracy with which the effort necessary for software development is estimated, and it is a significant success element in software project management. The estimated effort should be roughly equal to the actual effort in a good software estimation model. Accurate estimation enables managers to allocate resources for the planning and coordination of all activities. In this paper the effort estimation for small size projects involving technical and analytical aspects of engineering intent. The estimation of time resources is done in three steps firstly Identification of seven novel traits of software (LOC, N&C, CGPA, R, CC, AC, FP). Secondly PCA was implemented to decide the significant parameters among the software features. Lastly linear regression and polynomial regression models were developed using the significant features predicted by the PCA. The result of evaluation of the two models are encouraging with the minimum RMSE of 32-204 minutes and maximum regression coefficients(r2) of 0.95-0.98
{"title":"Software Effort Estimation Using Hard Limiting Techniques with Special Reference to Small Size Technical &Analytical Projects","authors":"T. Kumar, M. Jayaram","doi":"10.1109/ICERECT56837.2022.10060696","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10060696","url":null,"abstract":"Effort estimation is the process of predicting a software's likely cost and development time. Estimating software development effort is still a complex problem that receives a lot of research attention. The accuracy with which the effort necessary for software development is estimated, and it is a significant success element in software project management. The estimated effort should be roughly equal to the actual effort in a good software estimation model. Accurate estimation enables managers to allocate resources for the planning and coordination of all activities. In this paper the effort estimation for small size projects involving technical and analytical aspects of engineering intent. The estimation of time resources is done in three steps firstly Identification of seven novel traits of software (LOC, N&C, CGPA, R, CC, AC, FP). Secondly PCA was implemented to decide the significant parameters among the software features. Lastly linear regression and polynomial regression models were developed using the significant features predicted by the PCA. The result of evaluation of the two models are encouraging with the minimum RMSE of 32-204 minutes and maximum regression coefficients(r2) of 0.95-0.98","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123032239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-26DOI: 10.1109/ICERECT56837.2022.10059654
K. Nayana, S. Vinay, S. Ashwini
cardiovascular disease is one of the most common diseases in the modern world. If recognized early, then it can significantly reduce the damage to the patient. This work describes the detection of anomalies in electrocardiogram (ECG) readings. Anomaly detection in data mining finds instances, occurrences, and observations that differ from a dataset's regular pattern of activity. Using the ECG dataset as input, the initial step in this method is signal pre-processing. High pass, low pass, and notch filters are used to de-noise ECG signals as part of the signal pre-processing. ECG signal de-noising is a significant pre-processing step that highlights the characteristic waves in ECG data while attenuating the disturbances. The emergence of the ECG signal coefficients from signal pre-processing is trained and tested in the second stage. The classification of the ECG signal using LSTM RNN Model is the final stage. Recurrent neural networks are artificial neural networks that use sequential data or time series data (RNN). The LSTM RNN Model effectively separates out extraneous data, prevents signal information loss, lowers computational complexity, and classifies the ECG signal.
{"title":"Anomaly Detection in Cardiac Related Datasets","authors":"K. Nayana, S. Vinay, S. Ashwini","doi":"10.1109/ICERECT56837.2022.10059654","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10059654","url":null,"abstract":"cardiovascular disease is one of the most common diseases in the modern world. If recognized early, then it can significantly reduce the damage to the patient. This work describes the detection of anomalies in electrocardiogram (ECG) readings. Anomaly detection in data mining finds instances, occurrences, and observations that differ from a dataset's regular pattern of activity. Using the ECG dataset as input, the initial step in this method is signal pre-processing. High pass, low pass, and notch filters are used to de-noise ECG signals as part of the signal pre-processing. ECG signal de-noising is a significant pre-processing step that highlights the characteristic waves in ECG data while attenuating the disturbances. The emergence of the ECG signal coefficients from signal pre-processing is trained and tested in the second stage. The classification of the ECG signal using LSTM RNN Model is the final stage. Recurrent neural networks are artificial neural networks that use sequential data or time series data (RNN). The LSTM RNN Model effectively separates out extraneous data, prevents signal information loss, lowers computational complexity, and classifies the ECG signal.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115762005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-26DOI: 10.1109/ICERECT56837.2022.10060676
Wei Cheng, Xiaotang Xia
This paper introduces application of unmanned aerial vehicle (UAV) in topographic survey of Yangtze River. Traditional measurement methods are insufficient and inaccurate due to the limitations of natural conditions in field, and sometimes danger to on-site surveyors. By applying aerial mapping system and GPS differential technology for UAVs in waterway, the working hours and labour intensity of this task was significant reduced, the safety of the surveying personnel and the measurement accuracy were highly improved. A real case was also carried out in 2021 in relation to the maintenance project of constructions in the middle and lower reaches of the Yangtze River Waterway.
{"title":"The Practical Application of Mapping Yangtze River Waterway by UAV","authors":"Wei Cheng, Xiaotang Xia","doi":"10.1109/ICERECT56837.2022.10060676","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10060676","url":null,"abstract":"This paper introduces application of unmanned aerial vehicle (UAV) in topographic survey of Yangtze River. Traditional measurement methods are insufficient and inaccurate due to the limitations of natural conditions in field, and sometimes danger to on-site surveyors. By applying aerial mapping system and GPS differential technology for UAVs in waterway, the working hours and labour intensity of this task was significant reduced, the safety of the surveying personnel and the measurement accuracy were highly improved. A real case was also carried out in 2021 in relation to the maintenance project of constructions in the middle and lower reaches of the Yangtze River Waterway.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"389 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131695462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-26DOI: 10.1109/ICERECT56837.2022.10060598
Kushala V M, Supriya M C, Divakar H R
Agriculture being a prominent occupation of India with its contribution to GDP, effective, prominent technological methodology needs to be adopted for the process of agriculture to be more sustainable and eco-friendlier. Adoption and awareness towards organic fertilizers is a key concept in sustainable agriculture. Agriculture is the most dynamic field with multi-dimensional factors involving in the procedure. To handle the factors involved in the cultivation of a crop a knowledge base with organic fertilizers information and its availability is much needed information for the farmers in adopting them. To achieve this, we built a domain ontology for agriculture by considering factors like soil information, organic fertilizers, diseases, geographical information, crop demand, fertilizers availability. A semantic web helps in serving the ontology to the farmers.
{"title":"Construction of Domain Ontology Considering Organic Fertilizers for A Sustainable Agriculture","authors":"Kushala V M, Supriya M C, Divakar H R","doi":"10.1109/ICERECT56837.2022.10060598","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10060598","url":null,"abstract":"Agriculture being a prominent occupation of India with its contribution to GDP, effective, prominent technological methodology needs to be adopted for the process of agriculture to be more sustainable and eco-friendlier. Adoption and awareness towards organic fertilizers is a key concept in sustainable agriculture. Agriculture is the most dynamic field with multi-dimensional factors involving in the procedure. To handle the factors involved in the cultivation of a crop a knowledge base with organic fertilizers information and its availability is much needed information for the farmers in adopting them. To achieve this, we built a domain ontology for agriculture by considering factors like soil information, organic fertilizers, diseases, geographical information, crop demand, fertilizers availability. A semantic web helps in serving the ontology to the farmers.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123179606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-26DOI: 10.1109/ICERECT56837.2022.10059746
A. M, A. K, Amrutha K, A. M, Chandanashree K R
Today, one of the top priorities of governments istrafficsafety. Given the importance of the subject, identifying the causes of road accidents has become the primary goal in reducing the damage caused by traffic accidents. Machine learning and data mining concepts are usedto recognize the various factors that influence road accidents andtheir severity. The application uses at odd Machine Learning algorithms which includes K-Nearest-Neighbor, Decision tree, Random forest, etc make predictions based on various parameters, within allthese models KNN gives the best accuracy. This informationcan be used to analyze future inputs and improve the system'soutput accuracy. This model can be improved further to send the accident report to the appropriate authorities, such as hospitals, ambulances, and insurance companies, and can thus be very useful in reducing accident fatality rates in the country.
{"title":"Accident Prediction Using KNN Algorithm","authors":"A. M, A. K, Amrutha K, A. M, Chandanashree K R","doi":"10.1109/ICERECT56837.2022.10059746","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10059746","url":null,"abstract":"Today, one of the top priorities of governments istrafficsafety. Given the importance of the subject, identifying the causes of road accidents has become the primary goal in reducing the damage caused by traffic accidents. Machine learning and data mining concepts are usedto recognize the various factors that influence road accidents andtheir severity. The application uses at odd Machine Learning algorithms which includes K-Nearest-Neighbor, Decision tree, Random forest, etc make predictions based on various parameters, within allthese models KNN gives the best accuracy. This informationcan be used to analyze future inputs and improve the system'soutput accuracy. This model can be improved further to send the accident report to the appropriate authorities, such as hospitals, ambulances, and insurance companies, and can thus be very useful in reducing accident fatality rates in the country.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125287835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-26DOI: 10.1109/ICERECT56837.2022.10060032
Jingtai Li
The latest research results show that activity recognition based on frequent pattern mining is the main method. Its advantages are that the activity patterns obtained through learning mining are comprehensive and accurate, and the recognition effect of activities in the corresponding environment is good. Besides, some activities that can only be carried out in a specific environment can be identified. This study attempts to use data mining technology to help foreign language education systems to further transform massive data, so as to mine more valuable content, helping educators to further tap teaching potential, and improving the efficiency of teaching resources utilization.
{"title":"Mining and Recognition of English Learning Patterns of Mobile Users Based on Intelligent Algorithm","authors":"Jingtai Li","doi":"10.1109/ICERECT56837.2022.10060032","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10060032","url":null,"abstract":"The latest research results show that activity recognition based on frequent pattern mining is the main method. Its advantages are that the activity patterns obtained through learning mining are comprehensive and accurate, and the recognition effect of activities in the corresponding environment is good. Besides, some activities that can only be carried out in a specific environment can be identified. This study attempts to use data mining technology to help foreign language education systems to further transform massive data, so as to mine more valuable content, helping educators to further tap teaching potential, and improving the efficiency of teaching resources utilization.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114626954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-26DOI: 10.1109/ICERECT56837.2022.10060553
Nabila Sultana Anonnya, Md. Mostafizur Rahman
In this paper, a microstrip patch antenna is proposed for use with Wi-Max and general-purpose mobile radio. The antenna can receive signals at 1.24 GHz, making it suitable for Wi-Max and WLAN networks, and 1.04 GHz, making it suitable for satellite use. The microstrip antenna is planar in shape, and it is built from a defective ground, a base, a patch, a feed, a single slot in the patch, and a defective surface with a pie slot and decreased area on three sides other than the feed side. Antenna parameters are optimized by theoretical study and design iteration via simulation using the commercial program CST Microwave Studio. The obtained data demonstrate the suggested antenna's potential for use in Wi-Max (operating at a frequency of 5.5 GHz), WLAN (operating at a frequency of 5.2-5.8 GHz), and satellite (operating at a frequency of 6–7 GHz). Measurements of the ground and slot widths in the microstrip patch antenna have been analyzed in this work.
{"title":"A Proposed Microstrip Patch Antenna Design for Wi-Max, WLAN and Application for Satellite","authors":"Nabila Sultana Anonnya, Md. Mostafizur Rahman","doi":"10.1109/ICERECT56837.2022.10060553","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10060553","url":null,"abstract":"In this paper, a microstrip patch antenna is proposed for use with Wi-Max and general-purpose mobile radio. The antenna can receive signals at 1.24 GHz, making it suitable for Wi-Max and WLAN networks, and 1.04 GHz, making it suitable for satellite use. The microstrip antenna is planar in shape, and it is built from a defective ground, a base, a patch, a feed, a single slot in the patch, and a defective surface with a pie slot and decreased area on three sides other than the feed side. Antenna parameters are optimized by theoretical study and design iteration via simulation using the commercial program CST Microwave Studio. The obtained data demonstrate the suggested antenna's potential for use in Wi-Max (operating at a frequency of 5.5 GHz), WLAN (operating at a frequency of 5.2-5.8 GHz), and satellite (operating at a frequency of 6–7 GHz). Measurements of the ground and slot widths in the microstrip patch antenna have been analyzed in this work.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115107177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-26DOI: 10.1109/ICERECT56837.2022.10060451
K. N. Kumar, P. Badrinath, S. Vickraman, G. Satheesan
In the recent years, the globe has changed dramatically. is moving towards the usage of electric vehicles which is more comfortable as well as more economical than the normal petrol/diesel operated vehicles. This study has created a battery monitoring system based on the internet of things (IoT) to track the functionality and performance of batteries in a smart E vehicle system. The main concern of the electric car is battery charging time, which consumes more demand from the supply network. An intelligent electronic device (IED), PV system, battery pack, grid connection, and electrical load are all components of this smart microgrid. The Internet of Things (IoT) established in this study consists of an IED communication channel, a data collection mechanism, a cloud system, and a human-machine interface (HMI). The main idea behind this work creates a charging station with solar and implement the IoT concept to monitor the performance and to cut the greenhouse gases and the usage of fossil fuels.
{"title":"Charging Station for E-Vehicle Using Solar with IoT","authors":"K. N. Kumar, P. Badrinath, S. Vickraman, G. Satheesan","doi":"10.1109/ICERECT56837.2022.10060451","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10060451","url":null,"abstract":"In the recent years, the globe has changed dramatically. is moving towards the usage of electric vehicles which is more comfortable as well as more economical than the normal petrol/diesel operated vehicles. This study has created a battery monitoring system based on the internet of things (IoT) to track the functionality and performance of batteries in a smart E vehicle system. The main concern of the electric car is battery charging time, which consumes more demand from the supply network. An intelligent electronic device (IED), PV system, battery pack, grid connection, and electrical load are all components of this smart microgrid. The Internet of Things (IoT) established in this study consists of an IED communication channel, a data collection mechanism, a cloud system, and a human-machine interface (HMI). The main idea behind this work creates a charging station with solar and implement the IoT concept to monitor the performance and to cut the greenhouse gases and the usage of fossil fuels.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115519816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-26DOI: 10.1109/ICERECT56837.2022.10059743
Rajlakshmi Ghatkamble, P. Pareek, P. D.
The key to successful early recovery and treatment of breast cancer in today's healthcare system is an accurate and prompt diagnosis. Over the last several years, the IoT has undergone a transition that makes it possible to analyse both real-time techniques. Medical diagnostics are aided by the Internet of Medical Things, which connects various medical equipment and artificial intelligence applications with the healthcare network. Most women with breast cancer don't make it because the disease isn't detected early enough using today's best methods. Therefore, doctors and scientists are confronted with a significant challenge in recognizing breast cancer at an primary stage. We present a medical IoT-based diagnostic system that can distinguish between patients with cancer and those without it in an Internet of Things setting. Malignant vs benign categorization is performed using an unique transfer learning technique called BERT, which is based on a previously learned language model. In particular, this research looks at how well novel fine-tuning approaches based on transfer learning might improve BERT's capacity to capture significant context. This research improves the BERT model's classification accuracy by using a Black Widow-meta-heuristic Optimization (NBW-MHO) feature selection strategy to refine feature selection from the breast cancer dataset. The WDBC dataset served as a testbed for the suggested method. The suggested model's classification accuracy using the BERT model and NBW-MHO was 95.20 percent.
{"title":"Design of NBW-MHO with BERT model for prediction of Breast Cancer in IoT Healthcare System","authors":"Rajlakshmi Ghatkamble, P. Pareek, P. D.","doi":"10.1109/ICERECT56837.2022.10059743","DOIUrl":"https://doi.org/10.1109/ICERECT56837.2022.10059743","url":null,"abstract":"The key to successful early recovery and treatment of breast cancer in today's healthcare system is an accurate and prompt diagnosis. Over the last several years, the IoT has undergone a transition that makes it possible to analyse both real-time techniques. Medical diagnostics are aided by the Internet of Medical Things, which connects various medical equipment and artificial intelligence applications with the healthcare network. Most women with breast cancer don't make it because the disease isn't detected early enough using today's best methods. Therefore, doctors and scientists are confronted with a significant challenge in recognizing breast cancer at an primary stage. We present a medical IoT-based diagnostic system that can distinguish between patients with cancer and those without it in an Internet of Things setting. Malignant vs benign categorization is performed using an unique transfer learning technique called BERT, which is based on a previously learned language model. In particular, this research looks at how well novel fine-tuning approaches based on transfer learning might improve BERT's capacity to capture significant context. This research improves the BERT model's classification accuracy by using a Black Widow-meta-heuristic Optimization (NBW-MHO) feature selection strategy to refine feature selection from the breast cancer dataset. The WDBC dataset served as a testbed for the suggested method. The suggested model's classification accuracy using the BERT model and NBW-MHO was 95.20 percent.","PeriodicalId":205485,"journal":{"name":"2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122655761","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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