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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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    RETRACTED: Cancer detection using deep learning techniques (Retracted Article)
    (Springer Heidelberg, 2024) Alkurdi, Dunya Ahmed; Ilyas, Muhammad; Jamil, Akhtar
    Breast cancer has become the most common form of cancer in world recently having overtaken cervical cancer in urban cities. Immense research has been carried out on breast cancer and several automated machines for detection have been formed, however, they are far from perfection and medical assessments need more reliable services. Computer Assisted Diagnostics programs have been developed over the past 2 decades to help radiologists interpret mammogram screening. Deep convolutional neural networks (CNN), which have surpassed human output since 2012, have been an immense success in image recognition. Deep CNNs will revolutionize the analysis of medical images. We propose a method for breast cancer detection based on Faster R-CNN, the most common frameworks for object detection. In a non-human interference mammogram, the device detects and categorizes malignant or benign lesions. The method proposed sets the current status of the INbreast database public classification scheme, AUC = 0.95. In the digital mammography challenge DREAM with AUC = 0.85, the method mentioned here was second. When the device is used as a sensor, the accuracy of the INbreast data set is extremely low with very false positive image points.