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Sumit kumar Kushwaha

Bio: Sumit kumar Kushwaha is an academic researcher. The author has contributed to research in topics: Computer science & Artificial neural network. The author has co-authored 1 publications.

Papers
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Journal ArticleDOI
TL;DR: In this article , the authors used the intelligent model to examine the green finance for ecological advancement with regard to artificial intelligence, and the proposed algorithm is compared with the neural model and observed that the proposed model has obtained 98.85% of accuracy which is higher than neural model.
Abstract: Green finance can be referred to as financial investments made on sustainable projects and policies that focus on a sustainable economy. The procedures include promoting renewable energy sources, energy efficiency, water sanitation, industrial pollution control, transportation pollution control, reduction of deforestation, and carbon emissions, etc. Mainly, these green finance initiatives are carried out by private and public agents like business organizations, banks, international organizations, government organizations, etc. Green finance provides a financial solution to create a positive impact on society and leads to environmental development. In the age of artificial intelligence, all industries adopt AI technologies. In this research, we see the applications of the intelligent model to examine the green finance for ecological advancement with regard to artificial intelligence. Feasible transportation and energy proficiency and power transmission are two significant fields to be advanced and focused on minimizing the carbon impression in these industries. Renewable sources like solar energies for power generation and electric vehicles are to be researched and developed. This R&D requires a considerable fund supply, thus comes the green finance. Globally, green finance plays a vital role in creating a sustainable environment. In this research, for performing the green finance analysis, financial maximally filtered graph (FMFG) algorithm is implemented in different domains. The proposed algorithm is compared with the neural model and observed that the proposed model has obtained 98.85% of accuracy which is higher than the neural model.

15 citations

Journal ArticleDOI
TL;DR: It is argued that small ANNs can denoise small-scale texture patterns almost as effectively as their larger equivalents, and self-similarity and ANNs are complementary paradigms for patch denoising, as demonstrated by an algorithm that effectively complements BM3D withSmall ANNs, surpassing BM3d at a low cost.
Abstract: Artificial ANNs (ANNs) are relatively new computational tools used in the development of intelligent systems, some of which are inspired by biological ANNs, and have found widespread application in the solving of a variety of complex real-world problems. It boasts enticing features as well as remarkable data processing capabilities. In this paper, a comprehensive overview of the backpropagation algorithm for digital image denoising was discussed. Then, we presented a probabilistic analysis of how different algorithms address this challenge, arguing that small ANNs can denoise small-scale texture patterns almost as effectively as their larger equivalents. The results also show that self-similarity and ANNs are complementary paradigms for patch denoising, as demonstrated by an algorithm that effectively complements BM3D with small ANNs, surpassing BM3D at a low cost. Here, one of the most significant advantages of this learning technique is that, once taught, digital images may be recovered without prior knowledge of the degradation model (noise/blurring) that caused the digital image to become distorted.

11 citations

Proceedings ArticleDOI
20 Jul 2022
TL;DR: Machine learning (ML) has recently made remarkable progress, and this study suggests an automatic diagnosis of kidney stones based on coronal computed tomography scans (CT) based on Random Forest, Logistic Regression, and an ensemble are used.
Abstract: Many individuals visit the emergency room due to severe discomfort caused by kidney stones. A kidney stone is a hard, solid particle of a substance that forms as a result of the minerals in the urine. They are the outcome of a combination of genetic and environmental factors. Obesity, certain foods, drugs, and a lack of water can all contribute to this illness. Kidney stones can be of various shapes and sizes. Numerous research and imaging modalities have detected the existence of kidney stones. To completely interpret and diagnose, these pictures require the knowledge of a medical specialist. Clinicians who use computer-aided diagnosis systems as supplemental tools gain considerably from the practical techniques provided by these systems. Nonetheless, the presence of noise has resulted in some errors in kidney stone classification. Machine learning (ML) has recently made remarkable progress, and this study suggests an automatic diagnosis of kidney stones based on coronal computed tomography scans (CT). For research, cross-sectional CT images were collected, yielding a total of 2600 images. Random Forest (RF), Logistic Regression (LR), and an ensemble (combination of the LR and RF) are used. The different metrics are used to compare the ML model for identifying the best model. From the metrics comparison, it is identified as all the positive metrics will be high for the ensemble method and negative metrics will be low for the LR model.

2 citations

Journal ArticleDOI
TL;DR: A deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here.
Abstract: In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.

1 citations

Journal Article
TL;DR: In this article, Bagasse ash (Sugar-cane ash) from Wahid industries, Phagwara, has been used for stabilizing sub-grade soil.
Abstract: In the present world we are all aware of this thing that commercial-vehicles and the private vehicles are in the rate of increment day by day and with the vehicle’s increment the load of bearing on the soil will grow more. So, when the typical wheel load on the soil of sub-grade will grow more the conditions of the stresses will also grow more. So, if this sub-grade has low capacity of bearing the loads it will fail in that case and if we have to make this type of soil well suited for sub-grade construction we will have to stabilize that soil with the help of materials like Bagasse Ash (Sugarcane ash). After adding these materials in the soil there is change in soil’s physical and chemical properties. We will see the result and analyze that there is change in the geo-technical properties of the soil. We will find out that the liquid limit and the plastic index of soil will decrease but the value of C.B.R will have some increment. In this research paper we are using Bagasse ash (Sugar-cane ash) from Wahid industries, Phagwara, and after performing tests till now we have observed that the value of M.D.D decreases from 1.86 to 1.825gm/cm3, as we have increased the amount of Bagasse ash in our soil samples. We have done the stabilization by using different concentrations of Bagasse ash i.e. 2.5%, 5%, 7.5%&10%. While performing the sieve analysis of the soil sample it was seen that soil is poorly graded soil and the liquid limit will decrease from 24% to 21% after addition of Bagasse ash in it and our main purpose is to see that with the usage of Bagasse ash as admixtures in the sub-grade soil will decrease the thickness of the pavement and decrease the overall cost. The test of C.B.R was being done and the result was being analyzed by me and was seen that total thickness of the pavement will decrease from 809 mm to 550 mm and the value of C.B.R is coming maximum at 7.5% replacement of Bagasse ash. Key words: Local soil available, Bagasse ash, Stabilization.

Cited by
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Journal ArticleDOI
TL;DR: In this article , the non-repudiation and non-tampering features of the block chain are used to provide trust in collaborative edges, and a Proof-of-Collaboration consensus consensus technique is proposed for high computational reduction, in which edge devices participate in block formation by giving their PoC credits.

11 citations

Journal ArticleDOI
TL;DR: In this paper , an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible.
Abstract: Photographing images is used as a common detection tool during the process of bridge maintenance. The edges in an image can provide a lot of valuable information, but the detection and extraction of edge details are often affected by the image noise. This study proposes an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible. In this study, four wavelet functions and four decomposition levels are used to decompose the image, filter the coefficients and reconstruct the image. The PSNR and MSE of the denoised images were compared, and the results showed that the sym5 wavelet function with three-level decomposition has the best overall denoising performance, in which the PSNR and MSE of the denoised images were 23.48 dB and 299.49, respectively. In this study, the canny algorithm was used to detect the edges of the images, and the detection results visually demonstrate the difference between before and after denoising. In order to further evaluate the denoising performance, this study also performed edge detection on images processed by both wavelet transform and the current widely used Gaussian filter, and it calculated the Pratt quality factor of the edge detection results, which were 0.53 and 0.47, respectively. This indicates that the use of wavelet transform to remove noise is more beneficial to the improvement of the subsequent edge detection results.

9 citations

Journal ArticleDOI
TL;DR: In this paper , different evaluation based on the watermarking methods described is presented. But, the main purpose to upgrade image watermark is to fulfill the need of sufficient imperceptibility, capacity, and robustness of watermark against several attacks like JPEG compression parameter, salt and pepper noise, cropping factor of the image part which contains a watermark and rotating watermark at one particular angle.

7 citations

Journal ArticleDOI
TL;DR: In this paper , the impact of green credit policy on the sustainability performance of heavily polluting enterprises (HPEs) from the perspectives of technological innovation level (TIL) and credit resource allocation (CRA), using panel data for Chinese A-share listed manufacturing companies.
Abstract: Green credit policy (GCP), as one of the key financial instruments to achieve ’carbon peaking’ and ‘carbon neutrality’ targets, provides capital support for the green development of enterprises. This paper explores the impact mechanism of GCP on the sustainability performance of heavily polluting enterprises (HPEs) from the perspectives of technological innovation level (TIL) and credit resource allocation (CRA), using panel data for Chinese A-share listed manufacturing companies from 2010 to 2015 to construct a propensity score matching and differences-in-differences (PSM-DID) model. We find that GCP has a causal effect on corporate sustainability performance (CSP). Although GCP significantly improves CSP, there is no long-term effect. Heterogeneity analysis shows that the relationship between GCP and CSP is only significant in non-state-owned enterprises and in eastern and low-market-concentration enterprises. Mechanism tests indicate that GCP stimulates HPEs to invest more in technological innovation and thereby improves CSP through the innovation compensation effect; the credit constraint and information transfer effects caused by GCP reduce the credit resources available to HPEs but have a significant forced effect on CSP. This paper enriches the study of the economic consequences of GCP and provides implications for stakeholders to improve the green financial system and achieve green transformation of HPEs.

5 citations