Text generation is a field of natural language processing. Text generation enables the system to produce comprehensive, . grammatically correct texts like humans. Applications of text generation include image Captioning, poetry production, production of meteorological reports and environmental reports, production of business reports, automatic text summarization, . With the appearance of deep neural networks, research in the field of text generation has change to use of these networks, but the most important challenge in the field of text generation using deep neural networks is the data is discrete, which has made gradient inability to transmit. Recently, the use of a new approach in the field of deep learning, called Generative adversarial networks (GANs) for the generation of image, sound and text has been considered. The purpose of this research is to use this approach to generate Persian sentences. In this paper, three different algorithms of Generative adversarial networks were used to generate Persian sentences. to evaluate our proposed methods we use BLEU and self-BLEU because They compare the sentences in terms of quality and variety.

Background and Objectives: Investment has become a paramount concern for various individuals, particularly investors, in today's financial landscape. Cryptocurrencies, encompassing various types, hold a unique position among investors, with Bitcoin being the most prominent. Additionally, Bitcoin serves as the foundation for some other cryptocurrencies. Given the critical nature of investment decisions, diverse methods have been employed, ranging from traditional statistical approaches to machine learning and deep learning techniques. However, among these methods, the Generative adversarial network (GAN) model has not been utilized in the cryptocurrency market. This article aims to explore the applicability of the GAN model for predicting short-term Bitcoin prices.Methods: In this article, we employ the GAN model to predict short-term Bitcoin prices. Moreover, Data for this study has been collected from a diverse set of sources, including technical data, fundamental data, technical indicators, as well as additional data such as the number of tweets and Google Trends. In this research, we also evaluate the model's accuracy using the RMSE, MAE and MAPE metrics.Results: The results obtained from the experiments indicate that the GAN model can be effectively utilized in the cryptocurrency market for short-term price prediction.Conclusion: In conclusion, the results of this study suggest that the GAN model exhibits promise in predicting short-term prices in the cryptocurrency market, affirming its potential utility within this domain. These insights can provide investors and analysts with enhanced knowledge for making more informed investment decisions, while also paving the way for comparative analyses against alternative models operating in this dynamic field.

Background: One of the common limitations in the treatment of cancer is in the early detection of this disease. The customary medical practice of cancer examination is a visual examination by the dermatologist followed by an invasive biopsy. Nonetheless, this symptomatic approach is time‑,consuming and prone to human errors. An automated machine learning model is essential to capacitate fast diagnoses and early treatment. Objective: The key objective of this study is to establish a fully automatic model that helps Dermatologists in skin cancer handling process in a way that could improve skin lesion classification accuracy. Method: The work is conducted following an implementation of a Deep Convolutional Generative adversarial network (DCGAN) using the Python‑, based deep learning library Keras. We incorporated effective image filtering and enhancement algorithms such as bilateral filter to enhance feature detection and extraction during training. The Deep Convolutional Generative adversarial network (DCGAN) needed slightly more fine‑, tuning to ripe a better return. Hyperparameter optimization was utilized for selecting the best‑, performed hyperparameter combinations and several network hyperparameters. In this work, we decreased the learning rate from the default 0. 001 to 0. 0002, and the momentum for Adam optimization algorithm from 0. 9 to 0. 5, in trying to reduce the instability issues related to GAN models and at each iteration the weights of the discriminative and Generative network were updated to balance the loss between them. We endeavour to address a binary classification which predicts two classes present in our dataset, namely benign and malignant. More so, some well‑,known metrics such as the receiver operating characteristic ‑, area under the curve and confusion matrix were incorporated for evaluating the results and classification accuracy. Results: The model generated very conceivable lesions during the early stages of the experiment and we could easily visualise a smooth transition in resolution along the way. Thus, we have achieved an overall test accuracy of 93. 5% after fine‑, tuning most parameters of our network. Conclusion: This classification model provides spatial intelligence that could be useful in the future for cancer risk prediction. Unfortunately, it is difficult to generate high quality images that are much like the synthetic real samples and to compare different classification methods given the fact that some methods use non‑, public datasets for training.

Male infertility as an effective factor can affect the lives of infertile couples. Sperm morphology is an important step in evaluating and examining semen in male infertility. The lack of samples of sperm head abnormalities compared to natural sperm samples can make the classification of sperm head images into an imbalanced classification problem. With the inability of common classification algorithms, capsule neural networks (CapsNet) provide a suitable platform for designing imbalanced classification models compared to other deep networks. Also, Generative adversarial networks (GANs) help improve the imbalanced classification of images by producing appropriate artificial samples. To this end, in this paper a new architecture is introduced based on CapsNet and GAN to evaluate the imbalanced classification of human sperm images. Reviewing and comparing the proposed model with other deep learning models in the balanced and imbalanced classification of human sperm images showed the superiority of the proposed model. Investigating the general methods of increasing data with the proposed model to increase data, it was concluded that the general methods have less resistance to reducing the number of data than the proposed model. Balanced classification of human sperm images was done by proposed model with 98.1 % accuracy. The proposed model also maintained a high sensitivity to the minority to the majority of 1:25, indicating its proper performance in the imbalanced classification of sperm images.

Dominant and rare events detection is one of the most important subjects of image and video analysis field. Due to inaccessibility to all rare events, detecting of them is a challenging task. Today, deep networks are the best tool for video modeling but due to inaccessibility to tagged data of rare data, usual learning of a deep convolutional network is not possible. Due to the success of Generative adversarial networks, in this paper an end-to-end deep network based on Generative adversarial networks is presented for detecting rare events. This network is competitively trained only by dominant events. To evaluate performance of proposed method, two standard datasets: UCSDped1 and UCSDped2 are utilized. The proposed method can detect rare event with 0. 2 and 0. 17 equal error rate with the processing speed of 300 frames per second on the mentioned data respectively. In addition to end-to-end structure of the network and its simple train and test phase, this result is comparable to advanced methods results.

The image-to-image translation is one of the most challenging topics in artificial intelligence, which has recently made significant progress with the use of Generative adversarial networks (GANs). However, existing methods often fail to translate the noise source to the target domain. This article presents the WTGAN network, which includes a new generator and a local and global discriminator to solve this problem. The generating network is designed based on wavelet transform and attention. Due to the fact that wavelet transforms are powerful tools for removing general noise from the image, They have been used in the structure of the generator. Also, attention, residual and skip-connections can provide deeper surface information between the source and target image and help to improve the generator performance. Experiments were performed on the Cityscapes dataset and PSNR, SSIM, and LPIPS criteria were used for evaluation. The results have shown that the model can well reduce the effects of noise at the source, well reserve structure, and achieve the desired quality.

Based on Global Cancer 2015 statistics, the lung cancer of all types constitutes 27% of overall cancers while 19.5% of cancer deaths are due to lung cancer. In lieu of this, an effective lung cancer screening test using Computed Tomography (CT) scan is crucial to detect cancer at the early stage. The interpretation of the CT images requires an advanced CAD system of high accuracy for instance, in classifying the lung nodules. Recently, Deep Learning method that is Convolution Neural network (CNN) shows an outstanding success in lung nodules classification. However, the training of CNN requires a great number of images. Such a requirement is an issue in the case of medical images. Generative adversarial network (GAN) has been introduced to generate new image datasets for CNN training. Thus, the main objective of this study is to compare the performance of CNN architectures with and without the implementation of GAN for lung nodules classification in CT images. Here, the study used Conditional GAN (cGAN) to generate benign nodules images. The classification accuracy of the combined cGAN-CNN architecture was compared among CNN pretraining networks namely GoogleNet, ShuffleNet, DenseNet, and MobileNet based on classification accuracy, specificity, sensitivity, and AUC-ROC values. The experiment was tested on LIDC-IDRI database. The results showed cGAN-CNN architecture improves the overall classification accuracy as compared to CNN alone with the cGAN-ShuffleNet architecture performed the best, achieving 98.38% accuracy, 98.13% specificity, 100% sensitivity and AUC-ROC at 99.90%. Overall, the classification performance of CNN can be improved by integrating GAN architecture to mitigate the constraint of having a large medical image dataset, in this case, CT lung nodules images.

Channel is one of the most important parts of a communication system as the medium of the propagation of electromagnetic waves. Being aware of how the channel affects the propagation waves is essential for the design, optimization, and performance analysis of a communication system. Along with conventional modeling schemes, in this paper, we present a novel propagation channel model. The proposed modeling strategy considers the 2-dimensional time-frequency response of the channel as an image. It models the distribution of these channel images using Deep Convolutional Generative adversarial networks (DCGANs). In addition, for the measurements with different user speeds, the user speed is considered as an auxiliary parameter for the model. StarGAN is used as an image-to-image translation technique to change the generated channel images with respect to the desired user speed. The performance of the proposed model is evaluated using a few existing evaluation metrics. Furthermore, as modeling the 2D time- frequency response is more general than the modeling of the channel only in time, the conventional metrics for evaluation of the channel models are not sufficient; therefore, a new metric is introduced in this paper. This metric is based on the Cepstral Distance Measure (CDM) between the mean autocorrelation of the generated samples and measurement data. Using this metric, the generated channels show significant statistical similarity to the measurement data.