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This work focuses on the problem of constructing accurate prediction models using an adaptive neuro-fuzzy inference system (ANFIS) from data that are scarce and poorly scaled. Many real-world problems have limited data to work with and yet require models with high prediction accuracy. Due to the fuzzy nature of many data sets, it is appropriate to construct prediction models using fuzzy inference systems (FIS), rather than traditional artificial neural network (ANN) models. However, the scarcity of the data raises serious concerns when attempting to construct models of this type. In this paper, we address the problem of constructing accurate prediction models using ANFIS in circumstances where capturing large amounts of data is difficult. We propose a new approach, using robust data expansion methods to enhance the data carefully. This approach provides the learning algorithm with enough representative data to allow all of the parameters (premise and consequent) to be optimised. We compare and contrast the effectiveness in reducing prediction error for three specific expansion models using radial basis function (RBF) kernels. When expanding data, for which the underlying smoothness properties are not known, there is often a danger of over-smoothing. This is usually noticeable at turning points and can occur when fitting the data using polynomials or cubic splines for example. To avoid over-smoothing, we introduce a modified multiquadric approach that allows smooth, near-interpolation while maintaining the desirable shape-preserving properties of the data. The proposed method is explained through two case studies — the first to predict gasoline consumption for the oil/fuel industry in Iraq and the second for predicting natural gas consumption in Iran. This is the first study that uses adjustable, shape-preserving re-sampling methods for optimising ANFIS models in low data environments. • ANFIS networks capture complexities not easily found using traditional ANNs. • Model complexity versus data sample size is a significant practical problem. • Data augmentation methods can be constructed using robust continuous models. • Multiquadric RBFs can be easily tuned to provide robust, accurate data expansion. • Complex models can be built with confidence using reliable data expansion. [ABSTRACT FROM AUTHOR]

• Cascaded deep approach is introduced for license plate detection and recognition. • Partial labeling is considered for the plate number, city and country regions. • Three convolutional neural networks models are used independently. • Arabic characters and northern Iraq region cities recognition are carried out. • A new challenging license plate dataset for northern Iraq region is introduced. Automatic license plate (ALP) detection and recognition is an important task for both traffic surveillance and parking management systems, as well as being crucial to maintaining the flow of modern civic life. Various ALP detection and recognition methods have been proposed to date. These methods generally use various image processing and machine learning techniques. In this paper, a cascaded deep learning approach is proposed in order to construct an efficient ALP detection and recognition system for the vehicles of northern Iraq. The license plates in northern Iraq contain three regions, namely a plate number, a city region, and a country region. The proposed method initially employs several preprocessing techniques such as Gaussian filtering and adaptive image contrast enhancement to make the input images more suited to further processing. Then, a deep semantic segmentation network is used in order to determine the three license plate regions of the input image. Segmentation is then carried out via deep encoder-decoder network architecture. The determined license plate regions are fed into two separate convolutional neural network (CNN) models for both Arabic number recognition and the city determination. For Arabic number recognition, an end-to-end CNN model was constructed and trained, whilst for the city recognition, a pretrained CNN model was further fine-tuned. A new license plate dataset was also constructed and used in the experimental works of the study. The performance of the proposed method was evaluated both in terms of detection and recognition. For detection, recall, precision and F-measure scores were used, and for recognition, classification accuracy was used. The obtained results showed the proposed method to be efficient in both license plate detection and recognition. The calculated recall, precision and F-measure scores were 92.10%, 94.43%, and 91.01%, respectively. Moreover, the classification accuracies for Arabic numbers and city labels were shown to be 99.37% and 92.26%, respectively. [ABSTRACT FROM AUTHOR]