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In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial. [ABSTRACT FROM AUTHOR]

The amalgamation of the Internet of medical things with artificial intelligence shows tremendous benefits in health care. Accurate detection of the fetal QRS complex is highly demanded in fetal heart rate monitoring. Detecting fetal heart rate using electrophysiological signals obtained from abdominal electrodes seems a promising alternative approach. The challenges in determining fetal heart rate from abdominal ECG (AECG) require eliminating maternal components and other noises from the signal at higher accuracy. We propose a novel approach using an IoT-based deep learning architecture to detect fetal QRS complex without eliminating the maternal components in the abdominal ECG. The novelty of the proposed algorithm is twofold: (1) The method uses the time–frequency image (TFI) of abdominal signals as input to the deep neural network and hence promotes the availability of rich features and improves the accurate detection of the fetal QRS complex. (2) The algorithm adapts pre-trained models based on transfer learning for the classification task and thus improves the fetal QRS detection. Two time–frequency approaches, namely Hilbert Huang Transform (HHT) and Stockwell transform (ST), are used to represent input AECG signals as two-dimensional images. The 2013 challenge database is used to evaluate the performance of the proposed approach. The TFI representations of training data using HHT and ST are independently used to train the pre-trained models MobileNet and ResNet18. A comparative analysis is provided in the results between the TFI and deep network architecture. The proposed solution can be suitable for an IoT environment enabling remote fetal monitoring. [ABSTRACT FROM AUTHOR]

The similarity recommendation of twitter data is evaluated by using sentiment analysis method. In this paper, the deep learning processes such as classification, clustering and prediction are used to measure the data. Convolutional neural network is applied for analyzing multimedia contents which is received from various sources. Recurrent neural network is used for handling the natural language data. The content based recommendation system is proposed for selecting similarity index in twitter data using deep sentiment learning method. In this paper, sentiment analysis technique is used for finding similar images, contents, texts, etc. The content is selected based on repetitive comments and trending information. Hash tag is also considered for data collection and prediction. The number tweets are accountable and each character is taken for evaluation. Deep belief network is generated using 512 x 512 x 3 layers system and 1056 trained data, 512 test data that are taken for convolution process. The deep belief network is generated using TensorFlow. TensorFlow is used to simulate the deep learning environments. Semantic analysis is applied for handling Twitter Data. The deep learning processes are classified into clustering, regression and prediction that are evaluated by step by setup approach. The experiments are carried out using similarity index calculation and measuring of accuracy. The results of similarity recommendation are compared with existing method and the results are recorded. Our proposed system gives better results comparing with existing experiments. [ABSTRACT FROM AUTHOR]