Feasibility of Recurrent Neural Network for the Binary classification of Non stationary signals

Delsy, T. Thaj Mary; Nandhitha, N. M.; Rani, B. Sheela

doi:10.1016/j.micpro.2021.103955

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…For each user, we find the number of NN. Relation between users' ratings is found using PCS [18]. Items focus on the commonality between the item likes instead of the users.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: A novel hybrid feature combination method for enhanced movie recommendations with user resemblance and attitude mining

Lavanya

Bharathi

2021

Pers Ubiquit Comput

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Most movie recommendation methods use hard-clustering and simple collaborative filtering techniques in order to achieve their end results. However, these methods tend to overlook crucial aspects of both users and items. When these methods hard cluster a movie item into a cluster, they turn a blind eye to the fact that the item also exhibits some properties of another cluster’s items. Recommender systems facilitate users and relevant things expeditiously supported their requests and historic communications with alternative customers. Recommendation systems are a crucial portion of signifying things particularly in streaming amenities. For streaming motion-picture show services like Netflix, recommendation methods are vital for serving to users notice fresh movies to get pleasure from. However, massive amounts of information will turn out restrictions in recommendations due to accuracy as a result of diversity and meagerness problems. Our work proposes a unique hybrid technique that mixes collaborative filtering and characteristics of demographic filtering technique to point the close users, and associate against one another. This technique has been established over associate in tending analysis of the way to cut back the blunders in grading estimates supported users’ earlier communications that ends up in improved prediction accuracy in among completely different algorithms. Additionally, a feature combination technique is utilized that progresses the expectation accuracy and to check our method, using MovieLens 1M dataset, we contended an offline assessment, already available assessment tactics, and compared the same with the output factors to support authenticating the proposed procedure.

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“…For each user, we find the number of NN. Relation between users' ratings is found using PCS [18]. Items focus on the commonality between the item likes instead of the users.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: A novel hybrid feature combination method for enhanced movie recommendations with user resemblance and attitude mining

Lavanya

Bharathi

2021

Pers Ubiquit Comput

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“…T. Thaj Mary Delsy et. Al [15] has proposed a RNN classi er that is used for non ststionary signals in order to attain better accuracy. And also the author has proposed BPN network that is used for training and testing for the classi cation of non stationary signals [16].…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: Phased array ultrasonic test signal enhancement and classification using Empirical Wavelet Transform and Deep Convolution Neural Network

Jayasudha

Lalithakumari

2022

Concurrent Engineering

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In the recent past, Non-Destructive Testing (NDT) has become the most popular technique due to its efficiency and accuracy without destroying the object and maintaining its original structure and gathering while examining external and internal welding defects. Generally, the NDT environment is harmful which is distinguished by huge volatile fields of electromagnetic, elevated radiation emission instability, and elevated heat. Therefore, a suitable NDT approach could be recognized and practiced. In this paper, a novel algorithm is proposed based on a Phased array ultrasonic test (PAUT) for NDT to attain the proper test attributes. In the proposed methodology, the carbon steel welding section is synthetically produced with various defects and tested using the PAUT method. The signals which are acquired from the PAUT device are having noise. The Adaptive Least Mean Square (ALMS) filter is proposed to filter PAUT signal to eliminate random noise and Gaussian noise. The ALMS filter is the combination of low pass filter (LPF), high pass filter (HPF), and bandpass filter (BPF). The time-domain PAUT signal is converted into a frequency-domain signal to extract more features by applying the Empirical Wavelet Transform (EWT) algorithm. In the frequency domain signal, first order and second order features extraction techniques are applied to extract various features for further classification. The Deep Learning methodology is proposed for the classification of PAUT signals. Based on the PAUT signal features, the Deep Convolution Neural Network (DCNN) is applied for further classification. The DCNN will classify the welding signal as to whether it is defective or non-defective. The Confusion Matrix (CM) is used for the estimation of measurement of performance of classification as calculating accuracy, sensitivity, and specificity. The experiments prove that the proposed methodology for PAUT testing for welding defect classification is obtained more accurately and efficiently across existing methodologies by providing numerical and graphical results.

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A Medical Chatbot Embedding with Artificial Intelligence for Self-Diagnosis

Jegan,

Kavi Priya,

Sheeba

et al. 2023

2023 International Conference on Data Science, Agents &Amp; Artificial Intelligence (ICDSAAI)

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Feasibility of Recurrent Neural Network for the Binary classification of Non stationary signals

Cited by 7 publications

References 5 publications

RETRACTED ARTICLE: A novel hybrid feature combination method for enhanced movie recommendations with user resemblance and attitude mining

RETRACTED ARTICLE: A novel hybrid feature combination method for enhanced movie recommendations with user resemblance and attitude mining

RETRACTED: Phased array ultrasonic test signal enhancement and classification using Empirical Wavelet Transform and Deep Convolution Neural Network

A Medical Chatbot Embedding with Artificial Intelligence for Self-Diagnosis

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