Feature Dimensionality Reduction via Homological Properties of Observability]{Feature Dimensionality Reduction via Homological Properties of Observability

Feature selection and its subsequent dimensionality reduction are significant problems in machine learning and it is at the core of several data science techniques. The 'shape' of data, or in other words its related topological properties, can provide crucial insights into the corresponding data types and sources and it enables the identification of general properties that facilitate its analysis and assessment. In this article, we discuss an information theoretic approach combined with data homological properties to assess dimensionality reduction, which can be applied to semantic feature selection

Optimal control of system governed by nonlinear volterra integral and fractional derivative equations

AbstractThis work presents a novel formulation for the numerical solution of optimal control problems related to nonlinear Volterra fractional integral equations systems. A spectral approach is implemented based on the new polynomials known as Chelyshkov polynomials. First, the properties of these polynomials are studied to solve the aforementioned problems. The operational matrices and the Galerkin method are used to discretize the continuous optimal control problems. Thereafter, some necessary conditions are defined according to which the optimal solutions of discrete problems converge to the optimal solution of the continuous ones. The applicability of the proposed approach has been illustrated through several examples. In addition, a comparison is made with other methods for showing the accuracy of the proposed one, resulting also in an improved efficiency

Comparison Between Protein-Protein Interaction Networks CD4 + T and CD8 + T and a Numerical Approach for Fractional HIV Infection of CD4 + T Cells

This research examines and compares the construction of protein-protein interaction (PPI) networks of CD4+ and CD8+T cells and investigates why studying these cells is critical after HIV infection. This study also examines a mathematical model of fractional HIV infection of CD4+T cells and proposes a new numerical procedure for this model that focuses on a recent kind of orthogonal polynomials called discrete Chebyshev polynomials. The proposed scheme consists of reducing the problem by extending the approximated solutions and by using unknown coefficients to nonlinear algebraic equations. For calculating unknown coefficients, fractional operational matrices for orthogonal polynomials are obtained. Finally, there is an example to show the effectiveness of the recommended method. All calculations were performed using the Maple 17 computer code

Privacy-preserving malware detection in Android-based IoT devices through federated Markov chains

The continuous emergence of new and sophisticated malware specifically targeting Android-based Internet of Things devices is causing significant security hazards and is consequently fostering the need for effective detection models and strategies able to work with these hardware-constrained devices. In addition, since such models are often trained on confidential application data, many involved subjects are reluctant to share their data for this purpose. Accordingly, several Federated Learning-based solutions are emerging, which rely on the capabilities of Machine Learning models in malware detection/classification without sharing user data. However, Federated Learning methods are often adversely affected by non-independent and identically distributed data in terms of both the required training time and classification results. Therefore, a promising solution could be to overcome the Federated Learning-related issues by preserving the privacy of end-user data. In this direction, the capabilities of Markov chains and associative rules are extended within a federated environment to face malware classification tasks in the IoT scenario. The presented approach, evaluated on several malware families, has achieved an average accuracy of 99% in the presence of centralized and decentralized unbalanced training/testing data by overcoming the most common state-of-the-art approaches. Also, its runtime performance is comparable with centralized ones by considering several non independent and identically distributed dataset partitions, splitting criteria, and clients, respectively

A Galerkin approach for fractional delay differential equations using hybrid Chelyshkov basis functions

This study proposes a numerical technique based on a hybrid of block-pulse functions and Chelyshkov polynomials to solve fractional delay differential equations. The Galerkin approach transforms the solution of fractional delay differential equations into a system of algebraic equations using the fractional operational matrix of integration for these hybrid functions. The suggested method's accuracy and efficiency are demonstrated using numerical examples

Vehicle-to-Everything (V2X) Communication Scenarios for Vehicular Ad-hoc Networking (VANET): An Overview

Nowadays both sciences and technology, including Intelligent Transportation Systems, are involved in improving current approaches. Overview studies give you fast, comprehensive, and easy access to all of the existing approaches in the field. With this inspiration, and the effect of traffic congestion as a challenging issue that affects the regular daily lives of millions of people around the world, in this work, we concentrate on communications paradigms that can be used to address traffic congestion problems. Vehicular Ad-hoc Networking (VANET), a modern networking technology, provides innovative techniques for vehicular traffic control and management. Virtual traffic light (VTL) methods for VANET seek to address traffic issues through using vehicular network communication models. These communication paradigms can be classified into four scenarios: Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) and Vehicle-toPedestrian (V2P). In general, these four scenarios are included in the category of vehicle-to-everything (V2X). Therefore, in this paper, we provide an overview of the most important scenarios of V2X communications based on their characteristics, methodologies, and assessments. We also investigate the applications and challenges of V2X