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The aim of this paper is to explore the ethical implications of artificial intelligence (AI) in the realm of art. Drawing on the philosophy and ethics of AI, this study examines the ethical challenges that may arise in the art domain. Given the increasing use and penetration of AI in the field of art, there is a need for more careful attention to the ethical dimensions of this phenomenon by scholars of art and philosophy. To achieve the objective of the paper, concepts such as AI, some of its techniques, and the ethical issues of AI in art are explained and analyzed using the analytical-descriptive method. The findings of the study reveal that ethical issues such as privacy and surveillance, manipulation of behavior, opacity and transparency, bias in decision systems, and automation and employment can be investigated in the field of art. These ethical issues are sometimes in conflict and sometimes in direct relation with each other. This study suggests that addressing the ethical questions of AI in the field of art requires addressing the ethical questions of AI in general. [ABSTRACT FROM AUTHOR]

Generative models try to obtain a probability distribution that is similar to that of observed data. Two different solutions have been proposed in this regard in recent years: one is to minimize the divergence (distance) between the two distributions by maximizing the variational lower bound, and the other is to implicitly reduce the distance between the two distributions through adversarial processes. One of the problems in generative adversarial networks (GANs) is the mode collapse. Mode collapse is a phenomenon in which, for various inputs, the generative model generates low variety or similar images. This paper tries to provide a solution to the mode collapse problem proposing a novel method called variational generative adversarial networks (VGANs). This method exploits variational autoencoders to initialize GANs. In other words, in addition to maximizing the variational lower bound, it also implicitly reduces the distance between the two distributions. Experimental results show that this method can deal with the mode collapse problem better than the state-of-the-art. Moreover, in the qualitative analysis, according to a survey of 136 people on the authenticity of the generated images, the proposed method can generate images more similar to real ones. [ABSTRACT FROM AUTHOR]

The stochastic neuron has great importance in neural networks and is one of the most important subjects in machine learning algorithms. Hardware implementation of neural networks has always been of interest to researchers and can significantly increase the performance and applications of neural networks. Hence hardware implementation of the stochastic neuron is also important. In this paper, utilizing stochastic behavior of MTJs in subcritical current regime a hardware model for stochastic neurons is proposed. Using HSpice tool, the proposed model was simulated and simulation results show that the proposed model functionality is similar to the mathematical description of the stochastic neuron and the error of this model is always less than 4.8٥/٠ compared to the mathematical description of the stochastic neuron. Also using corner simulation, it was shown that this model performs properly even in the presence of process variation and its error rate is less than 15.46٥/o and 17.43٥/٠ compared to the mathematical model and ideal simulation, respectively. [ABSTRACT FROM AUTHOR]

Background: Accurate and early detection of non-alcoholic fatty liver, which is a major cause of chronic diseases is very important and is vital to prevent the complications associated with this disease. Ultrasound of the liver is the most common and widely performed method of diagnosing fatty liver. However, due to the low quality of ultrasound images, the need for an automatic and intelligent classification method based on artificial intelligence methods to accurately detect the amount of liver fat is essential. This paper aims to develop an advanced machine learning model based on texture features to assess liver fat levels based on liver ultrasound images. Methods: In this analytic study, which is done from April to November 2020 in Tehran, ultrasound images of 55 obese people who have undergone laparoscopic surgery have been used and the histological result of a liver biopsy has been employed as a reference for liver fat. First, 88 texture-based features were extracted from the images using the Gray-Level Co-Occurrence Matrix (GLCM) method. In the next step, using the method of minimum redundancy and maximum correlation, the top features were selected from among 88 features and applied to the classifier input. Finally, using the three classifiers of linear discriminant analysis, support vector machine and AdaBoost, the images were classified into 4 groups based on the amount of liver fat. Results: The accuracy of the automatic liver fat prediction model from ultrasound images for AdaBoost classification was 92.72%. However, the accuracies obtained for support vector machine and linear discriminant analysis classification were 87.88% and 75.76%, respectively. Conclusion: The proposed approach based on texture features using the GLCM and the AdaBoost classification from ultrasound images automatically detects the amount of liver fat with high accuracy and can help physicians and radiologists in the final diagnosis. [ABSTRACT FROM AUTHOR]

Given that the price signal in the electricity market is highly volatile or otherwise uncertain, short-term forecasting is significantly affected. Since time-series methods cannot estimate such nonlinear models appropriately with high accuracy, we need to provide an efficient model. For this reason, in this paper, a new hybrid algorithm for day-ahead electricity price forecasting is proposed. In order to achieve this model, we first divide the forecasting problem into three main layers: preprocessor, training, and regulator. In the first layer, we use the curvelet transform to reduce possible noise in the price signal. Then, using the extended data selection model based on increasing correlation and decreasing redundancy, we eliminate the unnecessary data and reduce the volume of computation significantly. Then the regularized data is entered into the learning layer which is a developed Extreme Learning Machine (ELM) to obtain and extract the best pattern from the input data. Since adjusting the control parameters of the proposed ELM can maximize its ability to derive a nonlinear pattern from the price signal, a new developed Virus Colony Search (VCS) method based on the time-varying coefficients theory is proposed in the last layer. The proposed algorithm is a novel optimization method based on the function of viruses to destroy host cells and penetrate the best ones into a cell for replication. The proposed method is applied to existing real electricity markets and the results are compared based on prediction error rates and error-based criteria. The obtained results show the appropriate and acceptable performance of the proposed forecasting method. [ABSTRACT FROM AUTHOR]

Today, with advances in use of intelligent systems technology for data analysis, processing in the diagnosis of various diseases is of particular importance. In this paper, an intelligent method that combines xcs and LA are offered to diagnose heart disease. Classification system (XCS) which is considered as one of the most successful agents in learning to solve problems (Learning Agents) is known, rules based on its accuracy in predicting the reward received from the environment, evaluating and using genetic algorithms to update. In this paper, using data from the UCI database, the proposed method deals with the education system And then use the test data to evaluate system performance. the Improve efficiency of the method compared to previous methods for detecting heart disease show. [ABSTRACT FROM AUTHOR]

Background and Objectives: Leukemia is a cancer type in the world. One of the most accurate methods for detection and prediction of Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia is to use DNA and genetic information of people. Microarray technology is a tool to study the expression of thousands of genes in shortest possible time. Analyzing the microarray datasets may not be possible without the statistical analysis and machine learning techniques. In this paper، microarray data sets and machine learning techniques are used for the diagnosis of leukemia. Materials and Methods:The data used in this descriptive study are the expression of 7129 genes of 72 patients with leukemia which have been achieved by the microarray technology. Then، the diagnosis of AML and ALL was performed using the microarray data based on anisotropic radial basis function with the gain ratio and information gain. Results:The proposed method using information gain with the selection of 230 important genes and using gain ratio with the selection of 86 important genes was able to detect AML and ALL with accuracy of 97.06%، whereas non-parametric kernel classification method based on the radial basis function has the accuracy of 35.29٪ with 7129 genes. Conclusions:The results of this study showed that the gene expression data and proposed method with gain ratio method are able to detect leukemia with high accuracy. Therefore، it seems that proposed method can help to accurately diagnose leukemia for a better decision making about the diagnosis of diseases and treatment of patients. [ABSTRACT FROM AUTHOR]