This work proposes a scheme that combines the advantages of a quantum secret sharing procedure and quantum dialogue. The proposed scheme enables the participants to simultaneously make mutual identity authentications, in a simulated scenario where the boss, Alice, shares a secret with her two agents Bob and Charlie. The secret is protected by checking photons to keep untrustworthy agents and outer attacks from getting useful information. Before the two agents cooperate to recover Alice’s secret, they must authenticate their identity using parts of a pre-shared key. In addition, the whole pre-shared key is reused as part of recovering the secret data to avoid any leaks of information. In comparison with previous schemes, the proposed method can efficiently detect eavesdropping and it is free from information leaks. Furthermore, the proposed scheme proved to be secure against man-in-the-middle attacks, impersonation attacks, entangled-and-measure attacks, participant attacks, modification attacks and Trojan-horse attacks.

In 2020, according to the publications of both the Global Cancer Observatory (GCO) and the World Health Organization (WHO), breast cancer (BC) represents one of the highest prevalent cancers in women worldwide. Almost 47% of the world’s 100,000 people are diagnosed with breast cancer, among females. Moreover, BC prevails among 38.8% of Egyptian women having cancer. Current deep learning developments have shown the common usage of deep convolutional neural networks (CNNs) for analyzing medical images. Unlike the randomly initialized ones, pre-trained natural image database (ImageNet)-based CNN models may become successfully fine-tuned to obtain improved findings. To conduct the automatic detection of BC by the CBIS-DDSM dataset, a CNN model, namely CoroNet, is proposed. It relies on the Xception architecture, which has been pre-trained on the ImageNet dataset and has been fully trained on whole-image BC according to mammograms. The convolutional design method is used in this paper, since it performs better than the other methods. On the prepared dataset, CoroNet was trained and tested. Experiments show that in a four-class classification, it may attain an overall accuracy of 94.92% (benign mass vs. malignant mass) and (benign calcification vs. malignant calcification). CoroNet has a classification accuracy of 88.67% for the two-class cases (calcifications and masses). The paper concluded that there are promising outcomes that could be improved because more training data are available.

Existing imputation methods may lead to biased predictions and decrease or increase the statistical influence which leads to improper estimations. Several missing value imputation approaches performance depends on the size of the dataset and the number of missing values within the dataset. In this work, the authors proposed a novel algorithm for manipulating missing data versus some common imputation approaches. The proposed algorithm imputes missing values in cumulative order depending on the gain ratio (GR) feature selection (to select the candidate feature to be manipulated) and the Bayesian Ridge Regression (BRR) technique (to build the predictive model). Each imputed feature will be used to manipulate the missing values in the following selected candidate feature. The proposed algorithm was implemented on eight different datasets after generating different missing values proportions from the missingness mechanisms. The imputation performance was calculated in terms of imputation time, mean absolute error (MAE), coefficient of determination (R2), and root-mean-square error (RMSE). The results show the efficiency of the proposed algorithm when imputing any dataset with any number of missing data from any missingness mechanism.

Personalized medicine provides more safe and effective treatment by individualizing the choice of drug and dose based on an individual’s genetic profile. Cancer patients’ response to anti-cancer treatments (drugs) is one of the foremost challenges in personalized medicine that releases the target treatment. Both size and availability of drug sensitivity data have motivated researchers to develop Artificial Intelligence (AI), based models, for predicting drug response to advance cancer treatment. The concerned AI models include Machine Learning (ML) and the recently advanced Deep Learning (DL) based models. This paper introduces both; a data federation method and a DL-based model for predicting drug response. The fundamental goal is to generalize the predictor so it will be able to predict the response to different drugs accurately. As the data has a considerable effect on any AI model, the data federation is utilized to consolidate the data. The proposed consolidation process is carried out to make each cell line contains gene expression data, its mutation profile, and drug response data. ML models such as Support Vector Machine (SVM) and Linear Regression (LR) are used along with Principal Component Analysis (PCA) for feature reduction, and the AI models have been tested with and without data federation. The results show that data federation enhanced the accuracy and decreased the Mean Square Error (MSE) by almost 25%. The proposed DL model uses dimension reduction encoders. The encoder is a DL model that uses unsupervised learning. It is trained by integrating an encoder with a decoder to achieve equality between the input and output. The proposed model has achieved the best accuracy compared to some other recent models in terms of the Pearson correlation coefficient (PCC) as a performance measure. In addition, the results show that the Enhanced Deep Drug Response prediction (Enhanced Deep-DR) model has achieved the best PCC value even with the largest number of genes and drugs, which proves the high capacity and efficiency of the proposed model. Convolutional Neural Network (CNN) based-model is also implemented; it achieves higher accuracy in predicting the drug response than in some other DL-based models but less than the Enhanced Deep learning. The Enhanced Deep-DR achieves better accuracy within the range of 5% to 12% than other DL-models.

In most scientific studies such as data analysis, the existence of missing data is a critical problem, and selecting the appropriate approach to deal with missing data is a challenge. In this paper, the authors perform a fair comparative study of some practical imputation methods used for handling missing values against two proposed imputation algorithms. The proposed algorithms depend on the Bayesian Ridge technique under two different feature selection conditions. The proposed algorithms differ from the existing approaches in that they cumulate the imputed features; those imputed features will be incorporated within the Bayesian Ridge equation for predicting the missing values in the next incomplete selected feature. The authors applied the proposed algorithms on eight datasets with different amount of missing values created from different missingness mechanisms. The performance was measured in terms of imputation time, root-mean-square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). The results showed that the performance varies depending on missing values percentage, size of the dataset, and the missingness mechanism. In addition, the performance of the proposed methods is slightly better.