Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169278
Anurag Dutta, J. Harshith, K. Lakshmanan, A. Ramamoorthy
The k - Sum Problem, which is a generic member of the family of which 2 - Sum and 3 - Sum problems are the youngest siblings is one of the most interesting problems in the domain of Optimization Techniques. Many researchers have shown that the k - Sum problem can be solved in no less than the order of $n_{k-1}$. On the other side, many researchers have tried and have successfully minimized its Computational Complexity, though quite negligibly. But since any subtle method doesn’t exist to minimize its Computational Complexity by a major pie, the Query - “Can k - Sum problem be solved in $O(n_{k-1-epsilon})$ for some $epsilon gt 0$ ” have been added in the list of UPCS (Unsolved Problems in Computer Science). In this article, we will effort to analyse the Complexity of Computing the $k - Sum$ problem, by exemplifying minimal bounds of Quantum Search, $Omegaleft(frac{sqrt[2]{log _2 n}}{log _2left(log _2 nright)}right)$ as stated by Buhrman. Now, one assumption that this minimal bound holds is that the element to be searched will be composed in some ordered manner. To extrude that, we will extend our work by making use of Grover’s Search, with Computational Complexity of the order, $O(sqrt[2]{n})$, which is not known to make use of any prerequisite.
{"title":"Computational Time Complexity for k-Sum Problem Amalgamated with Quantum Search","authors":"Anurag Dutta, J. Harshith, K. Lakshmanan, A. Ramamoorthy","doi":"10.1109/ICAIA57370.2023.10169278","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169278","url":null,"abstract":"The k - Sum Problem, which is a generic member of the family of which 2 - Sum and 3 - Sum problems are the youngest siblings is one of the most interesting problems in the domain of Optimization Techniques. Many researchers have shown that the k - Sum problem can be solved in no less than the order of $n_{k-1}$. On the other side, many researchers have tried and have successfully minimized its Computational Complexity, though quite negligibly. But since any subtle method doesn’t exist to minimize its Computational Complexity by a major pie, the Query - “Can k - Sum problem be solved in $O(n_{k-1-epsilon})$ for some $epsilon gt 0$ ” have been added in the list of UPCS (Unsolved Problems in Computer Science). In this article, we will effort to analyse the Complexity of Computing the $k - Sum$ problem, by exemplifying minimal bounds of Quantum Search, $Omegaleft(frac{sqrt[2]{log _2 n}}{log _2left(log _2 nright)}right)$ as stated by Buhrman. Now, one assumption that this minimal bound holds is that the element to be searched will be composed in some ordered manner. To extrude that, we will extend our work by making use of Grover’s Search, with Computational Complexity of the order, $O(sqrt[2]{n})$, which is not known to make use of any prerequisite.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130457899","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169273
S. Ajay, K. S. Sabarinathan, N. G. Santhosh Sudhaan, P. Uma Maheswari, S. Mohamed Mansoor Roomi, S.M.H. Sithi Shameem Fathima
Proper segmentation of the maxillofacial bones in OPG (OrthoPantomoGram) is vital for identification and prediagnosis planning for maxillofacial surgery. Traditional segmentation is time - consuming and demanding due to inherent properties of bones in the maxillofacial regions. Nevertheless, due to the large consistent dataset requirements of data driven segmentation techniques, such as deep learning, there is an impediment in their clinical applications. In this study, we proposed a modified Mask RCNN based Framework for the automatic and accurate segmentation of the condylar regions in Dental (OPG) images with limited datasets. This proposed technique comprises of three stages namely pre-processing, mask creations and segmentation. Initially the edges of the condylar region in dental (OPG) images are enhance using pre-processing filter subsequently the mask regions of the condylar has been created using polynomial approach then the mask images along with the original images are trained by the proposed deep network architecture finally the segmented condylar region is compared with the ground tooth images created by dental experts and achieves an accuracy of 87.24%. The results suggested that Modified Mask RCNN has segmentation performance that is comparable to other models and has better data compatibility.
{"title":"Automatic Segmentation of Mandibular Condylar in Dental OPG Images Using Modified Mask RCNN","authors":"S. Ajay, K. S. Sabarinathan, N. G. Santhosh Sudhaan, P. Uma Maheswari, S. Mohamed Mansoor Roomi, S.M.H. Sithi Shameem Fathima","doi":"10.1109/ICAIA57370.2023.10169273","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169273","url":null,"abstract":"Proper segmentation of the maxillofacial bones in OPG (OrthoPantomoGram) is vital for identification and prediagnosis planning for maxillofacial surgery. Traditional segmentation is time - consuming and demanding due to inherent properties of bones in the maxillofacial regions. Nevertheless, due to the large consistent dataset requirements of data driven segmentation techniques, such as deep learning, there is an impediment in their clinical applications. In this study, we proposed a modified Mask RCNN based Framework for the automatic and accurate segmentation of the condylar regions in Dental (OPG) images with limited datasets. This proposed technique comprises of three stages namely pre-processing, mask creations and segmentation. Initially the edges of the condylar region in dental (OPG) images are enhance using pre-processing filter subsequently the mask regions of the condylar has been created using polynomial approach then the mask images along with the original images are trained by the proposed deep network architecture finally the segmented condylar region is compared with the ground tooth images created by dental experts and achieves an accuracy of 87.24%. The results suggested that Modified Mask RCNN has segmentation performance that is comparable to other models and has better data compatibility.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126597533","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169418
N. Nithya, M. Sivaranjani, S. Itapu
Steganography is the art of employing digital graphics to conceal a message such that only the sender and the intended recipient can find out about its existence. The hyperchaotic encryption algorithm, which minimizes the computational complexity of the chaotic encryption technique and the foundation of the proposed system in this project to improve bit insertion. The suggested bitwise operation based on encryption changes the encryption phases from permutation to XOR and XOR to bit insertion. The next step is to create a four-dimensional hyperchaotic system that uses the chaotic system’s discrete time signal as the key generator and its necessary mapping. This result demonstrates how steganography may be used to increase security by monitoring the speed and quality of data sources. Additionally, an acceptable PSNR is obtained, which is higher than MSE (Mean-Square Error) and improves the quality of the steganosource.
{"title":"An Implementation of Hyperchaotic Encryption Based Steganography with XOR Operation in Wireless Transmission","authors":"N. Nithya, M. Sivaranjani, S. Itapu","doi":"10.1109/ICAIA57370.2023.10169418","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169418","url":null,"abstract":"Steganography is the art of employing digital graphics to conceal a message such that only the sender and the intended recipient can find out about its existence. The hyperchaotic encryption algorithm, which minimizes the computational complexity of the chaotic encryption technique and the foundation of the proposed system in this project to improve bit insertion. The suggested bitwise operation based on encryption changes the encryption phases from permutation to XOR and XOR to bit insertion. The next step is to create a four-dimensional hyperchaotic system that uses the chaotic system’s discrete time signal as the key generator and its necessary mapping. This result demonstrates how steganography may be used to increase security by monitoring the speed and quality of data sources. Additionally, an acceptable PSNR is obtained, which is higher than MSE (Mean-Square Error) and improves the quality of the steganosource.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125786136","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}
Recognizing human movements through computer vision is an important field of research, which can be used in various applications such as patient monitoring, observation, and human-machine interface. The ability to perceive these movements requires extremely complex judgments. Generally, the above-mentioned applications need to automatically recognize advanced operations, such as: a pair of easy movements of a man and a woman. If the action is well classified, then the proper information can be provided to the system. This paper addresses various machine learning algorithms such as logistic regression, RBF SVM, decision tree, random forest, linear SVM, gradient boosting DT by grouping different activities. This article classifies complex human behaviors by observing, comparing and evaluating the performance of algorithms by using large set of information.
{"title":"Performance Analysis of Different Classifiers for the Application of Human Activity Identification","authors":"Afzal Khan, Upendra Kumar Acharya, Anurag Rai, Abhishek Singh, Ajey Shakti Mishra, Sandeep Kumar","doi":"10.1109/ICAIA57370.2023.10169551","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169551","url":null,"abstract":"Recognizing human movements through computer vision is an important field of research, which can be used in various applications such as patient monitoring, observation, and human-machine interface. The ability to perceive these movements requires extremely complex judgments. Generally, the above-mentioned applications need to automatically recognize advanced operations, such as: a pair of easy movements of a man and a woman. If the action is well classified, then the proper information can be provided to the system. This paper addresses various machine learning algorithms such as logistic regression, RBF SVM, decision tree, random forest, linear SVM, gradient boosting DT by grouping different activities. This article classifies complex human behaviors by observing, comparing and evaluating the performance of algorithms by using large set of information.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127340228","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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