IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

Sabir, Zulqurnain; Nisar, Kashif; Raja, Muhammad Asif Zahoor; Haque, Muhammad Reazul; Umar, Muhammad; Ibrahim, Ag Asri Ag; Le, Dac‐Nhuong

doi:10.1109/access.2021.3113986

Cited by 22 publications

(8 citation statements)

References 129 publications

(128 reference statements)

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“…This connected network is called the Internet of Things (IoT) and may be illustrated by a smart oven that can communicate with a washing machine. Many effective systems have been developed based on the IoT, such as [18] and [19] for COVID-19 disease diagnosis and detection and [20] for modeling the mosquito release ecosystem in a heterogeneous atmosphere.…”

Section: Related Workmentioning

confidence: 99%

A Deep Trash Classification Model on Raspberry Pi 4

Tran¹,

Huynh²,

Le³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The deep learning-based ResNet-50 model, an improved version of the ResNet model, is used to classify trash from the dataset of trash images. The experimental results, which are tested both on the dataset and in real time, show that ResNet-50 had an average accuracy of 96%, higher than that of related models. Moreover, integrating the classification module into a Raspberry Pi computer, which controlled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste, created a complete trash classification system. This proves the efficiency and high applicability of the proposed system.

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Section: Related Workmentioning

confidence: 99%

A Deep Trash Classification Model on Raspberry Pi 4

Tran¹,

Huynh²,

Le³

et al. 2023

Intelligent Automation &Amp; Soft Computing

Self Cite

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“…GA works through the procedures of crossover, selection, elitism and mutation operators. In recent, GA is executed in lung cancer prognosis [35], wellhead backpressure control system [36], nonlinear circuits [37], heterogeneous mosquito release ecosystem [38], active noise control systems [39], multi-fractional multi-singular singular model [40], gas tungsten arc welding process [41], adjusting sensor acquisition frequency [42], multilayer piezoelectric transducer for broadband structures [43] and singular nonlinear system [44].…”

Section: Optimization: Ga-asamentioning

confidence: 99%

Numerical Computational Heuristic Through Morlet Wavelet Neural Network for Solving the Dynamics of Nonlinear SITR COVID-19

Sabir¹,

Alnahdi²,

Jeelani³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The present investigations are associated with designing Morlet wavelet neural network (MWNN) for solving a class of susceptible, infected, treatment and recovered (SITR) fractal systems of COVID-19 propagation and control. The structure of an error function is accessible using the SITR differential form and its initial conditions. The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The detail of each class of the SITR nonlinear COVID-19 system is also discussed. The obtained outcomes of the SITR system are compared with the Runge-Kutta results to check the perfection of the designed method. The statistical analysis is performed using different measures for 30 independent runs as well as 15 variables to authenticate the consistency of the proposed method. The plots of the absolute error, convergence analysis, histogram, performance measures, and boxplots are also provided to find the exactness, dependability and stability of the MWNN-GA-ASA.

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“…Moreover, time saving and improved privacy was achieved by this system. The transmitted data was constructed over the edge node in this framework to achieve optimization of data [20][21][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

Tuấn

Akila

Pal³

et al. 2021

Preprint

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This article presents a new scheme for data optimization in IoT assister sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of performance ratio, reliability ratio, coverage ratio and sensing error. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.

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IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

Cited by 22 publications

References 129 publications

A Deep Trash Classification Model on Raspberry Pi 4

A Deep Trash Classification Model on Raspberry Pi 4

Numerical Computational Heuristic Through Morlet Wavelet Neural Network for Solving the Dynamics of Nonlinear SITR COVID-19

Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

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