Weather prediction and meteorological analysis contribute significantly towards sustainable development to reduce the damage from extreme events which could otherwise set-back the progress in development by years. The change in surface temperature is as one of the important indicators in detecting climate change. In this research, we propose a novel deep learning model named Spatial Feature Attention Long Short Term Memory (SFA-LSTM) model to capture accurate spatial and temporal relations of multiple meteorological features to forecast temperature. Significant spatial feature and temporal interpretations of historical data aligned directly to output feature helps the model to forecast data accurately. The spatial feature attention captures mutual influence of input features on the target feature. The model is built using encoder-decoder architecture, where the temporal dependencies in data are learnt using LSTM layers in the encoder phase and spatial feature relations in the decoder phase. SFA-LSTM forecasts temperature by simultaneously learning most important time steps and weather variables. When compared with baseline models, SFA-LSTM maintains the state-of the-art prediction accuracy while offering the benefit of appropriate spatial feature interpretability. The learned spatial feature attention weights are validated from magnitude of correlation with target feature obtained from the dataset.
Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data.
Data transmission is a great challenge in any network environment. However, medical data collected from IoT devices need to be transmitted at high speed to ensure that the transmitted data are secure. This paper focuses on the security, speed and load of transmission. To prove security, combined steganographic methods involving cryptographic algorithms are used. The proposed model begins by updating two entries, medical image data and medical report data. Digital imaging and communications in medicine image data hold the medical report data to be encrypted and transmitted over the network channel. Although the proposed work follows the conventional method of data transmission from encryption until transmission, an effort has been made to split up the given data without transmitting them as such. As a public cryptography mechanism, the algorithm is also capable of transmission during decryption. The method of this article is genuine in proving its secure actions during the transmission of medical data and medical images. The proposed method justifies its performance when tested in hiding medical transcription data of different sizes varying across 30, 45, 64, 128 and 256 bytes in sample images with an average PSNR ranging from 55-70 dB, an MAE averaging from 0.2 to 0.7, and an SSIM, SC and correlation coefficient averaging to 1. This research is proven to work well in a simulation environment, and the results prove the genuine nature of the proposed technique.
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