Computer-aided diagnosis for early cancer detection using Adaptive Kernel Based Fuzzy Cuckoo Search Optimization Clustering from mammogram images

Srinivas, A.; Prasad, V. Kamakshi; Kumari, B. Leela

doi:10.1016/j.compeleceng.2022.108343

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Combining the novel Cuckoo Search Optimization Algorithm with the conventional K-means clustering approach improves the detection rate, false positive ratio, and calculations' speed results. [11]. Thus, the importance of the proposed study lies in opportunities to strengthen the security of IoT networks and protect the numerous and valuable data produced and shared by connected devices [12] [13].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things

Hassan,

Najim,

Al-Sharhanee

et al. 2024

Preprint

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Integrating machine learning (ML) into intrusion detection systems (IDS) is considered an important topic for preventing the spread of cyber threats. However, when it comes to machine learning techniques, IDSs face challenges in accurately identifying various types of attacks within the complex structures of a network. This study addresses the lack of research on combining metaheuristic optimization techniques with unsupervised machine learning algorithms in IDS design. The proposed model uses the cuckoo search metaheuristic and the K-means method to improve IDS precision. Here, the cuckoo search algorithm is used to increase the efficiency of feature selection. Meanwhile, the k-means clustering methodology is used to discretize the data and reduce its dimensionality by using two clusters, C1 and C2. The proposed model, developed carefully, includes data preprocessing (handling missing values), data transformation (label encoding), and data normalization. A stochastic operator assesses the impact of the K-means operator. The model is evaluated using an accessible intrusion dataset and compared with other state-of-the-art models. From the research conclusions, the presented model also demonstrates better results compared to the rest, especially when it reaches accuracy (99. 79%), precision (99. 78%), recall (99. 51%), and the F1-score (99).

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Section: Introductionmentioning

confidence: 99%

A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things

Hassan,

Najim,

Al-Sharhanee

et al. 2024

Preprint

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“…Moreover, CS also has remarkable features such as simple implementation and few control parameters. In view of these, CS algorithm has been widely used to address computational intelligence and engineering optimization problems, such as numerical optimization [2], path planning [3], soil water characteristics analysis [4], wastewater treatment [5], plant disease detection [6], IoT service placement [7] and cancer diagnosis [8].…”

Section: Introductionmentioning

confidence: 99%

Cuckoo search algorithm with ensemble strategy and the application in chaotic time series prediction

Cheng,

Lin,

Xiong

2023

Preprint

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Cuckoo search (CS) algorithm is a simple and effective search technique. However, CS algorithm may suffer from premature convergence as the complexity of the problem increases. To address this challenge, a cuckoo search algorithm with ensemble strategy, called CSES, is presented to strengthen the convergence performance. Specifically, three new search strategies with diverse properties are designed to well balance the trade-off between global exploration and local exploitation. After that, according to the idea of selective ensemble, a priority roulette method is employed to select the appropriate search strategy at different stages of the evolution process, so as to produce more promising results. To investigate the comprehensive performance of CSES algorithm, extensive experiments are carried out on 53 benchmark functions and three chaotic time series prediction problems. Simulation results illustrate that the proposed CSES is superior to six recently developed CS variants and several other advanced evolutionary algorithms.

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An automatic level set segmentation of breast tumor from mammogram images using optimized fuzzy c-means clustering

Ramudu,

Girija,

Raghotham Reddy

2024

Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images

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Computer-aided diagnosis for early cancer detection using Adaptive Kernel Based Fuzzy Cuckoo Search Optimization Clustering from mammogram images

Cited by 3 publications

References 23 publications

A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things

A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things

Cuckoo search algorithm with ensemble strategy and the application in chaotic time series prediction

An automatic level set segmentation of breast tumor from mammogram images using optimized fuzzy c-means clustering

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