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.

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.

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.

Image denoising has become a crucial task in medical ultrasound (US) imaging due to the presence of speckle or multiplicative noise and additive Gaussian noise. Recently, several denoising techniques such as adaptive wavelet thresholding & joint bilateral (AWT + JB) filter, adaptive fuzzy switching weighted mean (AFSWM) filter and median patch-based locally optimal Wiener (MPBLOW) filter have been proposed to remove the speckle noise. However, these denoising techniques were found to remove noise along with the essential parts of the actual image data which is known as over-filtering. Thereby, it reduces the accuracy of the recognition process. In this paper, a new hybrid filter technique is proposed by combining anisotropic diffusion (AD) with Butterworth band pass filter to overcome over-filtering of the image. In addition, the performance of the proposed hybrid filter and its design parameters are enhanced using the particle swarm optimization (PSO) algorithm. The simulation results show that the proposed filtering technique achieves a better denoising performance when compared with other filtering techniques in terms of peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), structural similarity index (SSIM) and edge preservation index (EPI). Moreover, the results validated that the proposed filtering technique using PSO achieves effective performance than using the harmony search algorithm (HSA) and other filtering techniques.

Biomedical and Pharmacology Journal (BPJ) is an international, open access, peer reviewed, quarterly research journal.