Using the Artificial Bee Colony (ABC) Algorithm in Collaboration with the Fog Nodes in the Internet of Things Three-layer Architecture

Vakilian, Shakoor; Moravvej, Seyed Vahid; Fanian, Ali

doi:10.1109/icee52715.2021.9544399

Cited by 32 publications

(23 citation statements)

References 12 publications

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“…The layered architecture of fog computing comprises a sensing layer, middleware layer and fog server, depicted in Figure 3 22 …”

Section: Fog Computing Architecturementioning

confidence: 99%

Security analysis of fog computing environment for ensuring the security and privacy of information

Gola,

Arya,

Khan

et al. 2023

Trans Emerging Tel Tech

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In fog computing, the management, measurement, configuration, control, communication, and storage tasks are carried out collaboratively by one or more near‐user or end users. Fog computing is widely utilized in major application domains like centralized fog utilized in a shopping complex (UXFog), agriculture domain (FoAgro), health‐care domain (MediFog), vehicle parking system, and energy Lattices. The execution of fog computing at the system's edge provides the elevated quality of service (QoS), promotes area mindfulness, and minimal dormancy for the continuous and online streaming application areas. Fog networking is extremely concerned about wireless system security due to the prevalence of wireless communication in fog computing. Sniffer attacks, Jamming assaults, and several other intrusions are considered examples of security issues. While considering network storage issues, factors like interaction management, dynamic workload, and low dormancy are the most challenging aspects of designing the network model. Thus, privacy and security issues are crucial while designing the network model, accomplished through intrusion detection, access control and authentication methods. Therefore, the security analysis of fog computing based on authentication, access control and intrusion detection mechanisms are analyzed in this research to design a novel technique with enhanced security and privacy features for sharing and accessing information.

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“…The layered architecture of fog computing comprises a sensing layer, middleware layer and fog server, depicted in Figure 3 22 …”

Section: Fog Computing Architecturementioning

confidence: 99%

Security analysis of fog computing environment for ensuring the security and privacy of information

Gola,

Arya,

Khan

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Because of its stratified architecture, deep learning proficiently captures sophisticated features to yield better classification performance, and are increasing being used in numerous applications (Wang et al 2020, Zeng et al 2020, Moravvej et al 2022b. Multilayer perceptron (MLP) is an estimator that was initially developed for nonlinear XOR, and has subsequently been effectively employed to resolve combinatorial optimization issues (Moravvej et al 2021a, Hong et al 2023, finding applications in information processing, pattern recognition, image processing, classification, linear and nonlinear optimization, and real data prediction (Duraković et al 2011, Moravvej et al 2022a. MLP functions as a universal approximation where input signals propagate forward.…”

Section: Introductionmentioning

confidence: 99%

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

Bostani,

Mirzaeibonehkhater,

Najafi

et al. 2023

Physiol. Meas.

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Objective. Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events. Approach. The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed. Main Results. The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals ‘at risk’ and ‘not at risk,’ respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4%; geometric mean 89.6%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model. Significance. This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.

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“…Few researchers have shown that population-based meta-heuristic (PBMH) algorithms [14,15] may help to overcome this problem [16]. Among PBMH algorithms, the ABC algorithm is one of the most effective optimizers [17,18]. It emulates the behavior of bees in nature and, unlike traditional optimization algorithms, dispenses with the need to calculate gradients, thereby reducing the probability of getting stuck in local optimizations [19].…”

Section: Introductionmentioning

confidence: 99%

RLMD‐PA: A Reinforcement Learning‐Based Myocarditis Diagnosis Combined with a Population‐Based Algorithm for Pretraining Weights

Moravvej

Alizadehsani

Khanam

et al. 2022

Contrast Media & Molecular Imaging

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Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the automatic screening of CMR images for triage. This paper presents an automatic model for myocarditis classification based on a deep reinforcement learning approach called as reinforcement learning-based myocarditis diagnosis combined with population-based algorithm (RLMD-PA) that we evaluated using the Z-Alizadeh Sani myocarditis dataset of CMR images prospectively acquired at Omid Hospital, Tehran. This model addresses the imbalanced classification problem inherent to the CMR dataset and formulates the classification problem as a sequential decision-making process. The policy of architecture is based on convolutional neural network (CNN). To implement this model, we first apply the artificial bee colony (ABC) algorithm to obtain initial values for RLMD-PA weights. Next, the agent receives a sample at each step and classifies it. For each classification act, the agent gets a reward from the environment in which the reward of the minority class is greater than the reward of the majority class. Eventually, the agent finds an optimal policy under the guidance of a particular reward function and a helpful learning environment. Experimental results based on standard performance metrics show that RLMD-PA has achieved high accuracy for myocarditis classification, indicating that the proposed model is suitable for myocarditis diagnosis.

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Using the Artificial Bee Colony (ABC) Algorithm in Collaboration with the Fog Nodes in the Internet of Things Three-layer Architecture

Cited by 32 publications

References 12 publications

Security analysis of fog computing environment for ensuring the security and privacy of information

Security analysis of fog computing environment for ensuring the security and privacy of information

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

RLMD‐PA: A Reinforcement Learning‐Based Myocarditis Diagnosis Combined with a Population‐Based Algorithm for Pretraining Weights

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