Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072843
Supreet Kaur, Vinit Grewal
Through the Wireless Sensor Network (WSN), researchers have made every effort in advancing sensing technology worldwide. However, the essence of communication is deeply affected by the limited battery operating nature of sensor nodes. A lot of research efforts are reported that deal with this concern. Besides, the routing algorithms that tend to promise energy-efficient and optimized routing still fail to achieve optimized performance. Henceforth, sink mobility is one of the eminent solutions that tend to optimize the network through energy-saving routing strategies. In this paper, we have reviewed the sink mobility-based routing algorithms that are proposed for the Year 2022. We believe this review will help the readers to improvise the routing strategy by identifying the research gaps in the existing techniques.
{"title":"Review on sink mobility-based routing algorithms in WSN proposed in the Year 2022","authors":"Supreet Kaur, Vinit Grewal","doi":"10.1109/IC3I56241.2022.10072843","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072843","url":null,"abstract":"Through the Wireless Sensor Network (WSN), researchers have made every effort in advancing sensing technology worldwide. However, the essence of communication is deeply affected by the limited battery operating nature of sensor nodes. A lot of research efforts are reported that deal with this concern. Besides, the routing algorithms that tend to promise energy-efficient and optimized routing still fail to achieve optimized performance. Henceforth, sink mobility is one of the eminent solutions that tend to optimize the network through energy-saving routing strategies. In this paper, we have reviewed the sink mobility-based routing algorithms that are proposed for the Year 2022. We believe this review will help the readers to improvise the routing strategy by identifying the research gaps in the existing techniques.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115855251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072725
Gaurav Srivastav, Mamoon Rashid, Richa Singh, A. Gehlot, Neha Sharma
Breast cancer is one of the most common cancer types. This is the second-leading cause of cancer-related death in women. It ranked 2nd according to available data lung cancers is the only one causing more causalities. It’s critical to receive a breast cancer diagnosis quickly. The MIAS data set is used in this study to examine machine learning-based categorization approaches used to study breast cancer. Important image data is fetched from mammograms. We choose eight distinct classifiers and assess each one’s precision, recall, accuracy, and F-score. The analysis’s findings were higher than 69.88%.
{"title":"Breast Cancer Detection in Mammogram Images using Machine Learning Methods and CLAHE Algorithm","authors":"Gaurav Srivastav, Mamoon Rashid, Richa Singh, A. Gehlot, Neha Sharma","doi":"10.1109/IC3I56241.2022.10072725","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072725","url":null,"abstract":"Breast cancer is one of the most common cancer types. This is the second-leading cause of cancer-related death in women. It ranked 2nd according to available data lung cancers is the only one causing more causalities. It’s critical to receive a breast cancer diagnosis quickly. The MIAS data set is used in this study to examine machine learning-based categorization approaches used to study breast cancer. Important image data is fetched from mammograms. We choose eight distinct classifiers and assess each one’s precision, recall, accuracy, and F-score. The analysis’s findings were higher than 69.88%.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134221393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072476
K. Lakshmi., P. Narayana, P. Bhavani, V. V. S. Madhavacharyulu, N. Lavanya, S. Pousia
The real estate market is one of the least transparent sectors of society as real estate prices change daily and are often overvalued rather than valued. Homebuyers use budget and market methods to find new homes. However, a fundamental problem with the current approach is the inability to predict future market trends that will lead to price spikes. It is very important for researchers to base their house price proposals on empirical studies. In order to accurately predict the price of a home, customers need to carefully evaluatefactors related to the home, which is very difficult. Using machine learning (ML) to solve this problem seems like a viable solution. To address this problem, ML models such as Linear Regression (LR), K Nearest Neighbors (KNN), Random Forests (RF); Ensembles (LR, KNN, RF) are used. A number of error metrics are used to select the best model, including mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The results in this disclosure show that a model combining linear regression (LR), random forest (RF) and K Nearest Neighbors (KNN) yields the lowest inaccuracies. A successful regression model should have a minimal error value. This eliminates the need to rely on realtors to determine a fair price for a home based on key features.
{"title":"An Enhanced Regression Technique for House Price Prediction","authors":"K. Lakshmi., P. Narayana, P. Bhavani, V. V. S. Madhavacharyulu, N. Lavanya, S. Pousia","doi":"10.1109/IC3I56241.2022.10072476","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072476","url":null,"abstract":"The real estate market is one of the least transparent sectors of society as real estate prices change daily and are often overvalued rather than valued. Homebuyers use budget and market methods to find new homes. However, a fundamental problem with the current approach is the inability to predict future market trends that will lead to price spikes. It is very important for researchers to base their house price proposals on empirical studies. In order to accurately predict the price of a home, customers need to carefully evaluatefactors related to the home, which is very difficult. Using machine learning (ML) to solve this problem seems like a viable solution. To address this problem, ML models such as Linear Regression (LR), K Nearest Neighbors (KNN), Random Forests (RF); Ensembles (LR, KNN, RF) are used. A number of error metrics are used to select the best model, including mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The results in this disclosure show that a model combining linear regression (LR), random forest (RF) and K Nearest Neighbors (KNN) yields the lowest inaccuracies. A successful regression model should have a minimal error value. This eliminates the need to rely on realtors to determine a fair price for a home based on key features.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134336209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073296
Kakoli Banerjee, Ajay Kumar, Akshansh Gupta, Pradeep Kumar, K. Harsha, Gauransh Verma, P. Vinooth, Dhruv Baliyan
For development, industries are prerequisite. And for setting the industries, one prime resource which is much needed is - Water. Water, as a resource is equally important not only being used in the production of goods, but also for discharging the harmful chemicals and hazardous waste, as the other method of discharge would cost high to the industries. The discharge of waste and chemicals affect the TDS (Total Dissolved Salts) in water, EC (Electric Conductivity), PH level, Temperature of water etc, and hence alters the Water Quality Index. According to UNEP, The amount of waste-water which goes untreated, which contains everything from human waste to highly toxic industrial discharge, globally accounts for 80%, and the authorities responsible for checking and treating the water are unaware of such happening, causing ultimate damage to the humans, flora and fauna.This paper presents an IOT-based solution to curb the water pollution level by alerting the concerned authorities as soon as the discharge of chemicals by the industries or water is being polluted, thus aiding not only in controlling water pollution level but also in effective water management by categorizing the water, as per its usage in household, industry, etc.
{"title":"Assessing Water Quality Index Near Industrial Regions and Aiding in Effective Water Management and Controlling Water Pollution Level","authors":"Kakoli Banerjee, Ajay Kumar, Akshansh Gupta, Pradeep Kumar, K. Harsha, Gauransh Verma, P. Vinooth, Dhruv Baliyan","doi":"10.1109/IC3I56241.2022.10073296","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073296","url":null,"abstract":"For development, industries are prerequisite. And for setting the industries, one prime resource which is much needed is - Water. Water, as a resource is equally important not only being used in the production of goods, but also for discharging the harmful chemicals and hazardous waste, as the other method of discharge would cost high to the industries. The discharge of waste and chemicals affect the TDS (Total Dissolved Salts) in water, EC (Electric Conductivity), PH level, Temperature of water etc, and hence alters the Water Quality Index. According to UNEP, The amount of waste-water which goes untreated, which contains everything from human waste to highly toxic industrial discharge, globally accounts for 80%, and the authorities responsible for checking and treating the water are unaware of such happening, causing ultimate damage to the humans, flora and fauna.This paper presents an IOT-based solution to curb the water pollution level by alerting the concerned authorities as soon as the discharge of chemicals by the industries or water is being polluted, thus aiding not only in controlling water pollution level but also in effective water management by categorizing the water, as per its usage in household, industry, etc.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131825182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072444
Meet Kumari, V. Arya, N. Sharma, Mamoon Rashid, Rajesh Singh
The rapid progress in emerging communication technologies, enhancing the next generation visible light communication (VLC) and passive optical network (PON) as a promising candidate for highly demanding sophisticated services and applications. The next generation hybrid PONVLC network provides high data rate, capacity, security, mobility, flexibility, energy efficiency and spectral efficiency to adapt existing technologies. In this paper, a hybrid PON-VLC architecture, its review and applications are discussed. Also, the major open challenges in hybrid PON-VLC are described.
{"title":"Emerging next generation hybrid PON-VLC system: A review, applications and challenges","authors":"Meet Kumari, V. Arya, N. Sharma, Mamoon Rashid, Rajesh Singh","doi":"10.1109/IC3I56241.2022.10072444","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072444","url":null,"abstract":"The rapid progress in emerging communication technologies, enhancing the next generation visible light communication (VLC) and passive optical network (PON) as a promising candidate for highly demanding sophisticated services and applications. The next generation hybrid PONVLC network provides high data rate, capacity, security, mobility, flexibility, energy efficiency and spectral efficiency to adapt existing technologies. In this paper, a hybrid PON-VLC architecture, its review and applications are discussed. Also, the major open challenges in hybrid PON-VLC are described.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134166508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072814
Karishma Sharma, S. Arora
Transfer learning has lately shown potential in diagnosing plant lesions, but it requires large and particular crop diseases data, both of which are uncommon. Plant malady leaf photos in full colour must be included to the data collection. The quality of the classifier may be increased thanks to research on a method for acquiring a comprehensive and unique picture of a crop diseases leaf presented in this publication. Our study has many advantages, including the following: To answer the topic of how a conceptual asymmetric networks (Gap) generates a disease picture with a certain form, we suggest a bipolar producer net. Secondly, utilizing rim and image stacking approaches, the issue of synthesizing a complete lesion digital image with numerous synthetic edge pixels and system out photos will be addressed. Continued studies on plant diseases will effectively rise thanks to our strategy, which will also improve the show’s classification accuracy. Our approach was shown to effectively expand the dataset of crop lesions and improve the class network’s recognition accuracy compared to experts with Alex Net.
{"title":"Diagnosing of zucchini leaf lesions using reconstruction of GAN","authors":"Karishma Sharma, S. Arora","doi":"10.1109/IC3I56241.2022.10072814","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072814","url":null,"abstract":"Transfer learning has lately shown potential in diagnosing plant lesions, but it requires large and particular crop diseases data, both of which are uncommon. Plant malady leaf photos in full colour must be included to the data collection. The quality of the classifier may be increased thanks to research on a method for acquiring a comprehensive and unique picture of a crop diseases leaf presented in this publication. Our study has many advantages, including the following: To answer the topic of how a conceptual asymmetric networks (Gap) generates a disease picture with a certain form, we suggest a bipolar producer net. Secondly, utilizing rim and image stacking approaches, the issue of synthesizing a complete lesion digital image with numerous synthetic edge pixels and system out photos will be addressed. Continued studies on plant diseases will effectively rise thanks to our strategy, which will also improve the show’s classification accuracy. Our approach was shown to effectively expand the dataset of crop lesions and improve the class network’s recognition accuracy compared to experts with Alex Net.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132526387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073088
Komal Sandhu, Durgesh Nandan
Emotion recognition serves as one of the key research areas as Facebook use on web. On instagram, Google, Wikipedia, as well as others, individuals share their views, ideas, emotions, feelings, and views, bringing a considerable measure with fire to modern life. Ai, sometimes referred to as recommender systems, focuses on categorizing and predicting men’s sentiments regarding that same issue. It is sometimes referred to as “emotional ore” or “mood coal.” It involves classifying written texts into pro or con groups depending on the stated viewpoint on a particular problem. Despite the simple fact that recommendation system may seem to be similar to text categorization, it faces a variety of issues which has inspired much study in this field. To improve the tone study, many automation (ML) and also dictionary strategies were created in the story. In this work, we just provide results of an university level that aims to assess the state of the science. In order for new called electronic to be developed by researchers in the future who have more information and can address in all issues and get the best outcomes.
{"title":"An Organized Study of Opinion Methods","authors":"Komal Sandhu, Durgesh Nandan","doi":"10.1109/IC3I56241.2022.10073088","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073088","url":null,"abstract":"Emotion recognition serves as one of the key research areas as Facebook use on web. On instagram, Google, Wikipedia, as well as others, individuals share their views, ideas, emotions, feelings, and views, bringing a considerable measure with fire to modern life. Ai, sometimes referred to as recommender systems, focuses on categorizing and predicting men’s sentiments regarding that same issue. It is sometimes referred to as “emotional ore” or “mood coal.” It involves classifying written texts into pro or con groups depending on the stated viewpoint on a particular problem. Despite the simple fact that recommendation system may seem to be similar to text categorization, it faces a variety of issues which has inspired much study in this field. To improve the tone study, many automation (ML) and also dictionary strategies were created in the story. In this work, we just provide results of an university level that aims to assess the state of the science. In order for new called electronic to be developed by researchers in the future who have more information and can address in all issues and get the best outcomes.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"267 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134271506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073024
Priyameet Kaur Keer, J. Al-Safi, S. B. G. T. Babu, G. Ramesh
While the integration of artificial intelligence and computer technology has enriched people's daily lives, it has also become an inaccessible part of people's lives. The trend of future social development is the advancement of computer network technology, which will be the tendency of future social development. Because of the artificial intelligence technology's high level of intelligence, it has emerged as one of the most promising technologies in the realm of computer networks and is dependent on computers for its development, which led to the establishment of the technology-based development. In order to hasten the incorporation of artificial intelligence into social production and living, as well as to make production and life easier, we need to make some changes. The purpose of this study was to investigate artificial intelligence, after which the model structure of intelligent anti-spam in network security management was established, and finally, the model was evaluated. According to the findings, the intelligent anti-spam model exhibited satisfactory filtering performance.
{"title":"Artificial Intelligence in Computer Network Technology in The Big Data Era","authors":"Priyameet Kaur Keer, J. Al-Safi, S. B. G. T. Babu, G. Ramesh","doi":"10.1109/IC3I56241.2022.10073024","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073024","url":null,"abstract":"While the integration of artificial intelligence and computer technology has enriched people's daily lives, it has also become an inaccessible part of people's lives. The trend of future social development is the advancement of computer network technology, which will be the tendency of future social development. Because of the artificial intelligence technology's high level of intelligence, it has emerged as one of the most promising technologies in the realm of computer networks and is dependent on computers for its development, which led to the establishment of the technology-based development. In order to hasten the incorporation of artificial intelligence into social production and living, as well as to make production and life easier, we need to make some changes. The purpose of this study was to investigate artificial intelligence, after which the model structure of intelligent anti-spam in network security management was established, and finally, the model was evaluated. According to the findings, the intelligent anti-spam model exhibited satisfactory filtering performance.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116819924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073080
Ashwini Kumar, V. Vekariya
The extensive usage of interconnection and interoperable of computing systems has become an unavoidable requirement for improving our daily lives. Similarly, it paves the way for exploitable flaws that are far beyond human control. Because of the flaws, cyber-security techniques are required in order to conduct communication. To resist concerns, reliable connectivity necessitates security protocols, as well as innovations in protection efforts to control growing security concerns. To identify and categorize networks assaults, this study suggests using deep learning architectures to construct an adaptable and resistant network intrusion detection system (IDS).The focus is about how deep learning or deep convolutional networks (DCNNs) may help adaptable IDS with growing capabilities distinguish known and novel or zero-day networking observable traits, disconnecting the intruder and lowering the risk of exposure. The UNSW-NB15 dataset, which reflects genuine current network interaction complementing synthetically created attack behaviours, was used to illustrate the performance of the model.
{"title":"Deep Convolutional Neural Networks for Intrusion Detection in Automotive Ethernet Networks","authors":"Ashwini Kumar, V. Vekariya","doi":"10.1109/IC3I56241.2022.10073080","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073080","url":null,"abstract":"The extensive usage of interconnection and interoperable of computing systems has become an unavoidable requirement for improving our daily lives. Similarly, it paves the way for exploitable flaws that are far beyond human control. Because of the flaws, cyber-security techniques are required in order to conduct communication. To resist concerns, reliable connectivity necessitates security protocols, as well as innovations in protection efforts to control growing security concerns. To identify and categorize networks assaults, this study suggests using deep learning architectures to construct an adaptable and resistant network intrusion detection system (IDS).The focus is about how deep learning or deep convolutional networks (DCNNs) may help adaptable IDS with growing capabilities distinguish known and novel or zero-day networking observable traits, disconnecting the intruder and lowering the risk of exposure. The UNSW-NB15 dataset, which reflects genuine current network interaction complementing synthetically created attack behaviours, was used to illustrate the performance of the model.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116304538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072302
A. Pandey, Amit Barve
The skin is the body’s outermost layer, concealing/covering numerous biological organs, muscles, and other innumerable body parts. The study found that the body’s exposure to ultraviolet radiation is the main contributor to skin cancer (UV). There are several layers to the skin, but the epidermis and dermis are where cancer first appears. Changes in your skin or the appearance of a mole in many locations on your body are the most common warning signs. The only way to prevent cancer is to stay as far away from UV rays as you can, which would stop your skin from coming into contact with the disease. According to statistics, cases of this cancer have not only increased but are increasing swiftly as a result of the ozone layer’s deterioration, which causes it to stop emitting dangerous light and, as a result, come into contact with our skin. For the following issue, numerous different strategies include machine learning, DL, and TL are being used. Naive Bayes, logistic regression, random forest, decision tree, artificial NN, and convolutional NN are just a few of the numerous techniques used. The study makes an effort to put both TL and DL techniques to use in order to provide a result that shows which performs better for the next challenge.
{"title":"Skin Cancer Prediction Comparative Analysis using TL and NNs","authors":"A. Pandey, Amit Barve","doi":"10.1109/IC3I56241.2022.10072302","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072302","url":null,"abstract":"The skin is the body’s outermost layer, concealing/covering numerous biological organs, muscles, and other innumerable body parts. The study found that the body’s exposure to ultraviolet radiation is the main contributor to skin cancer (UV). There are several layers to the skin, but the epidermis and dermis are where cancer first appears. Changes in your skin or the appearance of a mole in many locations on your body are the most common warning signs. The only way to prevent cancer is to stay as far away from UV rays as you can, which would stop your skin from coming into contact with the disease. According to statistics, cases of this cancer have not only increased but are increasing swiftly as a result of the ozone layer’s deterioration, which causes it to stop emitting dangerous light and, as a result, come into contact with our skin. For the following issue, numerous different strategies include machine learning, DL, and TL are being used. Naive Bayes, logistic regression, random forest, decision tree, artificial NN, and convolutional NN are just a few of the numerous techniques used. The study makes an effort to put both TL and DL techniques to use in order to provide a result that shows which performs better for the next challenge.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114768266","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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