An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing

Attiya, Ibrahim; Abualigah, Laith; Elsadek, Doaa; Chelloug, Samia Allaoua; Elaziz, Mohamed Abd

doi:10.3390/math10071100

Cited by 27 publications

(12 citation statements)

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“…At present, the dataset available for use is small, the proposed algorithm only recognizes isolated word sign language, and the model operation speed is slow. Therefore, expanding and building sign language datasets, studying efficient and accurate continuous sentence sign language recognition algorithms, and using cloud computing 43 – 45 and optimization algorithm 46 – 51 to speed up the efficiency of the algorithm are important directions of follow-up research work.…”

Section: Discussionmentioning

confidence: 99%

Improved 3D-ResNet sign language recognition algorithm with enhanced hand features

Wang

Yang

et al. 2022

Sci Rep

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In sign language video, the hand region is small, the resolution is low, the motion speed is fast, and there are cross occlusion and blur phenomena, which have a great impact on sign language recognition rate and speed, and are important factors restricting sign language recognition performance. To solve these problems, this paper proposes an improved 3D-ResNet sign language recognition algorithm with enhanced hand features, aiming to highlight the features of both hands, solve the problem of missing more effective information when relying only on global features, and improve the accuracy of sign language recognition. The proposed method has two improvements. Firstly, the algorithm detects the left and right hand regions based on the improved EfficientDet network, uses the improved Bi-FPN module and dual channel and spatial attention module are used to enhance the detection ability of the network for small targets like hand. Secondly, the improved residual module is used to improve the 3D-ResNet18 network to extract sign language features. The global, the left-hand and the right-hand image sequences are divided into three branches for feature extraction and fusion, so as to strengthen the attention to hand features, strengthen the representation ability of sign language features, and achieve the purpose of improving the accuracy of sign language recognition. In order to verify the performance of this algorithm, a series of experiments are carried out on CSL dataset. For example, in the experiments of hand detection algorithm and sign language recognition algorithm, the performance indicators such as Top-N, mAP, FLOPs and Parm are applied to find the optimal algorithm framework. The experimental results show that the Top1 recognition accuracy of this algorithm reaches 91.12%, which is more than 10% higher than that of C3D, P3D and 3D-ResNet basic networks. From the performance indicators of Top-N, mAP, FLOPs, Parm and so on, the performance of the algorithm in this paper is better than several algorithms in recent three years, such as I3D+BLSTM, B3D ResNet, AM-ResC3D+RCNN and so on. The results show that the hand detection network with enhanced hand features and three-dimensional convolutional neural network proposed in this paper can achieve higher accuracy of sign language recognition.

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Section: Discussionmentioning

confidence: 99%

Improved 3D-ResNet sign language recognition algorithm with enhanced hand features

Wang

Yang

et al. 2022

Sci Rep

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“…A hybrid of the chimp optimization algorithm with the marine predators algorithm was developed in Attiya et al 40 for task scheduling. In this work, the marine predators algorithm and disruption operator is used to avoid the local optima solutions and enhance the exploitation of the chimp optimization algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel membrane‐inspired multiverse optimizer algorithm for quality of service‐aware cloud web service composition with service level agreements

Maroosi

Muniyandi

2023

Int J Communication

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Summary Cloud computing is an emerging technology in computing that provides different services over the Internet. It needs composite services to perform a complex task. Optimal selection of services that provides both functionality and nonfunctionality requirements is an NP‐hard problem. This study uses nondeterministic parallel and distributed structures of membrane systems for the recently improved multiverse optimization algorithm to improve the quality of solutions. In the previous membrane‐inspired algorithm, the population was divided into subpopulations that evolve different dynamic membranes. This study not only uses a conventional membrane‐inspired approach to introduce a conventional membrane‐inspired multiverse optimizer (CMIMVO) for the first time but also proposes an algorithm that divides the variables (dimension) into subgroups for different membranes called proposed membrane‐inspired multiverse optimizer (PMIMVO). Thus, in PMIMVO, each membrane works on a subgroup to gain global information, which considers the best values obtained by other membranes for other variables. The PMIMVO shows promising results on benchmark function problems. Furthermore, simulation results show that the PMIMVO approach could achieve up to 38% improvement in integrated quality of service (QoS) with attributes including response time, price, availability, and reliability in comparison with the previous approaches, including genetics algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), moth–flame optimization (MFO) improved multiverse optimizer (MVO), and CMIMVO.

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“…According to [17], and the work scheduling may be improved by using a combination of chimpanzee optimization (ChOA), marine predators (MPA), and the disruption operator. The key limitations of the newly created algorithm, known as CHMPAD, are its inability to resist being sucked into local optima and to increase the original ChOA's exploitation capacity.…”

Section: Related Workmentioning

confidence: 99%

A Fog-Cluster Based Load-Balancing Technique

et al. 2022

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The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

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An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing

Cited by 27 publications

References 50 publications

Improved 3D-ResNet sign language recognition algorithm with enhanced hand features

Improved 3D-ResNet sign language recognition algorithm with enhanced hand features

A novel membrane‐inspired multiverse optimizer algorithm for quality of service‐aware cloud web service composition with service level agreements

A Fog-Cluster Based Load-Balancing Technique

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