This study aims to supplement the incomplete descriptions of dysmenorrheal syndrome types in ancient medical records and tap into the tacit knowledge in ancient medical records. Based on the dysmenorrhea medical recor...
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ISBN:
(纸本)9798400708138
This study aims to supplement the incomplete descriptions of dysmenorrheal syndrome types in ancient medical records and tap into the tacit knowledge in ancient medical records. Based on the dysmenorrhea medical record using the naivebayesian algorithm to establish the syndrome-type prediction model, its diagnostic accuracy is 73.77%. The model is applied to 20 medical records and found that three predicted syndrome types differ from the expert judgment. After a comprehensive analysis, this difference reflects the tacit knowledge in the text. The results show that the syndrome prediction model can assist experts in studying and understanding ancient medical records, make tacit knowledge explicit, and provide a reference for modern clinical diagnosis and treatment.
To achieve the classification of shoulder shape of young women,11 parameters of shoulder shape of 208 female samples were obtained by three-dimensional body *** 5 characteristic parameters of shoulders were selected b...
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To achieve the classification of shoulder shape of young women,11 parameters of shoulder shape of 208 female samples were obtained by three-dimensional body *** 5 characteristic parameters of shoulders were selected by descriptive analysis,factor analysis and correlation analysis,which included height,shoulder width,shoulder thickness,shoulder angle and dorsal ***,shoulder shapes were divided into 16 types by hierarchical cluster *** of shoulder classification were tested by naive bayes algorithm and the accuracy within and outside the sample were 91% and 94.2% respectively,which proved the classification methods were correct and effective.
Foreign currency exchange plays a vital role for currency trading in the financial market. To manage large volume of transactions in modern world, it requires support from the computer algorithms. There could be poten...
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ISBN:
(纸本)9781538641200
Foreign currency exchange plays a vital role for currency trading in the financial market. To manage large volume of transactions in modern world, it requires support from the computer algorithms. There could be potential problems like trading without a plan, having unrealistic expectation, failing to adapt to the market and many more. This paper examines on foreign exchange market prediction using neural network and sentiment analysis. There are various techniques and algorithms for prediction but different algorithms have different accuracy. Among them, one of the best and accurate method is Artificial Neural Network (ANN). Neural network parameters consist of number of neurons, use of bias neurons, number of hidden layers, activation functions and training methods. Root Mean Squared Error (RMSE) was found to be 0.0034 with 6 hidden nodes using ANN. As the price movement is also directly proportional to market sentiment, we applied sentiment analysis using combination of naivebayes and lexicon based algorithm to analyze the opinion of different traders and predict the overall sentiment. Sentiments are taken from tweets and were classified as positive or negative. In sentiment analysis, accuracy was found to be 90.625%.
With the process of software development and application basing on network and cloud, the change of software development requires new software defect prediction method for these kinds of software development, which ca...
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With the process of software development and application basing on network and cloud, the change of software development requires new software defect prediction method for these kinds of software development, which can solve the problems of the traditional software defect prediction method based on target project, such as the same predict background and higher cost of defect tagging. A new software defect prediction method based on multi source data oriented network and cloud development process is proposed. This method selects the predictive candidates from multisource projects which have similar characteristics as objective projects, and then guides the training data selection by the software modules, finishes the prediction based on naivebayesian algorithm. Finally through algorithm experiment this method is proved superior to the traditional WP prediction model.
Internet of Things (IoT) is extension of current internet to provide communication, connection, and internetworking between various devices or physical objects also known as "Things." In this paper we have r...
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ISBN:
(纸本)9781479980826
Internet of Things (IoT) is extension of current internet to provide communication, connection, and internetworking between various devices or physical objects also known as "Things." In this paper we have reported an effective use of IoT for Environmental Condition Monitoring and Controlling in Homes. We also provide fault detection and correction in any devices connected to this system automatically. Home Automation is nothing but automation of regular activities inside the home. Now a day's due to huge advancement in wireless sensor network and other computation technologies, it is possible to provide flexible and low cost home automation system. However there is no any system available in market which provide home automation as well as error detection in the devices efficiently. In this system we use prediction to find out the required solution if any problem occurs in any device connected to the system. To achieve that we are applying Data Mining concept. For efficient data mining we use naivebayes Classifier algorithm to find out the best possible solution. This gives a huge upper hand on other available home automation system, and we actually manage to provide a real intelligent system.
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