A Framework of Dimensionality Reduction Utilizing PCA for Neural Network Prediction

Kumar, G. Ravi; Nagamani, K.; Babu, G. Suresh

doi:10.1007/978-981-15-0978-0_16

Cited by 11 publications

(7 citation statements)

References 3 publications

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“…Some more advanced methods for mapping the multi-dimensional thermal analysis data for the input layer of neural network still need to be developed. Muravyev and Pivkina [ 21 ] propose using principal component analysis [ 57 , 58 , 59 ] or introducing additional hidden layers for this purpose. To the best of authors’ knowledge, no more advanced methods of introduction of TA data to ANN have been proposed so far.…”

Section: Technical Details Behind Annsmentioning

confidence: 99%

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Muravyev

Luciano²,

Ornaghi

et al. 2021

Molecules

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Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods.

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Section: Technical Details Behind Annsmentioning

confidence: 99%

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Muravyev

Luciano²,

Ornaghi

et al. 2021

Molecules

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“…A choice tree is a tree structure which groups an information test into one of its potential classes. Choice trees are utilized to extricate information by settling on choice guidelines from the enormous measure of accessible data [1,2] . A choice tree classifier has a basic structure which can be minimalistic ally put away and that effectively arranges new information.…”

Section: Decision Treementioning

confidence: 99%

Diagnosis spinal abnormalities utilizing machine learning algorithms

E¹,

B²

2021

Int. J. Comput. Programming Database Manage.

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This paper centers on the use of AI calculations for anticipating spinal anomalies. Various AI approaches specifically Decision tree, Naïve Bayes, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) strategies are considered for the conclusion of spinal anomaly. The presentation of arrangement of strange and typical spinal patients is assessed as far as various variables including preparing and testing exactness, accuracy and review. Be that as it may, SVM is the most appealing as it's anything but a higher exactness esteem. Henceforth, SVM is appropriate for the order of spinal patients when applied on the most five significant highlights of spinal examples.

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“…Finding an optimal component subset is obstinate and gives related part decisions have been wind up being NP-hard [9] . At this intersection, it is central to depict standard component decision measure, which involves four major advances, to be explicit, subset age, subset appraisal, ending standard, and endorsement [4,5] . Subset age is a chase communication that produces contender incorporate subsets for evaluation dependent on a particular pursuit approach.…”

Section: Feature Selectionmentioning

confidence: 99%

An efficient feature selection approach for detection of liver disease prediction

Ahamed¹,

Reddy²

2021

Int. J. Comput. Artif. Intell.

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The human liver issue is a hereditary issue because of the habituality of liquor or impact by the infection. It can prompt liver disappointment or liver malignancy, if not been recognized in introductory stage. The point of the proposed strategy is to recognize the liver issue in beginning stage utilizing liver capacity test dataset. There are numerous problems of the liver that require clinical consideration by a doctor or other medical services proficient. The dataset contains a colossal volume of highlights estimations which are diminished using ReliefF based component assurance methodology. The dataset contains a gigantic rundown of abilities which is diminished using an improved segment decision procedure named as covering method. The current examination centers on different component determination methods, which is perhaps the most significant and every now and again utilized in information preprocessing for information mining. In this paper the impact of highlight determination on the exactness of Decision Tree and Naïve Bayes, classifiers is introduced utilizing Liver problem information. These two classifiers are contrasted and genuine dataset which are pre-prepared with highlight determination strategies. The proposed covering method depends on a choice tree -RelifF and credulous bayes -RelifF computation to pick the primary highlights from the given dataset. The picked subset of highlights then goes through a preprocessing step to introduce a consistency in the allotment of data. Since guileless bayes is seen to enjoy the benefit of giving a famous execution in portrayal stage.

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A Framework of Dimensionality Reduction Utilizing PCA for Neural Network Prediction

Cited by 11 publications

References 3 publications

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Diagnosis spinal abnormalities utilizing machine learning algorithms

An efficient feature selection approach for detection of liver disease prediction

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