A Comparative Study of Different Curve Fitting Algorithms in Artificial Neural Network using Housing Dataset

Bataineh, Ali Al; Kaur, Devinder

doi:10.1109/naecon.2018.8556738

Cited by 50 publications

(22 citation statements)

References 7 publications

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“…MPG dataset is likely because the input, origin , should not be treated as a numerical input while the other categorical variables have some justification for being treated as numerical features. We note that the performance of LMGP and LVGP is either better or comparable to that of state-of-the-art NNs fitted to these datasets[41][42][43][44]. Unlike NNs, however,…”

mentioning

confidence: 83%

Latent map Gaussian processes for mixed variable metamodeling

Oune

Bostanabad

2021

Computer Methods in Applied Mechanics and Engineering

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mentioning

confidence: 83%

Latent map Gaussian processes for mixed variable metamodeling

Oune

Bostanabad

2021

Computer Methods in Applied Mechanics and Engineering

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“…After figuring out the optimal architecture of the autoencoder; the following task is to train the autoencoder on normal training data. This is achieved using the backpropagation algorithm [22,23]. Once autoencoder is trained, next task is to compute the threshold value which is required for detection of anomalies during testing phase.…”

Section: Methodsmentioning

confidence: 99%

Autoencoder based Semi-Supervised Anomaly Detection in Turbofan Engines

Bataineh¹,

Mairaj²,

Kaur³

2020

IJACSA

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This paper proposes a semi-supervised autoencoder based approach for the detection of anomalies in turbofan engines. Data used in this research is generated through simulation of turbofan engines created using a tool known as Commercial Modular Aero-Propulsion System Simulation (CMAPSS). C-MAPSS allows users to simulate various operational settings, environmental conditions, and control settings by varying various input parameters. Optimal architecture of autoencoder is discovered using Bayesian hyperparameter tuning approach. Autoencoder model with optimal architecture is trained on data representing normal behavior of turbofan engines included in training set. Performance of trained model is then tested on data of engines included in test set. To study the effect of redundant features removal on performance, two approaches are implemented and tested: with and without redundant features removal. Performance of proposed models is evaluated using various performance evaluation metrics like F1-score, Precision and Recall. Results have shown that best performance is achieved when autoencoder model is used without redundant feature removal.

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“…In (7), N c is the total number of clones created for a given antibody, β is a clonal factor, N is the size of the population, i is the antibody current rank where i ∈ [1, m], and round (.) is the operator that rounds its argument towards the closest integer.…”

Section: B Csamentioning

confidence: 99%

“…In many of these applications, i.e., classification from text, sound, or images, the performance of deep learning algorithms matched and sometimes exceeding human-level performance [4], [5]. Deep learning refers to neural networks with several hidden layers [6], [7]. Deep neural networks perform computing tasks similar to biological neurons in the human brain [8].…”

Section: Introductionmentioning

confidence: 99%

Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

Bataineh

Kaur

2021

IEEE Access

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This paper aims to automatically design optimal LSTM topologies using the clonal selection algorithm (CSA) to solve text classification tasks such as sentiment analysis and SMS spam classification. Designing optimal topologies involves determining the best configuration of hyperparameters that will give the best performance. The current state-of-the-art LSTM topologies are often designed using trial and error approaches which are incredibly time-consuming and require domain experts. Our proposed method, referred to as CSA-LSTM, is evaluated using the Large Movie Review Dataset (IMDB). Furthermore, to verify the robustness of the hyperparameters discovered by CSA for the IMDB dataset, we have used them for the other datasets, viz. the Twitter US Airline Sentiment and the SMS Spam Collection. Additionally, the discovered hyperparameters for the LSTM are combined with pre-determined convolutional neural network (CNN) layers to achieve the same or better results to fast the training time and fewer trainable parameters. For further verification and evaluation of the generalization ability and effectiveness of the proposed approach, it is compared with four machine learning algorithms widely used for text classification tasks: (1) random forest (RF), (2) logistic regression (LR), (3) support vector machine (SVM), and (4) multinomial naive Bayes (NB). The results of our experiments show that the LSTM topologies automatically designed by our CSA method are less expensive, reusable and outperform the machine learning algorithms and other models in the literature evaluated on the same three datasets. Through our proposed method, LSTM's best topology can be self-determined without any human intervention, making CSA-based algorithms a promising approach to automatically design optimal LSTM topologies that provide the best performance for a given task.INDEX TERMS clonal selection algorithm, deep learning, hyperparameters optimization, IMDB, immunocomputing, long short-term memory.

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A Comparative Study of Different Curve Fitting Algorithms in Artificial Neural Network using Housing Dataset

Cited by 50 publications

References 7 publications

Latent map Gaussian processes for mixed variable metamodeling

Latent map Gaussian processes for mixed variable metamodeling

Autoencoder based Semi-Supervised Anomaly Detection in Turbofan Engines

Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

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