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Öğe An optimal task scheduling method in IoT-Fog-Cloud network using multi-objective moth-flame algorithm(Springer, 2023) Salehnia, Taybeh; Seyfollahi, Ali; Raziani, Saeid; Noori, Azad; Ghaffari, Ali; Alsoud, Anas Ratib; Abualigah, LaithNowadays, cloud and fog computing have been leveraged to enhance Internet of Things (IoT) performance. The outstanding potential of cloud platforms accelerates the processing and storage of aggregated big data from IoT equipment. Emerging fog-based schemes can improve service quality to IoT applications and mitigate excessive delays and security challenges. Also, since energy consumption can directly cause CO2 emissions from fog and cloud nodes, an efficient task scheduling method reduces energy consumption. In this regard, the growing need for an efficient task scheduling mechanism considering the optimal management of IoT resources is increasingly felt. IoT's task scheduling based on fog-cloud computing plays a crucial role in responding to users' requests. Optimal task scheduling can improve system performance. Therefore, this study uses an IoT task request scheduling method on resources by the Multi-Objective Moth-Flame Optimization (MOMFO) algorithm. It enhances the quality of IoT services based on fog-cloud computing to reduce task requests' completion and system throughput times and energy consumption. If energy consumption is diminished, the percentage of CO2 emissions is also reduced. Then, the proposed scheduling method to solve the task scheduling problem is evaluated using the datasets. A comparison between the proposed scheme and Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Salp Swarm Algorithms (SSA), Harris Hawks Optimizer (HHO), and Artificial Bee Colony (ABC) is performed to assess the performance. According to experiments, the proposed solution has reduced the completion time of IoT tasks and throughput time, thus cutting down the delay due to the processing of tasks, energy consumption, and CO2 emissions and increasing the system's performance rate.Öğe Review of heterogeneous graph embedding methods based on deep learning techniques and comparing their efficiency in node classification(Springer Wien, 2024) Noori, Azad; Balafar, Mohammad Ali; Bouyer, Asgarali; Salmani, KhosroGraph embedding is an advantageous technique for reducing computational costs and effectively using graph information in machine learning tasks like classification, clustering, and link prediction. As a result, it has become a key method in various research areas. However, different embedding methods may be used depending on the variety of graphs available. One of the most commonly used graph types is the heterogeneous graph (HG) or heterogeneous information network (HIN), which presents unique challenges for graph embedding approaches due to its diverse set of nodes and edges. Several methods have been proposed for heterogeneous graph embedding in recent years to overcome these challenges. This paper aims to review the latest techniques used for this purpose, divided into two main parts: the first part describes the fundamental concepts and obstacles in heterogeneous graph embedding, while the second part compares the most critical methods. Finally, the results are summarized, outlining the challenges and opportunities for future directions.