Artificial Neural
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2022 ◽  
Vol 343 ◽  
pp. 126083
Author(s):  
Zhenzhong Hu ◽  
Yue Yuan ◽  
Xian Li ◽  
Zhengjun Tu ◽  
Omar Donovan Dacres ◽  
...  

2021 ◽  
Vol 249 ◽  
pp. 114821
Author(s):  
Wei Chen ◽  
Yingzong Liang ◽  
Xianglong Luo ◽  
Jianyong Chen ◽  
Zhi Yang ◽  
...  

2022 ◽  
Vol 306 ◽  
pp. 117960
Author(s):  
Matheus Soares Geraldi ◽  
Enedir Ghisi

2021 ◽  
Vol 142 ◽  
pp. 103985
Author(s):  
Afshin Hedayat

2021 ◽  
Vol 67 ◽  
pp. 102720
Author(s):  
Naoto Sugawara ◽  
Takeshi Fujisawa ◽  
Kodai Nakamura ◽  
Yusuke Sawada ◽  
Takayoshi Mori ◽  
...  

2021 ◽  
Vol 28 ◽  
pp. 101553
Author(s):  
Woo Su Lee ◽  
Moon Yong Park ◽  
Xuan Quang Duong ◽  
Mahdi Koushaeian ◽  
Nehad Ali Shah ◽  
...  

2021 ◽  
Vol 33 (4) ◽  
pp. 042025
Author(s):  
Cameron Vo ◽  
Boyang Zhou ◽  
Xiaoming Yu

2022 ◽  
Vol 52 (4) ◽  
Author(s):  
Raissa Oliveira Rocha Alves ◽  
Otávio Chedid Tomé ◽  
Pollyanna Cardoso Pereira ◽  
Camila Nair Batista Couto Villanoeva ◽  
Vanelle Maria da Silva

ABSTRACT: This research was performed to ascertain the most suitable Artificial Neural Network (ANN) model to quantify the degree of fraud in powdered milk through the addition of powdered whey via regular standard physicochemical analyses. In this study, an evaluation was done on 103 samples with different quantities of added whey powder to whole milk powder. Using Fourier Transform Infrared Spectroscopy the fat, cryoscopy, total solids, defatted dry extract, lactose, protein and casein were analyzed. The hyperbolic tangent transformation function was used with 45 topologies, and the Holdback and K-fold validation methods were tested. In the Holdback method, 75% of the database was employed for training, while 25% was used for validation. In the K-fold method, the database was categorized into five equal sized subsets, which alternated between training and validation. Of the two methods, the K-fold method was proven to have superior efficiency. Next, analysis was done on three models of multilayer perceptron networks with feedforward architecture. In Model 1, the input layer contained all the physicochemical analyses conducted, in model 2 the casein analysis was excluded, and in model 3 the routine analyses performed for dairy products was done (fat, defatted dry extract, cryoscopy and total solids). From Model 3 an ANN was derived which could satisfactorily predict fraud calculated from using the routine and standard analyses for dairy products, containing 64 nodes in the hidden layer, with R2 of 0.9935 and RMSE of 0.5779 for training, and R2 of 0.9964 and RMSE of 0.4358 for validation.


2022 ◽  
Vol 414 ◽  
pp. 126678
Author(s):  
Yixin Li ◽  
Xianliang Hu

Author(s):  
Sherly T.T ◽  
◽  
B. Rosiline Jeetha ◽  

When somebody, usually a teenager, abuses or harasses individual on the internet and other digital places, mainly on social networking platforms, this is termed as cyberbullying. Cyberbullying, like all types of bullying, produces psychological, emotional, and physical distress. Every individual's reaction to being bullied is diverse, but research has discovered certain common patterns. In a recent study, we introduced a technique called Hybrid Firefly Artificial Neural Networks (HFANN) to combat cyberbullying. Nevertheless, without considering the sentiment analysis features, accuracy of cyber bullying identification is lowered in this study. The Sentiment Analysis and Deep Learning based Cyber Bullying Detection (SADL-CDD) approach is used in the suggested research approach to address this issue. The punctuations, urls, html tags, and emoticons from the input tweet comments are removed first in this study project. Sentiment feature extraction is performed after pre-processing to improve classification accuracy. The Modified Fruit Fly Algorithm (MFFA) is used to choose the best features from the extracted features. Following feature selection, cyber bullying detection is carried out using a Hybrid Recurrent Residual Convolutional Neural Network (HRecRCNN). The experimental outcome of this study indicates the efficiency of the suggested approach. In comparison to current algorithms, the SADL-CDD method delivers improved classification performance with respect to reduced time complexity, greater precision, recall, f-measure, and accuracy.


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