Artificial Jellyfish Optimization with Deep-Learning-Driven Decision Support System for Energy Management in Smart Cities

Abstract

A smart city is a sustainable and effectual urban center which offers a maximal quality of life to its inhabitants with the optimal management of their resources. Energy management is the most difficult problem in such urban centers because of the difficulty of energy models and their important role. The recent developments of machine learning (ML) and deep learning (DL) models pave the way to design effective energy management schemes. In this respect, this study introduces an artificial jellyfish optimization with deep learning-driven decision support system (AJODL-DSSEM) model for energy management in smart cities. The proposed AJODL-DSSEM model predicts the energy in the smart city environment. To do so, the proposed AJODL-DSSEM model primarily performs data preprocessing at the initial stage to normalize the data. Besides, the AJODL-DSSEM model involves the attention-based convolutional neural network-bidirectional long short-term memory (CNN-ABLSTM) model for the prediction of energy. For the hyperparameter tuning of the CNN-ABLSTM model, the AJO algorithm was applied. The experimental validation of the proposed AJODL-DSSEM model was tested using two open-access datasets, namely the IHEPC and ISO-NE datasets. The comparative study reported the improved outcomes of the AJODL-DSSEM model over recent approaches.

Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification

“…This AJS is one of the newly proposed meta-heuristic swarm-based optimization algorithms derived by simulating the locomotion and dietary patterns [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] of jellyfish. Jellyfish are the most efficient swimmers of all aquatic animals widely seen in the oceans having umbrella-shaped bells and trailing tentacles.…”

“…The key advantages of the ensemble learning mechanism to design a robust feature selection model by proposing combined feature fusion strategies [ 19 , 20 , 21 ], such as combined feature set (CFS), adaptive weighted feature set (AWFS), model-based optimized weighted feature set (MOWFS), and feature-based optimized weighted feature set (FOWFS), are experimented and validated. In order to reduce the losses and selection of optimized weights of those three pre-trained networks, the advantages of a new meta-heuristic optimizer artificial jellyfish optimizer (AJS) [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] was used and finally, the performance of the proposed feature fusion strategies are likened to other combinations of the models with genetic algorithm (GA) [ 30 ] and particle swarm optimization (PSO) [ 31 ] such as MOWFA-GA, MOWFS-PSO, FOWFS-GA, and FOWPS-PSO, and it was observed that the proposed combination of FOWFS-AJS outperforms the other models used for classification of skin lesion diagnosis.…”

Path planning for a UGV using Salp Swarm Algorithm

“…Traditional path planning methods mainly face an optimization task of path length and computational efficiency in dynamic and unpredictable terrains . In the multitude of algorithms proposed for path planning, bio-inspired algorithms are a powerful solution since they are flexible, scalable, and capable of finding global optima in multi-dimensional spaces [67][68][69][70][71][72][73][74][75][76][77][78]. For example, the Salp Swarm Algorithm (SSA), which is based on the swarming behavior of salps in the ocean, is one of the new bio-inspired optimization techniques that have demonstrated potential in different optimization problems [79][80][81][82][83].…”