scholarly journals An AdaptiveWhale Optimization Algorithm Guided Smart City Big Data Feature Identification for Fair Resource Utilization

2019 ◽  
Vol 8 (12) ◽  
pp. 271-281
Keyword(s):  
Optimization Algorithm ◽  
Smart City ◽  
Smart Cities ◽  
Human Life ◽  
Support Vector ◽  
Feature Identification ◽  
Ideal Element ◽  
Course Of Action ◽  
Whale Optimization ◽  
The Ideal

World improvement is the development of every single province of the world. Smart city implies changed hardware to adjusted individuals. Smart cities have the most indispensable part in altering distinctive regions of human life, touching segments like transportation, wellbeing, vitality, and instruction. Productively to make measurements to improve distinctive smart city benefits huge information frameworks are put away, prepared, and mined in smart cities. For the change and course of action of huge information applications for smart cities, different difficulties are faces. In this paper, we propose a wrapper display based ideal element recognizable proof calculation for ideal use of assets given highlight subset age. Nine component determination techniques used for compelling element extraction. At last, which includes best add to the ideal usage of assets got by means of a novel element recognizable proof calculation made by the application out of a Whale Optimization Algorithm with Adaptive Multi-Population (WOA-AMP) system as inquiry process in a wrapper display driven by the notable relapse demonstrate regression model Random Forest with Support Vector Machine (RF-SVM). Our proposed calculation gives the exact method to choose the most agreeable feature blend, which prompts ideal asset usage.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

Whale optimization algorithm fused with SVM to detect stress in EEG signals

2021 ◽  
Vol 15 (1) ◽  
pp. 87-97
Author(s):  
Richa Gupta ◽  
M. Afshar Alam ◽  
Parul Agarwal
Keyword(s):  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Support Vector ◽  
Eeg Signals ◽  
Hunting Behavior ◽  
Positron Emission ◽  
Whale Optimization ◽  
Signal Activity ◽  
Work Done

Identifying stress and its level has always been a challenging area for researchers. A lot of work is going on around the world on the same. An attempt has been made by the authors in this paper as they present a methodology for detecting stress in EEG signals. Electroencephalogram (EEG) is commonly used to acquire brain signal activity. Though there exist other techniques to extract the same like Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) we have used EEG as it is economical. We have used an open-source dataset for EEG data. Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper.

A Comprehensive Survey on Current Literature, Standards, ‎Applications and Projects of Self-Organizing Aerial Ad Hoc Network ‎‎(AANET) in Smart Cities

2021 ◽  
Vol 01 ◽  
Author(s):  
Qutaiba Ibrahim Ali ◽  
Mustafa Siham Qassab
Keyword(s):  
Current Literature ◽  
Smart City ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Smart Cities ◽  
Human Life ◽  
Well Being ◽  
Research Area ◽  
Main Research ◽  
Self Organizing

Abstract : In the last few decades, Information and Communication Technology (ICT) has been introduced which aims to bring more comfort to human life by integrating smartness into daily objects, yields to the idea of the smart city. Guaranteeing the well-being of residents and assessing industry and urban planning from an ecological and sustainable perspective are the main goals for the smart city. Great potentials are brought to the public and civil areas by the Aerial Ad Hoc Network (AANET) concept, especially in applications that are risky to human lives. AANET, like any emerging technology, comes with many challenges that have to be overcome to be employed efficiently. In this paper, we make a detailed survey on current literature, standards, and projects of self-organizing AANET in smart cities. Also, we intend to present a profound knowledge of this active research area by identifying features, design characteristics, architectures, routing protocols, and security aspects for the design and implementation of self-organizing AANET. Furthermore, we discuss existing solutions, indicate assessment metrics along with current applications, finally we highlight the main research scope for further developments. This article surveys the work done toward AANET-related outstanding issues, intending to encourage further research in this field.

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