An adaptive fuzzy K-nearest neighbor approach for MR brain tumor image classification using parameter free bat optimization algorithm

Kaur, Taranjit; Saini, Barjinder Singh; Gupta, Savita

doi:10.1007/s11042-019-7498-3

Cited by 30 publications

(9 citation statements)

References 74 publications

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“…Improved performance of the PF-BAT on standard benchmark mathematical functions and a variety of medical image classification tasks has been already demonstrated in the previous work [27] . Also, the superiority of PF-BAT enhanced FKNN over other machine learning algorithms over a broad variety of classification problems has already been established in our previous works [29] . In the present work, its performance has been investigated over COVID CT scan image dataset.…”

Section: Methodsmentioning

confidence: 78%

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Kaur

Gandhi

Panigrahi

2021

IEEE J. Transl. Eng. Health Med.

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Background: Timely and precise identification of COVID-19 is an arduous task owing to the scarcity and inefficiency of the medical test kits. This has resulted in medical professionals turning towards Computed Tomography (CT) scans. Efforts are being made to design deep learning models capable of COVID-19 detection using CT scans. This has certainly reduced the manual intervention in disease detection but reported accuracy is limited. Methods: The present work proposes an automatic system for COVID-19 diagnosis based on deep features and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier. Typically, features are extracted from the fully connected layer of transfer learned MobileNetv2 that are then employed for FKNN training. The hyper-parameters of FKNN are fine-tuned using PF-BAT. Results: The experimental results on the benchmark COVID CT scan data reveal that the proposed algorithm attains a validation accuracy of 99.38% which is better than the existing state-of-the-arts. Conclusion: The outcomes presented in the paper will aid in speedy detection of this virus at the different phases. Such a detection system will also increase the opportunities for fast recovery of patients worldwide thereby releasing the pressure off the medical professionals and the healthcare system around the world.

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Section: Methodsmentioning

confidence: 78%

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Kaur

Gandhi

Panigrahi

2021

IEEE J. Transl. Eng. Health Med.

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“…For researchers, it is a big dilemma to find the optimal configurations for such parameters. Therefore, there are a considerable research works to build parameterless or parameter-free BA that has been recognized [ 152 , 153 ]. In the future, a robust parameter-free BA can be proposed to serve as a black box to reach better publicity.…”

Section: Discussionmentioning

confidence: 99%

“…Another domain where using BA shows its superiority is image and signal processing, in which the algorithm solves image segmentation [ 28 , 35 , 45 , 77 , 78 , 196 , 204 , 221 , 229 , 255 , 322 , 323 , 327 , 328 ], face recognition and fingerprint identification [ 23 , 44 , 80 , 230 ], magnetic resonance (MR) brain tumour classification [ 151 – 153 , 271 ], multilevel image thresholding [ 79 , 207 , 254 ], and signal processing application [ 5 , 161 , 302 ]. Figure 19 plots the applications of BA in image and signal processing domain.…”

Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

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“…As a commonly used machine learning algorithm, many scholars have also improved it, mainly around the adjustment of the

parameter [ 41 , 42 ]. However, no in-depth research has been performed on the determination of k , selection of distance functions, or setting of distance weights.…”

Section: An Edge-intelligent Wsn Intrusion Detection Systemmentioning

confidence: 99%

An Enhanced Intrusion Detection Model Based on Improved kNN in WSNs

Liu

Zhao

Fan

et al. 2022

Sensors

109

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Aiming at the intrusion detection problem of the wireless sensor network (WSN), considering the combined characteristics of the wireless sensor network, we consider setting up a corresponding intrusion detection system on the edge side through edge computing. An intrusion detection system (IDS), as a proactive network security protection technology, provides an effective defense system for the WSN. In this paper, we propose a WSN intelligent intrusion detection model, through the introduction of the k-Nearest Neighbor algorithm (kNN) in machine learning and the introduction of the arithmetic optimization algorithm (AOA) in evolutionary calculation, to form an edge intelligence framework that specifically performs the intrusion detection when the WSN encounters a DoS attack. In order to enhance the accuracy of the model, we use a parallel strategy to enhance the communication between the populations and use the Lévy flight strategy to adjust the optimization. The proposed PL-AOA algorithm performs well in the benchmark function test and effectively guarantees the improvement of the kNN classifier. We use Matlab2018b to conduct simulation experiments based on the WSN-DS data set and our model achieves 99% ACC, with a nearly 10% improvement compared with the original kNN when performing DoS intrusion detection. The experimental results show that the proposed intrusion detection model has good effects and practical application significance.

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An adaptive fuzzy K-nearest neighbor approach for MR brain tumor image classification using parameter free bat optimization algorithm

Cited by 30 publications

References 74 publications

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Automated Diagnosis of COVID-19 Using Deep Features and Parameter Free BAT Optimization

Recent advances of bat-inspired algorithm, its versions and applications

An Enhanced Intrusion Detection Model Based on Improved kNN in WSNs

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