On account of the exponential augmentation of documents on the internet, users need all the pertinent data at ?1? place with no hassle. Therefore, automatic text summarization (ATS) is needed to automate the procedure of summarizing text via extorting the salient details as of the documents. The goal is to propose an automatic, generic, in addition to extractive text summarization for a single document utilizing Deep Learning Modifier Neural Network (DLMNN) classifier for generating an adequately informative summary centered upon the entropy values. A proposed DLMNN framework comprises ?6? phases. In the initial phase, the input document is pre-processed which engages stop word removal, tokenization, along with stemming. Subsequently, the features are extorted as of the pre-processed data. Next, the most apposite features are selected employing the improved fruit fly optimization algorithm (IFFOA). The entropy value for every chosen feature is computed utilizing support as well as confident measure. Afterward, DLMNN classifier is utilized to classify these values into ?2? classes, a) highest entropy values and b) lowest entropy values. Lastly, the class that holds the highest entropy values are chosen besides, the informative sentences are selected as of the highest entropy values to form the last summary. Experimental outcomes are executed and the proposed DLMNN classifier?s performance is analyzed utilizing sensitivity, accuracy, recall, specificity, precision, and also f-measure. The proposed DLMNN provides the best outcomes amid all other techniques.

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The measurement of snow depth based on temperature brightness with passive microwave sensing is still achallenging problem. Snow depth for the snow cover hydrological model and climate model is asignificant input parameter. Hence, this study concentrates on Inversion Model Assisted Vector Analysis (IMAV) for estimating snow depth in north Xinjiang based on the brightness of temperature. Further, the estimated set of IMAV has been hybridized to address the problem. The results suggested that for both horizontal and vertically polarized PMW radiation the IMAV outperforms SVM at 11.05, 19.6, and 38.4 GHz. If the root mean square error (RMSE) in the IMAV performance is 8 K or below, compared with anormal SVM calculation, then the average over the nine-year study period across the North Xinjiang region of China, the failure correlation coefficient is 7 or greater. Compared with SVM tests, the RMSE was decreased by more than 17% for any of the six frequencies and polarization combinations evaluated, while the anomaly coefficient was raised by more than 50%.Such results suggest that the IMAV is asuperior alternative to the SVM for subsequent use in adata assimilation system as acalculating operator.

The Internet of Things (IoT) has become part of people's daily life, allowing physical and digital contact. The rise of mobile devices and scientific and technological advances in health have led to breakthroughs in meeting consumer needs. mHealth refers to using mobile devices to improve healthcare services, increase medical care, and reduce costs. Mobile cloud computing (MCC) lets users bypass mobile device limits on processing, storage, and battery life. A body area network uses implanted wireless sensors to remotely monitor patients (WBAN). These networks collect and distribute data for disease diagnosis and prevention. This mix of technologies allows hospitals and clinics new ways to treat and monitor patients. This research examines IoT availability in mHealth. The analysed architecture includes wireless sensors to monitor patients, an intra-BAN, a mobile device with a battery and communication interfaces, an extra-BAN with Wi-Fi and 4G connectivity, and a cloud environment to store data. Hierarchical models were developed using RBD and continuous-time Markov chains (CTMC). Each component's MTTF (mean time to failure) and MTTR (mean time to repair) values are used to quantify system or section availability. Experiments were conducted to test mHealth availability parameters. Intra-BAN, Zigbee, or Bluetooth protocols have no meaningful impact on system availability. For households, two small routers in the extra-BAN are more effective than one large router. Finally, backup batteries and power banks boost availability. The offered models can help developers and maintainers scale mHealth systems based on service needs.

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