Deep neural networks based error level analysis for lossless image compression based forgery detection.

The proposed model is implemented in deep learning based on counterfeit feature extraction and Error Level Analysis (ELA) techniques. Error level analysis is used to improve the efficiency of distinguishing copy-move images produced by Deep Fake from the real ones. Error Level Analysis is used on images in-depth for identifying whether the photograph has long passed through changing. This Model uses CNN on the dataset of images for training and to test the dataset for identifying the forged image. Convolution neural network (CNN) can extract the counterfeit attribute and detect if images are false. In the proposed approach after the tests were carried out, it is displayed with the pie chart representation based on percentage the image is detected. It also detects different image compression ratios using the ELA process. The results of the assessments display the effectiveness of the proposed method

Character recognition using tesseract enabling multilingualism.

Character recognition builds a recognizing factor for identifying the accuracy in characters. The accuracy of classifying the recognizing characters in an image is applied through deep learning methods. The character recognition is mainly focusing on the layers of text recognition through deep learning techniques. Well cleared python code assists to furnish all the levels of image by following deep learning that algorithmically analyse and recognize text from the given input image. This research work has been proposed for recognizing characters using deep learning techniques and recognize the input image with well-furnished and most efficient output. It provides a high level of accuracy-built output after the recognition of characters in the high-resolution image. This recognized character can be converted into user desired languages where the proposed model is trained to recognize some particular languages

Forecasting meteorological analysis using machine learning algorithms.

Weather prediction is gaining up ubiquity quickly in the current period of Machine learning and Technologies. It is fundamental to foresee the temperature of the climate for quite a while. Decision trees, K-NN, Random Forest algorithms are an integral asset which has been utilized in several prediction works for instance, flood prediction, storm detection etc. In this paper, a simple approach for weather prediction of future years by utilizing the past data analysis is proposed by the decision tree, K-NN and random forest algorithm calculations and showing the best accuracy result of these three algorithms. Weather prediction plays a significant job in everyday applications and in this paper the prediction is done based on the temperature changes of the certain area. All these algorithms calculate the mean values, median, confidence values, probability and show the difference between plots of all the three algorithms etc. Finally, using these algorithms in this work we can predict whether the temperature increases or decreases, is it a rainy day or not. The dataset is completely based on the weather of certain area including few objects like year, month, and temperature, predicted values and so on.