Multi-objective optimization of feature selection using hybrid cat swarm optimization

Gao, Xiao‐Zhi; Nalluri, Madhusudana Rao; Kannan, K.; Sinharoy, Diptendu

doi:10.1007/s11431-019-1607-7

Cited by 19 publications

(7 citation statements)

References 52 publications

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“…This experimental evaluation was undergone with the population count of 10 and a maximum iterations count of 20 for the proposed disease classification model. The proposed SI‐JKHO was compared with other meta‐heuristic algorithms like “MFO, 46 CSO, 47 KHA, 45 and JA 44 and machine learning algorithms like Recurrent Neural Network (RNN), 43 LSTM, 42 CNN 48 and LSRNN” 49 . The parameters of the LSTM, RNN, and Fuzzy deep structured architectures are depicted below.…”

Section: Resultsmentioning

confidence: 99%

Adaptive leaf abnormality segmentation and improved plant disease classification by tuned long short‐term memory with recurrent neural network

Y.¹,

Rao

Veeresh

et al. 2023

Concurrency and Computation

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The conventional models suffer in providing significant accuracy and detection speed, especially in detecting apple diseases, to assure the healthy development of the apple industry. The main aspect of the proposed scheme is to present a new plant disease classification using intelligent segmentation and classification models. The adaptive leaf abnormality segmentation is enhanced by the solution index-based Jaya-Krill Herd optimization (SI-JKHO). Further, the feature extraction is accomplished using "gray level co-occurrence matrix, hybrid local binary pattern with local gradient patterns," color features, and shape features. These collected features are subjected to feature selection using principle component analysis, in which the optimal features are obtained that are further considered to "tuned long short-term memory with recurrent neural network (T-LSRNN)." From the validating results, the performance of the enhanced-JKHO-LSRNN method is correspondingly secured at 6.66%, 6.66%, 7.86%, and 4.34% higher enriched performance RNN, long short-term memory, convolutional neural networks, and LSRNN at 35th learning percentage in dataset 3. The results confirm that the developed model can accurately determine plant diseases compared to conventional models based on diverse performance metrics like "accuracy, precision, specificity, sensitivity, false positive rate, and false negative rate," and so forth. K E Y W O R D Sadaptive fuzzy C-means clustering with thresholding, plant disease classification, solution index-based Jaya-Krill Herd optimization, tuned long short-term memory with recurrent neural network INTRODUCTIONPlant disease can be diagnosed through the plant leaves, which involve more complexities while considering optical observation. 1 Due to the increasing cultivation of plants, the complexities also increase along with the conventional phytopathological problems. Although experienced agronomists and pathologists diagnose plant diseases, sometimes it causes failures in diagnosing particular plant diseases and leads to wrong conclusions and further mistaken treatments. 2 The existing automatic computational system has offered significant support to the agronomist in detecting and diagnosing plant diseases while subjecting the optical observation of infected plants through their leaves. 3 Here, the developed system has to be simple

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Section: Resultsmentioning

confidence: 99%

Adaptive leaf abnormality segmentation and improved plant disease classification by tuned long short‐term memory with recurrent neural network

Y.¹,

Rao

Veeresh

et al. 2023

Concurrency and Computation

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show abstract

“…The assessment results of the SBCSO algorithm, compared to the binary single-objective optimization algorithms, such as the BACO [13], BBA [25], BCSO [23], BDA [26], BGSA [35], BGA [42], BPSO [21], and BGWO [22], are reported in subsection 4.3. Finally, the assessment results of the SBCSO algorithm, compared to the binary multi-objective optimization algorithms, such as BMOCSO [46], BNSGA-II [45], BMODE [43], BMOPSO [44], and BMOBBA [47], are reported in subsection 4.5. All of the simulations were executed in MATLAB on a computer equipped with a Core i7 CPU and 16G RAM.…”

Section: Resultsmentioning

confidence: 99%

A Self-adaptive Binary Cat Swarm Optimization Using New Time-Varying Transfer Function for Gene Selection in DNA Microarray Expression Cancer Data

Sharafi

Teshnehlab

2022

Preprint

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Microarray technology is beneficial in terms of diagnosing various diseases, including cancer. Despite all DNA microarray benefits, the high number of genes versus the low number of samples has always been a crucial challenge for this technology. Accordingly, we need new optimization algorithms to select optimal genes for faster disease diagnosis. In this article, a new version of the binary cat optimization algorithm, named SBCSO, for gene selection in DNA microarray expression cancer data is presented. The main contributions in this paper are listed as follows: First, the opposition-based learning (OBL) mechanism is employed to improve the proposed algorithm's population members' diversity. Second, a time-varying V-shape transfer function is employed to balance the two phases of exploration and extraction in the proposed algorithm. Third, the MR and 𝛌 parameters in the proposed algorithm are adapted over time, and finally, single-objective and multi-objective approaches are proposed to solve the gene selection problems. The fifteen datasets pertinent to microarray data of various cancer types are employed to compare the proposed method with other well-known binary optimization algorithms. The experiments' results indicate that the proposed algorithm has a better capability to select the optimal genes for a faster disease diagnosis.

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“…The evolutionary optimization algorithm [75,76] is based on the evolution of nature and simulates the biological evolution process. Most evolutionary algorithms [77,78] are based on genetic algorithms, involving selection, crossover, and mutation operations.…”

Section: ) Evolutionary Optimization Algorithmmentioning

confidence: 99%

Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

Zhang¹,

Geng²,

Cai³

2023

Computer Modeling in Engineering &Amp; Sciences

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Cloud computing technology is favored by users because of its strong computing power and convenient services. At the same time, scheduling performance has an extremely efficient impact on promoting carbon neutrality. Currently, scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours. Therefore, the scheduling problem has promising application prospects under the multi-cloud environment. This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks. This paper reviews the concepts, types, objectives, advantages, challenges, and research status of task scheduling in the multi-cloud environment. Task scheduling strategies proposed in the existing related references are analyzed, discussed, and summarized, including research motivation, optimization algorithm, and related objectives. Finally, the research status of the two kinds of task scheduling is compared, and several future important research directions of multi-cloud task scheduling are proposed.

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Multi-objective optimization of feature selection using hybrid cat swarm optimization

Cited by 19 publications

References 52 publications

Adaptive leaf abnormality segmentation and improved plant disease classification by tuned long short‐term memory with recurrent neural network

Adaptive leaf abnormality segmentation and improved plant disease classification by tuned long short‐term memory with recurrent neural network

A Self-adaptive Binary Cat Swarm Optimization Using New Time-Varying Transfer Function for Gene Selection in DNA Microarray Expression Cancer Data

Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

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