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The current work aimed to investigate the private tutoring phenomenon among Egyptian medical faculty students.

The present work is a cross-sectional observational study using an online, anonymous questionnaire disseminated to Egyptian medical students and instructors via social platforms and university e-mails. All subjects involved in the survey gave informed consent to begin the questionnaire. No financial incentives were awarded to finish the questionnaire.

In total, 79.2% of the surveyed students (n = 198) admitted taking private medical courses during their medical study courses till the date of the survey. The Egyptian students, 68.4% (n = 171), markedly surpassed the non-Egyptian participants (n = 79, 31.6%). Males were nearly double the female participants (n = 162 and 88 consecutively).The highest academic-level-seeking private medical tutoring was the fifth-year students (n = 66, 26.4%).

A large sample size is needed to strengthen the statistical power and permit the generalization over the population, so more research work in this aspect is recommended. Also, subject-specific data in private medical tutoring need to be investigated in future works. Similar global work is recommended to allow better comparison of data worldwide.

When conceptualizing medical education processes and developing its regulations, the dynamics of private medical instruction should be taken into account, especially concerning socioeconomic inequities and efficiency in medical school systems. This work has been the first to investigate the private tutoring phenomenon among Egyptian medical students to the authors' best knowledge.

Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.

In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.

The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.

In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.