Recognition of visual events as a video analysis task has become popular in machine learning community. While the traditional approaches for detection of video events have been used for a long time, the recently evolved deep learning based methods have revolutionized this area. They have enabled event recognition systems to achieve detection rates which were not reachable by traditional approaches. Convolutional neural networks (CNNs) are among the most popular types of deep networks utilized in both imaga and video recognition tasks. They are initially made up of several convolutional layers, each of which followed by proper activation and possibly pooling layers. They often encompass one or more fully connected layers as the last layers. The favorite property of them in this work is the ability of CNNs to extract mid-level features from video frames. Actually, despite traditional approaches based on low-level visual features, the CNNs make it possible to extract higher level semantic features from the video frames. The focus of this paper is on recognition of visual events in video using CNNs. In this work, image trained descriptor s are used to make video recognition can be done with low computational complexity. A tuned CNN is used as the frame descriptor and its fully connected layers are utilized as concept detectors. So, the featue maps of activation layers following fully connected layers act as feature vectors. These feature vectors (concept vectors) are actually the mid-level features which are a better video representation than the low level features. The obtained mid-level features can partially fill the semantic gap between low level features and high level semantics of video. The obtained descriptors from the CNNs for each video are varying length stack of feature vectors. To make the obtained descriptors organized and prepared for clasification, they must be properly encoded. The coded descriptors are then normalized and classified. The normaliztion may consist of conventional and normalization or more advanced power-law normalization. The main purpose of normalization is to change the distribution of descriptor values in a way to make them more uniformly distributed. So, very large or very small descriptors could have a more balanced impact on recognition of events. The main novelty of this paper is that spatial and temporal information in mid-level features are employed to construct a suitable coding procedure. We use temporal information in coding of video descriptors. Such information is often ignored, resulting in reduced coding efficiency. Hence, a new coding is proposed which improves the trade-off between the computation complexity of the recognition scheme and the accuracy in identifying video events. It is also shown that the proposed coding is in the form of an optimization problem that can be solved with existing algorithms. The optimization problem is initially non-convex and not solvable with the existing methods in polynomial time. So, it is transformed to a convex form which makes it a well defined optimization problem. While there are many methods to handle these types of convex optimization problems, we chose to use a strong convex optimization library to efficiently solve the problem and obtain the video descriptors. To confirm the effectiveness of the proposed descriptor coding method, extensive experiments are done on two large public datasets: Columbia consumer video (CCV) dataset and ActivityNet dataset. Both CCV and ActivityNet are popular publically available video event recognition datasets, with standard train/test splits, which are large enough to be used as reasonable benchmarks in video recognition tasks. Compared to the best practices available in the field of detecting visual events, the proposed method provides a better model of video and a much better mean average precision, mean average recall, and F score on the test set of CCV and ActivityNet datasets. The presented method not only improves the performance in terms of accuracy, but also reduces the computational cost with respect to those of the state of the art. The experiments vividly confirm the potential of the proposed method in improving the performance of visual recognition systems, especially in supervised video event detection.

Increasing the growth rate of container transportation industry in recent decades and predicting the continuation of this growth over the years will highlight the importance of container transport and the key role of containers in maritime transportation. With entering container into transportation area, this area has experienced a huge change. However, the imbalance problem between the input and output containers exists in ports until now. This imbalance between import and export of goods has created a problem called Empty Container. in this paper, investigated the empty containers repositioning problem under Uncertainty. A robust scenario-based approach to risk pooling effect has been used to model the failure of some of the problem parameters under different scenarios. Finally, the computational results indicate the proper performance of the proposed model under uncertainty conditions and the importance of using a robust approach to solving the problem of empty containers repositioning under uncertain conditions.

Convolutional neural network is one of the effective methods for classifying images that performs learning using convolutional, pooling and fully-connected layers. All kinds of noise disrupt the operation of this network. Noise images reduce classification accuracy and increase convolutional neural network training time. Noise is an unwanted signal that destroys the original signal. Noise changes the output values of a system, just as the value recorded in the output differs from its actual value. In the process of image encoding and transmission, when the image is passed through noisy transmission channel, the impulse noise with positive and negative pulses causes the image to be destroyed. A positive pulse in the form of white and a negative pulse in the form of black affect the image. The purpose of this paper is to introduce dynamic pooling which make the convolutional neural network stronger against the noisy image. The proposed method classifies noise images by weighting the values in the dynamic pooling region. In this research, a new method for modifying the pooling operator is presented in order to increase the accuracy of convolutional neural network in noise image classification. To remove noise in the dynamic pooling layer, it is sufficient to prevent the noise pixel processing by the dynamic pooling operator. Preventing noise pixel processing in the dynamic pooling layer prevents selecting the amount of noise to be applied to subsequent CNN layers. This increases the accuracy of the classification. There is a possibility of destroying the pixels of the entire window in the image. Due to the fact that the dynamic pooling operator is repeated several times in the layers of the convolutional neural network, the proposed method for merging noise pixels can be used many times. In the proposed dynamic pooling layer, pixels with a probability of p being destroyed by noise are not included in the dynamic pooling operation with the same probability. In other words, the participation of a pixel in the dynamic pooling layer depends on the health of that pixel value. If a pixel is likely to be noisy, it will not be processed in the proposed dynamic pooling layer with the same probability. To compare the proposed method, the trained VGG-Net model with medium and slow architecture has been used. Five convolutional layers and three fully connected layers are the components of the proposed model. The proposed method with 26% error for images corrupted with impulse noise with a density of 5% has a better performance than the compared methods. Increased efficiency and speed of convolutional neural network based on dynamic pooling layer modification for noise image classification is seen in the simulation results.

In the present study, the effects of indole acetic acid (IAA) on certain physiological and biochemical parameters in Glycine Max (L.) Merr under aluminum chloride (AlCl3)stress were studied. Seeds were sterilized and cultured in petri-dishes. Six days old seedlings were transferred to pots, and then they were irrigated with Hoagland solution in a growth chamber (with 16 h light period per 24 h, with day / night temperatures of 25/18oC respectively). Twenty days old plants were treated with different concentrations of AlCl3 (0, 50, 100, 200 and 300 mM) and IAA (0, 50 and 100 mM). Plants were harvested 15 days after treatment. The plants exhibited decline in the relative water content (RWC), protein, soluble sugars, the chlorophylls a and b, carotenoids contents and photosynthetic rate with increase of aluminum chloride concentration, but the respiration rate and CO2 compensation concentration were increased. With addition of IAA to culture solutions containing aluminum, the plants showed further decrease in the amount of soluble sugars, chlorophylls a and b, carotenoids and photosynthetic rate and further increase in other parameters. With ncreasing of aluminum concentration to culture solutions with and without IAA unsoluble sugars, proline and lipid peroxidation increased.

Over the past few years, real-time classification of vehicle types has become an increasingly popular and important topic, given its wide range of applications in traffic control and analysis. Among the various methods available for classifying car types, convolutional neural networks (CNNs) have emerged as particularly appealing. In this article, we introduce a new real-time CNN architecture that is specifically designed to detect different types of cars. This innovative structure incorporates several unique features, including novel network architecture and structural elements, as well as an advanced learning method based on the back-propagation algorithm. One key aspect of our proposed method is its use of feature extraction at three different locations within the network, which allows for more accurate and efficient classification of car types. To evaluate the performance of our approach, we conducted experiments on two popular datasets (IRVD and MIO-TCD), and compared our results against those obtained using traditional CNN structures. Our evaluation results demonstrate that our proposed CNN architecture outperforms existing approaches, achieving superior classification accuracy across a range of criteria. Overall, our work represents a significant advance in the field of real-time car type classification, with broad implications for traffic management and analysis.