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The detection of spatial and temporal changes (or change detection) in remote sensing images is essential in any decision support system about natural phenomena such as extreme weather conditions, climate change, and floods. In this paper, a new method is proposed to determine the inference process parameters of boundary point, rule coefficient, defuzzification coefficient, and dependency coefficient and present a new FWADAM+ method to train that set of parameters simultaneously. The initial data are clustered simultaneously according to each data group. This result will be the basis for determining a suitable set of parameters by using the FWADAM+ concurrent training algorithm. Eventually, these results will be inherited in the following data groups to build other complex fuzzy rule systems in a shorter time while still ensuring the model's efficiency. The weather imagery database of the United States Navy (US Navy) is used to evaluate and compare with some related methods using the root-mean-squared error (RMSE), R-squared (R2) measures, and the analysis of variance (ANOVA) model. The experimental results show that the proposed method is up to 30% better than the SeriesNet method, and the processing time is 10% less than that of the SeriesNet method. [ABSTRACT FROM AUTHOR]

Periapical Inflammation (PI) is one of the most popular diseases in adults due to complication of endodontitis or dental trauma with corresponding consequences to quality-of-life like tiredness and signs of infection. Specifically, patients having severe PI are often tiredness and high fever accompanied by signs of infection such as dry lips, dirty tongue, lymph node reaction in the area under the jaw. In X-Ray images, PI could be recognized by vague boundaries with signs of periapical ligament extensions. It is necessary to design a computerized diagnosis system based on the Deep Learning models for supporting clinicians in diagnosis of PI from X-Ray images. In this paper, we propose a new medical system called VNU for diagnosis of PI from X-Rays images. The VNU system uses Deep Learning to classify whether X-Ray images being PI or not. The Residual Neural Network (ResNet) with 34 layers is utilized with proper data augmentation and learning algorithms. The system is designed based on 7-layer enterprise architecture (User, Business, Application, Data, Technology, Infrastructure, and Security). It is used by both the clinicians and IT operators. The system has been validated on real data from Hanoi Medical University, Vietnam consisting of 900 images with PI and 500 normal images. Two scenarios of validation namely hyperparameter selection and performance comparison with other CNN-based Deep Learning models have been performed. It has been found from the experiments that the proposed system has better performance than the others in terms of sensitivity and specificity with the corresponding values of 96.70% and 93.87%. The system is deployed on the web interface that offers flexibility for clinicians in diagnosis and training. [ABSTRACT FROM AUTHOR]

In this world, travelling is the daily routine to reach the desired places such as schools, hospitals, offices and so on. Everyone makes use of their vehicles like two-wheelers or four-wheelers to reach the destination. Vehicle condition plays a major role which includes many parameters such as tyre condition, engine overheating, low fuel mileage. This paper concentrates on the examination of tyre track design for deformities. The mileage of a vehicle relies upon different components. However, it depends on different parameters like street condition, tyre weight and numerous others. For smooth driving and for safe traffic reasons, the auspicious maintenance of tyres plays a vital task. Consequently, the likelihood of prediction of tyre imperfections is assumed to play a vital role to fleet proprietors, government and tyre makers. Researchers make use of recent technologies such as machine learning and deep learning concepts to provide a solution to various real-life problems. The creation of a weighted quality-based convolutional neural network with the purpose of discovery of imperfections on tyres by the examination of pictures of tyre track designs is being proposed. The proposed system with the accessible data-set made public by different tyre producers is included for evaluation. The consequences of our tests demonstrate that it is conceivable to predict the deformities and durability of a tyre from its track designs through a weighted quality-based convolutional neural network. The proposed methodology is used to identify the group of feature values from the tyre image. These kinds of feature values are utilized to implement the dissimilarity of the feature related pixels. The number of epochs is varied as a callback mechanism has been used to validate the proposed model. Binary accuracy and mean absolute error are the best metrics to evaluate the performance which are discussed in the results section. Also, precision, recall and F-measure are used as validation parameters for the proposed model. [ABSTRACT FROM AUTHOR]

Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellite images can be integrated with predictions based on sensors' information to improve the accuracy of a forecasting system and subsequently trigger warning systems. The existing Deep Learning models such as UNET has been effectively used to segment the flash flood with high performance, but there are no ways to determine the most suitable model architecture with the proper number of layers showing the best performance in the task. In this paper, we propose a novel Deep Learning architecture, namely PSO-UNET, which combines Particle Swarm Optimization (PSO) with UNET to seek the best number of layers and the parameters of layers in the UNET based architecture; thereby improving the performance of flash flood segmentation from satellite images. Since the original UNET has a symmetrical architecture, the evolutionary computation is performed by paying attention to the contracting path and the expanding path is synchronized with the following layers in the contracting path. The UNET convolutional process is performed four times. Indeed, we consider each process as a block of the convolution having two convolutional layers in the original architecture. Training of inputs and hyper-parameters is performed by executing the PSO algorithm. In practice, the value of Dice Coefficient of our proposed model exceeds 79.75% (8.59% higher than that of the original UNET model). Experimental results on various satellite images prove the advantages and superiority of the PSO-UNET approach. [ABSTRACT FROM AUTHOR]