Abstract: Disclosed are a financial analysis system and a financial analysis method for unstructured text data. In the financial analysis system, a user interface is configured to input a keyword and display an analysis result, a server is configured to manage at least one database, and a memory is configured to store an analysis program. In addition, a processor is configured to execute the analysis program for implementing the financial analysis method. The financial analysis method includes: according to the keyword, searching for a plurality of news related to the keyword within a predetermined time segment; and according to the news, executing a vocabulary analysis at every time point within the predetermined time segment to calculate an overall optimistic factor and an overall encouraging factor as the analysis result.

Abstract: The invention provides a method for forming a fermented composition of mung bean hulls, including: mixing mung bean hulls and water to form a mixture; adding a fungus into the mixture, wherein the fungus includes Rhizopus spp. or Aspergillus spp.; and fermenting the mixture to form a fermented composition. The invention also includes a fermented composition of mung bean hulls by the method and an anti-oxidation and anti-inflammation composition containing the fermented composition of mung bean hulls.

Abstract: The invention can recognize all languages and input words. It needs m unknown voices to represent m categories of known words with similar pronunciations. Words can be pronounced in any languages, dialects or accents. Each will be classified into one of m categories represented by its most similar unknown voice. When user pronounces a word, the invention finds its F most similar unknown voices. All words in F categories represented by F unknown voices will be arranged according to their pronunciation similarity and alphabetic letters. The pronounced word should be among the top words. Since we only find the F most similar unknown voices from m (=500) unknown voices and since the same word can be classified into several categories, our recognition method is stable for all users and can fast and accurately recognize all languages (English, Chinese and etc.) and input much more words without using samples.

Abstract: A speech recognition method on all sentences in all languages is provided. A sentence can be a word, name or sentence. All sentences are represented by E×P=12×12 matrices of linear predict coding cepstra (LPCC) 1000 different voices are transformed into 1000 matrices of LPCC to represent 1000 databases. E×P matrices of known sentences after deletion of time intervals between two words are put into their closest databases. To classify an unknown sentence, use the distance to find its F closest databases and then from known sentences in its F databases, find a known sentence to be the unknown one. The invention needs no samples and can find a sentence in one second using Visual Basic. Any person without training can immediately and freely communicate with computer in any language. It can recognize up to 7200 English words, 500 sentences of any language and 500 Chinese words.

Abstract: The present invention can recognize both English and Chinese at the same time. The most important skill is that the features of all English words (without samples) are entirely extracted from the features of Chinese syllables. The invention normalizes the signal waveforms of variable lengths for English words (Chinese syllables) such that the same words (syllables) can have the same features at the same time position. Hence the Bayesian classifier can recognize both the fast and slow utterance of sentences. The invention can improve the feature such that the speech recognition of the unknown English (Chinese) is guaranteed to be correct. Furthermore, since the invention can create the features of English words from the features of Chinese syllables, it can also create the features of other languages from the features of Chinese syllables and hence it can also recognize other languages, such as German, French, Japanese, Korean, Russian, etc.

Abstract: The invention can recognize any several languages at the same time without using samples. The important skill is that features of known words in any language are extracted from unknown words or continuous voices. These unknown words represented by matrices are spread in the 144-dimensional space. The feature of a known word of any language represented by a matrix is simulated by the surrounding unknown words. The invention includes 12 elastic frames of equal length without filter and without overlap to normalize the signal waveform of variable length for a word, which has one to several syllables, into a 12×12 matrix as a feature of the word. The invention can improve the feature such that the speech recognition of an unknown sentence is correct. The invention can correctly recognize any languages without samples, such as English, Chinese, German, French, Japanese, Korean, Russian, Cantonese, Taiwanese, etc.

Abstract: The invention can recognize all languages and input words. It needs m unknown voices to represent m categories of known words with similar pronunciations. Words can be pronounced in any languages, dialects or accents. Each will be classified into one of m categories represented by its most similar unknown voice. When user pronounces a word, the invention finds its F most similar unknown voices. All words in F categories represented by F unknown voices will be arranged according to their pronunciation similarity and alphabetic letters. The pronounced word should be among the top words. Since we only find the F most similar unknown voices from m (=500) unknown voices and since the same word can be classified into several categories, our recognition method is stable for all users and can fast and accurately recognize all languages (English, Chinese and etc.) and input much more words without using samples.

Abstract: The invention provides a method for forming a fermented composition of mung bean hulls, including: mixing mung bean hulls and water to form a mixture; adding a fungus into the mixture, wherein the fungus includes Rhizopus spp. or Aspergillus spp.; and fermenting the mixture to form a fermented composition. The invention also includes a fermented composition of mung bean hulls by the method and an anti-oxidation and anti-inflammation composition containing the fermented composition of mung bean hulls.

Abstract: The invention can recognize any several languages at the same time without using samples. The important skill is that features of known words in any language are extracted from unknown words or continuous voices. These unknown words represented by matrices are spread in the 144-dimensional space. The feature of a known word of any language represented by a matrix is simulated by the surrounding unknown words. The invention includes 12 elastic frames of equal length without filter and without overlap to normalize the signal waveform of variable length for a word, which has one to several syllables, into a 12×12 matrix as a feature of the word. The invention can improve the feature such that the speech recognition of an unknown sentence is correct. The invention can correctly recognize any languages without samples, such as English, Chinese, German, French, Japanese, Korean, Russian, Cantonese, Taiwanese, etc.

Abstract: The present invention can recognize both English and Chinese at the same time. The most important skill is that the features of all English words (without samples) are entirely extracted from the features of Chinese syllables. The invention normalizes the signal waveforms of variable lengths for English words (Chinese syllables) such that the same words (syllables) can have the same features at the same time position. Hence the Bayesian classifier can recognize both the fast and slow utterance of sentences. The invention can improve the feature such that the speech recognition of the unknown English (Chinese) is guaranteed to be correct. Furthermore, since the invention can create the features of English words from the features of Chinese syllables, it can also create the features of other languages from the features of Chinese syllables and hence it can also recognize other languages, such as German, French, Japanese, Korean, Russian, etc.