Bitcoin is an innovative decentralized digital currency without intermediaries. Bitcoin price prediction is a demanding need in the present situation. This paper makes an investigation on the Bitcoin price forecast with a Bi-directional Gated Recurrent Unit (GRU) time series method, combined with opinion mining based on Twitter and Reddit feeds. An hourly basis sentimental analysis through the implementation of Natural Language Processing presents a positive impact of sentimental analysis on the Bitcoin price prediction. For prediction, RNN, long-short memory, GRU has been utilized. Unidirectional and Bi-directional versions of all three networks with and without sentimental analysis were implemented for comparison. Of all the techniques implemented Bi-directional GRU along with sentimental analysis gives a minimum RMSE and Minimum absolute percentage error of 1108.33 and 7.384%. Thus, the framework including Bi-Directional GRU along with Sentimental Analysis provides better results than the State-of-art methods.
In this article, COVID‐19 detection and classification framework based on anopheles search optimized AlexNet convolutional deep neural network for random forest classifier is implemented. Here, the COVID‐19 dataset is taken from Joseph Paul Cohen database. Then, the input images are preprocessed with the help of fuzzy gray level difference histogram equalization technique (FGLHE) and fuzzy stacking technique for color enhancement and noise elimination in the input images. The FGLHE technique and fuzzy stacking technique are combined together and forms into stacked dataset image. This stacked dataset are trained with AlexNet convolutional deep neural network model and the feature packages acquired via the models are processed by the anopheles search algorithm. Subsequently, the efficient features are combined and delivered to random forest (RF) classifier. The proposed approach is implemented in MATLAB. The proposed ADCNN‐ASA‐RFC provides 91.66%, 69.13%, 34.86%, and 70.13% higher accuracy, 79.13%, 60.33%, and 63.34% higher specificity and 77.13%, 58.45%, 25.86%, and 55.33%, higher sensitivity compared with existing algorithms. At last, the simulation outcomes demonstrate that the proposed system can be able to find the optimal solutions efficiently and accurately with COVID‐19 diagnosis.
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