Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072618
N. Kadu, Pramod E Jadhav, M. Nirmal
Virtual machines (VMs) have become increasingly valuable for resource consolidation and management due to their efficient and secure containers, as well as their capability to offer desired execution environments for applications. The cloud is also becoming increasingly popular, which is hosted in large data centers. The vast majority of these large data centers use a virtualized server infrastructure, known as a virtual machine (VM), that is managed by a cloud infrastructure service such as an open stack or cloud stack, etc. Computing, storage, and communication resources are increasingly needed in large Cloud Data Centers (CDCs). With the growth of cloud environments, VM migration has become an important criterion to ensure Quality of Service (QoS), save energy, and reduce resource usage. This paper examines the merits, challenges, and resource management aspects of virtual machines migration. In addition to providing load balancing, online system maintenance, proactive fault tolerance, power management, and resource sharing, it also helps to manage system power consumption.
{"title":"A Survey of Virtual machine migration, Optimal Resource Management and Challenges","authors":"N. Kadu, Pramod E Jadhav, M. Nirmal","doi":"10.1109/IC3I56241.2022.10072618","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072618","url":null,"abstract":"Virtual machines (VMs) have become increasingly valuable for resource consolidation and management due to their efficient and secure containers, as well as their capability to offer desired execution environments for applications. The cloud is also becoming increasingly popular, which is hosted in large data centers. The vast majority of these large data centers use a virtualized server infrastructure, known as a virtual machine (VM), that is managed by a cloud infrastructure service such as an open stack or cloud stack, etc. Computing, storage, and communication resources are increasingly needed in large Cloud Data Centers (CDCs). With the growth of cloud environments, VM migration has become an important criterion to ensure Quality of Service (QoS), save energy, and reduce resource usage. This paper examines the merits, challenges, and resource management aspects of virtual machines migration. In addition to providing load balancing, online system maintenance, proactive fault tolerance, power management, and resource sharing, it also helps to manage system power consumption.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"96 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":"115678293","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/IC3I56241.2022.10072745
A. Dixit, T. V. Kumar, Abhishek Joshi, H. Bedi, M. Chakravarthi, D. P. Singh
Today, all manufacturing businesses are expected to have the ability to produce high-quality goods with shorter lead times and the capacity to produce to a variety of customer specifications. Any industry’s production capacity has increased exponentially as a result of the use of robots in manufacturing. To resolve the aforementioned problems and maintain a nation’s revenue in the fiercely competitive international market, robotics and computer integrated manufacturing (CIM) technology are required. The main emphasis is placed on the robot’s accuracy during milling operations, and its capacity to carry out the work with the required precision is assessed. The manipulator stiffness model is utilized to estimate failures in conformity caused by the baseline cutting force, which is the same for all robots under consideration, in order to calculate this performance metric. The feasibility of the suggested method was demonstrated in experimental research including the milling of circular notches using a robot for a variety of workgroup samples and locations. As cutting force increases, the circularity index raises as well, and it may be calculated by simple scaling. The study suggests a system that is focused on the industry and allows users to rate industrial robots according to their machining precision. Some industrial robots from the KUKA family that have been graded for various machining jobs are used to test the established methodology.
{"title":"Trends in Robotics and Computer Integrated Manufacturing","authors":"A. Dixit, T. V. Kumar, Abhishek Joshi, H. Bedi, M. Chakravarthi, D. P. Singh","doi":"10.1109/IC3I56241.2022.10072745","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072745","url":null,"abstract":"Today, all manufacturing businesses are expected to have the ability to produce high-quality goods with shorter lead times and the capacity to produce to a variety of customer specifications. Any industry’s production capacity has increased exponentially as a result of the use of robots in manufacturing. To resolve the aforementioned problems and maintain a nation’s revenue in the fiercely competitive international market, robotics and computer integrated manufacturing (CIM) technology are required. The main emphasis is placed on the robot’s accuracy during milling operations, and its capacity to carry out the work with the required precision is assessed. The manipulator stiffness model is utilized to estimate failures in conformity caused by the baseline cutting force, which is the same for all robots under consideration, in order to calculate this performance metric. The feasibility of the suggested method was demonstrated in experimental research including the milling of circular notches using a robot for a variety of workgroup samples and locations. As cutting force increases, the circularity index raises as well, and it may be calculated by simple scaling. The study suggests a system that is focused on the industry and allows users to rate industrial robots according to their machining precision. Some industrial robots from the KUKA family that have been graded for various machining jobs are used to test the established methodology.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"15 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":"115914285","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/IC3I56241.2022.10073343
Shubham Saxena, Sameer Sonawane, B. P. Lohani, Ashish Garg, A. Deepak, Deepika Arora
This paper provides a comprehensive analysis of the steps necessary to disable self-interference (SI) in wireless networks and allow full duplex (FD) broadcasting. As a way of constructing mobile networks in regions where there is a scarcity of broadcast signals, the adoption of a frequency division method, also known as an in-band FD approach, has seen a rise in popularity. This method is also known as an in-band frequency division approach. Although the study does include reviews of methods for mitigating self-interference, great care has been taken to highlight not only the various strategies for mitigating the self-interference that occurs when FD equipment is activated, but also other methods that have a significant impact on self-interference in satellite propagation. While the study does include reviews of methods for mitigating self-interference, it is important to note that great care has been taken to highlight not only the various strategies for mitigating the self-interference that This review gives a scientific classification of self-interference and illustrates the usefulness of various kinds of self-interference as well as the limits that come with them. A synopsis of the study, a discussion of the difficulties encountered in the research, and a list of recommendations for the future stages are all necessary components of the review. The findings of this study might be used as a starting point and direction for future work on SI to perform FD propagation in mobile contexts with varied surrounds, such as the Internet of Things.
{"title":"A Study on Methods for Managing Full-Duplex Self-Interference","authors":"Shubham Saxena, Sameer Sonawane, B. P. Lohani, Ashish Garg, A. Deepak, Deepika Arora","doi":"10.1109/IC3I56241.2022.10073343","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073343","url":null,"abstract":"This paper provides a comprehensive analysis of the steps necessary to disable self-interference (SI) in wireless networks and allow full duplex (FD) broadcasting. As a way of constructing mobile networks in regions where there is a scarcity of broadcast signals, the adoption of a frequency division method, also known as an in-band FD approach, has seen a rise in popularity. This method is also known as an in-band frequency division approach. Although the study does include reviews of methods for mitigating self-interference, great care has been taken to highlight not only the various strategies for mitigating the self-interference that occurs when FD equipment is activated, but also other methods that have a significant impact on self-interference in satellite propagation. While the study does include reviews of methods for mitigating self-interference, it is important to note that great care has been taken to highlight not only the various strategies for mitigating the self-interference that This review gives a scientific classification of self-interference and illustrates the usefulness of various kinds of self-interference as well as the limits that come with them. A synopsis of the study, a discussion of the difficulties encountered in the research, and a list of recommendations for the future stages are all necessary components of the review. The findings of this study might be used as a starting point and direction for future work on SI to perform FD propagation in mobile contexts with varied surrounds, such as the Internet of Things.","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":"124344946","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/IC3I56241.2022.10072763
R. Mittal, Varun Malik, S. V. Singh
Diabetes affects a large number of people in modern culture. Individuals must keep track of food calories and total calories consumed daily to maintain a balanced diet. Type 2 diabetes is a devastating metabolic illness that may manifest in many symptoms and complications throughout the body. In the modern day, diabetics may be found throughout all age groups in society. The increased number of reported diabetes patients may be attributed to different causes, including but not limited to harmful or chemical components blended into the food, obesity, working culture and improper diet plan, atypical lifestyle, consuming food habits, and environmental variables. As a result, saving human life requires a proper diagnosis of diabetes. When used in the healthcare industry, machine learning techniques may help doctors foresee the onset of diabetes and other complications. This research proposes the Diabetic Food Recommendation System (DFR-HL) to identify diabetes and advice patients on managing their condition via diet (DFRS). The datasets are normalized using a standard scalar with an improved Decision Tree (IDT), and the feature is selected using a Random forest. Finally, the classification has been done with Hybrid (CNN with Resnet50) DL algorithms. The experimental results are compared with performance metrics.
{"title":"DFR-HL: Diabetic Food Recommendation Using Hybrid Learning Methods","authors":"R. Mittal, Varun Malik, S. V. Singh","doi":"10.1109/IC3I56241.2022.10072763","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072763","url":null,"abstract":"Diabetes affects a large number of people in modern culture. Individuals must keep track of food calories and total calories consumed daily to maintain a balanced diet. Type 2 diabetes is a devastating metabolic illness that may manifest in many symptoms and complications throughout the body. In the modern day, diabetics may be found throughout all age groups in society. The increased number of reported diabetes patients may be attributed to different causes, including but not limited to harmful or chemical components blended into the food, obesity, working culture and improper diet plan, atypical lifestyle, consuming food habits, and environmental variables. As a result, saving human life requires a proper diagnosis of diabetes. When used in the healthcare industry, machine learning techniques may help doctors foresee the onset of diabetes and other complications. This research proposes the Diabetic Food Recommendation System (DFR-HL) to identify diabetes and advice patients on managing their condition via diet (DFRS). The datasets are normalized using a standard scalar with an improved Decision Tree (IDT), and the feature is selected using a Random forest. Finally, the classification has been done with Hybrid (CNN with Resnet50) DL algorithms. The experimental results are compared with performance metrics.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"205 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":"114407218","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/IC3I56241.2022.10072552
Amit Jain, Tripti Misra, Neha Tyagi, M. V. S. Kumar, Bhasker Pant
Protection of networks, programs and systems from cyberattacks is the practice of cybersecurity technology. The cyberattacks are capable of gaining access for deleting or altering of data that are sensitive, demanding money from the users and obstruction of regular operations of corporates. In the present situation many devices are getting smarter than that of the hackers and humans that makes the tasks difficult for implementing the measures of cybersecurity. In terms of cyber security, the ability of collecting huge amount of data is known as big data analytics. The functions are performed through displaying, interpretation and extraction of the insights of future that can enable early detection of catastrophic cyber threats and attacks. Organizations can better understand all the activities and acts that could potentially result in cyber-attacks by having a stronger and more effective cyber defensive posture. As offices became more remote, cloud computing became crucial for gaining access to important documents, programs, and computing resources. Although connection can be both a benefit and a curse, as easily accessible files can become an easy target for hostile actors, we expect the global economy to transfer even more into the cloud over time. Therefore, efforts in strengthening cybersecurity measures are vital to safeguard distant vaults of sensitive data. The management depends on a combination of technology and advice to secure the cloud. It involves managing the framework, information applications, safe-secure guidelines, consistency leads, and secure infrastructure data applications that pertain to cloud computing. The same principles that underpin security for modern cloud computing platforms and historic knowledge centers are secrecy, integrity, and usability. The novel method of DDoS attack detection is proposed.
{"title":"A Comparative Study on Cyber security Technology in Big data Cloud Computing Environment","authors":"Amit Jain, Tripti Misra, Neha Tyagi, M. V. S. Kumar, Bhasker Pant","doi":"10.1109/IC3I56241.2022.10072552","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072552","url":null,"abstract":"Protection of networks, programs and systems from cyberattacks is the practice of cybersecurity technology. The cyberattacks are capable of gaining access for deleting or altering of data that are sensitive, demanding money from the users and obstruction of regular operations of corporates. In the present situation many devices are getting smarter than that of the hackers and humans that makes the tasks difficult for implementing the measures of cybersecurity. In terms of cyber security, the ability of collecting huge amount of data is known as big data analytics. The functions are performed through displaying, interpretation and extraction of the insights of future that can enable early detection of catastrophic cyber threats and attacks. Organizations can better understand all the activities and acts that could potentially result in cyber-attacks by having a stronger and more effective cyber defensive posture. As offices became more remote, cloud computing became crucial for gaining access to important documents, programs, and computing resources. Although connection can be both a benefit and a curse, as easily accessible files can become an easy target for hostile actors, we expect the global economy to transfer even more into the cloud over time. Therefore, efforts in strengthening cybersecurity measures are vital to safeguard distant vaults of sensitive data. The management depends on a combination of technology and advice to secure the cloud. It involves managing the framework, information applications, safe-secure guidelines, consistency leads, and secure infrastructure data applications that pertain to cloud computing. The same principles that underpin security for modern cloud computing platforms and historic knowledge centers are secrecy, integrity, and usability. The novel method of DDoS attack detection is proposed.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"17 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":"114619480","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/IC3I56241.2022.10073307
I. S. Chakrapani, Shubhi Gupta, Narender Chinthamu, H. S. Pokhariya, B. Babu, Annam Takshitha Rao
Retinal microvascular is a dependable marker of abnormalities in vessel morphology, that have been linked to a variety of clinical disorders, both in ocular and metastatic disease. However, accurate vessel segmentation, which would be intricate- and time-intensive, is required for objective and statistical evaluation of the retinal blood vessels. In terms of segmenting retinal vessels, artificial intelligence (AI) has shown a significant amount of promise. In this study, the fundus images retinal blood vessel is segmented using deep learning methods. The data set required for this study is collected from the Kaggle website and pre-processed using various techniques to make it compatible with the deep learning models. The pre-processed images are then segmented using deep learning models such as LadderNet and UNet. The efficiency of the deep learning models are validated using performance metrics such as Intersection of Union (IoU), accuracy and F1 score. This study shows an accuracy of 0.98% using the UNet deep learning model and it is deemed to be an efficient model than the pre-existing models.
{"title":"Retinal blood vessel segmentation using AI","authors":"I. S. Chakrapani, Shubhi Gupta, Narender Chinthamu, H. S. Pokhariya, B. Babu, Annam Takshitha Rao","doi":"10.1109/IC3I56241.2022.10073307","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073307","url":null,"abstract":"Retinal microvascular is a dependable marker of abnormalities in vessel morphology, that have been linked to a variety of clinical disorders, both in ocular and metastatic disease. However, accurate vessel segmentation, which would be intricate- and time-intensive, is required for objective and statistical evaluation of the retinal blood vessels. In terms of segmenting retinal vessels, artificial intelligence (AI) has shown a significant amount of promise. In this study, the fundus images retinal blood vessel is segmented using deep learning methods. The data set required for this study is collected from the Kaggle website and pre-processed using various techniques to make it compatible with the deep learning models. The pre-processed images are then segmented using deep learning models such as LadderNet and UNet. The efficiency of the deep learning models are validated using performance metrics such as Intersection of Union (IoU), accuracy and F1 score. This study shows an accuracy of 0.98% using the UNet deep learning model and it is deemed to be an efficient model than the pre-existing models.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"254 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":"114938507","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/IC3I56241.2022.10073139
Soumitra Das, Durgaprasad Gangodkar, R. Singh, P. Vijay, Ankit Bhardwaj, Amit Semwal
The skin is the body’s outermost layer, concealing/covering many physical organs, muscles, and other innumerable bodily parts. The research found that the body’s exposure to ultraviolet light is the main contributor to skin cancer (UV). There are many layers to the surface, but the top and dermis are where cancer first appears. Variations in you complexion or the appearance of a blemish 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 Uvr as you can, that could stop their skin from coming into contact with the disease. According to statistics, cases of this cancer are not only increased but are increasing swiftly as a result of the ozone layer’s deterioration, which causes it to stop emitting dangerous energy and, as a result, come into contact with our skin. For the following problem, several different strategies including machine learning, deep learning, and data augmentation are being used. Bayes Classifier, linear regression, random woodland, retiree, artificial neural network, and dnn are just a few of the many techniques used. The research makes an effort to put both transfer learning and deep learning approaches to use in order to provide a result that shows which performed best for the next challenge.
{"title":"Comparative Analysis of Skin Cancer Prediction using Neural Networks and Transfer Learning","authors":"Soumitra Das, Durgaprasad Gangodkar, R. Singh, P. Vijay, Ankit Bhardwaj, Amit Semwal","doi":"10.1109/IC3I56241.2022.10073139","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073139","url":null,"abstract":"The skin is the body’s outermost layer, concealing/covering many physical organs, muscles, and other innumerable bodily parts. The research found that the body’s exposure to ultraviolet light is the main contributor to skin cancer (UV). There are many layers to the surface, but the top and dermis are where cancer first appears. Variations in you complexion or the appearance of a blemish 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 Uvr as you can, that could stop their skin from coming into contact with the disease. According to statistics, cases of this cancer are not only increased but are increasing swiftly as a result of the ozone layer’s deterioration, which causes it to stop emitting dangerous energy and, as a result, come into contact with our skin. For the following problem, several different strategies including machine learning, deep learning, and data augmentation are being used. Bayes Classifier, linear regression, random woodland, retiree, artificial neural network, and dnn are just a few of the many techniques used. The research makes an effort to put both transfer learning and deep learning approaches to use in order to provide a result that shows which performed best for the next challenge.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"221 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":"116000841","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/IC3I56241.2022.10073107
A. Raizada, Manbir Kaur Brar
A need for enhancing our everyday lives is the widespread use of interconnected and interoperable computer systems. The same is true for exploitable defects that are uncontrollable by humans. Computer security methods are necessary to handle contact because of the weaknesses. Reliable connection requires security standards and advancements in protection measures to counter escalating security issues. This paper offers building an adaptive and durable intrusion detection system utilizing deep learning systems to recognize and categorise cyber-attacks. The emphasis is on how learning or deep neuronal systems (DCNNs) may aid adaptive IDS with developing capabilities discern between known and new or negligible networking detectable qualities, disconnecting the intrusive party and reducing the danger of exposure. The effectiveness of the model was shown using the UNSW-NB15 database, whose represents real current network activity in addition to artificially constructed attack behavior.
{"title":"Neural Networks for Vulnerability Scanning in Automobiles Ethernet Connections","authors":"A. Raizada, Manbir Kaur Brar","doi":"10.1109/IC3I56241.2022.10073107","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073107","url":null,"abstract":"A need for enhancing our everyday lives is the widespread use of interconnected and interoperable computer systems. The same is true for exploitable defects that are uncontrollable by humans. Computer security methods are necessary to handle contact because of the weaknesses. Reliable connection requires security standards and advancements in protection measures to counter escalating security issues. This paper offers building an adaptive and durable intrusion detection system utilizing deep learning systems to recognize and categorise cyber-attacks. The emphasis is on how learning or deep neuronal systems (DCNNs) may aid adaptive IDS with developing capabilities discern between known and new or negligible networking detectable qualities, disconnecting the intrusive party and reducing the danger of exposure. The effectiveness of the model was shown using the UNSW-NB15 database, whose represents real current network activity in addition to artificially constructed attack behavior.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"44 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":"116365931","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/IC3I56241.2022.10072832
D. Mitra, Neha Sharma, Mamoon Rashid, R. Singh
One of the medical field’s most researched issues is cancer diagnosis. Many researchers have concentrated on performance enhancement and achieving successful outcomes. One of the most lethal forms of cancer is breast cancer. A significant issue in cancer diagnosis research is the diagnosis of this cancer. A kind of artificial intelligence called machine learning allows a machine to grow over time. In bio informatics, machine learning is frequently employed, notably in the detection of breast cancer. supervised learning method known as K-nearest neighbors’ approach, is one well-liked techniques. It’s really intriguing to use the K-NN in medical diagnostics. The value of parameter “k” & distance have a significant impact on the findings’ quality. This indicates how many neighbors are in proximity. In this paper, we assess the performance of various K-NN algorithmic distances. Additionally, we investigate this distance using various “k” parameter values and classification algorithms (the formula used to determine a sample’s classification).
{"title":"Classification Rules based Breast Cancer Detection using Machine Learning Approach","authors":"D. Mitra, Neha Sharma, Mamoon Rashid, R. Singh","doi":"10.1109/IC3I56241.2022.10072832","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072832","url":null,"abstract":"One of the medical field’s most researched issues is cancer diagnosis. Many researchers have concentrated on performance enhancement and achieving successful outcomes. One of the most lethal forms of cancer is breast cancer. A significant issue in cancer diagnosis research is the diagnosis of this cancer. A kind of artificial intelligence called machine learning allows a machine to grow over time. In bio informatics, machine learning is frequently employed, notably in the detection of breast cancer. supervised learning method known as K-nearest neighbors’ approach, is one well-liked techniques. It’s really intriguing to use the K-NN in medical diagnostics. The value of parameter “k” & distance have a significant impact on the findings’ quality. This indicates how many neighbors are in proximity. In this paper, we assess the performance of various K-NN algorithmic distances. Additionally, we investigate this distance using various “k” parameter values and classification algorithms (the formula used to determine a sample’s classification).","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"208 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":"116386933","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}
In today's IT industry, cloud computing, and the broader cloud services sector have gained widespread acceptance. To put it simply, cloud services provide cloud computing, which enables on-demand access to IT resources through the internet and employs a pay-per-use pricing model based on subscription authorization and centralized hosting. The need for public cloud services is increasing at an unprecedented rate as the 21st century becomes more digital. Cloud computing makes it easy to access servers, storage, databases, and various application services via the internet. The cloud environment supports a variety of advantages, but it also has a few drawbacks. Regardless of the highest industry certifications and security requirements implemented by cloud service providers, there are always risks when storing sensitive data on third-party service providers. Security and privacy must play a significant role when discussing data security, especially when handling sensitive data. Many solutions have been created to deal with this problem. It is necessary to discover, classify, and analyze the significant existing work because there is a shortage of analysis among the current solutions. This paper relates and briefly analyses the top methods for safely exchanging and safeguarding data in a cloud setting. The discussion of each specific technique covers its role in data protection, prospective and reliable solutions in the field, scope, future directions, etc. The applicability and integrity of the methodologies are then explored regarding the demands and results.
{"title":"A Review: Data Security in Cloud Computing Using Machine Learning","authors":"Pravallika Kandi, Sujith Raj Tarapatla, Surinder Kumar, Harshitha Kadiyam, Dinesh Chowdary, Nageswara Rao Moparthi","doi":"10.1109/IC3I56241.2022.10072968","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072968","url":null,"abstract":"In today's IT industry, cloud computing, and the broader cloud services sector have gained widespread acceptance. To put it simply, cloud services provide cloud computing, which enables on-demand access to IT resources through the internet and employs a pay-per-use pricing model based on subscription authorization and centralized hosting. The need for public cloud services is increasing at an unprecedented rate as the 21st century becomes more digital. Cloud computing makes it easy to access servers, storage, databases, and various application services via the internet. The cloud environment supports a variety of advantages, but it also has a few drawbacks. Regardless of the highest industry certifications and security requirements implemented by cloud service providers, there are always risks when storing sensitive data on third-party service providers. Security and privacy must play a significant role when discussing data security, especially when handling sensitive data. Many solutions have been created to deal with this problem. It is necessary to discover, classify, and analyze the significant existing work because there is a shortage of analysis among the current solutions. This paper relates and briefly analyses the top methods for safely exchanging and safeguarding data in a cloud setting. The discussion of each specific technique covers its role in data protection, prospective and reliable solutions in the field, scope, future directions, etc. The applicability and integrity of the methodologies are then explored regarding the demands and results.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"542 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":"122069154","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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