Peer Clouds: A P2P-Based Resource Discovery Mechanism for the Intercloud

Kapoor, Lohit; Bawa, Seema; Gupta, Ankur

doi:10.47164/ijngc.v6i3.399

Cited by 11 publications

(9 citation statements)

References 7 publications

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“…e successful execution of the Bitcoin application data format has given rise to a new concept for software development based on Blockchain technology. A Blockchain data structure can be compared to a linked list that is shared across the network, with each node having a copy of every block (linked to the longest chain) generated by its generator [11]. In domains such as the IoT, e-governance, and e-document handling, many authentic solutions have lately been developed.…”

Section: Review Of Literaturementioning

confidence: 99%

A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis

Kumar

Nassa

Uike

et al. 2022

Journal of Food Quality

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The growing need for access to safer food items is increasing, and hence, there is a need for a better supply chain management system in the food industry is increasing. The increased complexity of the existing systems tends to introduce more issues to the stakeholders, and also, the cost of product traceability is quite high. Hence, the industry is looking for effective solutions in relation to drug traceability, and the application of Blockchain technology enables the stakeholders in the food and beverage (F&B) sector to track the movement of goods, supported in gathering the required details so that the contaminated products can be identified and recalled without much delay and lesser recall costs to protect the lives of the individuals. The tampered food items are increasing and are impacting the supply chain process, brand name of the companies, and claim assurance. They create an adverse impact on the health of the individuals and cause higher economic loss to the health-care industry. The existing studies tend to focus on laying emphasis of the need for an enhanced, effective, and end tracking systems in the industry. The emergence of Blockchain technology enables centralized tracking of information support in enhancing the data privacy and increasing transparency and support in eradicating the tampered food products in the supply chain system. These approaches leverage the usage of smart contracts and decentralize the storage of information in a secure manner for enhanced product traceability in the F&B industry. The implementation of smart contracts generates better data governance, which tends to meet the needs and requirements of the stakeholders, and applies effective measures of food traceability. The primary objective of the study is to perform an analysis of Blockchain in enhancing drug traceability in the food sector. The researcher uses quantitative analysis for the study as it helps in understanding the critical determinants influencing drug traceability in food effectively, the survey method is used to gather the information, and past reviews are also used to possess a better understanding of the subject area effectively.

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Section: Review Of Literaturementioning

confidence: 99%

A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis

Kumar

Nassa

Uike

et al. 2022

Journal of Food Quality

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“…Among the many technological choices available to geneticists and plant breeders, heterosis propagating in crops has been the most victorious. Heterosis has been shown to boost yield and other beneficial features, making it a feasible option for improving Indian mustard production [22]. To successfully exploit heterosis, one must identify hybrids that outperform the parent or standard control cultivar.…”

Section: Introductionmentioning

confidence: 99%

Contextualization of Trait Nexus and Gene Action for Quantitative and Qualitative Characteristics in Indian Mustard

Chand

Debnath

Rahimi

et al. 2022

Journal of Food Quality

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After soybean and palm oil, the world’s third largest oil seed crop is rapeseed mustard. Out of the seven consumable oil seed crops grown in India, rapeseed mustard is responsible for one-third of production. Mustard aphid (Lipaphis erysimi Kalt) is considered the primary pest causing mayhem in crop production. Understanding the genetics behind resistance will aid breeders in developing a resistant/tolerant strain. Appropriate parent selection and analyzing gene action contribute to economic benefit maximization. The aims of the current study are to use seven lines and five testers to determine the best-performing parents and crosses based on their general and specific capacity to combine and to examine the level of heterosis for yield and related features like mustard aphid resistance. Due to the self-pollination nature of Indian mustard, Kempthorne’s line X tester method is helpful to judge the combining ability. Therefore, seven lines and five testers of Indian mustard (Brassica juncea L.) were employed in the present study. The findings suggested that there was substantial genetic variation for all traits examined. The mean oil yield of R1 B2-26 × R1 B2-25, JD6 × R1 B2-25, and JD6 × R1 B2-29 hybrids was more significant than that of the ancestors. The results show that R1 B2-26 × R1 B2-25, JD6 × R1 B2-25, and JD6 × R1 B2-29 hybrids produced more oil than their parents. The variance explained by SCA was greater than that explained by GCA, as indicated by the Ϭ2 gca/Ϭ2 sca ratio being less than one for all characters, implying that nonadditive gene actions such as dominance, epistasis, and other interaction effects played an important role in the presence of these attributes. Punjab Local was discovered to be an excellent general merge for reducing crop duration, whereas JD6 was an excellent combiner for seed yield per crop, aphid infestation indicator, seed yield per crop, and oil production per plant. The predictability ratio was found to be less than 0.5 for almost all traits, denoting that the nonadditive gene measure is involved in controlling the nature except days to 50% flowering, aphid infestation index, oil content, and oil yield per plant. Thus, based on these four traits, selection for superior plants may be practiced in later generations.

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“…After the rows are normalized, the association matrix A can be regarded as a fuzzy relationship matrix of clustering and labeling. According to the fuzzy logic reasoning mechanism, the fuzzy synthesis operation is performed on the association matrix and the membership degree, and the membership degree of the sample to the label is obtained [ 31 ]. After normalization, it is the label distribution.…”

Section: Cost-sensitive Marker Distribution Learning For Asd-aided Di...mentioning

confidence: 99%

“…According to the fuzzy logic reasoning mechanism, the fuzzy synthesis operation is performed on the association matrix and the membership degree, and the membership degree of the sample to the label is obtained [ 31 ]. After normalization, it is the label distribution.…”

Section: Cost-sensitive Marker Distribution Learning For Asd-aided Di...mentioning

confidence: 99%

Supervised Approach to Identify Autism Spectrum Neurological Disorder via Label Distribution Learning

Munagala

Saravanan²,

Almukhtar

et al. 2022

Computational Intelligence and Neuroscience

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Autism Spectrum Disorder (ASD) is a complicated collection of neurodevelopmental illnesses characterized by a variety of developmental defects. It is a binary classification system that cannot cope with reality. Furthermore, ASD, data label noise, high dimension, and data distribution imbalance have all hampered the existing classification algorithms. As a result, a new ASD was proposed. This strategy employs label distribution learning (LDL) to deal with label noise and uses support vector regression (SVR) to deal with sample imbalance. The experimental results show that the proposed method balances the effects of majority and minority classes on outcomes. It can effectively deal with imbalanced data in ASD diagnosis, and it can help with ASD diagnosis. This study presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis.

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Peer Clouds: A P2P-Based Resource Discovery Mechanism for the Intercloud

Cited by 11 publications

References 7 publications

A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis

A Comparative Analysis of Blockchain in Enhancing the Drug Traceability in Edible Foods Using Multiple Regression Analysis

Contextualization of Trait Nexus and Gene Action for Quantitative and Qualitative Characteristics in Indian Mustard

Supervised Approach to Identify Autism Spectrum Neurological Disorder via Label Distribution Learning

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