Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data

Neural Computing and Applications - Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model...     

Design and simulation of autonomous military vehicle control system based on machine vision and ensemble movement approach

The Journal of Supercomputing - On the battlefield, early detection of armored vehicles can have a positive effect. Because according to this issue, timely and appropriate reactions can be done....     

Predicting Mobile Cross-Platform Adaptation Using a Hybrid Sem–ANN Approach

Owing to constant changes in user needs, new technologies have been introduced to keep pace by building sustainable applications. Researchers and practitioners are keen to understand the factors that create an attractive user interface. Although the use of cross-platform applications and services is increasing, limited research has examined and evaluated cross-platforms for developing mobile applications for different operating systems. This study evaluates cross-platform features, identifying the main factors that help to create an attractive user adaptation when building sustainable applications for both Android and iOS. Flutter and React Native were selected so end-users could test their features using the cross-platform usability assessment model. Usability, satisfaction, and navigation were tested to measure the cross-platform adaptation and end-user experience. The data were analyzed using hybrid structural equation modeling (SEM) and artificial neural network (ANN) approaches. The study results show that usability and navigation both have a positive effect on adaptation on Flutter and React Native, while satisfaction only has an effect on Flutter. The navigation variable was also the most significant predictor of adaptation for both models. This study has several implications and makes contributions to the research field, to developers, and to end-users.     

ETAS: predictive scheduling of functions on worker nodes of Apache OpenWhisk platform

The Journal of Supercomputing - Fast execution of functions is an inevitable challenge in the serverless computing landscape. Inefficient dispatching, fluctuations in invocation rates, burstiness...     

Spectral Measurement of the Film-Substrate Index Difference in Proton-Exchanged LiNbO(3) Waveguides

We report the spectral characterization of proton-exchanged lithium niobate (PE:LiNbO(3)) waveguides in terms of the variation of the refractive-index difference between the waveguiding layer and the substrate. The dispersion of the extraordinary refractive-index increase (deltan(e)) is measured from 405 to 1319 nm with several light sources. Two types of proton-exchanged waveguide, prepared under different conditions, are studied. These measurements should be of use in the optimization of PE:LiNbO(3) waveguides for nonlinear optical applications, particularly in second-harmonic generation in the blue-green wavelength region.     

Geometry and topology optimization of geodesic domes using charged system search

Dome structures provide cost-effective solutions for covering large areas without intermediate supports. In this article, simple procedures are developed to reach the configuration of the geodesic domes. A new definition of dome optimization problems is given which consists of finding optimal sections for elements (size optimization), optimal height for the crown (geometry optimization) and the optimum number of elements (topology optimization) under determined loading conditions. In order to find the optimum design, the recently developed meta-heuristic algorithm, known as the Charged System Search (CSS), is applied to the optimum design of geodesic domes. The CSS takes into account the nonlinear response of the domes. Using CSS, the optimum design of the geodesic domes is efficiently performed.     

Determining malicious executable distinguishing attributes and low-complexity detection

Detection of rapidly evolving malware requires classification techniques that can effectively and efficiently detect zero-day attacks. Such detection is based on a robust model of benign behavior and deviations from that model are used to detect malicious behavior. In this paper we propose a low-complexity host-based technique that uses deviations in static file attributes to detect malicious executables. We first develop simple statistical models of static file attributes derived from the empirical data of thousands of benign executables. Deviations among the attribute models of benign and malware executables are then quantified using information-theoretic (Kullback-Leibler-based) divergence measures. This quantification reveals distinguishing attributes that are considerably divergent between benign and malware executables and therefore can be used for detection. We use the benign models of divergent attributes in cross-correlation and log-likelihood frameworks to classify malicious executables. Our results, using over 4,000 malicious file samples, indicate that the proposed detector provides reasonably high detection accuracy, while having significantly lower complexity than existing detectors.