Abstract: A method and a system for recognizing plant diseases and a recording medium are provided. The method includes: providing a plant disease database, for storing plant disease and disease characteristic image corresponding to the plant disease; after obtaining plant image by an image capture device, obtaining segmented plant image by an image processing unit according to a first processing technique, and obtaining suspected region image according to a second processing technique; calculating an area of the suspected region image, and when the area is greater than a preset area, using the suspected region image as syndrome image, and comparing the syndrome image with the disease characteristic image; and when the syndrome image matches any specific disease characteristic image, obtaining a corresponding specific plant disease.

Abstract: The present invention solves problems of cold start, first rater, sparsity and scalability for recommendation. A recommendation system according to the present invention finds association rules through data mining. Then, the recommendation system integrates a rough-set algorithm and a statistical analysis prediction for recommendation. The recommendation is dynamically made from a result of the rough-set algorithm and a result of the statistical analysis prediction by setting a standard deviation as a threshold.

Abstract: A music recommendation method and a computer readable recording medium storing a computer program performing the method are provided. In the music recommendation method, music items and rating data matrix comprising ratings and user IDs are first provided. Then, the ratings of each music item are classified into positive ratings and negative ratings. Thereafter, a pre-processing phase comprising a frame-based clustering step and a sequence-based clustering step is performed to transform the music items into perceptual patterns. Then, a prediction phase is performed to determine an interest value of a plurality of target music items for an active user. Thereafter, the target music items arranged into a music recommendation list in accordance with the first interest value and the second interest values, wherein the music recommendation list is a reference for the active user to select one of the target items.

Abstract: A system and a method for imputing missing values and a computer program product thereof are applicable to a data matrix. The system includes a storage unit having the data matrix and a computing device. The computing device finds complete and incomplete data transactions from the data matrix, finds at least one target data transaction approximate to each incomplete data transaction from the complete data transactions, and obtains known data at corresponding positions to compute an initial estimated data to replace unknown data. Then, a correction data transaction containing the initial estimated data is selected from the incomplete data transactions, a rough set of the selected initial estimated data is found in a manner of grouping same data into one group, and a numerical value correlated to the initial estimated data is found and used to compute an imputed data, so as to impute the imputed data into the original estimated data.

Abstract: A music recommendation method and a computer readable recording medium storing a computer program performing the method are provided. In the music recommendation method, music items and rating data matrix comprising ratings and user IDs are first provided. Then, the ratings of each music item are classified into positive ratings and negative ratings. Thereafter, a pre-processing phase comprising a frame-based clustering step and a sequence-based clustering step is performed to transform the music items into perceptual patterns. Then, a prediction phase is performed to determine an interest value of a plurality of target music items for an active user. Thereafter, the target music items arranged into a music recommendation list in accordance with the first interest value and the second interest values, wherein the music recommendation list is a reference for the active user to select one of the target items.

Abstract: The present invention solves problems of cold start, first rater, sparsity and scalability for recommendation. A recommendation system according to the present invention finds association rules through data mining. Then, the recommendation system integrates a rough-set algorithm and a statistical analysis prediction for recommendation. The recommendation is dynamically made from a result of the rough-set algorithm and a result of the statistical analysis prediction by setting a standard deviation as a threshold.

Abstract: A video annotation method by integrating visual features and frequent patterns is disclosed. This method is featured in integrating a statistical model based on visual features with a sequential model and an association model constructed by data mining skills for automatically annotating unknown videos. This method takes both of visual features and semantic patterns into consideration simultaneously through the combination of three different models so as to enhance the accuracy of annotation.

Abstract: A video annotation method by integrating visual features and frequent patterns is disclosed. This method is featured in integrating a statistical model based on visual features with a sequential model and an association model constructed by data mining skills for automatically annotating unknown videos. This method takes both of visual features and semantic patterns into consideration simultaneously through the combination of three different models so as to enhance the accuracy of annotation.