In this research, a soft computing approach based on a Nature-inspired technique, the Fractional-Order Darwinian Particle Swarm Optimization (FO-DPSO) algorithm, is hybridized with feed-forward artificial neural network (FF-ANN) to suggest and calculate better solutions for non-linear second-order ordinary differential equation (ODE) representing the corneal shape model (CSM). The unknown weights involved in approximate solutions obtained through ANN are tuned with the help of FO-DPSO. To test the robustness of our approach and conditionality of CSM, we have considered several cases of CSM with different aspects of the problem. Solutions obtained by Adam's method are used as a reference point for the sake of comparison. We establish it that FO-DPSO is a suitable technique for tuning the unknown weights involved in the solution designed with ANNs. Our results suggest that the proposed approach is a suitable candidate for solving real-world problems involving differential equations. INDEX TERMS Non-linear differential equations, meta-heuristics, soft computing, corneal shape model, feed-forward artificial neural networks, fractional order Darwinian particle swarm optimization.

Real application problems in physics, engineering, economics, and other disciplines are often modeled as differential equations. Classical numerical techniques are computationally expensive when we require solutions to our mathematical problems with no prior information. Hence, researchers are more interested in developing numerical methods that can obtain better solutions with fewer efforts and computational time. Heuristic algorithms are considered suitable candidates for such type of problems. In this research, we have developed a new neuroevolutionary algorithm that combines the power of feed-forward artificial neural networks (ANNs) and a modern metaheuristic, the Symbiotic Organism Search (SOS) algorithm. With our new approach, we have analyzed the simultaneous surface convection and radiation during heat transfer in different models of fins/ heat exchangers. Longitudinal fins are considered with concave parabolic, rectangular and trapezoidal shapes. We have analyzed our problem in two scenarios and six sub-cases. Our solutions are of high quality, with minimum residual errors in all cases. We have established the quality of our results by calculating values of different performance indicators like Root-mean-square error (RMSE), Absolute error (AE), Generational distance (GD), Mean absolute deviation (MAD), Nash-Sutcliffe efficiency (NSE), Error in Nash-Sutcliffe efficiency (ENSE). Statistical and graphical analysis of our results suggests that our approach is suitable for handling real application problems. We have compared our results with state-of-the-art results, and the outcome of our analysis points to the superiority of our approach. INDEX TERMS Approximate solutions, Artificial neural networks, Heat transfer analyses, Longitudinal fins, Metaheuristics, Symbiotic organism search optimizer.

This paper aims at the analysis of the VdP heartbeat mathematical model. We have analysed the conditionality of a mathematical model which represents the oscillatory behaviour of the heart. A novel neuroevolutionary approach is chosen to analyse the mathematical model. The characteristics of the cardiac pulse of the heart are examined by considering two major scenarios with sixteen different cases. Artificial neural networks (ANNs) are constructed to obtain the best solutions for the heartbeat model. Unknown weights are finely tuned by a combination of a global search technique the Harris Hawks Optimizer (HHO) and a local search technique the Interior Point Algorithm (IPA). Stable behaviour of solutions obtained by considering different cases demonstrates that the model under consideration is well-conditioned. The accuracy of our novel procedure is established by getting the lowest residual errors in our solution for all cases. Graphical and statistical analysis are added to further elaborate the accuracy of our approach. INDEX TERMSCardiac pulse model, hybridized soft computing, artificial neural networks, non-linear ordinary differential equations, heuristics, interior-point algorithm, Harris Hawks optimizer. Ph.D. degree in software engineering from De Montfort University, in 2015. From 2004 to 2007, he worked in the Software Development Industry, where he implemented several systems and solutions for a National Academic Institution. His research interests include algorithms, semantic web, and optimization techniques. He focuses on enhancing real-world matching systems using machine learning and data analytics in the context of supporting decision-making.

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In stenography, embedding data within an image has a trade-off between image quality and embedding capacity. Specifically, the more data are concealed within a carrier image, the further distortion the image suffers, causing a decline in the resultant stego image quality. Embedding high capacity of data into an image while preserving the quality of the carrier image can be seen as an optimization problem. In this paper, we propose a novel spatial steganography scheme using genetic algorithms (GAs). Our scheme utilizes new operations to increase least significant bits (LSB) matching between the carrier and the stego image which results in increased embedding capacity and reduced distortion. These operations are optimized pixel scanning in vertical and horizontal directions, circular shifting, flipping secret bits and secret data transposing. We formulate a general GA-based steganography model to search for the optimum solutions. Finally, we use LSB substitution for data embedding. We conduct extensive experimental testing of the proposed scheme and compare it to the state-of-art steganography schemes. The proposed scheme outperforms the relevant GA-based steganography methodologies.

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The rapid growth of social media content during the current pandemic provides useful tools for disseminating information which has also become a root for misinformation. Therefore, there is an urgent need for fact-checking and effective techniques for detecting misinformation in social media. In this work, we study the misinformation in the Arabic content of Twitter. We construct a large Arabic dataset related to COVID-19 misinformation and gold-annotate the tweets into two categories: misinformation or not. Then, we apply eight different traditional and deep machine learning models, with different features including word embeddings and word frequency. The word embedding models (FASTTEXT and word2vec) exploit more than two million Arabic tweets related to COVID-19. Experiments show that optimizing the area under the curve (AUC) improves the models' performance and the Extreme Gradient Boosting (XGBoost) presents the highest accuracy in detecting COVID-19 misinformation online.

Information systems that utilise contextual information have the potential of helping a user identify relevant information more quickly and more accurately than systems that work the same for all users and contexts. Contextual information comes in a variety of types, often derived from records of past interactions between a user and the information system. It can be individual or group based. We are focusing on the latter, harnessing the search behaviour of cohorts of users, turning it into a domain model that can then be used to assist other users of the same cohort. More specifically, we aim to explore how such a domain model is best utilised for profile-biased summarisation of documents in a navigation scenario in which such summaries can be displayed as hover text as a user moves the mouse over a link. The main motivation is to help a user find relevant documents more quickly. Given the fact that the Web in general has been studied extensively already, we focus our attention on Web sites and similar document collections. Such collections can be notoriously difficult to search or explore. The process of acquiring the domain model is not a research interest here; we simply adopt a biologically inspired method that resembles the idea of ant colony optimisation. This has been shown to work well in a variety of application areas. The model can be built in a continuous learning cycle that exploits search patterns as recorded in typical query log files. Our research explores different summarisation techniques, some of which use the domain model and some that do not. We perform task-based evaluations of these different techniques—thus of the impact of the domain model and profile-biased summarisation—in the context of Web site navigation.