A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Kshirsagar, Pravin R.; Manoharan, Hariprasath; Shitharth, S.; Althubiti, Sara A.; Alenezi, Fayadh; Srivastava, Gautam; Lin, Jerry Chun‐Wei

doi:10.3390/electronics11131950

Cited by 14 publications

(5 citation statements)

References 33 publications

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“…This value chain connects local businesses across a variety of levels, including forward, backward, and vertical. Technology spillover may be increased thanks to the vertical integration of technology, which makes it possible for local firms to learn sophisticated ideas, green management, and green technology and to imitate them [55]. In spite of this, the results of this study lend credence to the conclusions of previously published research on China [56], Belt and Road Initiative nations [57], and Azerbaijan [58].…”

Section: Resultssupporting

confidence: 67%

Nexus between Cyclical Innovation in Green Technologies and CO2 Emissions in Nordic Countries: Consent toward Environmental Sustainability

et al. 2022

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Several economies have acknowledged that environmental degradation poses a serious danger to worldwide sustainable production and consumption. Policy makers concur that the increased use and production of carbon-intensive technologies has intensified the detrimental consequences of carbon dioxide emissions. In response, a number of nations have reacted by enacting stringent regulations and encouraging green technology innovations across corporate and governmental organizations. Evidence that already exists suggests that research and development is a cyclical process; nevertheless, the non-linear influence of shocks in research and development and innovation in green technologies on CO2 emissions in the Nordic nations has not been well investigated. Using panel data from 1995 to 2019, this research explores the asymmetric link between innovation in green technologies and CO2 emissions. The cointegration link between the chosen variables was validated using the Westerlund cointegration test and the Johansen–Fisher panel cointegration test. The findings of both tests confirm the presence of cointegration association between dependent and independent variables. The outcomes of CS-ARDL revealed that negative shocks in creating green technologies contribute to carbon dioxide emissions during recessions. Second, the findings supported the notion that innovation in green technology may reduce carbon dioxide emissions during times of economic expansion. Thirdly, the GDP increases the CO2 emissions, but the usage of renewable energy decreases CO2 emissions. In addition, the robustness analysis validated the consistency and precision of the existing findings. In summary, the findings suggest that the link between advances in environmentally friendly technologies and levels of carbon dioxide emissions were inversely proportional.

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Section: Resultssupporting

confidence: 67%

Nexus between Cyclical Innovation in Green Technologies and CO2 Emissions in Nordic Countries: Consent toward Environmental Sustainability

et al. 2022

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“…The design model in the proposed method is generated based on back propagation neural network where initial weights are tuned for reducing the error rates. In addition, clear information about design model for detecting neural digestive problems can only be achieved if inputs and hidden weights are minimized as high data processing is needed in the detection problem (Khadidos et al, 2023;Kshirsagar et al, 2022;Shitharth et al, 2022). However it is essential to check the hidden units as the designed device can able to provide additional data weights inside the monitored area where gastro intestinal activity occurs.…”

Section: Optimization Algorithmmentioning

confidence: 99%

A comparative recognition research on excretory organism in medical applications using artificial neural networks

Shitharth

Manoharan²,

Iwendi

et al. 2023

Front. Bioeng. Biotechnol.

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Purpose: In the contemporary era, a significant number of individuals encounter various health issues, including digestive system ailments, even during their advanced years. The major purpose of this study is based on certain observations that are made in internal digestive systems in order to prevent severe cause that usually occurs in elderly people.Approach: To solve the purpose of the proposed method the proposed system is introduced with advanced features and parametric monitoring system that are based on wireless sensor setups. The parametric monitoring system is integrated with neural network where certain control actions are taken to prevent gastrointestinal activities at reduced data loss.Results: The outcome of the combined process is examined based on four different cases that is designed based on analytical model where control parameters and weight establishments are also determined. As the internal digestive system is monitored the data loss that is present with wireless sensor network must be reduced and proposed approach prevents such data loss with an optimized value of 1.39%.Conclusion: Parametric cases were conducted to evaluate the efficacy of neural networks. The findings indicate a significantly higher effectiveness rate of approximately 68% when compared to the control cases.

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“…Research on bare PCB defect detection algorithms based on machine vision theory can provide new ideas for machine vision technology, promote technological progress in related fields, and help expand the application field of machine vision and defect detection technology to areas such as medical ( Coraci, Tognolo & Masiero, 2023 ; Gao et al, 2023 ), security ( Mallaiyan Sathiaseelan et al, 2021 ; Ge, Dan & Li, 2020 ), and smart home technology ( Yu & Pei, 2021 ; Talaat, Arafa & Metwally, 2020 ). The continuous improvement of this technology can also promote the cross-integration of machine vision and other disciplines, further promoting the improvement of industrial automation ( Kshirsagar et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Defect identification of bare printed circuit boards based on Bayesian fusion of multi-scale features

Han,

Li,

Wang

et al. 2024

PeerJ Computer Science

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The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale co-occurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%.

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A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Cited by 14 publications

References 33 publications

Nexus between Cyclical Innovation in Green Technologies and CO2 Emissions in Nordic Countries: Consent toward Environmental Sustainability

Nexus between Cyclical Innovation in Green Technologies and CO2 Emissions in Nordic Countries: Consent toward Environmental Sustainability

A comparative recognition research on excretory organism in medical applications using artificial neural networks

Defect identification of bare printed circuit boards based on Bayesian fusion of multi-scale features

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