The roots of innovation are extending towards every field to provide ace solution. We cater an ace solution for aquaculture, where their yields (shrimp, fish, etc.) depends on the ponds water characteristics. The parameters depending on water must kept at certain optimal levels for better cultivation of Aqua. The parameters of water extremely project alterations during the day and also alter depending upon the environmental conditions i.e., it is necessary to monitor these parameters with high frequency. We adopt wireless sensor networks to monitor aqua forms. This system consists of two modules, they are transmitter and receiver station. We navigate data to database at receiver station through the GSM. The graphical user interface was designed in such a manner that the observations are forwarded to the farmer as message in their respective local languages to their mobile phones. That alerts them in unhygienic environmental conditions for adopting suitable measures.
The amount of data generated is increasing day by day due to the development in remote sensors, and thus it needs concern to increase the accuracy in the classification of the big data. Many classification methods are in practice; however, they limit due to many reasons like its nature for data loss, time complexity, efficiency and accuracy. This paper proposes an effective and optimal data classification approach using the proposed Ant Cat Swarm Optimization-enabled Deep Recurrent Neural Network (ACSO-enabled Deep RNN) by Map Reduce framework, which is the incorporation of Ant Lion Optimization approach and the Cat Swarm Optimization technique. To process feature selection and big data classification, Map Reduce framework is used. The feature selection is performed using Pearson correlation-based Black hole entropy fuzzy clustering. The classification in reducer part is performed using Deep RNN that is trained using a developed ACSO scheme. It classifies the big data based on the reduced dimension features to produce a satisfactory result. The proposed ACSO-based Deep RNN showed improved results with maximal specificity of 0.884, highest accuracy of 0.893, maximal sensitivity of 0.900 and the maximum threat score of 0.827 based on the Cleveland dataset.
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.
The leading death cause all over the world is heart disease. The presence of arrhythmias has to be examined to detect heart disease in early stage. The abnormality in heart beat rhythm is known as Arrhythmia. The speed of heart beat can be detected by Arrhythmia, it may be too slow, too fast or irregular patterns of heart beat are considered as Arrhythmia and there are various types of Arrhythmia. The electrocardiogram (ECG) produces signals, classification of such signals is very crucial for knowing the irregularity in the patterns of heart beat. As detection of arrhythmia is a challenging task, there is a great demand for an automatic detection technique to identify abnormal signals produced by heart which cannot be done manually. Therefore this paper provides a method for detection of Cardiac Arrhythmia using Multi-Perspective Convolutional Neutral Network (MPCNN) for ECG Heartbeat Classification. Basing on Physionet's MIT-BIH Arrhythmia Dataset the signals of ECG arrhythmia can be categorized into five classes. Number of layers, filters and size of the filter are appropriate parameters which are heuristically optimized for operating swiftly for operation of MPCNN effectively. Compared with the ultra-modern methods as Quantum Neural Networks and Deep Convolutional Neural Networks, the proposed method results in efficient performance having high level accuracy of 96.46%, 98.1% and 96.2% are the F1 scores for SVP and PVC respectively. The effective detection of heartbeat rhythm and irregularities can be identified using this model.
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