In the Internet of Things (IoT) scenario, many devices will communicate in the presence of the cellular network; the chances of availability of spectrum will be very scary given the presence of large numbers of mobile users and large amounts of applications. Spectrum prediction is very encouraging for high traffic next-generation wireless networks, where devices/machines which are part of the Cognitive Radio Network (CRN) can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sensing radio spectrum. Long short-term memory (LSTM) is employed to simultaneously predict the Radio Spectrum State (RSS) for two-time slots, thereby allowing the secondary node to use the prediction result to transmit its information to achieve lower waiting time hence, enhanced performance capacity. A framework of spectral transmission based on the LSTM prediction is formulated, named as positive prediction and sensing-based spectrum access. The proposed scheme provides an average maximum waiting time gain of 2.88 ms. The proposed scheme provides 0.096 bps more capacity than a conventional energy detector.

A prevalent diabetic complication is Diabetic Retinopathy (DR), which can damage the retina's veins, leading to a severe loss of vision. If treated in the early stage, it can help to prevent vision loss. But since its diagnosis takes time and there is a shortage of ophthalmologists, patients suffer vision loss even before diagnosis. Hence, early detection of DR is the necessity of the time. The primary purpose of the work is to apply the data fusion/feature fusion technique, which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater accuracy. Mechanized procedures for diabetic retinopathy analysis are fundamental in taking care of these issues. While profound learning for parallel characterization has accomplished high approval exactness's, multi-stage order results are less noteworthy, especially during beginning phase sickness. Densely Connected Convolutional Networks are suggested to detect of Diabetic Retinopathy on retinal images. The presented model is trained on a Diabetic Retinopathy Dataset having 3,662 images given by APTOS. Experimental results suggest that the training accuracy of 93.51% 0.98 precision, 0.98 recall and 0.98 F1-score has been achieved through the best one out of the three models in the proposed work. The same model is tested on 550 images of the Kaggle 2015 dataset where the proposed model was able to detect No DR images with 96% accuracy, Mild DR images with 90% accuracy, Moderate DR images with 89% accuracy, Severe DR images with 87% accuracy and Proliferative DR images with 93% accuracy.