JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY最新文献_第10页

Comparative analysis of validating parameters in the deep learning models for remotely sensed images 遥感图像深度学习模型验证参数的对比分析

IF 1.4

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY Pub Date : 2022-05-19 DOI: 10.1080/09720529.2022.2068602

Ravi Kumar, Deepak Kumar

{"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}

引用次数: 1

Improving the visual quality of a size deterministic visual cryptography scheme for Grayscale Images 提高灰度图像尺寸确定性视觉密码方案的视觉质量

IF 1.4

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY Pub Date : 2022-05-19 DOI: 10.1080/09720529.2022.2075087

Amit Kumar, Ashutosh Kumar, Girraj Khandelwal, Y. Bhardwaj, V. Sharma, G. Sharma

引用次数: 0

Disease prediction model for secure patient data over cloud using machine learning 使用机器学习的云上安全患者数据的疾病预测模型

IF 1.4

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY Pub Date : 2022-05-19 DOI: 10.1080/09720529.2022.2072438

Dinesh Goyal, Ruchi Goyal, S. Bhargava, Priyanka Sharma

引用次数: 0

Detection and tracking of moving cloud services from video using saliency map model 使用显著性图模型检测和跟踪视频中的移动云服务

IF 1.4

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY Pub Date : 2022-05-19 DOI: 10.1080/09720529.2022.2072436

S. Kamble, D. K. Saini, Vinay Kumar, A. Gautam, Shikha Verma, Ashish Tiwari, Dinesh Goyal

引用次数: 13

Guest Editors 客人编辑

IF 1.4

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY Pub Date : 2022-05-19 DOI: 10.1080/09720529.2022.2102212

Dinesh Goyal, Anil Kumar, Amit Kumar Gupta, Carlos M. Travieso-Gonzalez

引用次数: 0

Blockchain in healthcare : Moving towards a methodological framework for protecting Biomedical Databases 区块链在医疗保健:迈向保护生物医学数据库的方法框架

IF 1.4

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY Pub Date : 2022-05-19 DOI: 10.1080/09720529.2022.2068598

G. Ramesh, Avinash Sharma, D. V. Lalitha Parameswari, Ch. Mallikarjuna Rao, J. Somasekar

{"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}

引用次数: 3

Stress Ocare : An advance IoMT based physiological data analysis for anxiety status prediction using cloud computing 压力Ocare：一种基于IoMT的先进生理数据分析，用于云计算的焦虑状态预测

IF 1.4

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY Pub Date : 2022-05-19 DOI: 10.1080/09720529.2022.2072426

Bhupendra Ramani, Warish D. Patel, K. Solanki

{"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}

引用次数: 2

A comparative approach for classifying retinal OCT images based on deep learning framework 基于深度学习框架的视网膜OCT图像分类比较方法

IF 1.4

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY Pub Date : 2022-05-19 DOI: 10.1080/09720529.2022.2068595

Aman Dureja, P. Pahwa

{"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}

引用次数: 0

Construction of Petersen graph via graph product and correlation of topological descriptors of Petersen graph in terms of cyclic graph C 5 用图积构造Petersen图及循环图C5上Petersen图拓扑描述符的相关性

IF 1.4

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY Pub Date : 2022-05-15 DOI: 10.1080/09720529.2022.2060921

Muhammad Waheed, Umair Saleem, M. Cancan, Ziyattin Taş, M. Alaeiyan, M. Farahani

引用次数: 0

A review of fog computing and its simulators 雾计算及其模拟器的研究进展

IF 1.4

JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY Pub Date : 2022-04-03 DOI: 10.1080/09720529.2021.2016222

Sonam Kaler, Ajay Sharma, Arshad Ahmad Yatoo

引用次数: 1