A Bayesian regularized feed-forward neural network model for conductivity prediction of PS/MWCNT nanocomposite film coatings

Demirbay, Barış; Kara, Duygu Bayram; Uǧur, Şaziye

doi:10.1016/j.asoc.2020.106632

Cited by 20 publications

(8 citation statements)

References 55 publications

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“…An Artificial Neural Network (ANN) mimics the nervous system of a human [ 69 ]. The single hidden layer feed-forward artificial neural network is the simplest form of the ANN.…”

Section: Related Terminologiesmentioning

confidence: 99%

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura

Al-Zoubi

Faris

et al. 2021

Sensors

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The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.

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“…An Artificial Neural Network (ANN) mimics the nervous system of a human [ 69 ]. The single hidden layer feed-forward artificial neural network is the simplest form of the ANN.…”

Section: Related Terminologiesmentioning

confidence: 99%

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura

Al-Zoubi

Faris

et al. 2021

Sensors

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“…In addition, it prevents numerical difficulties during the calculation [ 37 ]. Equation (3) is the formula for normalization [ 14 ].

where

is the scale value,

is the

th actual value of data,

is the maximum value of data, and

is the minimum value of data.…”

Section: Methodsmentioning

confidence: 99%

“…The proper design of ANN training (so-called ANN topology) is crucial in order to produce models with good accuracy [ 10 , 11 , 12 ]. The determination of network parameters requires a large number of its different configurations and is performed by the trial-and-error method [ 13 , 14 ]. The network parameters of the number of hidden layers, the neuron in the first hidden layer and the neuron in the second hidden layer [ 13 ], transfer functions, and hidden neurons [ 14 ] need to be varied until their optimal condition is determined.…”

Section: Introductionmentioning

confidence: 99%

Improved Artificial Neural Network Training Based on Response Surface Methodology for Membrane Flux Prediction

Ibrahim

Wahab

2022

Membranes

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This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural network parameters. The technique has the advantage of training performance, with a reduced training time and number of repetitions in achieving good model prediction for the permeate flux of palm oil mill effluent. The conventional training process is performed by the trial-and-error method, which is time consuming. In this work, Levenberg–Marquardt (lm) and gradient descent with momentum (gdm) training functions are used, the feed-forward neural network (FFNN) structure is applied to predict the permeate flux, and airflow and transmembrane pressure are the input variables. The network parameters include the number of neurons, the learning rate, the momentum, the epoch, and the training functions. To realize the effectiveness of the DoE strategy, central composite design is incorporated into neural network methodology to achieve both good model accuracy and improved training performance. The simulation results show an improvement of more than 50% of training performance, with less repetition of the training process for the RSM-based FFNN (FFNN-RSM) compared with the conventional-based FFNN (FFNN-lm and FFNN-gdm). In addition, a good accuracy of the models is achieved, with a smaller generalization error.

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“…As it has been shown already, ANNs dominate in a considerable amount of publications due to their unique ability to solve complex real-life engineering problems beyond AM, by the utilization of previously measured characterization data and due to the ability to achieve significant time savings [ 41 ]. The interpretability of ANNs in decision making has been popular in data science, commonly by solving the inverse problem, where the experimental data are correlated to the microstructure and functionalities of nanomaterials to gain new insights by using first-principles-based tools to accelerate the time-to-market of novel nanomaterials/nanocomposite materials/systems [ 93 ].…”

Section: In Silico Materials Developmentmentioning

confidence: 99%

“…Beyond process control, AI and machine learning hold a lot of promise to bridge this gap with their empirical character, as till today rapid characterization tools have been exploited with high accuracy for the discovery of materials and immunotherapies, materials reinforcement/failure/toxicity mechanism recognition, structural characterization, phase detection and quantification, and anomaly detection [ 3 , 25 , 31 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. While machine learning and AI are already well established in the fields of statistics, economics, and bioinformatics, their utilization in nanotechnology is relatively new [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Konstantopoulos

Koumoulos²,

Charitidis

2022

Nanomaterials

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Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

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A Bayesian regularized feed-forward neural network model for conductivity prediction of PS/MWCNT nanocomposite film coatings

Cited by 20 publications

References 55 publications

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Improved Artificial Neural Network Training Based on Response Surface Methodology for Membrane Flux Prediction

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

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