Pub Date : 2022-05-19DOI: 10.1080/09720529.2022.2068602
Ravi Kumar, Deepak Kumar
Abstract The recognition of object in remotely sensed images is a complex task. The immense research is running in the field of remote sensing due to the availability of high resolution satellite images. The detection of object is a challenging task due to the complex background and small object size in remotely sensed images. The object detection in remote sensing images has a vital role in the field of navigation, salvage, and military. The performance of traditional algorithms is very less due to the usage of handcrafted features. With the initiation of Deep Learning algorithms, various Convolutional Neural Networks (CNN) based model have been utilized to detect the objects with high-resolution remotely sensed images. In this research paper various CNN based models has been compared and analyzed. Object detection approaches are broadly categorized in two ways-one based on the region matching and second based on the one-stage target detection. The researchers have compared the result of R-CNN, SPP Net , fast R-CNN, faster R-CNN, R-FCN, Mask R-CNN SSD (Single Shot Multibox Detector), DSSD (Deconvolution Single Shot Multibox Detector), FSSD , YOLO v1,YOLO v2, YOLO v3, Gaussian YOLO v3, RetinaNet which conclude that the minimal average precision for the region based category is best shown by Mask R-CNN with 39.8 mAP in the COCO parameter test and for the one stage detector YOLO v3 shows the best case for the COCO parameter test with 69.1 mAP. In the second phase of the review the researchers found that in comparison to the region based and one stage detector the YOLO v3 model from one stage detector shows the best detection precision percentage with the highest 87% in identifying the object called ship.
{"title":"Comparative analysis of validating parameters in the deep learning models for remotely sensed images","authors":"Ravi Kumar, Deepak Kumar","doi":"10.1080/09720529.2022.2068602","DOIUrl":"https://doi.org/10.1080/09720529.2022.2068602","url":null,"abstract":"Abstract The recognition of object in remotely sensed images is a complex task. The immense research is running in the field of remote sensing due to the availability of high resolution satellite images. The detection of object is a challenging task due to the complex background and small object size in remotely sensed images. The object detection in remote sensing images has a vital role in the field of navigation, salvage, and military. The performance of traditional algorithms is very less due to the usage of handcrafted features. With the initiation of Deep Learning algorithms, various Convolutional Neural Networks (CNN) based model have been utilized to detect the objects with high-resolution remotely sensed images. In this research paper various CNN based models has been compared and analyzed. Object detection approaches are broadly categorized in two ways-one based on the region matching and second based on the one-stage target detection. The researchers have compared the result of R-CNN, SPP Net , fast R-CNN, faster R-CNN, R-FCN, Mask R-CNN SSD (Single Shot Multibox Detector), DSSD (Deconvolution Single Shot Multibox Detector), FSSD , YOLO v1,YOLO v2, YOLO v3, Gaussian YOLO v3, RetinaNet which conclude that the minimal average precision for the region based category is best shown by Mask R-CNN with 39.8 mAP in the COCO parameter test and for the one stage detector YOLO v3 shows the best case for the COCO parameter test with 69.1 mAP. In the second phase of the review the researchers found that in comparison to the region based and one stage detector the YOLO v3 model from one stage detector shows the best detection precision percentage with the highest 87% in identifying the object called ship.","PeriodicalId":46563,"journal":{"name":"JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY","volume":"25 1","pages":"913 - 920"},"PeriodicalIF":1.4,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47682989","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 : 2022-05-19DOI: 10.1080/09720529.2022.2075087
Amit Kumar, Ashutosh Kumar, Girraj Khandelwal, Y. Bhardwaj, V. Sharma, G. Sharma
Abstract To enhance the security choices in transmission of information over the web, various methods such as cryptography, steganography and digital watermarking have been developed. Visual cryptography has developed in the past decade as an entity that splits the information into two parts in order to complete integration. This system is also secured in a lesser quantity. In this paper a secret message multi-share method is used. In the proposed work input picture is divided into eight portions. The eight portions shares are encrypted before embedding the image in the patchwork image, photo sharing and the image retrieved is the same as the hidden starting image. Results show that the proposed method has optimized the optimal time by 8%, improved PSNR by 11% and lower overhead communication.
{"title":"Improving the visual quality of a size deterministic visual cryptography scheme for Grayscale Images","authors":"Amit Kumar, Ashutosh Kumar, Girraj Khandelwal, Y. Bhardwaj, V. Sharma, G. Sharma","doi":"10.1080/09720529.2022.2075087","DOIUrl":"https://doi.org/10.1080/09720529.2022.2075087","url":null,"abstract":"Abstract To enhance the security choices in transmission of information over the web, various methods such as cryptography, steganography and digital watermarking have been developed. Visual cryptography has developed in the past decade as an entity that splits the information into two parts in order to complete integration. This system is also secured in a lesser quantity. In this paper a secret message multi-share method is used. In the proposed work input picture is divided into eight portions. The eight portions shares are encrypted before embedding the image in the patchwork image, photo sharing and the image retrieved is the same as the hidden starting image. Results show that the proposed method has optimized the optimal time by 8%, improved PSNR by 11% and lower overhead communication.","PeriodicalId":46563,"journal":{"name":"JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY","volume":"25 1","pages":"1113 - 1123"},"PeriodicalIF":1.4,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42765127","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 : 2022-05-19DOI: 10.1080/09720529.2022.2072438
Dinesh Goyal, Ruchi Goyal, S. Bhargava, Priyanka Sharma
Abstract Patient data at hospitals is non-sharable in current systems and cost of repeated medication is curse to the patients. Also medical treatment perspective changes at every hospital or doctor or diagnostic center. To resolve the issue of availability of medicinal past history of the patients & reduction in cost of treatment with decisive & secure availability of patient records, like prescriptions and lab reports, we implemented an ERP system over cloud in five hospitals. Based on data retrieved from the hospitals of registration (only to ensure privacy of patients) we implement data analytics using machine learning for the prediction of the disease of patients by tracking the record of medical history and also analyzed hospital and doctors information and performance. Our aim is to provide solution for less cost treatment and for regulation and monitoring of health care to Indian Medical Industry.
{"title":"Disease prediction model for secure patient data over cloud using machine learning","authors":"Dinesh Goyal, Ruchi Goyal, S. Bhargava, Priyanka Sharma","doi":"10.1080/09720529.2022.2072438","DOIUrl":"https://doi.org/10.1080/09720529.2022.2072438","url":null,"abstract":"Abstract Patient data at hospitals is non-sharable in current systems and cost of repeated medication is curse to the patients. Also medical treatment perspective changes at every hospital or doctor or diagnostic center. To resolve the issue of availability of medicinal past history of the patients & reduction in cost of treatment with decisive & secure availability of patient records, like prescriptions and lab reports, we implemented an ERP system over cloud in five hospitals. Based on data retrieved from the hospitals of registration (only to ensure privacy of patients) we implement data analytics using machine learning for the prediction of the disease of patients by tracking the record of medical history and also analyzed hospital and doctors information and performance. Our aim is to provide solution for less cost treatment and for regulation and monitoring of health care to Indian Medical Industry.","PeriodicalId":46563,"journal":{"name":"JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY","volume":"25 1","pages":"1183 - 1193"},"PeriodicalIF":1.4,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41313484","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 : 2022-05-19DOI: 10.1080/09720529.2022.2072436
S. Kamble, D. K. Saini, Vinay Kumar, A. Gautam, Shikha Verma, Ashish Tiwari, Dinesh Goyal
Abstract In cloud computing, the services are observed in the video stream and clustering their pixels is the initial task in service detection. Tracking is the practice to observe or tracking the moments of a given item in each frame. Numerous false positives are included in the frame. Using the saliency map model and the Extended Kalman Filter, the proposed approach can recognize and track moving objects in video. The item is tracked using an Extended Kalman Filter. In the proposed research the evaluation is based on the delay and accuracy of the evaluation parameter. Finally, the suggested method is compared to existing object tracking methods, with an accuracy of greater than 90% attained.
{"title":"Detection and tracking of moving cloud services from video using saliency map model","authors":"S. Kamble, D. K. Saini, Vinay Kumar, A. Gautam, Shikha Verma, Ashish Tiwari, Dinesh Goyal","doi":"10.1080/09720529.2022.2072436","DOIUrl":"https://doi.org/10.1080/09720529.2022.2072436","url":null,"abstract":"Abstract In cloud computing, the services are observed in the video stream and clustering their pixels is the initial task in service detection. Tracking is the practice to observe or tracking the moments of a given item in each frame. Numerous false positives are included in the frame. Using the saliency map model and the Extended Kalman Filter, the proposed approach can recognize and track moving objects in video. The item is tracked using an Extended Kalman Filter. In the proposed research the evaluation is based on the delay and accuracy of the evaluation parameter. Finally, the suggested method is compared to existing object tracking methods, with an accuracy of greater than 90% attained.","PeriodicalId":46563,"journal":{"name":"JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY","volume":"25 1","pages":"1083 - 1092"},"PeriodicalIF":1.4,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49257431","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 : 2022-05-19DOI: 10.1080/09720529.2022.2068598
G. Ramesh, Avinash Sharma, D. V. Lalitha Parameswari, Ch. Mallikarjuna Rao, J. Somasekar
Abstract Biomedical databases or repositories have scientific information that is evidence based and protecting such documents from tampering or non-repudiation is very significant. The traditional techniques for the same have limitations in the distributed environments. Scientific contributions are to be safeguarded and it is one of the challenging problems. Blockchain is the promising technology that can support distributed ledger of transactions and thus it is found suitable for protecting biomedical repositories. As blockchain is a proven technology associated with crypto-currency known as Bitcoin in finance domain, it has plenty of opportunities in other domains. In this paper, a framework that is based on blockchain technology (BCT) for protection of biomedical databases with integrity and non-repudiation is presented. The framework will have underlying mechanisms to exploit blockchain to have a protection service and smart contracts to be more flexible and dynamic to adapt new requirements from time to time. The framework is domain specific but can pave way for motivation for adapting it to new domains as well.
{"title":"Blockchain in healthcare : Moving towards a methodological framework for protecting Biomedical Databases","authors":"G. Ramesh, Avinash Sharma, D. V. Lalitha Parameswari, Ch. Mallikarjuna Rao, J. Somasekar","doi":"10.1080/09720529.2022.2068598","DOIUrl":"https://doi.org/10.1080/09720529.2022.2068598","url":null,"abstract":"Abstract Biomedical databases or repositories have scientific information that is evidence based and protecting such documents from tampering or non-repudiation is very significant. The traditional techniques for the same have limitations in the distributed environments. Scientific contributions are to be safeguarded and it is one of the challenging problems. Blockchain is the promising technology that can support distributed ledger of transactions and thus it is found suitable for protecting biomedical repositories. As blockchain is a proven technology associated with crypto-currency known as Bitcoin in finance domain, it has plenty of opportunities in other domains. In this paper, a framework that is based on blockchain technology (BCT) for protection of biomedical databases with integrity and non-repudiation is presented. The framework will have underlying mechanisms to exploit blockchain to have a protection service and smart contracts to be more flexible and dynamic to adapt new requirements from time to time. The framework is domain specific but can pave way for motivation for adapting it to new domains as well.","PeriodicalId":46563,"journal":{"name":"JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY","volume":"25 1","pages":"891 - 901"},"PeriodicalIF":1.4,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41971286","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 : 2022-05-19DOI: 10.1080/09720529.2022.2072426
Bhupendra Ramani, Warish D. Patel, K. Solanki
Abstract In modern times individuals are facing an important social challenge in the form of stress. Combining sensor devices that capture physiological, and brain waves data, this study develops a machine learning technique using cloud computing to recognize stress in people in social contexts. In this paper, we are comparing several classifiers, including Random Forest, Support Vector Machine, k-nearest neighbor and AdaBoost, and also inventing a method that uses sensor data in day-to-day life. It detects stress levels with high accuracy. Our results show that by combining data from all the sensors, we are able to accurately differentiate between the stressful and normal situations of humans. In addition, this paper also evaluates the individual capabilities of each sensor modality and its applicability for stress detection in real-time situations. Methods: We have provided unique technology to incorporate sensor signals using cloud computing. It monitors the user’s sweat level, temperature, heart rate variation, and EEG under various motion estimations and also chooses the best model to detect the anxiety level based on the user’s motion conditions. Results: Evaluation of algorithms using sample data reveals an overall concern detection accuracy of 94% along with a significant reduction in false positives compared to the ultramodern techniques.
{"title":"Stress Ocare : An advance IoMT based physiological data analysis for anxiety status prediction using cloud computing","authors":"Bhupendra Ramani, Warish D. Patel, K. Solanki","doi":"10.1080/09720529.2022.2072426","DOIUrl":"https://doi.org/10.1080/09720529.2022.2072426","url":null,"abstract":"Abstract In modern times individuals are facing an important social challenge in the form of stress. Combining sensor devices that capture physiological, and brain waves data, this study develops a machine learning technique using cloud computing to recognize stress in people in social contexts. In this paper, we are comparing several classifiers, including Random Forest, Support Vector Machine, k-nearest neighbor and AdaBoost, and also inventing a method that uses sensor data in day-to-day life. It detects stress levels with high accuracy. Our results show that by combining data from all the sensors, we are able to accurately differentiate between the stressful and normal situations of humans. In addition, this paper also evaluates the individual capabilities of each sensor modality and its applicability for stress detection in real-time situations. Methods: We have provided unique technology to incorporate sensor signals using cloud computing. It monitors the user’s sweat level, temperature, heart rate variation, and EEG under various motion estimations and also chooses the best model to detect the anxiety level based on the user’s motion conditions. Results: Evaluation of algorithms using sample data reveals an overall concern detection accuracy of 94% along with a significant reduction in false positives compared to the ultramodern techniques.","PeriodicalId":46563,"journal":{"name":"JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY","volume":"25 1","pages":"1019 - 1029"},"PeriodicalIF":1.4,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46878977","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 : 2022-05-19DOI: 10.1080/09720529.2022.2068595
Aman Dureja, P. Pahwa
Abstract Convolutional Networks are category of deep optimizing networks used to interpret images in Deep Learning concepts. Image recognition and medical image analysis are two areas where they are useful. The increasing scale of clinical feature spaces is raising a significant obstacle, creating issues with extensive database management, and afterward compiling those repositories for retrieval and storage, that could only be addressed using content based medical image retrieval systems. The objective of this paper is to demonstrate a deep CNN architecture for retrieving research and clinical images quickly and efficiently for identifying multi-class retinal disease objects. To train the network, the datasets used are inter-modal and divided into 4 groups. The transfer learning method is used for the multi-classification of retinal images. Another augmentation technique is used for comparing the accuracy, precision, and evaluation metrics with the transfer learning method. The accuracy of 97.1%, with a recall of 97.2%, and a precision of 97.0% was achieved in research that is higher when compared with the previous methods that were deployed. With the augmentation technique, it achieved an accuracy of 94.0% with a 94.6% precision and a recall of 95.1% for the testing data which suggests that decreasing the size of data did not impact the accuracy of the model. The proposed model helps diagnose various categories of medical images for the development of a comprehensive system that can work better than the human experts and help to detect and diagnose various diseases in the medical and clinical fields.
{"title":"A comparative approach for classifying retinal OCT images based on deep learning framework","authors":"Aman Dureja, P. Pahwa","doi":"10.1080/09720529.2022.2068595","DOIUrl":"https://doi.org/10.1080/09720529.2022.2068595","url":null,"abstract":"Abstract Convolutional Networks are category of deep optimizing networks used to interpret images in Deep Learning concepts. Image recognition and medical image analysis are two areas where they are useful. The increasing scale of clinical feature spaces is raising a significant obstacle, creating issues with extensive database management, and afterward compiling those repositories for retrieval and storage, that could only be addressed using content based medical image retrieval systems. The objective of this paper is to demonstrate a deep CNN architecture for retrieving research and clinical images quickly and efficiently for identifying multi-class retinal disease objects. To train the network, the datasets used are inter-modal and divided into 4 groups. The transfer learning method is used for the multi-classification of retinal images. Another augmentation technique is used for comparing the accuracy, precision, and evaluation metrics with the transfer learning method. The accuracy of 97.1%, with a recall of 97.2%, and a precision of 97.0% was achieved in research that is higher when compared with the previous methods that were deployed. With the augmentation technique, it achieved an accuracy of 94.0% with a 94.6% precision and a recall of 95.1% for the testing data which suggests that decreasing the size of data did not impact the accuracy of the model. The proposed model helps diagnose various categories of medical images for the development of a comprehensive system that can work better than the human experts and help to detect and diagnose various diseases in the medical and clinical fields.","PeriodicalId":46563,"journal":{"name":"JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY","volume":"25 1","pages":"859 - 870"},"PeriodicalIF":1.4,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46389366","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 : 2022-05-15DOI: 10.1080/09720529.2022.2060921
Muhammad Waheed, Umair Saleem, M. Cancan, Ziyattin Taş, M. Alaeiyan, M. Farahani
Abstract Graph product yields a new structure from two initial given structures. The computation of topological indices for these sophisticated structures using the graph product is a critical endeavor. Petersen graph is a structure which consists of ten vertices and fifteen edges. It is commonly used as a counter example to graph theory conjectures. In this paper, we generate simple Petersen graph by using graph product and then explicit expressions of the first and second Zagreb indices, forgotten topological index, first hyper and first reformulated Zagreb index, reduced second Zagreb index and Y-index of the Peterson graph in terms of cyclic graph C5 are computed.
{"title":"Construction of Petersen graph via graph product and correlation of topological descriptors of Petersen graph in terms of cyclic graph C 5","authors":"Muhammad Waheed, Umair Saleem, M. Cancan, Ziyattin Taş, M. Alaeiyan, M. Farahani","doi":"10.1080/09720529.2022.2060921","DOIUrl":"https://doi.org/10.1080/09720529.2022.2060921","url":null,"abstract":"Abstract Graph product yields a new structure from two initial given structures. The computation of topological indices for these sophisticated structures using the graph product is a critical endeavor. Petersen graph is a structure which consists of ten vertices and fifteen edges. It is commonly used as a counter example to graph theory conjectures. In this paper, we generate simple Petersen graph by using graph product and then explicit expressions of the first and second Zagreb indices, forgotten topological index, first hyper and first reformulated Zagreb index, reduced second Zagreb index and Y-index of the Peterson graph in terms of cyclic graph C5 are computed.","PeriodicalId":46563,"journal":{"name":"JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY","volume":"25 1","pages":"1525 - 1534"},"PeriodicalIF":1.4,"publicationDate":"2022-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41827985","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 : 2022-04-03DOI: 10.1080/09720529.2021.2016222
Sonam Kaler, Ajay Sharma, Arshad Ahmad Yatoo
Abstract Fog computing is defined as the distribution of computing resources between the data devices and the cloud or any other data centre in a distributed computing infrastructure or process. This paper briefly reviews the various definitions, applications, architecture and fog simulators proposed by researchers over the years. In this paper, a comparison table is presented which highlights the key features of simulators available like FogtorchII, iFogSim, Fogbus, MyiFogSim etc.
{"title":"A review of fog computing and its simulators","authors":"Sonam Kaler, Ajay Sharma, Arshad Ahmad Yatoo","doi":"10.1080/09720529.2021.2016222","DOIUrl":"https://doi.org/10.1080/09720529.2021.2016222","url":null,"abstract":"Abstract Fog computing is defined as the distribution of computing resources between the data devices and the cloud or any other data centre in a distributed computing infrastructure or process. This paper briefly reviews the various definitions, applications, architecture and fog simulators proposed by researchers over the years. In this paper, a comparison table is presented which highlights the key features of simulators available like FogtorchII, iFogSim, Fogbus, MyiFogSim etc.","PeriodicalId":46563,"journal":{"name":"JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY","volume":"25 1","pages":"745 - 756"},"PeriodicalIF":1.4,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47599783","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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