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    Improving stock prediction accuracy using CNN and LSTM
    (Institute of Electrical and Electronics Engineers Inc., 2020) Rasheed, Jawad; Jamil, Akhtar; Hameed, Alaa Ali; Ilyas, Muhammad; Özyavaş, Adem; Ajlouni, Naim
    Stock price modeling and prediction is a challenging task due to its non-stationary and dynamic nature of data. Developing an accurate stock prediction method can help investors in making profitable decisions by reducing the investment risks. This paper proposes a deep learning-based method for significantly improving the stock prediction accuracy using deep learning-based methods. Two well-known methods were investigated, namely one dimensional Convolutional Neural Network (1D-CNN) and the Long Short-Term Memory (LSTM). In addition, we also investigated the effect of dimensionality reduction using principal component analysis (PCA) on the prediction accuracy of both 1D-CNN and LSTM. Two separate experiments were performed for each method, one with PCA and one without PCA. The experimental results indicated that LSTM with PCA produced the best results with mean absolute error (MAE) of 0.032, 0.084, and 0.044 while a root mean square error (RMSE) of 0.0643, 0.172, 0.079 on Apple Inc., Amerisource Bergen Corporation, and Cardinal Health datasets. The LSTM network with PCA took an average of 421.8s for training. Contrarily, 1D-CNN model with PCA performed better in terms of computational time as it took only 37s for training and attained MAE of 0.039 and RMSE of 0.0706 on Apple Inc. dataset. Similarly, 1D-CNN took 36.5s for training while achieving 0.099 MAR and 0.2021 RMSE on Amerisource Bergen Corporation dataset, while 37.5s for training that secured 0.067 MAE and 0.1037 RMSE on Cardinal Health dataset. © 2020 IEEE.
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    Öğe
    Polygon number algorithm for peak-to-average ratio reduction of massive 5G systems using modified partial transmit sequence scheme
    (Wiley, 2021) Alkatrani, Hayder; İlyas, Muhammad; Alyassri, Salam; Nahar, Ali; Al-Turjman, Fadi; Rasheed, Jawad; Alshahrani, Ali; Al-Kasasbeh, Basil
    The high peak-to-average power ratio (PAPR) of the transmit signal is a major shortcoming of OFDM systems, which results in band radiation and distortion due to the nonlinearity of the high-power amplifier (H.P). To resolve the traditional OFDM highPAPR issue, where the transmit sequence is designed to avoid similar data from being sent in the same order to reduce PAPR, there are numerous conventional ways for lowering the PAPR for OFDM system, such as selective mapping, tone reservation, block coding, filtering, clipping, and partial transmit sequence (PTS). This study proposes a new method called polygon number algorithm (PN) with conventional partial transmit sequence (C-PTS). This method (PN-PTS) processes the entered data before sending it, taking advantage of the number nonsimilarity according to the geometry of the number to prevent direct sending of similar data via PTS, and thus, this improved the level of PAPR rise in the proposed system. The amount of reduction that can be achieved in PAPR is up to 8 dB by different techniques. The best result obtained was the amount of reduction between the conventional method and the proposed method is 4.5683 where N = 64. Besides this, there is no transmission of side information (SI), which improves transmission efficiency. Finally, this method is easy in the calculation process and the ciphering and deciphering of data, which adds a few calculations.