Disclosed is an apparatus for generating an artificial-neural-network-based prediction model. The apparatus includes an input data conversion unit configured to convert input data of an L-dimensional array (L is a natural number) into normalized vector data and input the normalized vector data, a modeling unit configured to model an artificial-neural-network-based prediction model for learning the input vector data and output a value predicted through the modeling, and an adjustment unit configured to compare the value predicted by the modeling unit with an actually measured value to calculate an error value and adjust learning parameters of an artificial neural network using the error value and a back-propagation algorithm.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus for generating an artificial-neural-network-based prediction model, the apparatus comprising: an input data conversion unit configured to convert input data of an L-dimensional array (a tensor) (L is a natural number) into normalized vector data and input the normalized vector data; a modeling unit configured to model an artificial-neural-network-based prediction model for learning the input vector data and output a value predicted through the modeling; and an adjustment unit configured to compare the value predicted by the modeling unit with an actually measured value to calculate an error value and adjust learning parameters of an artificial neural network using the error value and a back-propagation algorithm. 2. The apparatus of claim 1, wherein the input data conversion unit comprises: an input data normalization unit configured to normalize L vectors corresponding to predetermined element values of the L-dimensional array (L is a natural number); and a normalization vector input processing unit configured to input the L vectors to the artificial neural network. 3. The apparatus of claim 2, wherein the input data normalization unit performs the normalization by multiplying the number of elements of each of the L vectors by a length of each of the vectors divided by the number of non-zero elements. 4. The apparatus of claim 2, wherein the normalization vector input processing unit inputs a row vector formed by sequentially connecting the L vectors to the artificial neural network. 5. The apparatus of claim 1, wherein the modeling unit allows batch normalization for normalizing a data distribution between an input layer and a hidden layer of the artificial neural network, between hidden layers of the artificial neural network, and between the hidden layer and an output layer of the artificial neural network to be performed at least one or more times. 6. The apparatus of claim 5, wherein the modeling unit calculates an average of input values, calculates a variance of the input values by using the average, finds normalized values by using the variance, and calculates batch-normalized values by scaling and shifting the normalized values. 7. The apparatus of claim 5, wherein the modeling unit applies a non-linear function to the batch-normalized values in the hidden layer. 8. The apparatus of claim 5, wherein the modeling unit applies a softmax function to the batch-normalized values in the output layer to calculate a probability of each rating and outputs the predicted value by applying weights to the values and summing the values. 9. The apparatus of claim 2, further comprising a control unit configured to, when the artificial-neural-network-based prediction model is modeled and then additional data is input, control the input data conversion unit and the modeling unit to generate an expansive L-dimensional array (L is a natural number) obtained by adding the additional data to the input data and output a predicted value as L vectors corresponding to element values included in additional data intended to be predicted by the L-dimensional array, wherein the L vectors corresponding to the additional data have the same size as L vectors corresponding to the input data. 10. The apparatus of claim 2, further comprising a control unit configured to, when the artificial-neural-network-based prediction model is modeled and then additional data is input, control the input data conversion unit, the modeling unit, and the adjustment unit to generate an L-expansive dimensional array (L is a natural number) obtained by adding the additional data to the input data and additionally learn the prediction model with L vectors corresponding to element values included in the additional data intended to be learned by the L-dimensional array, wherein the L vectors corresponding to the additional data have the same size as L vectors corresponding to the input data. 11. An apparatus for converting data to be input to an artificial neural network, the apparatus comprising: an input data normalization unit configured to normalize L vectors corresponding to predetermined element values of an L-dimensional array (a tensor) (L is a natural number); and a normalization vector input processing unit configured to input the L vectors to the artificial neural network. 12. The apparatus of claim 11, wherein the input data normalization unit performs the normalization by multiplying the number of elements of each of the L vectors by a length of each of the vectors divided by the number of non-zero elements. 13. The apparatus of claim 11, wherein the normalization vector input processing unit inputs a row vector formed by sequentially connecting the L vectors to the artificial neural network. 14. A method of generating an artificial-neural-network-based prediction model, the method comprising: converting input data of an L-dimensional array (a tensor) (L is a natural number) into normalized vector data; modeling an artificial-neural-network-based prediction model for learning the normalized vector data and outputting a value predicted through the modeling; and comparing the value predicted by the modeling unit with an actually measured value to calculate an error value and adjusting learning parameters of an artificial neural network using the error value and a back-propagation algorithm. 15. The method of claim 14, wherein the conversion comprises: normalizing L vectors corresponding to predetermined element values of the L-dimensional array (L is a natural number); and inputting the L vectors to the artificial neural network. 16. The method of claim 15, wherein the normalization comprises: performing the normalization by multiplying the number of elements of each of the L vectors by a length of each of the vectors divided by the number of non-zero elements. 17. The method of claim 15, wherein the inputting comprises inputting a row vector formed by sequentially connecting the L vectors to the artificial neural network. 18. The method of claim 14, wherein the outputting comprises batch-normalizing a data distribution between an input layer and a hidden layer of the artificial neural network, between hidden layers of the artificial neural network, and between the hidden layer and an output layer of the artificial neural network at least one time. 19. The method of claim 18, wherein the outputting comprises: calculating an average of input values; calculating a variance of the input values using the average; calculating normalized values using the variance; and calculating batch-normalized values by scaling and shifting the normalized values. 20. The method of claim 18, wherein the outputting comprises applying a non-linear function to the batch-normalized values in the hidden layer. 21. The method of claim 18, wherein the outputting comprises: applying a softmax function to the batch-normalized values in the output layer to calculate a probability of each rating; and outputting the predicted value by applying weights to the values and summing the values. 22. The method of claim 15, further comprising, when the artificial-neural-network-based prediction model is modeled and then additional data is input, performing control to re-perform the conversion and the outputting to generate an expansive L-dimensional array (L is a natural number) obtained by adding the additional data to the input data and output a predicted value as L vectors corresponding to element values included in additional data intended to be predicted by the L-dimensional array, wherein the L vectors corresponding to the additional data have the same size as L vectors corresponding to the input data. 23. The method of claim 16, further comprising, when the artificial-neural-network-based prediction model is modeled and then additional data is input, performing control to re-perform the conversion, the outputting, and the adjustment to generate an expansive L-dimensional array (L is a natural number) obtained by adding the additional data to the input data and additionally learn the prediction model by using L vectors corresponding to element values included in additional data intended to be learned by the L-dimensional array, wherein the L vectors corresponding to the additional data have the same size as L vectors corresponding to the input data. 24. A method of converting data to be input to an artificial neural network, the method comprising: normalizing L vectors corresponding to predetermined element values of an L-dimensional array (a tensor) (L is a natural number); and inputting the L vectors to the artificial neural network. 25. The method of claim 24, wherein the normalization comprises performing the normalization by multiplying the number of elements of each of the L vectors by a length of each of the vectors divided by the number of non-zero elements. 26. The method of claim 24, wherein the inputting comprises inputting a row vector formed by sequentially connecting the L vectors to the artificial neural network.