Artificial Neural Network (ANN) has played a significant role in many areas because of its ability to solve many complex problems that mathematical methods failed to solve. However, it has some shortcomings that lead it to stop working in some cases or decrease the result accuracy. In this research the authors propose a new approach combining particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to increase the classification accuracy of ANN. The proposed approach utilizes the advantages of both PSO and GA to overcome the local minima problem of ANN, which prevents ANN from improving the classification accuracy. The algorithms start with using backpropagation algorithm, then it keeps repeating applying GA followed by PSO until the optimum classification is reached. The proposed approach is domain independent and has been evaluated by applying it using nine datasets with various domains and characteristics. A comparative study has been performed between the authors' proposed approach and other previous approaches, the results show the superiority of our approach.
COVID-19 is a global pandemic that hit the world in 2019-2020 and caused massive losses. Every day, hundreds of thousands of tests are being done on possible infected cases. It usually takes several hours to get the results of virus test in advanced countries, whereas in other countries might take days. The aim of this study is to investigate whether normal blood medical tests help in detecting covid-19 using various machine learning approaches. If true, this would give an indication to people who should undergo the virus test. In this paper we independently use machine learning algorithms including support vector machines, adaptive boosting, random forest and k-nearest neighbors. These algorithms are then merged to form ensemble learning which leads to the classification. The results show that the ensemble learning is having the highest true positive rate of 30%. The obtained results show that normal blood tests do not help much in giving right indications about detecting COVID-19.
In this article, we explore the usage of long short-term memory neural network (NN) in generating music pieces and propose an approach to do so. Bach's musical style has been selected to train the NN to make it able to generate similar music pieces. The proposed approach takes midi files, converting them to song files and then encoding them to be as inputs for the NN. Before inputting the files into the NNs, an augmentation process which augments the file into different keys is performed then the file is fed into the NN for training. The last step is the music generation. The main objective is to provide the NN with an arbitrary note and then the NN starts amending it gradually until producing a good piece of music. Various experiments have been conducted to explore the best values of parameters that can be selected to obtain good music generations. The obtained generated music pieces are accepted in terms of rhythm and harmony; however, some other problems exist such as in certain cases the tone stops or in some other cases getting short melodies that do not change.
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