A large number of natural products secluded from sea atmosphere has been identified for the pharmacodynamic probable in varied illness handlings, such as, tumor or inflammatory states. Sea cucumber culturing and fishing is mainly reliant on physical works. For quick and precise programmed recognition, deep residual networks with various forms used to recognize the submarine sea cucumber. The imageries have been taken by a C-Watch distantly worked submarine automobile. To improve the pixel quality of the image, a training algorithm called Stochastic Gradient Descent algorithm (SGD) has been proposed in this paper. It explains how efficiently fetching the picture characteristics to expand the accurateness of sea cucumber detection, that might be reached by higher training information set and preprocessing information set with remove and denoising procedures towards increase picture eminence. Furthermore, the DL network might be linked through faster expertise to settle the location, also recognize the number of sea cucumber inimages, and weightiness valuation modeling is similarly required to be progressed to execute programmed take actions. The functioning of the planned technique specifies excellent latent for manual sea cucumber detection..

This paper presents a hybrid machine learning method of classifying residential requests in natural language to responsible departments that provide timely responses back to residents under the vision of digital government services in smart cities. Residential requests in natural language descriptions cover almost every aspect of a city's daily operation. Hence the responsible departments are fine-grained to even the level of local communities. There are no specific general categories or labels for each request sample. This causes two issues for supervised classification solutions, namely 1) the request sample data is unbalanced and 2) lack of specific labels for training. To solve these issues, we investigate a hybrid machine learning method that generates meta-class labels by means of unsupervised clustering algorithms; applies two-word embedding methods with three classifiers (including two hierarchical classifiers and one residual convolutional neural network); and selects the best performing classifier as the classification result. We demonstrate our approach performing better classification tasks compared two benchmarking machine learning models, Naive Bayes classifier and a Multiple Layer Perceptron (MLP). In addition, the hierarchical classification method provides insights into the source of classification errors.

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Monodisperse α-Fe 2 O 3 nanocubes with a mean size of 85 nm have been prepared by heating an aqueous solution of iron (III) nitrate in the presence of a certain amount of alkali without any additional organic reagents or templates in a hydrothermal route. The structure and morphology of the products were characterised by X-ray diffraction, transmission electron microscopy and field-emission scanning electron microscopy. The results have shown that the crystal morphology continually changes at higher temperatures and α-FeOOH was generated as the intermediate during the hydrothermal dehydration reaction. In addition, the optimum temperature and reaction time of the hydrothermal synthesis of these α-Fe 2 O 3 nanocubes were 200°C and 5 h, respectively.