Dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic and static swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared to several well-known meta-heuristics available in the literature. This paper presents a comprehensive review of DA and its new variants classified into modified and hybrid versions. It also describes the main diverse applications of DA in several fields and areas such as machine learning, neural network, image processing, robotics, and engineering. Finally, the paper suggests some possible interesting research on the applications and hybridizations of DA for future works.

Dragonfly algorithm: a comprehensive review and applications

Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores.

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An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study

Quantum-inspired metaheuristic algorithms: comprehensive survey and classification

Discovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson's correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN.

Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks

The synchronisation of n≥ 2 heterogenously typed models requires a thorough understanding of global consistency rules. After having related these models by determining identical entities in them, we express the global rules in terms of diagrammatic predicates imposed on a comprehensive metamodel, which integrates the structural properties of all involved model spaces. If the global rules are violated, a possible subsequent consistency restoration can make use of formal descriptions for the verification of these rules. The comprehensive metamodel is constructed in the category of directed graphs. If there is an arbitrary number of related models, comprehensive artefacts can formally be represented by colimits, i.e. by universal categorical constructions. The goal of the paper is to establish a practical algorithm for this construction. The main example, a web service integration scenario from the health care domain, also shows that relating graph morphisms may be non-injective, which is incorporated into the algorithm.

Abdelmalek Amine

Papers

Modelling and Implementation of Complex Systems