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Publication Metadata only Editorial Recent trends in reservoir computing(WORLD SCIENTIFIC PUBL CO PTE LTD, 2023) Ahmadian, Ali; Balas, Valentina E.; Salahshour, Soheil; Universita Mediterranea di Reggio Calabria; Bahcesehir UniversityPublication Metadata only Using different Heuristic strategies and an adaptive Neuro-Fuzzy inference system for multi-objective optimization of Hybrid Nanofluid to provide an efficient thermal behavior(ELSEVIER, 2024) Wang, Zhe; Shami, Hayder Oleiwi; Kazim, Khudhaier J.; Basem, Ali; Al-fanhrawi, Halah Jawad; Dacto, Karina Elizabeth Cajamarca; Salahshour, Soheil; Khajehkhabaz, Mohammad; Eftekhari, S. Ali; Chinese Academy of Sciences; National Center for Nanoscience & Technology, CAS; Al-Amarah University College; Misan University; University of Warith Alanbiyaa; Al-Mustaqbal University College; Universidad Nacional de Chimborazo; Okan University; Bahcesehir University; Lebanese American University; Islamic Azad UniversityThe importance of multi-objective optimization in hybrid nanofluid research lies in its wide-ranging applications across fields such as microelectronics, aerospace, and renewable energy. These specialized fluids hold the potential to elevate the performance and efficiency of diverse systems through enhanced heat transfer capabilities. This research endeavor is centered around optimizing a hybrid nanofluid composed of Silicon Oxide-MWCNTAlumina/Water by leveraging a mix of heuristic approaches and an adaptive neuro-fuzzy inference system. To this end, the most influential set of input parameters has been identified using four state-of-the-art algorithms: Non-dominated Genetic Algorithm, multi-objective particle swarm optimization, Strength Pareto Evolutionary Algorithm 2, and Pareto Envelope-based Selection Algorithm 2. The goal of the optimization process is to modify the temperature (T = 20 degrees C to 60 degrees C) and the volume fraction of nanoparticles (SVF=0.1 % to 0.5 %). Finding the optimal combination of these parameters that results in the hybrid nanofluid with the maximum thermal conductivity (knf) and the lowest dynamic viscosity is the main objective. The findings of this research have the potential to drastically improve the performance of systems in a variety of applications and to change the creation of sophisticated, high-efficiency heat transfer fluids.Publication Metadata only Impact of predation in the spread of an infectious disease with time fractional derivative and social behavior(WORLD SCIENTIFIC PUBL CO PTE LTD, 2021) Bentout, Soufiane; Ghanbari, Behzad; Djilali, Salih; Guin, Lakshmi Narayan; Universite Abou Bekr Belkaid; Kermanshah University of Technology; Bahcesehir University; Universite Hassiba Ben Bouali de Chlef; Visva Bharati UniversityThe main purpose of this paper is to explore the influence of predation on the spread of a disease developed in the prey population where we assume that the prey has a social behavior. The memory of the prey and the predator measured by the time fractional derivative plays a crucial role in modeling the dynamical response in a predator-prey interaction. This memory can be modeled to articulate the involvement of interacting species by the presence of the time fractional derivative in the considered models. For the purpose of studying the complex dynamics generated by the presence of infection and the time-fractional-derivative we split our study into two cases. The first one is devoted to study the effect of a non-selective hunting on the spread of the disease, where the local stability of the equilibria is investigated. Further the backward bifurcation is obtained concerning basic reproduction rate of the infection. The second case is for explaining the impact of selecting the weakest infected prey on the edge of the herd by a predator on the prevalence of the infection, where the local behavior is scrutinized. Moreover, for the graphical representation part, a numerical simulation scheme has been achieved using the Caputo fractional derivative operator.Publication Metadata only A new service composition method in the cloud-based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework(WILEY, 2024) Vakili, Asrin; Al-Khafaji, Hamza Mohammed Ridha; Darbandi, Mehdi; Heidari, Arash; Jafari Navimipour, Nima; Unal, Mehmet; Islamic Azad University; Al-Mustaqbal University College; Istanbul Atlas University; Kadir Has University; National Yunlin University Science & Technology; Bahcesehir UniversityCloud computing is quickly becoming a common commercial model for software delivery and services, enabling companies to save maintenance, infrastructure, and labor expenses. Also, Internet of Things (IoT) apps are designed to ease developers' and users' access to networks of smart services, devices, and data. Although cloud services give nearly infinite resources, their reach is constrained. Designing coherent and organized apps is made possible by integrating the cloud and IoT. Expanding facilities by combining services is a critical component of this technology. Various services may be presented in this environment based on the user's demands. Considering their Quality of Service (QoS) attributes, discovering the appropriate available atomic services to construct the needed composite service with their collaboration in an orchestration model is an NP-hard issue. This article suggests a service composition method using Grey Wolf Optimization (GWO) and MapReduce framework to compose services with optimized QoS. The simulation outcomes illustrate cost, availability, response time, and energy-saving improvements through the suggested approach. Comparing the suggested technique to three baseline algorithms, the average gain is a 40% improvement in energy savings, a 14% decrease in response time, an 11% increase in availability, and a 24% drop in cost.Publication Metadata only SCORING: Towards Smart Collaborative cOmputing, caching and netwoRking paradIgm for Next Generation communication infrastructures(IEEE, 2022) Hmitti, Zakaria Ait; Ben Ammar, Hamza; Soyak, Ece Gelal; Kardjadja, Youcef; Malektaji, Sepideh; Ali, Soukaina Ouledsidi; Rayani, Marsa; Saqib, Muhammad; Taghizadeh, Seyedreza; Ajib, Wessam; Elbiaze, Halima; Ercetin, Ozgur; Ghamri-Doudane, Yacine; Glitho, Roch; University of Quebec; University of Quebec Montreal; Sabanci University; Concordia University - Canada; Bahcesehir UniversityThe unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.Publication Metadata only Metaheuristic-based task scheduling for latency-sensitive IoT applications in edge computing(SPRINGER, 2025) Satouf, Aram; Hamidoglu, Ali; Gul, Omer Melih; Kuusik, Alar; Ata, Lutfiye Durak; Kadry, Seifedine; Bahcesehir University; University of Alberta; University of Alberta; Istanbul Technical University; Tallinn University of Technology; Lebanese American UniversityThe increasing amount of data produced by Internet of Things (IoT) devices imposes significant limitations on the resources available in conventional cloud data centers, undermining their capacity to accommodate time-sensitive IoT applications. Cloud-fog computing has emerged as a promising paradigm that extends cloud services to the network edge. However, the distribution of tasks in a cloud-fog environment presents new challenges. Our research paper introduces a semi-dynamic real-time task scheduling system designed explicitly for the cloud-fog environment. This algorithm effectively assigns jobs while minimizing energy consumption, cost, and makespan. An adapted version of the grey wolf optimizer is introduced to optimize task scheduling by considering various criteria such as task duration, resource requirements, and execution time. Our approach outperforms existing methods, such as genetic algorithm, particle swarm optimization, and artificial bee colony algorithm, in terms of makespan, total execution time, cost, and energy consumption.Publication Metadata only Characterizing Attenuation of Bumper Modifications: A Comparative Study in the E-Band(IEEE, 2024) Neubauer, Michael; Hirschmugl, Michael; Petanjek, David; Kiebach, Helge; Karamzadeh, Saeid; Kolosovs, D; Anstalt fur Verbrennungskraftmaschinen List; Bahcesehir UniversityHaving knowledge about the attenuation of the materials surrounding a radar sensor is of great importance in the automotive sector. Only then can the manufacturer guarantee the desired functionality of the sensor. Due to the fact that a lot of automotive manufacturers also started integrating radar sensors behind rear bumpers for improved visibility to the back of a car, it is vital to know how those bumpers and possible repair scenarios done to them can affect the radar sensor. This paper proposes a way to measure the attenuation in the E-band of not only square slab samples representing various types of paint and repair scenarios on automotive bumpers, but also actual rear bumpers with different types of repair scenarios. The results are then presented and compared relative to each other.Publication Metadata only Assistive Visual Tool: Enhancing Safe Navigation with Video Remapping in AR Headsets(SPRINGER INTERNATIONAL PUBLISHING AG, 2025) Sadeghzadeh, Arezoo; Islam, Md Baharul; Uddin, Md Nur; Aydin, Tarkan; DelBue, A; Canton, C; Pont-Tuset, J; Tommasi, T; Bahcesehir University; State University System of Florida; Florida Gulf Coast UniversityVisual Field Loss (VFL) is characterized by blind spots or scotomas that poses detrimental impact on fundamental movement activities of individuals. Addressing the challenges (e.g., low video quality, content loss, high levels of contradiction, and limited mobility assessment) faced by existing Extended Reality (XR) systems as vision aids, we introduce a groundbreaking method that enriches the real-time navigation using Augmented Reality (AR) glasses. Our novel vision aid employs advanced video processing techniques to enhance visual perception in individuals with moderate to severe VFL, bridging the gap to healthy vision. A unique optimal video remapping function, tailored to our selected AR glasses characteristics, dynamically maps live video content to the largest intact region of the Visual Field (VF) map. Our method preserves video quality, minimizing blurriness and distortion. Through a comprehensive empirical user study involving 29 subjects with artificially induced scotomas, statistical analyses of object counting and multi-tasking walking track tests demonstrate the promising performance of our method in enhancing visual awareness and navigation capability in real-time.Publication Metadata only A reliable method for data aggregation on the industrial internet of things using a hybrid optimization algorithm and density correlation degree(SPRINGER, 2024) Heidari, Arash; Shishehlou, Houshang; Darbandi, Mehdi; Navimipour, Nima Jafari; Yalcin, Senay; Halic University; Islamic Azad University; Kadir Has University; National Yunlin University Science & Technology; Bahcesehir UniversityThe Internet of Things (IoT) is a new information technology sector in which each device may receive and distribute data across a network. Industrial IoT (IIoT) and related areas, such as Industrial Wireless Networks (IWNs), big data, and cloud computing, have made significant strides recently. Using IIoT requires a reliable and effective data collection system, such as a spanning tree. Many previous spanning tree algorithms ignore failure and mobility. In such cases, the spanning tree is broken, making data delivery to the base station difficult. This study proposes an algorithm to construct an optimal spanning tree by combining an artificial bee colony, genetic operators, and density correlation degree to make suitable trees. The trees' fitness is measured using hop count distances of the devices from the base station, residual energy of the devices, and their mobility probabilities in this technique. The simulation outcomes highlight the enhanced data collection reliability achieved by the suggested algorithm when compared to established methods like the Reliable Spanning Tree (RST) construction algorithm in IIoT and the Hop Count Distance (HCD) based construction algorithm. This proposed algorithm shows improved reliability across diverse node numbers, considering key parameters including reliability, energy consumption, displacement probability, and distance.Publication Metadata only Throughput-maximizing OFDMA Scheduler for IEEE 802.11ax Networks(IEEE, 2020) Kuran, Mehmet Sukru; Dilmac, A.; Topal, Omer; Yamansavascilar, Baris; Avallone, Stefano; Tugcu, Tuna; Bahcesehir University; Bogazici University; University of Naples Federico IIIn this paper, we develop a novel throughput-maximizing OFDMA scheduler for the multi-user MAC framework for the IEEE 802.11ax networks. The scheduler works both in the downlink and uplink directions and assigns resource units to stations using a linear programming technique considering load of each client, possible resource unit configurations, modulation-coding scheme of each client, and ageing factor of each client's load. The performance of the proposed scheduler has been evaluated using the NS3 simulator and compared against the legacy MAC layer mechanism of IEEE 802.11 protocol (i.e., DCF/EDCA). Simulation results show that our proposed throughput-maximizing scheduler increases the total throughput in the network as well as decrease the average end-to-end delay regardless of the number of stations connected to the access point by prioritizing the traffic of clients connected via high modulation-coding schemes.