Pub Date : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702613
M. Maheswari, N. Brintha
Manufacturing is the process of producing effective products through the use of machinery, labor, tools and well-formulated theory. During the manufacturing process, industries experience various kinds of provocations, including unexpected failures of equipment and machines, downtime failures and products delivered in an imperfect way. In industry 4.0, smart manufacturing is used to rectify these challenges and faults during the manufacturing process and it includes all intermediate processes required for the integration of smart manufacturing technologies into industry. In detail, digital technology has been applied to the face of the industrial and manufacturing world, which is called smart manufacturing. This paper presents the technologies involved in the manufacturing process in a smart way in Industry 4.0. The merging of various emerging technologies such as the Internet of Things (IoT), Cyber Security, Big Data, Cloud Computing, Automation, Augmented Reality and virtual reality have been enabled in the industry 4.0. These technologies are used to upgrade how manufacturers improve and enhance operational efficiency, develop and launch new products with quality, design customized products and AI with digital transformations are used to make the manufacturing process smarter in the industry.
{"title":"Smart Manufacturing Technologies in Industry-4.0","authors":"M. Maheswari, N. Brintha","doi":"10.1109/ICIIP53038.2021.9702613","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702613","url":null,"abstract":"Manufacturing is the process of producing effective products through the use of machinery, labor, tools and well-formulated theory. During the manufacturing process, industries experience various kinds of provocations, including unexpected failures of equipment and machines, downtime failures and products delivered in an imperfect way. In industry 4.0, smart manufacturing is used to rectify these challenges and faults during the manufacturing process and it includes all intermediate processes required for the integration of smart manufacturing technologies into industry. In detail, digital technology has been applied to the face of the industrial and manufacturing world, which is called smart manufacturing. This paper presents the technologies involved in the manufacturing process in a smart way in Industry 4.0. The merging of various emerging technologies such as the Internet of Things (IoT), Cyber Security, Big Data, Cloud Computing, Automation, Augmented Reality and virtual reality have been enabled in the industry 4.0. These technologies are used to upgrade how manufacturers improve and enhance operational efficiency, develop and launch new products with quality, design customized products and AI with digital transformations are used to make the manufacturing process smarter in the industry.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116683537","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702658
N. Brintha, J. Jappes, S. Lakshmi
Security has become a major concern in today’s IT scenario because of the surge in use of technology aids and wide usage of publically available social networks among people because of its economic viability. Due to these concerns, common users are facing lot of issues which has become a life threatening issue. Public in terms of application provider needs some advertisement business model to offer the application at free of cost, even though the application which are already available have some security features, they completely lag in preserving user privacy, and vice versa. The user of such applications are not aware of the privacy settings configured in the application. Apparently, when a user (X) of an application takes and shares picture from a public place (Museum), the photo may have other users (Y) image as well, without their (Y’s) knowledge it may be available online. Hence, the basic idea behind the solution is to use some face recognition algorithm to identify the other (Y’s) face in the current user (X’s) picture and to intimate the other user (Y) about the occurrence of their picture. With this, the user can provide their decision on whether their picture could be shared or not so as to have control on their access privilege. The proposed approach solves the above problem and provides privacy in posting of photos in online sources. This provides an intimation to the users on their photo sharing.
{"title":"Privacy Enabled Dynamic Regimentation of Photo Posting on Online Social Networks","authors":"N. Brintha, J. Jappes, S. Lakshmi","doi":"10.1109/ICIIP53038.2021.9702658","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702658","url":null,"abstract":"Security has become a major concern in today’s IT scenario because of the surge in use of technology aids and wide usage of publically available social networks among people because of its economic viability. Due to these concerns, common users are facing lot of issues which has become a life threatening issue. Public in terms of application provider needs some advertisement business model to offer the application at free of cost, even though the application which are already available have some security features, they completely lag in preserving user privacy, and vice versa. The user of such applications are not aware of the privacy settings configured in the application. Apparently, when a user (X) of an application takes and shares picture from a public place (Museum), the photo may have other users (Y) image as well, without their (Y’s) knowledge it may be available online. Hence, the basic idea behind the solution is to use some face recognition algorithm to identify the other (Y’s) face in the current user (X’s) picture and to intimate the other user (Y) about the occurrence of their picture. With this, the user can provide their decision on whether their picture could be shared or not so as to have control on their access privilege. The proposed approach solves the above problem and provides privacy in posting of photos in online sources. This provides an intimation to the users on their photo sharing.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124980329","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702684
Parul Parihar, Devanand, N. Kumar
Product promotion for increasing the sale of the product is critical in today’s competitive environment. Online medium of promotion is vital in this regard. Product promotion especially in the field of movies goes through fake promotion issues. Movies are promoted by the entities through fake rating. This work primary focuses on the detection of fake profiles. To accomplish this collaborative filtering with the pre-processing mechanism is used. Demonstration of work will be done through movie lense dataset. The nature of proposed approach is modular; this means entire work will be divided into phase. Data acquisition is performed in the first phase. After collecting the dataset, pre-processing mechanism is applied by using nominal conversion. Collaborative filtering is applied along with clustering to determine the fake promotion of within movie lense dataset. Nominal conversion is also required since recommender system may not able to handle string values. By the classification accuracy we can show the result of the proposed work.
{"title":"Fake Profile Detection from the Social Dataset for Movie Promotion","authors":"Parul Parihar, Devanand, N. Kumar","doi":"10.1109/ICIIP53038.2021.9702684","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702684","url":null,"abstract":"Product promotion for increasing the sale of the product is critical in today’s competitive environment. Online medium of promotion is vital in this regard. Product promotion especially in the field of movies goes through fake promotion issues. Movies are promoted by the entities through fake rating. This work primary focuses on the detection of fake profiles. To accomplish this collaborative filtering with the pre-processing mechanism is used. Demonstration of work will be done through movie lense dataset. The nature of proposed approach is modular; this means entire work will be divided into phase. Data acquisition is performed in the first phase. After collecting the dataset, pre-processing mechanism is applied by using nominal conversion. Collaborative filtering is applied along with clustering to determine the fake promotion of within movie lense dataset. Nominal conversion is also required since recommender system may not able to handle string values. By the classification accuracy we can show the result of the proposed work.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126183138","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702706
F. Fayaz, A. Malik, Arshad Ahmad Yatoo
In conjunction with data generated by intelligent machines, cognitive IoT uses cognitive computing technology and the actions these devices can accomplish. The Cognitive Internet of Things (CIoT) is seen as the new IoT is combined with mental and mutual frameworks to facilitate success and intelligence. This leading research area has recently emerged as intelligent sensing. Researchers examine the sensing data performance problems with Smarter technologies, in which people usually use smart gadgets, contribute training datasets towards the Cognitive Internet of things collected by sensors. Moreover, Cognitive Intent of Things (CIOT), shortcomings in the scope of sensing data, contribute to the loss of human life and civil instability. To answer this problem, we propose a new metric in this article, called the Quality of Information Coverage (QIC), which will personify information distribution and data sensing incentives to leverage the QIC. In addition, a market-based compensation system is being developed to pledge the QIC. To produce optimum kickbacks for CIoT and news outlets, we evaluate the optimal business solution and examine an acceptable representation. Then, by detailed computations, the results of a competition reward system are studied. The findings suggest that the way the method of reward management hits the balance point with a greater QIC than most current systems. The QIC told a system in this work guarantees that, relative to existing algorithms, the sample variance number obtained datasets for specific regions decreases by approximately less than 40 to 55 percent since these data sets are calibrated. Compared to these non-QIC-aware algorithms, the average sale price is Sensing proposed should be less than 17 to 18 percent.
{"title":"Cognitive Internet of things (CIoT) a success for data collection","authors":"F. Fayaz, A. Malik, Arshad Ahmad Yatoo","doi":"10.1109/ICIIP53038.2021.9702706","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702706","url":null,"abstract":"In conjunction with data generated by intelligent machines, cognitive IoT uses cognitive computing technology and the actions these devices can accomplish. The Cognitive Internet of Things (CIoT) is seen as the new IoT is combined with mental and mutual frameworks to facilitate success and intelligence. This leading research area has recently emerged as intelligent sensing. Researchers examine the sensing data performance problems with Smarter technologies, in which people usually use smart gadgets, contribute training datasets towards the Cognitive Internet of things collected by sensors. Moreover, Cognitive Intent of Things (CIOT), shortcomings in the scope of sensing data, contribute to the loss of human life and civil instability. To answer this problem, we propose a new metric in this article, called the Quality of Information Coverage (QIC), which will personify information distribution and data sensing incentives to leverage the QIC. In addition, a market-based compensation system is being developed to pledge the QIC. To produce optimum kickbacks for CIoT and news outlets, we evaluate the optimal business solution and examine an acceptable representation. Then, by detailed computations, the results of a competition reward system are studied. The findings suggest that the way the method of reward management hits the balance point with a greater QIC than most current systems. The QIC told a system in this work guarantees that, relative to existing algorithms, the sample variance number obtained datasets for specific regions decreases by approximately less than 40 to 55 percent since these data sets are calibrated. Compared to these non-QIC-aware algorithms, the average sale price is Sensing proposed should be less than 17 to 18 percent.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126206505","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702688
Rajneesh Rani, Akshay Kumar, Amrita Rai
Law enforcement in the 21st century works on the evidence present in digital images or videos. Digital image processing is, thus, being heavily applied in the field of law enforcement, especially when it comes to detecting whether digital evidence related to legal matters has been tampered with or not. Due to the easy availability of various software, it is effortless for any law offender to have evidence such as digital images or videos transformed for their cause. Hence, two types of tampering detection techniques are used to maintain the integrity of digital evidence, namely Active and Passive. The active methods require that some kind of pre-embedded data be present in the image, using which detection can be performed while the passive techniques are applicable without any such condition. The differences, working, and classifications of these techniques are elaborately discussed here.
{"title":"A Brief Review on Existing Techniques for Detecting Digital Image Forgery","authors":"Rajneesh Rani, Akshay Kumar, Amrita Rai","doi":"10.1109/ICIIP53038.2021.9702688","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702688","url":null,"abstract":"Law enforcement in the 21st century works on the evidence present in digital images or videos. Digital image processing is, thus, being heavily applied in the field of law enforcement, especially when it comes to detecting whether digital evidence related to legal matters has been tampered with or not. Due to the easy availability of various software, it is effortless for any law offender to have evidence such as digital images or videos transformed for their cause. Hence, two types of tampering detection techniques are used to maintain the integrity of digital evidence, namely Active and Passive. The active methods require that some kind of pre-embedded data be present in the image, using which detection can be performed while the passive techniques are applicable without any such condition. The differences, working, and classifications of these techniques are elaborately discussed here.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115246824","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702657
Rudrajit Choudhuri, Sayan Halder, A. Halder
The primary focus of the paper is towards image enhancement via removal of salt and pepper noise from images. In this paper, a novel statistical approach based on the properties of rough set theory is proposed, where noisy pixel identification and removal are controlled by decision rough parameters. Each enhancement decision is directly governed by four parameters – the pixel is noisy or not, the pixel has any non-noisy neighbor compatible enough to replace it, the deviation of the neighboring pixel from the central pixel value, and matching of the threshold criterion. The four phase decision making algorithm fetches highly accurate results and with consecutive iterations and upgradation, the algorithm is able to remove all noisy pixels while maintaining fine details of the image for even 95% corruption levels.
{"title":"A Novel Rough Set Based Image Denoising Algorithm","authors":"Rudrajit Choudhuri, Sayan Halder, A. Halder","doi":"10.1109/ICIIP53038.2021.9702657","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702657","url":null,"abstract":"The primary focus of the paper is towards image enhancement via removal of salt and pepper noise from images. In this paper, a novel statistical approach based on the properties of rough set theory is proposed, where noisy pixel identification and removal are controlled by decision rough parameters. Each enhancement decision is directly governed by four parameters – the pixel is noisy or not, the pixel has any non-noisy neighbor compatible enough to replace it, the deviation of the neighboring pixel from the central pixel value, and matching of the threshold criterion. The four phase decision making algorithm fetches highly accurate results and with consecutive iterations and upgradation, the algorithm is able to remove all noisy pixels while maintaining fine details of the image for even 95% corruption levels.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"423 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115642413","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702588
A. Srinivas, S. Reddy
Social Network is a standout amongst the most prominent intuitive medium to share, impart and disperse data. Informal organization is the stage to manufacture social relations among individuals. Clients can stay in contact with companions by trading distinctive kinds of data or messages. Now and again individuals send mail which causes a difficult issue similar to irritating or extorting to clients. The mail substance might exist inconsiderate. The terminology like hostile, detest, disgusting and so on are accessible in the mail. Those mails are recognizing as spam utilizing data sifting. Information filtering can be done by using synopsis and Machine learning substance gathering methodologies. Neural substance classifier is used for representing summary of delivered messages and associated request. In light of content rendering, we have to check the sent messages are spam or not spam.
{"title":"A Novel Approach for Excavating Communication Using Taxonomy and Outline Mechanisms","authors":"A. Srinivas, S. Reddy","doi":"10.1109/ICIIP53038.2021.9702588","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702588","url":null,"abstract":"Social Network is a standout amongst the most prominent intuitive medium to share, impart and disperse data. Informal organization is the stage to manufacture social relations among individuals. Clients can stay in contact with companions by trading distinctive kinds of data or messages. Now and again individuals send mail which causes a difficult issue similar to irritating or extorting to clients. The mail substance might exist inconsiderate. The terminology like hostile, detest, disgusting and so on are accessible in the mail. Those mails are recognizing as spam utilizing data sifting. Information filtering can be done by using synopsis and Machine learning substance gathering methodologies. Neural substance classifier is used for representing summary of delivered messages and associated request. In light of content rendering, we have to check the sent messages are spam or not spam.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115282015","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702591
Vidit Kumar
From the past few years, face recognition has become critical for security and surveillance applications, and is now necessary in many different settings, including offices, educational institutions, airports, corporations, and social spaces. In this paper, we present a framework of multi-face recognition for real time monitoring, resulting in simultaneous face tracking and recognition. First, the faces are detected in the video frames by using viola-jones algorithm. To remove the outliers from the detected face region, we design a face skeleton based on YCBCR color space for further feature points detection and extraction. Then harris corner feature points and SURF feature points are detected from each face, where harris points are used to track the faces in the video and the SURF feature points are used to extract facial features from the cropped faces. As the face tracking is going on, faces are simultaneously recognized by the trained classifier (support vector machine). The experiments conducted on publicly available dataset suggest that our method is reliable, accurate, and robust that can be deployed for real-world multi-face recognition systems.
{"title":"A Multi-Face Recognition Framework for Real Time Monitoring","authors":"Vidit Kumar","doi":"10.1109/ICIIP53038.2021.9702591","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702591","url":null,"abstract":"From the past few years, face recognition has become critical for security and surveillance applications, and is now necessary in many different settings, including offices, educational institutions, airports, corporations, and social spaces. In this paper, we present a framework of multi-face recognition for real time monitoring, resulting in simultaneous face tracking and recognition. First, the faces are detected in the video frames by using viola-jones algorithm. To remove the outliers from the detected face region, we design a face skeleton based on YCBCR color space for further feature points detection and extraction. Then harris corner feature points and SURF feature points are detected from each face, where harris points are used to track the faces in the video and the SURF feature points are used to extract facial features from the cropped faces. As the face tracking is going on, faces are simultaneously recognized by the trained classifier (support vector machine). The experiments conducted on publicly available dataset suggest that our method is reliable, accurate, and robust that can be deployed for real-world multi-face recognition systems.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131312221","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702623
Preeti Kale, Vijashree. A. More, U. Shinde
This paper discusses the Techniques for the Ro-bust Copy Move Forgery detection for the different datasets MICCF8multi,MICCF600,MICCF220,CoMoFoD DB. Thepro-posedmethodology computes two thresholds dynamically, for each input image one to detect the candidate block & another to detect the forged block. The performance of the proposed algorithm is evaluated along with the different state-of-art techniques. The results have shown that the proposed SWT-SVD algorithm out-performs 2D-DWT,BDF,YU-SUN,DRHFMS in terms of accuracy & computational time.
{"title":"Copy Move Forgery Detection-A Robust Technique","authors":"Preeti Kale, Vijashree. A. More, U. Shinde","doi":"10.1109/ICIIP53038.2021.9702623","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702623","url":null,"abstract":"This paper discusses the Techniques for the Ro-bust Copy Move Forgery detection for the different datasets MICCF8multi,MICCF600,MICCF220,CoMoFoD DB. Thepro-posedmethodology computes two thresholds dynamically, for each input image one to detect the candidate block & another to detect the forged block. The performance of the proposed algorithm is evaluated along with the different state-of-art techniques. The results have shown that the proposed SWT-SVD algorithm out-performs 2D-DWT,BDF,YU-SUN,DRHFMS in terms of accuracy & computational time.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122075698","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 : 2021-11-26DOI: 10.1109/ICIIP53038.2021.9702570
P. Gupta, Anuj Gupta, Digvijay Puri
The COVID-19 epidemic has forced several organizations to undergo major shift, to examine essential aspects of their economic cycles and to make use of invention to maintain activities whilst maintaining a shifting rule scene and unique method. This review provides a comprehensive understanding via a framework of facts and an original approach of huge no of key issues and fundamental subtleties impacting organizations and society from COVID-19. The views for different welcoming industry professionals are analyzed and broken down when the specific interpretations may be understood Web learning, modern technology, man-made brainpower, data board, social communication, security of networks, information giant, blockchain, security, multi-faceted invention and approach from the present emergency standpoint and influence on such specific areas. The master perspectives give the extent of the elements optimum comprehension, distinguishing central questions and proposals for hypothesis and practice by utilizing chest X-Ray pictures with ML approach. In the paper, the use of these ML methods to cope with the COVID-19 pandemic flow situation is a promising aspect, just as the prevention of Covid infection model is proposed. Result shows the proposed hybrid approach gives better accuracy as compared to other
{"title":"ML Based Hybrid Approach for COVID Disease Detection Using X-Ray Images","authors":"P. Gupta, Anuj Gupta, Digvijay Puri","doi":"10.1109/ICIIP53038.2021.9702570","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702570","url":null,"abstract":"The COVID-19 epidemic has forced several organizations to undergo major shift, to examine essential aspects of their economic cycles and to make use of invention to maintain activities whilst maintaining a shifting rule scene and unique method. This review provides a comprehensive understanding via a framework of facts and an original approach of huge no of key issues and fundamental subtleties impacting organizations and society from COVID-19. The views for different welcoming industry professionals are analyzed and broken down when the specific interpretations may be understood Web learning, modern technology, man-made brainpower, data board, social communication, security of networks, information giant, blockchain, security, multi-faceted invention and approach from the present emergency standpoint and influence on such specific areas. The master perspectives give the extent of the elements optimum comprehension, distinguishing central questions and proposals for hypothesis and practice by utilizing chest X-Ray pictures with ML approach. In the paper, the use of these ML methods to cope with the COVID-19 pandemic flow situation is a promising aspect, just as the prevention of Covid infection model is proposed. Result shows the proposed hybrid approach gives better accuracy as compared to other","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122812282","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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