RETRACTED ARTICLE: The health of things for classification of protein structure using improved grey wolf optimization

Sharma, Prerna; Gupta, Apoorva; Aggarwal, Aastha; Gupta, Deepak; Khanna, Ashish; Hassanien, Aboul Ella; Albuquerque, Victor Hugo C. de

doi:10.1007/s11227-018-2639-4

Cited by 22 publications

(9 citation statements)

References 30 publications

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“…A study in ( 17 ) developed a resampling-based preprocessing technique to address the skewing of unbalanced data sets and classified various types of tumor in patients. Sharma et al ( 18 ) deals with development of an optimized meta-heuristic model for attribute selection to accurately categorize protein structures. The proposed model was experimentally compared with other meta-heuristic algorithms, and it was found that it yielded optimum results than others.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Skewed Data Using Sampling-Based Preprocessing Approach

Mishra

Mallick

Jena

et al. 2020

Front. Public Health

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In the past few years, classification has undergone some major evolution. With a constant surge of the amount of data gathered from different sources, efficient processing and analysis of data is becoming difficult. Due to the uneven distribution of data among classes, data classification with machine-learning techniques has become more tedious. While most algorithms focus on major data samples, they ignore the minor class data. Thus, the data-skewing issue is one of the critical problems that need attention of researchers. The paper stresses upon data preprocessing using sampling techniques to overcome the data-skewing problem. Here, three different sampling techniques such as Resampling, SpreadSubSampling, and SMOTE are implemented to reduce this uneven data distribution issue and classified with the K-nearest neighbor algorithm. The performance of classification is evaluated with various performance metrics to determine the efficiency of classification.

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Section: Related Workmentioning

confidence: 99%

Optimization of Skewed Data Using Sampling-Based Preprocessing Approach

Mishra

Mallick

Jena

et al. 2020

Front. Public Health

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“…The % removal region flotsam and jetsam assurance is just about similar among Brahmaputra and Ganga bowl and is lower in the Indus bowl. The removal district ice uncovered will be the top in Indus bowl [17]. The removal area ice uncovered is just about indistinguishable among Brahmaputra and Ganga bowl.…”

Section: Review Of Glacier Inventorymentioning

confidence: 95%

Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

Malik¹,

Pande²,

Nishi³

et al. 2020

AIS

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Climate change issues societal operation, likely wanting considerable adaptation to deal with doing well altered weather patterns. Machine learning (ML) algorithms have progressed considerably, triggering breakthroughs in some other investigation sectors, along with only lately suggested as helping climate evaluation. Though a significant volume of isolated Earth System functions are analyzed with ML techniques, much more generic phone system to find out better the whole temperature unit hasn't happened. For instance, ML is able to aid remote identification, in which complex feedbacks make characterization tough from instantaneous equation analysis or perhaps possibly visualization of sizes plus Earth System design (ESM) diagnostics. Artificial intelligence (AI) may thus build on determined climate associates to provide enhanced alerts of approaching eco-friendly functions, which includes intense events. While ESM development is actually completely necessary, a parallel concentrate on utilizing ML and AI to determine as well as capitalize a great deal more on pre pre-existing simulations as well as info is suggested by us.

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“…The remarkable advantages of GWO has led to tremendous research applications from several critical research domains. These application domains can be categorized into: machine learning (Emary et al, 2016(Emary et al, , 2015Al-Tashi et al, 2019), engineering (Kohli and Arora, 2018;Yıldız and Yıldız, 2018), wireless sensor network (Kaushik et al, 2019;Rajakumar et al, 2017), environmental modeling (Tikhamarine et al, 2020;Sweidan et al, 2016), medical and bio informatics (Sharma et al, 2019(Sharma et al, , 2020 and image processing (Khairuzzaman and Chaudhury, 2017;Li et al, 2017a).…”

Section: Gwo For Pid Tuningmentioning

confidence: 99%