Modeling of Compressive Strength of Sustainable Self-Compacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network

Al‐Mughanam, Tawfiq; Aldhyani, Theyazn H. H.; Alsubari, Belal; Al-Yaari, Mohammed

doi:10.3390/su12229322

Cited by 45 publications

(10 citation statements)

References 52 publications

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“…In a study, the Adaptive Neuro‐Fuzzy Inference System is adapted for predicting the CS of concrete containing treated palm oil fuel ash. The prediction results of the CS were very close to the experimental values presented a highly efficient performance to predict the CS 29 . Precise evaluation of UCS of OPSC is of critical significance before construction.…”

Section: Introductionsupporting

confidence: 65%

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Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence‐based algorithms

Zhu

Huang

Mao

et al. 2021

Structural Concrete

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Because natural coarse aggregates were depleting rapidly, concrete industry has been trended toward substitute aggregates from industrial by-products or waste. One of the waste materials is oil palm ash (OPS), which is widely generated in the processing of palm oil in the tropics. Concretes made with OPS to estimate the compressive strength (CS) is cost and time consuming. This study aims to propose novel hybrid models by concepts of extreme gradient boosting (XGB) model optimized with different optimization algorithms such as sinecosine algorithm, multiverse optimization algorithm (MVO), and particle swarm optimization for predicting the uniaxial CS (UCS) of oil palm shell lightweight aggregate concrete (OPS). Also, the multivariate adaptive regression spline model is also developed to present a meaningful relationship between input and output variables. To this aim, a data set containing data samples for concrete made with OPS was gathered from the published literature. Results show that all models have acceptable performance in predicting the UCS, representing the admissible correlation between observed and predicted values and models' robustness. In the training step, the value of R 2 , the root mean square error, and the variance accounted factor for MVO-XGB are 0.9713, 1.5777, and 97.129. These values in testing phase are 0.9019, 2.6786, and 89.158. Therefore, the MVO-XGB model outperforms others, and the results demonstrate the ability of the MVO algorithm to determine the optimal value of XGB parameters.

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Section: Introductionsupporting

confidence: 65%

“…The prediction results of the CS were very close to the experimental values presented a highly efficient performance to predict the CS. 29 Precise evaluation of UCS of OPSC is of critical significance before construction. So, a hybrid AI model based on random forest (RF) was proposed for predicting UCS of OPSC.…”

Section: Introductionmentioning

confidence: 99%

Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence‐based algorithms

Zhu

Huang

Mao

et al. 2021

Structural Concrete

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“…erefore, LSTM can abstract the long-distance holding of sequences mining. Corresponding to a normal LSTM, the bidirectional LSTM [46][47][48][49] can extract bidirectional sequence data from the input sequence. Figure 3 shows the process of RNN bidirectional long-short memory model.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain

Alsubari

Aldhyani

Deshmukh

et al. 2022

Computational Intelligence and Neuroscience

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Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully addressed. Thus, deceptive reviews present wrong and misguiding opinions that are harmful to consumers and e-commerce. People called fraudsters who intentionally write deceptive reviews to target and deceive potential consumers, as they target businesses that have a well-built reputation or fame for their personal promotion, create such reviews. Therefore, developing a deceptive review detection system is essential for identifying and classifying online product reviews as truthful or fake/deceptive reviews. The main objective of this research work is to analyze and identify online deceptive reviews in electronic product reviews in the Amazon and Yelp domains. For this purpose, two experiments were conducted individually. The first was executed on standard Yelp product reviews. The second was performed on Amazon product review datasets. For this dataset, we created and labeled it using a deceptiveness score calculated based on features extracted from the review text using the linguistic inquiry and word count (LIWC) tool. These features were authenticity, negative words, comparing words negation words, analytical thinking, and positive words as well as the given rating value by a user. The recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model, was used to both datasets in order to conduct the evaluation. The application of this model was contingent upon the learning of words embedding of the review text. Finally, we evaluated the RNN-BLSTM model’s performance using the Yelp and Amazon datasets and compared the results. The results were 89.6% regarding testing accuracy for both datasets. From our experimental results, we observed that the LIWC feature with word embedding in the review text provided better accuracy performance compared with other existing methods.

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“…Figure 7 displays the structure of the LSTM model for detecting intrusions from the IoT network dataset. e LSTM model consisted of three important gates: the forget, input, and output gates [48].…”

Section: Long Short-term Memory Recurrent Neuralmentioning

confidence: 99%

Intrusion Detection System to Advance Internet of Things Infrastructure‐Based Deep Learning Algorithms

Alkahtani

Aldhyani

2021

Complexity

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Smart grids, advanced information technology, have become the favored intrusion targets due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid environment. These data are sent to the cloud, which is a huge network of super servers that provides different services to different smart infrastructures, such as smart homes and smart buildings. These can provide a large space for attackers to launch destructive cyberattacks. The novelty of this proposed research is the development of a robust framework system for detecting intrusions based on the IoT environment. An IoTID20 dataset attack was employed to develop the proposed system; it is a newly generated dataset from the IoT infrastructure. In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short-term memory (LSTM), and a hybrid convolution neural network with the long short-term memory (CNN-LSTM) model. The complexity of the network dataset was dimensionality reduced, and to improve the proposed system, the particle swarm optimization method (PSO) was used to select relevant features from the network dataset. The obtained features were processed using deep learning algorithms. The experimental results showed that the proposed systems achieved accuracy as follows: CNN = 96.60%, LSTM = 99.82%, and CNN-LSTM = 98.80%. The proposed framework attained the desired performance on a new variable dataset, and the system will be implemented in our university IoT environment. The results of comparative predictions between the proposed framework and existing systems showed that the proposed system more efficiently and effectively enhanced the security of the IoT environment from attacks. The experimental results confirmed that the proposed framework based on deep learning algorithms for an intrusion detection system can effectively detect real-world attacks and is capable of enhancing the security of the IoT environment.

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Modeling of Compressive Strength of Sustainable Self-Compacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network

Cited by 45 publications

References 52 publications

Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence‐based algorithms

Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence‐based algorithms

Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain

Intrusion Detection System to Advance Internet of Things Infrastructure‐Based Deep Learning Algorithms

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