Pub Date : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753693
Manan Khanna, Dhruv Pathak
About 9.2% of the world, or 689 million people, live in extreme Poverty according to the World Bank. The social agencies or trusts are limited in their capability and coverage to help these underprivileged people for food and main essentials. This paper focuses on the solution which enables mass Donors to seamlessly connect to recipients or Point-of-Contacts (PoCs) for easily donating food and essentials to underprivileged people while sitting at their homes/places. The solution introduces a Video-based Mobile App Platform that can be used by Donors anytime from anywhere to post and schedule their donations. Recipients directly or through agencies/organizations' PoCs are notified of the scheduled donations and pick-up/drop can be arranged internally or through 3rd party pick-up agencies. One of the other key features is SOS (emergency alert). If any poor person or family needs urgent help in terms of expensive medicines, blood, etc., this platform can be used by recipient PoCs to trigger SOS which will be received by all the people registered to this platform and hence can offer help immediately. The Application platform is designed and developed with frontend user interface in React Native/JavaScript and backend server in Python/Django. The application offers AI based Donation Prediction Engine (AIDPE) for adaptive video upload/upload processing and efficient PoC allocation based on multiple factors.
{"title":"D-Eazy Donation Platform – An Artificial Intelligence and Video based Mobile Application","authors":"Manan Khanna, Dhruv Pathak","doi":"10.1109/ICCMC53470.2022.9753693","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753693","url":null,"abstract":"About 9.2% of the world, or 689 million people, live in extreme Poverty according to the World Bank. The social agencies or trusts are limited in their capability and coverage to help these underprivileged people for food and main essentials. This paper focuses on the solution which enables mass Donors to seamlessly connect to recipients or Point-of-Contacts (PoCs) for easily donating food and essentials to underprivileged people while sitting at their homes/places. The solution introduces a Video-based Mobile App Platform that can be used by Donors anytime from anywhere to post and schedule their donations. Recipients directly or through agencies/organizations' PoCs are notified of the scheduled donations and pick-up/drop can be arranged internally or through 3rd party pick-up agencies. One of the other key features is SOS (emergency alert). If any poor person or family needs urgent help in terms of expensive medicines, blood, etc., this platform can be used by recipient PoCs to trigger SOS which will be received by all the people registered to this platform and hence can offer help immediately. The Application platform is designed and developed with frontend user interface in React Native/JavaScript and backend server in Python/Django. The application offers AI based Donation Prediction Engine (AIDPE) for adaptive video upload/upload processing and efficient PoC allocation based on multiple factors.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"20 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133043170","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753964
P. S, B. P, V. S, Sasmita. K
Epileptic seizures happen owing to anarchy in intellect functionality that can influence patient's physical condition. Finding of epileptic seizures inception is fairly valuable for medication and emergency alerts. Machine learning techniques and computational methods play a key part in detecting epileptic seizures from Electroencephalograms (EEG) signals. The main objective of this work is to provide an ANN framework with optimized performance related to seizure detection. Here, a machine learning framework is employed for seizure detection where the two-layer feature extraction with ANN classifiers are used to categorize seizure and non-seizure data. To get better performance, the best parameters related to ANN with the dataset are identified through bayes-optimization method. This model affords a trustworthy feature extraction and optimization for training a detection model. The proposed model is evaluated using the popular public dataset CHB-MIT.
{"title":"Epileptic Seizure Detection using Two-Layer Feature Extraction and Hyper-Parameter Optimization","authors":"P. S, B. P, V. S, Sasmita. K","doi":"10.1109/ICCMC53470.2022.9753964","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753964","url":null,"abstract":"Epileptic seizures happen owing to anarchy in intellect functionality that can influence patient's physical condition. Finding of epileptic seizures inception is fairly valuable for medication and emergency alerts. Machine learning techniques and computational methods play a key part in detecting epileptic seizures from Electroencephalograms (EEG) signals. The main objective of this work is to provide an ANN framework with optimized performance related to seizure detection. Here, a machine learning framework is employed for seizure detection where the two-layer feature extraction with ANN classifiers are used to categorize seizure and non-seizure data. To get better performance, the best parameters related to ANN with the dataset are identified through bayes-optimization method. This model affords a trustworthy feature extraction and optimization for training a detection model. The proposed model is evaluated using the popular public dataset CHB-MIT.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133809657","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753934
D. Akhil Reddy, V. Puneet, S. Siva Rama Krishna, S. Kranthi
Advancement of computer networktechnology and the IT business lead to new security issues in networks emerge on a regular basis, making it increasingly difficult to ignore. How to successfully prevent dangerous network hackers from invading, so that network systems and computers are safe and regular functioning, is a critical job for today's network administrators. In recent decades, network security has become increasingly important due to the rapid growth of the Internet and the growing number of users. Intrusion detection systems (IDSs), which attempt to maintain the maximum level of security, have recently become one of the most popular research subjects in network security. Deep learning neural network is used to extract features of network monitoring data, and classify intrusion types. The method will be validated using KDD CUP’99 dataset or any other relevant dataset. The results will be compared with other algorithms to show that the proposed method has a significant improvement over the traditional machine learning model accuracies.
随着计算机网络技术和IT业务的发展，新的网络安全问题层出不穷，越来越不容忽视。如何成功地防止危险的网络黑客入侵，使网络系统和计算机安全正常运行，是当今网络管理员的重要工作。近几十年来，由于互联网的快速发展和用户数量的不断增加，网络安全变得越来越重要。入侵检测系统(Intrusion detection system, ids)是近年来网络安全领域研究的热点之一。利用深度学习神经网络提取网络监控数据的特征，并对入侵类型进行分类。该方法将使用KDD CUP ' 99数据集或任何其他相关数据集进行验证。将结果与其他算法进行比较，表明所提出的方法比传统的机器学习模型精度有显著提高。
{"title":"Network Attack Detection And Classification using ANN Algorithm","authors":"D. Akhil Reddy, V. Puneet, S. Siva Rama Krishna, S. Kranthi","doi":"10.1109/ICCMC53470.2022.9753934","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753934","url":null,"abstract":"Advancement of computer networktechnology and the IT business lead to new security issues in networks emerge on a regular basis, making it increasingly difficult to ignore. How to successfully prevent dangerous network hackers from invading, so that network systems and computers are safe and regular functioning, is a critical job for today's network administrators. In recent decades, network security has become increasingly important due to the rapid growth of the Internet and the growing number of users. Intrusion detection systems (IDSs), which attempt to maintain the maximum level of security, have recently become one of the most popular research subjects in network security. Deep learning neural network is used to extract features of network monitoring data, and classify intrusion types. The method will be validated using KDD CUP’99 dataset or any other relevant dataset. The results will be compared with other algorithms to show that the proposed method has a significant improvement over the traditional machine learning model accuracies.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130302253","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753803
G. L. Salvin, J. Arul Linsely, I. Kathīr
This manuscript presents a complete study of self-interference(SI) managing plans needed to accomplish a full duplex (FD) broadcasting in wireless networks. A FD is regularly alluded to as in-band FD approach have arisen as an intriguing answer for the subsequently invention movable networks in mobile environments anywhere the shortage of accessible broadcasting signal is a significant concern. Despite the fact that reviews on the alleviation of self-interference have been recorded in the study, all encompassing effort to introduce not simply the different strategies accessible for dealing with self-interference that emerges when a FD equipment is empowered, as an overview, however it likewise examines other methodologies hindrances that considerably influence the self-interference for satellite propagation. The overview gives a scientific classification of self-interference demonstrates through examinations the qualities and restrictions of different self- interference. Significantly, the reviews sums up the study, identifies and unwrap research difficulty and important study intended for upcoming. This study is proposed to exist a lead and obtain inedible spot for additional effort on SI to accomplish FD propagation in portable environments, with assorted environments, that is verifiably the Internet of things to come remote frameworks.
{"title":"A Study on Full Duplex Self Interference Managing Approaches","authors":"G. L. Salvin, J. Arul Linsely, I. Kathīr","doi":"10.1109/ICCMC53470.2022.9753803","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753803","url":null,"abstract":"This manuscript presents a complete study of self-interference(SI) managing plans needed to accomplish a full duplex (FD) broadcasting in wireless networks. A FD is regularly alluded to as in-band FD approach have arisen as an intriguing answer for the subsequently invention movable networks in mobile environments anywhere the shortage of accessible broadcasting signal is a significant concern. Despite the fact that reviews on the alleviation of self-interference have been recorded in the study, all encompassing effort to introduce not simply the different strategies accessible for dealing with self-interference that emerges when a FD equipment is empowered, as an overview, however it likewise examines other methodologies hindrances that considerably influence the self-interference for satellite propagation. The overview gives a scientific classification of self-interference demonstrates through examinations the qualities and restrictions of different self- interference. Significantly, the reviews sums up the study, identifies and unwrap research difficulty and important study intended for upcoming. This study is proposed to exist a lead and obtain inedible spot for additional effort on SI to accomplish FD propagation in portable environments, with assorted environments, that is verifiably the Internet of things to come remote frameworks.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130348447","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753721
Mei Wang
Aiming at the big data security and privacy protection issues in the smart grid, the current key technologies for big data security and privacy protection in smart grids are sorted out, and a privacy-protecting smart grid association rule is proposed according to the privacy-protecting smart grid big data analysis and mining technology route The mining plan specifically analyzes the risk factors in the operation of the new power grid, and discusses the information security of power grid users from the perspective of the user, focusing on the protection of privacy and security, using safe multi-party calculation of the support and confidence of the association rules. Privacy-protecting smart grid big data mining enables power companies to improve service quality to 7.5% without divulging customer private information.
{"title":"Big Data Analysis and Mining Technology of Smart Grid Based on Privacy Protection","authors":"Mei Wang","doi":"10.1109/ICCMC53470.2022.9753721","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753721","url":null,"abstract":"Aiming at the big data security and privacy protection issues in the smart grid, the current key technologies for big data security and privacy protection in smart grids are sorted out, and a privacy-protecting smart grid association rule is proposed according to the privacy-protecting smart grid big data analysis and mining technology route The mining plan specifically analyzes the risk factors in the operation of the new power grid, and discusses the information security of power grid users from the perspective of the user, focusing on the protection of privacy and security, using safe multi-party calculation of the support and confidence of the association rules. Privacy-protecting smart grid big data mining enables power companies to improve service quality to 7.5% without divulging customer private information.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124503760","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9754043
Bharath Naidu Vangapandu, Anu Chalil
This paper provides an efficient Montgomery modular multiplication technique, such as high-performance Montgomery modular multiplier, which is the most important arithmetic functional unit. The throughput of this multiplier is critical to the overall performance of these digital multiplication systems, which is measured in bits per second. The suggested work in this study proposes a Montgomery modular multiplier architecture that incorporates a unique adaptive hold logic (AHL) circuit to achieve a high level of performance. Because by the variable latency, the multiplier can deliver increased throughput while also adjusting the AHL circuit to prevent performance decline caused by the aging aware effect. Therefore, this proposed multiplier was developed using Verilog HDL and Synthesized in a Xilinx FPGA, which reduced the number of clock cycles required for operand pre-computation and conversion of format. As a result, high throughput can be achieved by hiding the additional clock cycles required for operand pre-computation and conversion of format. According to the experimental findings, our suggested design with 32-bit multipliers may provide up to a significant performance boost when compared to current 32-bit Montgomery Multipliers in terms of speed and efficiency.
{"title":"FPGA Implementation of High-Performance Montgomery Modular Multiplication with Adaptive Hold Logic","authors":"Bharath Naidu Vangapandu, Anu Chalil","doi":"10.1109/ICCMC53470.2022.9754043","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9754043","url":null,"abstract":"This paper provides an efficient Montgomery modular multiplication technique, such as high-performance Montgomery modular multiplier, which is the most important arithmetic functional unit. The throughput of this multiplier is critical to the overall performance of these digital multiplication systems, which is measured in bits per second. The suggested work in this study proposes a Montgomery modular multiplier architecture that incorporates a unique adaptive hold logic (AHL) circuit to achieve a high level of performance. Because by the variable latency, the multiplier can deliver increased throughput while also adjusting the AHL circuit to prevent performance decline caused by the aging aware effect. Therefore, this proposed multiplier was developed using Verilog HDL and Synthesized in a Xilinx FPGA, which reduced the number of clock cycles required for operand pre-computation and conversion of format. As a result, high throughput can be achieved by hiding the additional clock cycles required for operand pre-computation and conversion of format. According to the experimental findings, our suggested design with 32-bit multipliers may provide up to a significant performance boost when compared to current 32-bit Montgomery Multipliers in terms of speed and efficiency.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124914292","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753720
Aswin Kumar.K, S. Gowri, John Wilifred David .J, Y. Bevish Jinila
Naïve Bayes classification categorization in machine learning is employed to check the patient's entire heart illness in this proposed work. As a result, the percentage of patients that contract disease as both positive and negative data is used. Most database management systems and desktop analytics and visualization applications make working with big data difficult. As a result of this machine learning can be employed from the standpoint of data mining, and the proposal displays a machine learning methodology. The classifiers are used to process heart percentages, and the results are given as a confusion matrix. In the presence of a training dataset, a unique classification strategy is introduced that can effectively increase classification performance. Heart disease stent diagnostic In addition, the generated method has a high identification of rates, making It's a useful tool for junior cardiologists to check the cardio vascular patients with a high risk for certain diseases and refer them to expert cardiologists for further evaluation.
{"title":"An Efficient Association Rule Mining from Distributed Medical Database for Predicting Heart Disease","authors":"Aswin Kumar.K, S. Gowri, John Wilifred David .J, Y. Bevish Jinila","doi":"10.1109/ICCMC53470.2022.9753720","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753720","url":null,"abstract":"Naïve Bayes classification categorization in machine learning is employed to check the patient's entire heart illness in this proposed work. As a result, the percentage of patients that contract disease as both positive and negative data is used. Most database management systems and desktop analytics and visualization applications make working with big data difficult. As a result of this machine learning can be employed from the standpoint of data mining, and the proposal displays a machine learning methodology. The classifiers are used to process heart percentages, and the results are given as a confusion matrix. In the presence of a training dataset, a unique classification strategy is introduced that can effectively increase classification performance. Heart disease stent diagnostic In addition, the generated method has a high identification of rates, making It's a useful tool for junior cardiologists to check the cardio vascular patients with a high risk for certain diseases and refer them to expert cardiologists for further evaluation.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125281520","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753689
E. Ajitha, B. Diwan, M. Roshini
Among several different types of cancer the one that causes high mortality in every country is lung carcinoma. The possibility of survival from this deadly disease can be enhanced by identifying cancer at an early stage. This paper focuses on an Extended version of the KNN Algorithm that is used for the prediction of lung carcinoma based on the Computed Tomography (CT) - Images given as the input. The 2-D image undergoes a Modified Gabor Filtration technique wherein the images are used to extract the features for Edge Detection. This further undergoes Feature Extraction followed by Binarization which is fed as Production data to the Machine Learning model. Based on the Extended KNN Algorithm, the model evaluates the testing data and corresponding predictions are made. The model predicts the Cancer Stage based on the input CT - Image which is passed to the doctor for further medication.
{"title":"Lung Cancer Prediction using Extended KNN Algorithm","authors":"E. Ajitha, B. Diwan, M. Roshini","doi":"10.1109/ICCMC53470.2022.9753689","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753689","url":null,"abstract":"Among several different types of cancer the one that causes high mortality in every country is lung carcinoma. The possibility of survival from this deadly disease can be enhanced by identifying cancer at an early stage. This paper focuses on an Extended version of the KNN Algorithm that is used for the prediction of lung carcinoma based on the Computed Tomography (CT) - Images given as the input. The 2-D image undergoes a Modified Gabor Filtration technique wherein the images are used to extract the features for Edge Detection. This further undergoes Feature Extraction followed by Binarization which is fed as Production data to the Machine Learning model. Based on the Extended KNN Algorithm, the model evaluates the testing data and corresponding predictions are made. The model predicts the Cancer Stage based on the input CT - Image which is passed to the doctor for further medication.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134126104","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9753970
S. Velmurugan, T. Subashini
Today, image forensic is an emerging area which aims at authenticating the credibility of an image. Sophisticating image editing tools make it easy to forge images in different ways and one amongst them is copy-move (CM) forgery which is considered in this paper. CM forgery modifies the content of an image by copying a portion of an image and pasting it in a distinct location in the similar image. Fraudsters, in order to conceal the fraud and to deceive the human eyes, sometimes do some post-processing operations such as rotation, scaling, multiple CM, etc. The widely used block-based methods for CM forgery detection are not robust enough to affine transformation and are not invariant to scaling, rotation, and noise. So, in this work, key-point-based CM forgery detection methods based on BRISK and ORB descriptors are proposed for detecting CM forgeries in digital images. The presented methods are dependent upon blobs, detecting using DoG operator, from which BRISK and ORB features are extracted. The extracted features are matched using Hamming distance metrics to find similar key points to identify the CM regions. The work was implemented in Python and synthesized images were used in this to analyze and compare the efficacy of the presented techniques. The experimental outcomes demonstrates that the presented technique was effectual for multi-CM attacks and geometric transformations namely rotation and scaling. Though both the methods were able to detect the CM forgeries efficiently, ORB executed faster compared to BRISK.
{"title":"Binary descriptors for Copy-Move Forgery Detection in Digital Photographs","authors":"S. Velmurugan, T. Subashini","doi":"10.1109/ICCMC53470.2022.9753970","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9753970","url":null,"abstract":"Today, image forensic is an emerging area which aims at authenticating the credibility of an image. Sophisticating image editing tools make it easy to forge images in different ways and one amongst them is copy-move (CM) forgery which is considered in this paper. CM forgery modifies the content of an image by copying a portion of an image and pasting it in a distinct location in the similar image. Fraudsters, in order to conceal the fraud and to deceive the human eyes, sometimes do some post-processing operations such as rotation, scaling, multiple CM, etc. The widely used block-based methods for CM forgery detection are not robust enough to affine transformation and are not invariant to scaling, rotation, and noise. So, in this work, key-point-based CM forgery detection methods based on BRISK and ORB descriptors are proposed for detecting CM forgeries in digital images. The presented methods are dependent upon blobs, detecting using DoG operator, from which BRISK and ORB features are extracted. The extracted features are matched using Hamming distance metrics to find similar key points to identify the CM regions. The work was implemented in Python and synthesized images were used in this to analyze and compare the efficacy of the presented techniques. The experimental outcomes demonstrates that the presented technique was effectual for multi-CM attacks and geometric transformations namely rotation and scaling. Though both the methods were able to detect the CM forgeries efficiently, ORB executed faster compared to BRISK.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"430 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132245601","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 : 2022-03-29DOI: 10.1109/ICCMC53470.2022.9754122
J. Song
With the continuous development of science and technology, the application of computer information technology in various fields has become more and more extensive, and the world is entering the information age. The development and application of information technology has brought new opportunities and challenges to library management. This article combines the concept of library information technology, analyzes the challenges of library management under information technology, and then proposes the practical application of information technology in library management, and reviews in detail the research status of the massive data mining process and Faced with challenges, and discussed the processing mode in the process of massive data mining from the perspective of game theory, granular computing model and big data processing thinking.
{"title":"Functions and Ways of Computer Information Technology in Optimizing Library Operation and Management Considering Massive Data Mining","authors":"J. Song","doi":"10.1109/ICCMC53470.2022.9754122","DOIUrl":"https://doi.org/10.1109/ICCMC53470.2022.9754122","url":null,"abstract":"With the continuous development of science and technology, the application of computer information technology in various fields has become more and more extensive, and the world is entering the information age. The development and application of information technology has brought new opportunities and challenges to library management. This article combines the concept of library information technology, analyzes the challenges of library management under information technology, and then proposes the practical application of information technology in library management, and reviews in detail the research status of the massive data mining process and Faced with challenges, and discussed the processing mode in the process of massive data mining from the perspective of game theory, granular computing model and big data processing thinking.","PeriodicalId":345346,"journal":{"name":"2022 6th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"502 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131951214","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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