Computational intelligence methods, such as fuzzy logic and deep neural networks, are robust models to solve real-world problems. In many dynamic and complex problems, statistical attributes frequently change over the time. Recurrent neural networks (RNN) are suitable to model dynamic high-dimensional and non-linear state-space systems. Nevertheless, the RNN is incapable of modelling long-term dependencies in temporal data, and its learning using gradient descent is a complex and difficult task. Long Short-Term Memory (LSTM) networks were introduced to overcome the RNN issues, but coping with uncertainty is still a major challenge for the LSTM models. This research presents a Hybrid Type-2 Fuzzy LSTM (HHT2FLSTM) deep approach to learn long-term dependencies in order to obtain a reliable prediction in uncertain time series circumstances. The proposed model was applied to the air quality prediction problem to evaluate the model’s robustness in handling uncertainties in a real-world application. The proposed model has been evaluated on a real dataset that contains the outdoor pollutants from July 2011 to October 2020 in Tehran and Beijing by a 10-fold cv with an average area under the ROC curve of 97 % with a 95% confidence interval [95-97] %. Manuscript profile

Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hybrid Fuzzy-Evolutionary algorithms to predict the dust phenomenon. For this, first a fuzzy expert system was designed and then it was optimized using evolutionary algorithms like Genetic and Differential Evolutionary algorithms. Evolutionary nature of these algorithms have been taken into account to optimize the fuzzy system in the complex area of the dust phenomenon. To evaluate the proposed hybrid models a real dataset including 55 years of the dust phenomenon in Zanjan province in Iran was considered. Performance of these methods was investigated through an ROC curve analysis in combination with a 10-fold cross validation technique. The accuracy of the fuzzy expert system was 92.13% and after optimization through the Fuzzy-Genetic model and hybrid differential evolutionary model was reached to 93.5% and 97.30%, respectively. The results are promising for early forecasting of the dust phenomena and preventing its consequences. Manuscript profile

In recent years, the advancement of digital technology has led to an increase in data transmission on the Internet. Security of images is one of the biggest concern of many researchers. Therefore, numerous algorithms have been presented for image encryption. An efficient encryption algorithm should have high security and low search time along with high complexity. DNA encryption is one of the fastest emerging technologies performing based on the concepts of DNA computing and can be used for data storage and transfer. Very high speed and minimum memory and power requirements in the DNA calculations are of the advantages of this new encryption algorithm. In this study, a new encryption algorithm has been proposed for grayscale digital images using DNA algorithm and lattice map function. In the first step, the initial value of the Logistic Map function has been obtained from a 120-bit key using the proposed method, then in the second stage, the original image was encrypted with the Lattice Map function sequence using the logistic map function sequence generated in the previous step and the DNA rules. The results of the simulations showed a high level of resistance and security against statistical attacks, so that the entropy of the proposed method was obtained as 7.9996. Manuscript profile

Encryption is an important issue in information security which is usually provided using a reversible mathematical model. Digital image as a most frequently used digital product needs special encryption algorithms. This paper presents a new encryption algorithm high security for digital gray images using genetic algorithm and Lattice Map function. At the first the initial value of Logistic Map function from a 120 bits key is offered, and then by using the produced chaos series moves original picture pixels. In third step, the original image with Lattice Map function series create by sequence of Logistic Map function from latest level to encrypt the image. This process goes under evolution through the generation of the genetic algorithm until the algorithm converges to an encrypted image with a highest entropy and lowest correlation coefficient among pixels. The results reveal the highest level of resistance and security against statistical attacks. With obtained entropy results from the proposed method were 7.9993 which shows its proficiency compared to the counterpart methods. Manuscript profile

Fuzzy expert systems are one of the most practical intelligent models with the high potential for managing uncertainty associated to the medical diagnosis. In this paper, a fuzzy inference system (FIS) for diagnosing of acute lymphocytic leukemia in children has been introduced. The fuzzy expert system applies Mamdani reasoning model that has high interpretability to explain system results to experts in a high level. The system has been designed based on the specialist physician’s knowledge. The proposed systems, has been implemented in Matlab and evaluated on real patients’ dataset. High accuracy of this system (with an accuracy about 96%) revealed its capability for helping experts to early diagnosis of the disease. that the results are promising for more earlier diagnosis and then providing good treatment of patients and consequently saving more children’s lives. Manuscript profile

Concurrency control is one of the important problems in operation systems. Various studies have been reported to present different algorithms to address this problem, although a few attempts have been made to represent an overall view of the characteristics of these algorithms and comparison of their capabilities to each other. This paper presents a survey of the current methods for controlling concurrency in operating systems. Classification of current algorithms in operating systems has been proposed. Current concurrency control algorithms are classified into four groups: 1) software-based algorithms, 2) hardware-based algorithms, 3) based operating system, and 4) based on message passing. Furthermore, it presents an analysis of the capabilities and characteristics of current algorithms' in their own category (intra-group comparison analysis) and between different categories (inter-group comparison analysis) to put a light on the way of selecting a proper algorithm for various circumstances in operating systems. Manuscript profile