Abstract: A method for establishing robust prediction model is adapted for solving the problem that the conventional prediction model cannot generate stable and credible results with missing data. The method of the present invention includes the following steps: obtaining pre-established single-modality standard models respectively based on each type of modalities from samples; extracting modality sets each having the same modality types from the samples to establish corresponding multi-modalities standard models; extracting multiple combinations of the modality sets from the samples having complete modalities to be training data, wherein the multiple combinations of the modality sets can be classified into single-modality, multi-modalities and complete-modalities; inputting said training data into a to-be trained prediction model, and modifying the prediction model by said single-modality standard models and said multi-modalities standard models to obtain a well-trained prediction model.

Abstract: An episode mining method that includes the steps outlined below is provided. Real-time operation parameters are retrieved. At least one incremental candidate pattern in every incremental time period is generated, wherein the incremental candidate pattern is an incremental episode including a set of events of the real-time operation parameters and having an incremental occurrence frequency larger than an incremental threshold. At least one batch candidate pattern in every batch time period is generated, wherein the batch candidate pattern is a batch episode including a set of events of the real-time operation parameters within the batch time period and having a batch occurrence frequency larger than a batch threshold. At least one newly-add candidate episode is determined from the incremental candidate pattern and the batch candidate pattern having an occurrence frequency larger than a determine threshold. At least one detection rule is generated based on the newly-add candidate episode.

Abstract: An episode mining method that includes the steps outlined below is provided. Real-time operation parameters are retrieved. At least one incremental candidate pattern in every incremental time period is generated, wherein the incremental candidate pattern is an incremental episode including a set of events of the real-time operation parameters and having an incremental occurrence frequency larger than an incremental threshold. At least one batch candidate pattern in every batch time period is generated, wherein the batch candidate pattern is a batch episode including a set of events of the real-time operation parameters within the batch time period and having a batch occurrence frequency larger than a batch threshold. At least one newly-add candidate episode is determined from the incremental candidate pattern and the batch candidate pattern having an occurrence frequency larger than a determine threshold. At least one detection rule is generated based on the newly-add candidate episode.

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 process debug method used to identify at least one excursive machine in a manufacturing process comprising the following steps: First, a series of validity identification data is collected, and the serial validity identification data is associated with its pathway to obtain a plurality of validity identification data sequences in corresponding to the machines. Subsequently, a sorting process is conducted to cluster the validity identification data sequence into several groups, and the clustered groups are ranked into a first order. The validity identification data sequences are subjected a continuity analysis to determine the continuity of the defects occurring in a particular machine. And the continuities of the machines involved in a particular group are ranked into a second order. Accordingly, the excursive machines causing the defective end products in the manufacturing process can be identified by the way of joining the second orders according to the first order.

Abstract: A process debug method used to identify at least one excursive machine in a manufacturing process comprising the following steps: First, a series of validity identification data is collected, and the serial validity identification data is associated with its pathway to obtain a plurality of validity identification data sequences in corresponding to the machines. Subsequently, a sorting process is conducted to cluster the validity identification data sequence into several groups, and the clustered groups are ranked into a first order. The validity identification data sequences are subjected a continuity analysis to determine the continuity of the defects occurring in a particular machine. And the continuities of the machines involved in a particular group are ranked into a second order. Accordingly, the excursive machines causing the defective end products in the manufacturing process can be identified by the way of joining the second orders according to the first order.

Abstract: A method of monitoring physiological status is periodically inspecting physiological status of a user, obtaining a physiological signal value, and determining whether the physiological signal value is normal or not. The method includes collecting a plurality of specific physiological values, calculating a reference value and a reference range according to the specific physiological values, and determining and displaying whether the physiological signal value of the specific section of the next inspection period falls within the reference value and the reference range according to the reference value and the reference range. The update of the above reference value and reference range is determined when a next physiological value is collected, so as to maintain the accuracy of the reference value and reference range. A care device of monitoring physiological status is also included in the embodiment.

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.