The effects of uncontrolled fires on natural ecosystems and the factors affecting their occurrence are widely studied worldwide. Fire occurrence modeling is important to prioritize the fire risk in a given area and identify practices to prevent it from happening. The purpose of the present study was to simulate the prediction of fire and identify the most important factors in fire occurrence using Bayesian Belief Network in Chaharmahal and Bakhtiari Province. Data from 205 fire sites and 205 sites with ‘ no-fire’ experience were recorded. Climatic factors (e. g. annual precipitation and annual mean temperature), topography (elevation, slope, direction), land cover, and human factors (e. g. distance from the residential area, distance from agricultural land and distance from the road) of the sites were selected and embedded into the BBN model. The results indicated that the ability of the BBN model to predict the occurrence of fire was excellent (AUC= 0. 923). Since there are many fire events in this province, the results of this study can be used as a fundamental and powerful tool for decision-makers to reduce the incidence of fire and its hazard.

During the past decades, recognition of human activities has attracted the attention of numerous researches due to its outstanding applications including smart houses, health-care and monitoring the private and public places. Applying to the video frames, this paper proposes a hybrid method which combines the features extracted from the images using the ‘ scale-invariant features transform’ (SIFT), ‘ histogram of oriented gradient’ (HOG) and ‘ global invariant features transform’ (GIST) descriptors and classifies the activities by means of the Deep Belief Network (DBN). First, in order to avoid ineffective features, a pre-processing course is performed on any image in the dataset. Then, the mentioned descriptors extract several features from the image. Due to the problems of working with a large number of features, a small and distinguishing feature set is produced using the bag of words (BoW) technique. Finally, these reduced features are given to a Deep Belief Network in order to recognize the human activities. Comparing the simulation results of the proposed approach with some other existing methods applied to the standard PASCAL VOC Challenge 2010 database with nine different activities demonstrates an improvement in the accuracy, precision and recall measures (reaching 96. 39%, 85. 77% and 86. 72% respectively) for the approach of this work with respect to the other compared ones in the human activity recognition.

Spatial recommendation systems allow users to provide useful information by reducing duplicate and irrelevant information on the web widely. Recommendation systems are widely used in various fields, including tourism. Tourism recommendation systems can be used as tools by the tourist. A tourist can visit the tourist attractions of the region in a short time and with the least facilities, cost, and knowledge. Recommendation systems generally offer the necessary suggestions to different users based on participatory refinement and the similarity between the groups of the users. However, many services do not match the personal characteristics of the individual, and this reduces the effectiveness of such systems. The purpose of this study is to develop a recommendation algorithm based on the similarities between the users and personalization concepts. The innovation of this research is the use of a Deep Belief neural Network to personalize the suggestions that can be offered to the tourists. The research scenario is as follows: first different tourists register in the system,then they express their personal information and general preferences and specific personalization factors for visiting the tourist centres. In the proposed approach, there is no need to separate the users,rather, due to the learning power of Deep neural Networks, it is possible to differentiate and personalize the user suggestions. In this regard, the related data to 400 tourists were received based on 14 input and distinguishing elements. Furthermore, based on the trained Network, the predictability of personalized tourist places for 30 new users was examined. The results were evaluated based on these three indicators: Precision, Recall, and F-Score, as well as the user satisfaction. The results showed the high accuracy as well as the satisfaction of more than 79% of the users.

Background: One of the fields of research in recent years that has been under focused is emotion recognition in electroencephalography (EEG) signals. This study provides a four‑ layer method to improve people’ s emotion recognition through these signals and Deep Belief neural Networks. Methods: In this study, using DEAP dataset, a four-layer method is established, which includes (1) preprocessing, (2) extracting features, (3) dimension reduction, and (4) emotion identification and estimation. To find the optimal choice in some of the steps of these layers, three different tests have been conducted. The first is finding the perfect window in feature extraction section that resulted in superiority of Hamming window to the other windows. The second is choosing the most appropriate number of filter bank and the best result was 26. The third test was also emotion recognition that its accuracy was 92. 93 for arousal dimension, 92. 64 for valence dimension, 93. 14 for dominance dimension in two‑ class experiment and 76. 28 for the arousal, 74. 83 for the valence, and 75. 64 for dominance in three‑ class experiment. Results: The results of this method show an improvement of 12. 34% and 7. 74% in two‑ and three‑ class levels in the arousal dimension. This improvement in the valence is 12. 77 and 8. 52, respectively. Conclusion: The results show that the proposed method can be used to improve the accuracy of emotion recognition.

Identification of the exact location of an exon in a DNA sequence is an important research area of bioinformatics. The main issues of the previous signal processing techniques are accuracy and robustness for the exact locating of exons. To address the mentioned issues, in this study, a method has been proposed based on Deep learning. The proposed method includes a new preprocessing, a new mapping method, and a multi-scale modified and hybrid Deep neural Network. The proposed preprocessing method enriches the Network to accept and encode genes at any length in a new mapping method. The proposed multi-scale Deep neural Network uses a combination of an embedding layer, a modified CNN, and an LSTM Network. In this study, HMR195, BG570, and F56F11.4 datasets have been used to compare this work with previous studies. The accuracies of the proposed method have been 0.982, 0.966, and 0.965 on HMR195, BG570, and F56F11.4 databases, respectively. The results reveal the superiority and effectiveness of the proposed hybrid multi-scale CNN-LSTM Network.