one of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug design and discovery. therefore, using classical methods such as the protein bank database and quantum mechanical calculations are insufficient owing to the dimensionality of search spaces. In this paper, we introduce a hybrid metaheuristic algorithm called cHHo-cS, which combines Harris hawks optimizer (HHo) with two operators: cuckoo search (cS) and chaotic maps. the role of cS is to control the main position vectors of the HHo algorithm to maintain the balance between exploitation and exploration phases, while the chaotic maps are used to update the control energy parameters to avoid falling into local optimum and premature convergence. feature selection (fS) is a tool that permits to reduce the dimensionality of the dataset by removing redundant and non desired information, then fS is very helpful in cheminformatics. FS methods employ a classifier that permits to identify the best subset of features. the support vector machines (SVMs) are then used by the proposed cHHo-cS as an objective function for the classification process in FS. The CHHO-CS-SVM is tested in the selection of appropriate chemical descriptors and compound activities. Various datasets are used to validate the efficiency of the proposed CHHO-CS-SVM approach including ten from the UCI machine learning repository. Additionally, two chemical datasets (i.e., quantitative structure-activity relation biodegradation and monoamine oxidase) were utilized for selecting the most significant chemical descriptors and chemical compounds activities. the extensive experimental and statistical analyses exhibit that the suggested CHHO-CS method accomplished much-preferred trade-off solutions over the competitor algorithms including the HHO, CS, particle swarm optimization, moth-flame optimization, grey wolf optimizer, Salp swarm algorithm, and sine-cosine algorithm surfaced in the literature. the experimental results proved that the complexity associated with cheminformatics can be handled using chaotic maps and hybridizing the meta-heuristic methods. The prediction and analysis of molecules are essential tasks in cheminformatics, which use methods from mathematics and computer science to enhance their performance. The implementation of these methods depends on databases. The processes that generate most of the affectations are the storage and retrieval of molecular structures and properties (e.g., pharmacogenomics data). Typically, the behavior of the compounds can be investigated using molecular analysis. The molecular analysis helps to develop and test molecules for decreasing the effects of specific diseases 1. One drawback associated with cheminformatics is the exponential increment of the search space owing to features in the dataset 2. However, cheminformatics is still being widely used in drug design, where ...
Despite the great efforts to find an effective way for coronavirus disease 2019 (COVID-19) prediction, the virus nature and mutation represent a critical challenge to diagnose the covered cases. However, developing a model to predict COVID-19 via chest X-ray images with accurate performance is necessary to help in early diagnosis. In this paper, a hybrid quantum-classical convolutional neural network (HQ-CNN) model using random quantum circuits as a base to detect COVID-19 patients with chest X-ray images is presented. A collection of 5445 chest X-ray images, including 1350 COVID-19, 1350 normal, 1345 viral pneumonia, and 1400 bacterial pneumonia images, were used to evaluate the HQ-CNN. The proposed HQ-CNN model has achieved higher performance with an accuracy of 98.6% and a recall of 99% on the first experiment (COVID-19 and normal cases). Besides, it obtained an accuracy of 98.2% and a recall of 99.5% on the second experiment (COVID-19 and viral pneumonia cases). Also, it obtained 98% and 98.8% for accuracy and recall, respectively, on the third dataset (COVID-19 and bacterial pneumonia cases). Lastly, it achieved accuracy and recall of 88.2% and 88.6%, respectively, on the multiclass dataset cases. Moreover, the HQ-CNN model is assessed with the statistical analysis (i.e. Cohen’s Kappa and Matthew correlation coefficients). The experimental results revealed that the proposed HQ-CNN model is able to predict the positive COVID-19 cases.
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