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The dissolved oxygen (DO) is oxygen dissolved in water, which is an important factor for the aquaculture. Using BP neural network method with the combination of purelin, logsig, and tansig activation functions is proposed for the prediction of aquaculture's dissolved oxygen. The input layer, hidden layer, and output layer are introduced in detail including the weight adjustment process. The breeding data of three ponds in actual 10 consecutive days were used for experiments; these ponds were located in Beihai, Guangxi, a traditional aquaculture base in southern China. The data of the first 7 days are used for training, and the data of the latter 3 days are used for the test. Compared with the common prediction models, curve fitting (CF), autoregression (AR), grey model (GM), and support vector machines (SVM), the experimental results show that the prediction accuracy of the neural network is the highest, and all the predicted values are less than 5% of the error limit, which can meet the needs of practical applications, followed by AR, GM, SVM, and CF. The prediction model can help to improve the water quality monitoring level of aquaculture which will prevent the deterioration of water quality and the outbreak of disease. PrefaceDissolved oxygen [1] refers to the amount of oxygen dissolved in water, usually expressed in DO. It is an important indicator to study the water self-purification ability. The level of DO directly affects the aquaculture's food intake, feed conversion rate, and disease resistance. Low DO or hypoxic water environment has a great impact on aquaculture organisms. As an example, if the aquaculture shrimp is often raised in the low DO water environment, it will intake little food, which will lead to low conversion rate of food, slow growth, and low disease resistance. The hypoxia can directly or indirectly lead to a large number of live shrimp.Based on the study of the lowest time of DO in aquaculture water and the analysis of the daily variation of low DO values, we can estimate and forecast its development trend. The basis for decision-making is provided to improve water quality for preventing water quality deterioration with the low DO. It helps to control and reduce aquaculture risk. At present, the common prediction models include curve fitting (CF) [2], autoregression (AR) [3], neural network (NN) [4][5][6][7][8], grey models (GM) [9,10], support vector machine (SVM) [11,12], and other models [13]. But there is no literature on the comparison and experimentation of these methods. Therefore, in order to study an accurate and practical method of DO prediction, in this paper, NN method, CF method, AR method, GM method, and SVM method are compared using the 10 consecutive days of aquaculture data of southern China traditional aquaculture base in three shrimp culture ponds in Beihai, Guangxi Province. The experimental results show that the accuracy of NN prediction is high, and all the predicted values are less than 5%, which is the maximum acceptable predicted error rate. So the method can...

The rise of health care cost is one of the world's most important problems. Disease prediction is also a vibrant research area. Researchers have approached this problem using various techniques such as support vector machine, artificial neural network, etc. This study typically exploits the immune system's characteristics of learning and memory to solve the problem of liver disease diagnosis. The proposed system applies a combination of two methods of artificial immune and genetic algorithm to diagnose the liver disease. The system architecture is based on artificial immune system. The learning procedure of system adopts genetic algorithm to interfere the evolution of antibody population. The experiments use two benchmark datasets in our study, which are acquired from the famous UCI machine learning repository. The obtained diagnosis accuracies are very promising with regard to the other diagnosis system in the literatures. These results suggest that this system may be a useful automatic diagnosis tool for liver disease.