Aquaculture becomes very popular in economic where aquatic organisms, like fishes and prawns, are mainly dependent on the quality of water in aquaculture pond. Also, the water quality constraints, which include turbidity, carbon dioxide, temperature, pH level, dissolved oxygen and phosphorus, are considered for achieving better performance. Hence, this paper presents an approach for aqua status prediction based on Deep Long Short‐Term Memory (Deep LSTM) classifier. The sensor nodes are placed in the aqua pond for measuring the parameters of water quality, and then the cell network transformation is done using the Voronoi partition. After that, the Cluster Head (CH) selection is carried out using Piecewise Fuzzy C‐means clustering (piFCM). Once the clusters are selected, the Chronological Harris Hawks (Chronological HH) optimization algorithm is introduced for optimal sink placement where the constraints for enabling the optimal sink placement are the distance and energy of the nodes. Finally, the aqua status is predicted using Deep LSTM. The performance of the Chronological HH‐based Deep LSTM is computed in terms of accuracy, energy and the number of dead nodes. The proposed Chronological HH‐based Deep LSTM outperformed other methods with maximal accuracy of 92.65%, maximal energy of 0.976 and the minimal dead nodes of 32.
Health care prosperity is the most challenging task for human being in the present dangerous COVID scenario and the discovery proposes an augmented reality based personalized smart diet assistance system which provides diet recommendations, appropriate time, type, quantity and method of consumption of a food item diet based on user health parameters based on location and event activities. The augmented reality based system comprises a user data input, an image processing, food consumption assistance, transmissible disease information retrieval and diet planning modules. The system incorporates an AI based camera to scan a food item before or after cooking and utilizes augmented reality to indicate the nutritional information. The proposed system provides personalized diet recommendations to the user based on personal data such as height, weight, existing medical conditions and thereof of a user. The system retrieves existing transmissible diseases data from world health organizations and data from news articles about any viral infections or diseases to suggest immunity boosting foods to the user to thereby safeguard the user against such diseases or infections.
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