Quantum information processing introduces novel approaches for classical data encoding to encompass the complex patterns of input data of practical computational challenges using basic principles of quantum mechanics. The classification of diabetes is an example of a problem that can be efficiently resolved by using quantum unitary operations and the variational quantum classifier (VQC). This study demonstrates the effects of the number of qubits, types of feature maps, optimizers’ class, and the number of layers in the parametrized circuit, and the number of learnable parameters in ansatz influences the effectiveness of the VQC. In total, 76 variants of VQC are analyzed for four and eight qubits’ cases and their results are compared with six classical machine learning models to predict diabetes. Three different types of feature maps (Pauli, Z, and ZZ) are implemented during analysis in addition to three different optimizers (COBYLA, SPSA and SLSQP). Experiments are performed using the PIMA Indian Diabetes Dataset (PIDD). The results conclude that VQC with six layers embedded with an error correction scaling factor of 0.01 and having ZZ feature map and COBYLA optimizer outperforms other quantum variants. The optimal proposed model attained the accuracy of 0.85 and 0.80 for eight and four qubits’ cases, respectively. In addition, the final quantum model among 76 variants was compared with six classical machine learning models. The results suggest that the proposed VQC model has outperformed four classical models including SVM, random forest (RF), decision tree (DT), and linear regression (LR).

In 2019, a deadly coronaviral infection (COVID‐19) that infected millions of people globally was detected in China. This fatal virus affects the respiratory system and currently spreads to more than 200 nations worldwide. COVID‐19 may be found using a chest X‐ray scan, a reliable imaging method. Although an expert may examine an X‐ray scan manually, this process takes a lot of time. Therefore, deep convolutional neural networks (CNNs) may be utilized to automate this procedure. In this work, at the first step, a novel isolated 19‐layer CNN model is developed from scratch to detect chest infections using X‐rays. Then, the developed model is reutilized to distinguish the type of chest infection, such as COVID‐19, fibrosis, pneumonia, and tuberculosis, using the transfer learning approach. Stochastic gradient descent with momentum is utilized to optimize the model. The proposed multistage framework shows 98.85% and 97% classification accuracies for chest infection detection (binary classification between normal and patient) and four‐class subclassification (COVID‐19, fibrosis, pneumonia, and tuberculosis) for an online chest X‐ray dataset. The reliability of the proposed multistage CNN model was further validated through a new dataset, showing an accuracy of 98.5%. The proposed multistage methodology took minimal training time compared to publically available pretrained models. Therefore, the presented multistage deep learning framework can help doctors in clinical practices.

In this study, a channel and feature selection methodology is devised for brain‐computer interface (BCI) applications using functional near‐infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter‐based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left‐ and right‐hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two‐class BCI applications (i.e., left‐ vs. right‐hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four‐class BCI applications (i.e., left‐ vs. right‐hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two‐class and four‐class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four‐class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four‐class) yielded high average classification accuracy (p < 0.05). However, the results of the ReliefF algorithm are more stable and significant.