This work gives a survey by comparing the different methods of image denoising with the help of wavelet transforms and Convolutional Neural Network. To get the better method for Image denoising, there is distinctive merging which have been used. The vital role of communication is transmitting visual information in the appearance of digital images, but on the receiver side we will get the image with corruption. Therefore, in practical analysis and facts, the powerful image denoising approach is still a legitimate undertaking. The algorithms which are very beneficial for processing the signal like compression of image and denoising the image is Wavelet transforms. To get a better quality image as output, denoising methods includes the maneuver of data of that image. The primary aim is wavelet coefficient modification inside the new basis, by that the noise within the image data can be eliminated. In this paper, we suggested different methods of image denoising from the corrupted images with the help of different noises like Gaussian and speckle noises. This paper implemented by using adaptive wavelet threshold( Sure Shrink, Block Shrink, Neigh Shrink and Bivariate Shrink) and Convolutional Neural Network(CNN) Model, the experimental consequences the comparative accuracy of our proposed work.

Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with actionable insights for decision-making. The results demonstrate that the proposed method effectively estimates crop yield damage with the ability to assess risk under varying conditions, such as extreme weather. This approach can help farmers optimize crop management strategies, minimize losses, and improve overall productivity. Future work will explore the application of this method to other crops and farming systems to expand its utility.

An experiment laid down to evaluate the effect of biofertilizers, growth regulator gibbralic acid (GA₃) and micronutrients on yield and yield attributes of Sapota cv. Kalipatti at Fruit Research Station, Navsari Agricultural University, Gandevi, Gujarat. The treatments of recommended dose of fertilizer (RDF), i.e.100 kg FYM and 1000:500:500 g/plant NPK along with application of biofertilizers (Azospirillum + PSB), growth regulator (GA₃) and micronutrients. The pooled results of 75% RDF + biofertilizers- Azospirillum + PSB @ 40 ml/tree + GA₃ @ 50 ppm + Grade 4 (micronutrient @ 0.5%) were exhibited significantly higher number of fruit (2815.45), yield (20.61 t/ha)along with net return (Rs. 3,03,951/ha) and Benefit Cost Ratio (2.81). The said application enhanced fruit yield up to 43.41% over control comprising RDF only. However, significantly higher fruit weight (81.34 g) was recorded in application of 100% RDF + biofertilizers- Azospirillum + PSB @ 40ml/tree + GA₃ @ 50 ppm + Grade-4 (micronutrient @ 0.5%).

The software reliability model estimates the probability of data failure in a specific environment, significantly impacting reliability and trustworthiness. The paper study focuses on cluster crime data, i.e., indeterministic in Neutrosophic Logic, using a software reliability model. The study utilizes reinforcement learning, Neutrosophic logic, and non-homogeneous Poisson process crime data to estimate indeterministic cluster data in crime. The "Non-homogeneous Poisson Process with Neutrosophic Logic" technique performs well in evaluating and deterring crime based on crime data analysis. The crime cluster involving offenders correctly classified as failure to accomplish does better than uncertain cluster reliability estimation with least squares and logistic regression analysis. The method enables crime prediction and prevention by using concave growth models to create an uncertain crime cluster, penalizing the correct person.