Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  compare two approaches in selecting neural network learning parameters and architecture. Traditionally they are found by trial and error (handcrafted) and alternatively, can be found using a genetic algorithm. Trial and error can find good solutions but the drawback is this method is time consuming and it can only try a few possible solutions while the genetic algorithm is known to be able to search for a good solution intelligently and faster with greater diversity of possible solutions.  tested the approaches on ten isolated Malay digits from 0 to 9. Three factors are compared beten the two approaches: time to get a good solution; network learning convergence; and the recognition rate. Our findings show that the neural network using the genetic algorithm achieved 94% recognition rate while the handcrafted neural network achieved 95%. Hover, using the genetic algorithm, a good solution can be found within days while with the handcrafted method it took eks. The network learning convergence for both approaches re relatively the same.
['M. S. Salam', 'Dzulkifli Mohamad', 'Sazilah Salleh']
I am writing a research paper on a idea Neural network speaker dependent isolated Malay speech recognition system: handcrafted vs genetic algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Neural network speaker dependent isolated Malay speech recognition system: handcrafted vs genetic algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  compare two approaches in selecting neural network learning parameters and architecture. Traditionally they are found by trial and error (handcrafted) and alternatively, can be found using a genetic algorithm. Trial and error can find good solutions but the drawback is this method is time consuming and it can only try a few possible solutions while the genetic algorithm is known to be able to search for a good solution intelligently and faster with greater diversity of possible solutions.  tested the approaches on ten isolated Malay digits from 0 to 9. Three factors are compared beten the two approaches: time to get a good solution; network learning convergence; and the recognition rate. Our findings show that the neural network using the genetic algorithm achieved 94% recognition rate while the handcrafted neural network achieved 95%. Hover, using the genetic algorithm, a good solution can be found within days while with the handcrafted method it took eks. The network learning convergence for both approaches re relatively the same."}]