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The price forecasting of the digital currencies in the financial market is of great importance, especially after the recent global economic crises. Due to the nonlinear dynamics, which is including inherent fractality and chaoticity of the digital currencies, it is understood from the research conducted by many researchers that a single model is not sufficient in the forecasting of the digital currencies with very high accuracy. Since the single models used in the forecasting of digital currencies have weaknesses as well as their own strengths, they might not grant the best forecasting achievement in all situations for all the time. A new hybridforecasting framework has been proposed in digital currency time-series to minimize this negative situation and increase forecasting achievement. In this study, a novel hybrid forecasting model based on long short-term memory (LSTM) neural network and empirical wavelet transform (EWT) decomposition along with cuckoo search (CS) algorithm is developed for digital currency time series. The model is obtained by combining the LSTM neural network and EWT decomposition technique, and optimizing the intrinsic mode *Manuscript Click here to view linked References function (IMF) estimated outputs with CS. The price of the four most traded digital currencies such as BTC, XRP, DASH and LTC, is estimated by the proposed model and the performance of the model has been tested. The experimental results show that the hybrid model proposed for digital currency forecasting can capture nonlinear properties of digital currency time series.

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Purpose In the production processes of electronic devices, production activities are interrupted due to the problems caused by soldering defects during the assembly of surface-mounted elements on printed circuit boards (PCBs), and this leads to an increase in production costs. In solder paste applications, defects that may occur in electronic cards are usually noticed at the last stage of the production process. This situation reduces the efficiency of production and causes delays in the delivery schedule of critical systems. This study aims to overcome these problems, optimization based deep learning model has been proposed by using 2D signal processing methods. Design/methodology/approach An optimization-based deep learning model is proposed by using image-processing techniques to detect solder paste defects on PCBs with high performance at an early stage. Convolutional neural network, one of the deep learning methods, is trained using the data set obtained for this study, and pad regions on PCB are classified. Findings A total of six types of classes used in the study consist of uncorrectable soldering, missing soldering, excess soldering, short circuit, undefined object and correct soldering, which are frequently used in the literature. The validity of the model has been tested on the data set consisting of 648 test data. Originality/value The effect of image processing and optimization methods on model performance is examined. With the help of the proposed model, defective solder paste areas on PCBs are detected, and these regions are visualized by taking them into a frame.