Recognition Covid-19 cases using deep type-2 fuzzy neural networks based on chest X-ray image

Today, the new coronavirus (Covid-19) has become a major global epidemic. Every day, a large proportion of the world's population is infected with the Covid-19 virus, and a significant proportion of those infected dies as a result of this virus. Because of the virus's infectious nature, prompt diagnosis, treatment, and quarantine are considered critical. In this paper, an automated method for detecting Covid-19 from chest X-ray images based on deep learning networks is presented. For the proposed deep learning network, a combination of convolutional neural networks with type-2 fuzzy activation function is used to deal with noise and uncertainty. In this study, Generative Adversarial Networks (GANs) were also used for data augmentation. Furthermore, the proposed network is resistant to Gaussian noise up to 10 dB. The final accuracy for the classification of the first scenario (healthy and Covid-19) and the second scenario (healthy, Pneumonia and Covid-19) is about 99% and 95%, respectively. In addition, the results of the proposed method in terms of accuracy, precision, sensitivity, and specificity in comparison with recent research are promising. For example, the proposed method for classifying the first scenario has 100% and 99% sensitivity and specificity, respectively. In the field of medical application, the proposed method can be used as a physician's assistant during patient treatment

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

Intelligent parking assist

In this paper a new parking guiding and information system is described. The system assists the user to find the most suitable parking space based on his/her preferences and learned behavior. The system takes into account parameters such as driver's parking duration, arrival time, destination, type preference, cost preference, driving time, and walking distance as well as time-varying parking rules and pricing. Moreover, a prediction algorithm is proposed to forecast the parking availability for different parking locations for different times of the day based on the real-time parking information, and previous parking availability/occupancy data. A novel server structure is used to implement the system. Intelligent parking assist system reduces the searching time for parking spots in urban environments, and consequently leads to a reduction in air pollutions and traffic congestion. On-street parking meters, off-street parking garages, as well as free parking spaces are considered in our system. © 2013 IEEE. 1156 1161 "p"Conference code: 99950 Cited By :1

Identification of Effective Genes of Multiple Cancers Using Neural Network

Cancer is a major health concern that affects a significant number of people worldwide and can often result in fatalities. Therefore, there is a growing need to develop effective approaches for early diagnosis and classification of different types of cancer. Early detection of cancer is crucial for prompt and accurate treatment. Thus, researchers have been working to identify non-invasive and precise methods for the early diagnosis, monitoring, and control of cancer. Leukemia and prostate cancer are two of the most common types of cancer globally. Microarray data analysis has become a valuable tool for diagnosing and classifying different types of cancerous tissues. To improve the accuracy of diagnosis, hybrid algorithms and neural networks are being employed. This paper provides a review of different biomarkers for leukemia and prostate cancer and proposes a novel method for distinguishing between the two cancers. The proposed method includes appropriate gene selection, a new hybrid model, and differential analysis of microarray data to create a diagnostic tool. The results indicate that the proposed algorithm is highly accurate and efficient in selecting a small set of valuable genes to improve classification accuracy. In conclusion, the accurate diagnosis and classification of cancer are essential for timely and effective treatment. The proposed method can contribute to the development of a reliable diagnostic tool for leukemia and prostate cancer, and the application of microarray data and hybrid algorithms can be useful for diagnosing other types of cancer as well