convolutional neural network (cnn)

Misdiagnosis of skin diseases is a common occurrence. Psoriasis is a skin disease that has many similarities with other diseases, and its incorrect diagnosis causes many problems in the treatment process. Misdiagnosis of this disease causes doctors to face problems during treatment. The lack of images of the disease and the database of skin diseases reduces the diagnosis and the coordination of diagnostic methods, therefore, diagnosis using different images is very useful.
Today, diagnosis methods using deep features in medical images have received much attention.
Artificial intelligence is one of the automatic methods of diagnosis. These methods can detect new data entering the system and keep it in memory. Therefore, in this article, two different groups of data have been identified using deep features based on artificial intelligence.
In this method, the data of the first group in the form of training and testing and the data of the second group are studied gradually. If they are correctly identified, the next 0.1 chunks of data enter the network without testing. If they are wrongly recognized, they enter the training section and this reduces the training process. In this work, by training 20% of the data, i.e. the first 10% and the fourth 10%, there was no need for training because the accuracy was not less than98%.
In this article, deep features of images were first extracted using convolutional neural network, and then psoriasis and eczema were diagnosed with average accuracy of98.3%and sensitivity of 97.9% in skin images using artificial intelligence.

In recent years, emotion recognition as a new method for human-computer interaction has attracted the attention of researchers. Automatic speech emotion recognition has become one of the practical methods to increase engagement in most industries. It is expected that emotion recognition based on audio information can result in better accuracy. The purpose of this article is to present an efficient method for recognizing emotional states from speech signals, based on a new cognitive model. Due to the importance of the topic, this article presents an efficient method for recognizing emotional states from speech signals based on a mixed deep learning and cognitive model called SOAR. To implement each part of this model, two main steps have been introduced. The first step is reading the video and converting it to images and preprocessing it. The next step is to use the combination of convolutional neural network (CNN) and learning automata (LA) to classify and detect the rate of facial emotional recognition. The reason for choosing CNN in our model is that no dimension is removed from the speech signal and considering the temporal information in dynamic speech leads to more efficient and better classification. Also, the training of the CNN network in calculating the backpropagation error is adjusted by LA so that the efficiency of the proposed model is increased and the working memory part of the SOAR model can be implemented. In the proposed model, audio databases available in the field of multimodal emotion recognition eNTERFACE' 05 and SAVEE have been used for various experiments. The recognition accuracy of the presented model in the best case from eNTERFACE' 05 and SAVEE databases is equal to 85.3% and 84.5%, respectively.

Deep convolutional neural networks (CNNs) have attained remarkable success in numerous visual recognition tasks. There are two challenges when adopting CNNs in real-world applications: a) Existing CNNs are computationally expensive and memory intensive, impeding their use in edge computing; b) there is no standard methodology for designing the CNN architecture for the intended problem. Network pruning/compression has emerged as a research direction to address the first challenge, and it has proven to moderate CNN computational load successfully. For the second challenge, various evolutionary algorithms have been proposed thus far. The algorithm proposed in this paper can be viewed as a solution to both challenges. Instead of using constant predefined criteria to evaluate the filters of CNN layers, the proposed algorithm establishes evaluation criteria in online manner during network training based on the combination of each filter’s profit in its layer and the next layer. In addition, the novel method suggested that it inserts new filters into the CNN layers. The proposed algorithm is not simply a pruning strategy but determines the optimal number of filters. Training on multiple CNN architectures allows us to demonstrate the efficacy of our approach empirically. Compared to current pruning algorithms, our algorithm yields a network with a remarkable prune ratio and accuracy. Despite the relatively high computational cost of an epoch in the proposed algorithm in pruning, altogether it achieves the resultant network faster than other algorithms.

Today, electricity theft is one of the main challenges for energy distribution and transmission companies around the world. Early detection of abnormal consumers can prevent security and financial losses. Extensive research studies have been done to detect electricity theft by analyzing customer consumption patterns. Today, one of the most widely used methods is convolutional neural networks (CNNs). These networks contain a large number of hyper-parameters.  The accuracy of these networks is low in most studies due to the lack of attention to the adjustment of these hyper-parameters.  Network accuracy and achieving a robust learning model are influenced by the optimal adjusting of these hyper-parameters, which requires exploring a complex and large search space. Meta-heuristic-based search methods are suitable for solving these problems. Therefore, the main contribution of this paper is to use the high ability of the cheetah optimization algorithm (CHOA) to optimally extract CNN hyper-parameters. In this paper, in order to balance the dataset, abnormal samples are created using artificial attacks and added to the dataset. Also, in order to increase the accuracy of the network, abnormal data are clustered using the CHOA algorithm. ISSDA dataset is used to test and evaluate the results. Based on the results obtained and comparing them with the other works, it was proved that the proposed framework with high accuracy identifies abnormal consumers.

Multiple Sclerosis (MS) is a disease that destructs the central nervous system cell protection, destroys sheaths of immune cells, and causes lesions. Examination and diagnosis of lesions by specialists is usually done manually on Magnetic Resonance Imaging (MRI) images of the brain. Factors such as small sizes of lesions, their dispersion in the brain, similarity of lesions to some other diseases, and their overlap can lead to the misdiagnosis. Automatic image detection methods as auxiliary tools can increase the diagnosis accuracy. To this end, traditional image processing methods and deep learning approaches have been used. Deep Convolutional Neural Network is a common method of deep learning to detect lesions in images. In this network, the convolution layer extracts the specificities; and the pooling layer decreases the specificity map size. The present research uses the wavelet-transform-based pooling. In addition to decomposing the input image and reducing its size, the wavelet transform highlights sharp changes in the image and better describes local specificities. Therefore, using this transform can improve the diagnosis. The proposed method is based on six convolutional layers, two layers of wavelet pooling, and a completely connected layer that had a better amount of accuracy than the studied methods. The accuracy of 98.92%, precision of 99.20%, and specificity of 98.33% are obtained by testing the image data of 38 patients and 20 healthy individuals.