Image In painting is the process of reconstructing lost or deteriorated parts of images and videos. It is an important problem in computer vision and holds several importance in many imaging and graphics applications, e.g. restoring old photos and videos, automatic scene editing, denoising, compression and image based rendering. The traditional method of Image In painting which are mostly based on machine learning models work well for background in painting, they cannot hallucinate novel image contents for challenging tasks such as in painting of faces and complex objects as well as failing to capture high level objects semantics. It has been discovered that by simply introducing a small bit of noise to the original data, most mainstream neural nets may be readily misled into misclassifying items. This is because most machine learning models only learn from a little quantity of data and the input-to-output mapping is nearly linear, which is a major disadvantage and leads to overfitting. The present method where we use GANs, or Generative Adversarial Networks, are a type of generative modelling that employs deep learning techniques such as convolutional neural networks. GANs has a capability of learning from data that is unstructured or unlabeled, the algorithms try to learn using method of feature extraction which is very different, more reliable and fully automatic. Celeb Faces Attributes Dataset (Celeb A) is large scale face attributes dataset with more than 200K celebrity images, each with 40 attributes annotations.

Lactic acid is the most common and important chemical compound used in the pharmaceutical, cosmetic, chemical, and food industries. Various attempts have been made to produce lactic acid efficiently from inexpensive raw materials. Lactic acid can be produced by various methods such as fermentation of sugars and food waste. In this way, lactic acid is an environmentally friendly product that has gotten a lot of attention in recent years. The strains were isolated from five separate samples of curd, kimchi, garlic, rice water, and mango peel for the study. The study discusses the generation of lactic acid from various culinary wastes, including potato peel, orange peel, sugarcane peel, garlic peel, and mango peel. In the future, more emphasis should be placed on achieving maximum productivity and yield. Purification methods must be efficient enough to increase output while reducing product loss.

Gears are the most essential and commonly utilised power transmission components. It is really essential to operate machines involving different weights and speeds. When a load is increased beyond a certain limit, gear teeth frequently fail. Composite materials, in comparison to other metallic gears, offer significantly better mechanical qualities, such as a higher strength-to-weight ratio, increased hardness, and hence a lower risk of failure. Al6063 and SiC were employed to build a metal matrix composite for spur gear production in this work. This study discovered that composite materials outperformed steel alloys and cast iron in terms of characteristics, and that composites can thus be utilised to replace metallic gears.

One of the most well-known challenges in machine learning and computer vision applications is handwritten digit recognition. To overcome the challenge of handwritten digit and/or letter recognition, a variety of machine learning algorithms have been used. The current research focuses on using Neural Networks to overcome the problem. Deep neural networks, deep belief networks, and convolutional neural networks are the three most well-known methodologies. The accuracy and performance of these three neural network algorithms are compared and assessed in terms of a variety of variables such as accuracy and performance. However, the rate of recognition accuracy and performance is not the only factor in the assessment process; there are more relevant metrics like execution time. Random and standard dataset of handwritten digit will be used for conducting the experiments. The trials will be conducted using a random and standard dataset of handwritten digits. The results demonstrate that Deep belief network is the most accurate algorithm among the three neural network techniques, with a 98.08 percent accuracy rate. Deep belief network, on the other hand, has a comparable execution time to the other two methods. Each method, on the other hand, has a 1-2 percent error rate due to digit form similarities, particularly with the digits (1,7), (3,5), (3,8), (8,5), and (9,5). (6, 9).