A Comprehensive Review on Nature Inspired Neural Network based Adaptive Filter for Eliminating Noise in Medical Images

Kumar, Manish; Mishra, Sumit

doi:10.2174/1573405614666180801113345

Cited by 10 publications

(4 citation statements)

References 61 publications

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“…Bioinspired algorithms are used in many applications such as the Internet of Things [18], image processing [19], agriculture [20], healthcare [21], and security [22]. Recently, these algorithms have provided effective solutions regarding routing, safety, and efficient parking for vehicular networks.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Harris Hawks Optimization Based Cluster Optimization Scheme for VANETs

et al. 2022

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In recent years, intelligent vehicles with cutting-edge vehicular applications have grown in popularity, enabling the growth of Vehicular Ad hoc Networks (VANETs). Vehicular Ad hoc Networks (VANETs) are a network of vehicles that share and analyze real-time data and require a well-organized and efficient data delivery method. The stability of clusters and dynamic topology change in VANETs are the major issues in finding an optimal route amongst the vehicles. The cooperative approach and surprise pounce chasing technique of Harris Hawks in nature serve as the main sources of inspiration for Harris Hawks Optimization. In this technique, several hawks work together to attack a victim from various angles to surprise it. Due to the unpredictable nature of situations and the prey’s fleeing movements, Harris Hawks can exhibit a variety of intelligent strategies. This study proposes a novel route clustering optimization technique that takes into account communication range, the number of nodes, velocity, orientations, and grid size. To create and evaluate ideal cluster head (CH), the proposed method is based on Harris Hawks Intelligent Optimization Algorithm for route Clustering (iCHHO) which finds optimal and reliable routes amongst the vehicles. Other state-of-the-art methods, such as the Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimization (GWO), and Whale Optimization Algorithm (WOACNET), are utilized to evaluate and validate the proposed method. Our findings show that the developed method outperforms other current methods in terms of number of clusters, variable communication ranges, network size, and the number of vehicles. Furthermore, the statistical analysis concludes that the proposed method improves cluster optimization by 79% and increases cluster stability by an adjusted R -squared of 91.22.

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Section: Related Workmentioning

confidence: 99%

An Intelligent Harris Hawks Optimization Based Cluster Optimization Scheme for VANETs

et al. 2022

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“…used the heuristic learning‐based simple neural filtre model that supress the Gaussian and impulse noises from the CT images [43]. Similarly, Kumar and Mishra have presented a comprehensive review of the nature‐inspired NN‐based adaptive filtre for eliminating noise from the various medical images [44]. Qiao et.…”

Section: Related Workmentioning

confidence: 99%

Adaptive comprehensive particle swarm optimisation‐based functional‐link neural network filtre model for denoising ultrasound images

Kumar

Mishra

Joseph

et al. 2021

IET Image Processing

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Multiplicative speckle is a dominant type of noise that spoils the inherent features of the medical ultrasound (US) images. Apart from the speckle, impulse and Gaussian noises also appear in the US image due to the error encountered during the data transmission and transition of switching circuits and sensors. The noise not only deteriorates the visual quality of the US but also creates complications in the diagnosis. In this study, an adaptive comprehensive particle swarm optimisation‐based functional‐link neural network (ACPSO‐FLNN) filtre has been proposed and implemented in filtering noisy US images in different noise conditions. The proposed filtre is compared with some state‐of‐the‐art filtering techniques. Quantitative and qualitative measures such as training time, time complexity, convergence rate, and statistical test are included to study the performance of the proposed filtre. Furthermore, sensitivity, computational complexity, and order of the proposed filtre are also investigated. Friedman's test with 50 images is performed for statistical validation. The lower rank, that is, 6 and critical value of 21 × 10–4 of the proposed ACPSO‐FLNN filtre validates its dominance over other filtres.

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“…Nature‐inspired algorithms have wide applications in numerous sectors like healthcare, 93 agriculture, 94 security, 95 IoT, 96 and image processing 97 for tasks related to optimization and finding the best optimal solution. In this section, the focus is on the applicability of nature‐inspired algorithms in IoV.…”

Section: Applicability Of Nature‐inspired Algorithms In Iovmentioning

confidence: 99%

A survey on nature‐inspired algorithms and its applications in the Internet of Vehicles

Sharma

Kaushik

2021

Int J Communication

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The concept of the Internet of Vehicles (IoV) was evolved to cater to the increasing demands of emerging and advanced vehicular applications such as intelligent transportation systems (ITS). IoV plays a vital role in the development of smart cities and smart transportation. As IoV involves real-time data, so efficient routing mechanisms, as well as secure protocols, are very much needed to enhance the efficiency and security of the IoV network. In the existing literature, numerous researchers have focused on enhancing the efficiency of the IoV network. However, there still exist some flaws keeping in view the new security vulnerabilities that keep on emerging. So, there is a need to optimize the capabilities of existing frameworks for dealing with such issues.Nature-inspired algorithms are a category of powerful tools to solve problems of optimization. In this first of its kind state-of-the-art study, the potential of applicability of nature-inspired algorithms in IoV networks for improving the overall performance of IoV is explored. First, various categories of natureinspired algorithms are presented to make the readers well acquainted with the concepts of nature-inspired algorithms. Different nature-inspired algorithms having their applicability in different aspects of IoV are studied with a special focus on security, routing, and parking space management for strengthening the existing IoV network. Various open issues and research challenges in the use of different nature-inspired algorithms in IoV are also presented. Based upon the study conducted, it has been analyzed that natureinspired algorithms can optimize the overall performance of IoV networks.

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A Comprehensive Review on Nature Inspired Neural Network based Adaptive Filter for Eliminating Noise in Medical Images

Cited by 10 publications

References 61 publications

An Intelligent Harris Hawks Optimization Based Cluster Optimization Scheme for VANETs

An Intelligent Harris Hawks Optimization Based Cluster Optimization Scheme for VANETs

Adaptive comprehensive particle swarm optimisation‐based functional‐link neural network filtre model for denoising ultrasound images

A survey on nature‐inspired algorithms and its applications in the Internet of Vehicles

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