Energy efficient offloading mechanism using particle swarm optimization in 5G enabled edge nodes

Bačanin, Nebojša; Antonijevic, Milos; Bezdan, Timea; Živković, Miodrag; Venkatachalam, K.; Malebary, Sharaf J.

doi:10.1007/s10586-022-03609-z

Cited by 32 publications

(16 citation statements)

References 28 publications

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“…Other successful applications of metaheuristics optimizers include tuning of the cloud, edge and fog computing [2,5,15,23,46,59], feature selection challenge [8,19,22,32,37,49,61], dropout regularization [11], a variety of COVID-19 applications [25,58,[62][63][64], tuning artificial neural networks [3,6,7,10,13,18,44], text clustering [21,50] and cryptocurrency price forecast [42].…”

Section: Metaheuristics Optimizationmentioning

confidence: 99%

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

Gajevic¹,

Milutinovic²,

Krstovic³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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This paper explores classification of datasets for Healthcare 4.0 using artificial neural networks which are tuned by improved sine cosine algorithm (SCA). Healthcare 4.0 themes include internet of things (IoT), industrial IoT (IIoT), cognitive computing, artificial intelligence, cloud computing, fog computing, edge computing, and other industry 4.0 procedures. Health issues identification are critical since prompt treatment improves the quality of life for individuals affected. One of the most difficult challenges for artificial intelligence (AI) is selecting control parameters that are appropriate for the situation at hand. This paper presents a metaheuristics-based method for training the artificial neural network, by utilizing the SCA.

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Section: Metaheuristics Optimizationmentioning

confidence: 99%

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

Gajevic¹,

Milutinovic²,

Krstovic³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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“…Step iv) Update radius: When a complete lap is covered by the search agent after s iterations, the new radius value γ new is computed using the following expression, γ new = γ 0.99 (18) Step v) Update variable range: A variable G ls is used to restrict the agents to the most hopeful regions in the search space, thus performing an exclusive local search. The exclusive local search begins at the q th iteration, while the proportion of s to the total iteration count is higher than G ls .…”

Section: Cioa For Weight Optimization Of the Deep-lstmmentioning

confidence: 99%

“…Artificial Neural Networks (ANN) and regression models use the concept of the empirical scheme in forecasting [12]. Furthermore, Deep learning (DL) schemes and optimization algorithms, like Firefly Algorithm [13][14][15][16], Multi-Swarm Algorithm [17], particle swarm optimization [18], sine cosine algorithm [19], Moth Flame Optimizer [20] have shown promising results for solving complicated issues with less computational complexities and hence used in dealing with hydrological variable prediction at different spatial and temporal scales [21].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Optimization Based Deep Learning for Rainfall Prediction

Vijaya¹,

Chander²,

Palanisamy³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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Rainfall hugely impacts every aspect of human life, such as transportation, agriculture, water management, and so on. It also is a grave cause of several natural calamities, like landslides, floods, and drought, which pose a serious threat to the well-being of individuals. These concerns have necessitated the need for devising an effective technique to predict rainfall, which enables the undertaking of effective preventive measures. Several works have focused on developing efficient rainfall forecasting techniques; however, the uncertain nature of rainfall and the lack of rainfall data limit their effectiveness. This paper proposes an efficient rainfall prediction strategy using an optimized Deep Learning approach. Here, prediction is carried out using a Deep Long Short Term Memory network based on the time series data of the rainfall. Further, the prediction efficiency is enhanced by the utilization of the Circle Inspired Optimization Algorithm for the weight optimization of the Deep Long Short Term Memory. Experimental results show that the devised Circle Inspired Optimization Algorithm-Deep Long Short Term Memory reveals enhanced performance by attaining a minimal value of Relative Absolute Error at 0.023 Mean Square Error of 0.151, and Root Mean Square Error of 0.389.

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“…NP-hard complexity with real world problems is common and hence the application of these algorithms is diverse. Some notable examples are artificial neural network optimization [7][8][9][10]12,14,15,19,21,26,32,36,48,53,54], wireless sensors networks (WSNs) [4,11,13,52,65,75], cryptocurrency trends estimations [44,49], finally the COVID-19 global epidemic-associated applications [22,25,64,66,[69][70][71]73], computer-conducted MRI classification and sickness determination [17,20,24,33,55], cloud-edge and fog computing and task scheduling [3,5,6,16,23,50,67], and lastly securing networks through intrusion detection [2,31,43,62,…”

Section: Swarm Intelligence Applications In Machine Learningmentioning

confidence: 99%

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

Petrovic¹,

Antonijevic²,

Strumberger³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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Artificial intelligence and internet of things (IoT) fields have contributed to the flourishment of the industry 4.0 concept. The main benefits include the improvements in terms of device communication, productivity, and efficiency. Nevertheless, there is a downside concerning the security of these systems. The amount of devices and their diversity prove a security risk. Due to this intrusion detection systems are paramount. This paper proposes a novel framework exploiting extreme gradient boosting machine learning model which is optimized by a modified version of the multi-verse optimizer metaheuristic. The UNSW-NB intrusion dataset was used for experimental purposes on which the other cutting-edge techniques were tested and compared. The results provide the proof of improvement as the proposed method outperformed all other overall metaheuristic performances. Furthermore, the units for truthfulness and polarity for the case have been established as a standard evaluation system. True and false positives exist alongside the same negative counterparts. The results provided by these metrics have been visualized and used for further comparison proving the superiority of the performance of the proposed solution.

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Energy efficient offloading mechanism using particle swarm optimization in 5G enabled edge nodes

Cited by 32 publications

References 28 publications

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

Artificial Neural Network Tuning by Improved Sine Cosine Algorithm for HealthCare 4.0

Feature Selection and Optimization Based Deep Learning for Rainfall Prediction

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

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