Evolution of Industry and Blockchain Era: Monitoring Price Hike and Corruption Using BIoT for Smart Government and Industry 4.0

Hasan, Mohammad Kamrul; Akhtaruzzaman, Md.; Kabir, S. Rayhan; Gadekallu, Thippa Reddy; Islam, Shayla; Magalingam, Pritheega; Hassan, Rosilah; Alazab, Mamoun; Alazab, Moutaz

doi:10.1109/tii.2022.3164066

Cited by 51 publications

(23 citation statements)

References 23 publications

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“…The proposed approach appears to perform better than the architecture without interpolation [32][33][34]. This framework shows that the delay decreases from 50 to 300 simulations with LI, and it increases with interations without LI [35][36][37]. comparison to the work conducted without LI architecture, the total performanc of the proposed work has improved by 30%.…”

mentioning

confidence: 71%

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

Monga

Gupta

Prasad³

et al. 2022

Sustainability

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The security framework in Ad-hoc Networks (ANET) continues to attract the attention of researchers, although significant work has been accomplished already. Researchers in the last couple of years have shown quite an improvement in Identity Dependent Cryptography (IDC). Security in ANET is hard to attain due to the vulnerability of links (Wireless). IDC encompasses Polynomial Interpolations (PI) such as Lagrange, curve-fitting, and spline to provide security by implementing Integrated Key Management (IKM). The PI structure trusts all the available nodes in the network and randomly picks nodes for the security key generation. This paper presents a solution to the trust issues raised in Lagrange’s-PI (LI) utilizing an artificial neural network and attribute-based tree structure. The proposed structure not only improves the trust factor but also enhances the accuracy measures of LI to provide a sustainable network system. Throughput, PDR, noise, and latency have been increased by 47%, 50%, 34%, and 30%, respectively, by using LI and incorporating the aforementioned techniques.

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mentioning

confidence: 71%

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

Monga

Gupta

Prasad³

et al. 2022

Sustainability

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“…The authors in [30] proposed Blockchain-Integrated Remote Database (BIRD) paradigm that connects blockchain to a Relational Database Management System (RDBMS) that employs the Remote Database Access (RDA) protocol and a Cloud Server. This report gives a comprehensive history of blockchain and industry generations.…”

Section: Related Workmentioning

confidence: 99%

A Novel CNN-Inception-V4-Based Hybrid Approach for Classification of Breast Cancer in Mammogram Images

Nazir

Khan

Mohiyuddin

et al. 2022

Wireless Communications and Mobile Computing

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Breast cancer is the most frequent disease in women, with one in every 19 women at risk. Breast cancer is the fifth leading cause of cancer death in women around the world. The most effective and efficient technique of controlling cancer development is early identification. Mammography helps in the early detection of cancer, which saves lives. Many studies conducted various tests to categorize the tumor and obtained positive findings. However, there are certain limits. Mass categorization in mammography is still a problem, although it is critical in aiding radiologists in establishing correct diagnoses. The purpose of this study is to develop a unique hybrid technique to identify breast cancer mass pictures as benign or malignant. The combination of two networks helps accelerate the categorization process. This study proposes a novel-based hybrid approach, CNN-Inception-V4, based on the fusing of these two networks. Mass images are used in this research from the CBIS-DDSM dataset. 450 images are taken for benign, and 450 images are used for malignant. The images are first cleaned by removing pectoral muscles, labels, and white borders. Then, CLAHE is used to these images to improve their quality in order to produce promising classification results. Following preprocessing, our model classifies cancer in mammography pictures as benign or malignant abnormalities. Our proposed model’s accuracy is 99.2%, with sensitivity of 99.8%, specificity of 96.3%, and F1-score of 97%. We also compared our proposed model to CNN, Inception-V4, and ResNet-50. Our proposed model outperforms existing classification models, according to the results.

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“…In Italy, it is observed that Vo Euganeo, which is 50km west of Venice, has been completely shut down. Furthermore, it is being discovered that among several inhabitants who have been given pharyngeal swabs, about 50-75% are positive without any symptoms [2] . The solution available to identify the COVID-19 infected person has collected the swab and needs to analyze biological material via polymerase chain reaction [3] .…”

Section: Introductionmentioning

confidence: 99%

COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario

Babukarthik

Dhasarathan²,

Tripathi³

et al. 2022

Computers and Electrical Engineering

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Evolution of Industry and Blockchain Era: Monitoring Price Hike and Corruption Using BIoT for Smart Government and Industry 4.0

Cited by 51 publications

References 23 publications

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

A Novel CNN-Inception-V4-Based Hybrid Approach for Classification of Breast Cancer in Mammogram Images

COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario

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