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Today, it is known that there are great difficulties and problems in signature and signature examinations, which have a very important place in both our private life and business and commercial life. The major issue arises when the manuscript’s signature is so illegible and unclear that it is difficult, if not impossible, to authenticate it with the human eye. Researchers have proposed traditional deep learning techniques to solve or improve this challenge. However, the results are not satisfactory. In this study, a new use of Generative Adversarial Network (GAN) model is proposed as a high-quality data synthesis method to address the unreadable data problem on signature verification. A unique signature verification method based on Lightweight deep learning architecture is also proposed. The suggested data synthesizing approach is evaluated using three frequently used Convolutional Neural Network (CNN) methods: MobileNet, SqueezeNet, and ShuffleNet. In addition, in preprocessing phase, we added three different types of high-intensity noise, including Salt & Pepper (S&P), Gaussian, and Gaussian Blur, to the images to make the signature unreadable. We utilized Indic scripts dataset to train GAN and CNN models in our approach. The great quality of images generated by GAN model, as well as the signature verification of the generated images, point to the suggested model’s strong performance.

Recently, emotion analysis has become widely used. Therefore, increasing the accuracy of existing methods has become a challenge for researchers. The proposed method in this paper is a hybrid model to improve the accuracy of emotion analysis; Which uses a combination of convolutional neural network and ensemble learning. In the proposed method, after receiving the dataset, the data is pre-processed and converted into process able samples. Then the new dataset is split into two categories of training and test. The proposed model is a structure for machine learning in the form of ensemble learning. It contains blocks consisting of a combination of convolutional networks and basic classification algorithms. In each convolutional network, the base classification algorithms replace the fully connected layer. Evaluate the proposed method, in IMDB, PL04 and SemEval dataset with accuracy, precision, recall and F1 criteria, shows that, on average, for all three datasets, the precision of polarity detection is 90%, the recall of polarity detection is 93%, the F1 of polarity detection is 91% and finally the accuracy of polarity detection is 92%.