Cycle reservoir with regular jumps for forecasting ozone concentrations: two real cases from the east of Croatia

Sheta, Alaa; Faris, Hossam; Rodan, Ali; Kovač-Andrić, Elvira; Al-Zoubi, Ala’ M.

doi:10.1007/s11869-018-0561-9

Cited by 22 publications

(6 citation statements)

References 34 publications

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“…Thus, applying machine learning for IoT system security is considered an optimal opportunity to protect them from intrusion attacks, especially by detecting any outlier activity that emerges in the system. It is worth noting that machine learning also shows excellent performance in other areas such as [ 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…At this stage, in order to overcome the gradient disappearance and gradient explosion problems that may be caused by the classic recurrent neural network model, the nodes of the network usually use complex structures such as LSTM (Long-Short Term Memory) and its variant GRU (Gated Recurrent Unit), so that model training is slow. Subsequently, in order to strengthen the accuracy of model training, Sheta [ 11 ] introduced a translation model based on convolutional neural networks, which uses convolutional neural networks to window and hierarchically extract sentence features, while retaining the accuracy of recurrent neural networks. Next, model training is accelerated through parallel computing.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing

Tuo

Kong

et al. 2021

Computational Intelligence and Neuroscience

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This paper uses neural network as a predictive model and genetic algorithm as an online optimization algorithm to simulate the noise processing of Chinese-English parallel corpus. At the same time, according to the powerful random global search mechanism of genetic algorithm, this paper studied the principle and process of noise processing in Chinese-English parallel corpus. Aiming at the task of identifying isolated words for unspecified persons, taking into account the inadequacies of the algorithms in standard genetic algorithms and neural networks, this paper proposes a fast algorithm for training the network using genetic algorithms. Through simulation calculations, different characteristic parameters, the number of training samples, background noise, and whether a specific person affects the recognition result were analyzed and discussed and compared with the traditional dynamic time comparison method. This paper introduces the idea of reinforcement learning, uses different reward mechanisms to solve the inconsistency of loss function and evaluation index measurement methods, and uses different decoding methods to alleviate the problem of exposure bias. It uses various simple genetic operations and the survival of the fittest selection mechanism to guide the learning process and determine the direction of the search, and it can search multiple regions in the solution space at the same time. In addition, it also has the advantage of not being restricted by the restrictive conditions of the search space (such as differentiable, continuous, and unimodal). At the same time, a method of using English subword vectors to initialize the parameters of the translation model is given. The research results show that the neural network recognition method based on genetic algorithm which is given in this paper shows its ability of quickly learning network weights and it is superior to the standard in all aspects. The performance of the algorithm in genetic algorithm and neural network, with high recognition rate and unique application advantages, can achieve a win-win of time and efficiency.

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“…Feature selection techniques have been widely used in different computational applications including but not limited to medical science [40]- [42], sales forecasting [43], face recognition [44], and customer churn prediction [45]. When designing a machine learning technique [46], reducing the number of features in a dataset contributes to decreasing the required learning time by removing the redundant features. Also, it enhances the performance of the employed learning technique by removing the irrelevant, misleading, and inappropriate features [25].…”

Section: Review Of Related Workmentioning

confidence: 99%

A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

et al. 2021

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Classification tasks often include, among the large number of features to be processed in the datasets, many irrelevant and redundant ones, which can even decrease the efficiency of classifiers. Feature Selection (FS) is the most common preprocessing technique utilized to overcome the drawbacks of the high dimensionality of datasets and often has two conflicting objectives: The first function aims to maximize the classification performance or reduce the error rate of the classifier. In contrast, the second function is designed to minimize the number of features. However, the majority of wrapper FS techniques are developed for single-objective scenarios. Multi-verse optimizer (MVO) is considered as one of the well-regarded optimization approaches in recent years. In this paper, the binary multi-objective variant of MVO (MOMVO) is proposed to deal with feature selection tasks. The standard MOMVO suffers from local optima stagnation, so we propose an improved binary MOMVO to deal with this issue using the memory concept and personal best of the universes. The experimental results and comparisons indicate that the proposed binary MOMVO approach can effectively eliminate irrelevant and/or redundant features and maintain a minimum classification error rate when dealing with different datasets compared with the most popular feature selection techniques. Furthermore, the 14 benchmark datasets showed that the proposed approach outperforms the stat-of-art multi-objective optimization algorithms for feature selection.

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Cycle reservoir with regular jumps for forecasting ozone concentrations: two real cases from the east of Croatia

Cited by 22 publications

References 34 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

[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing

A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

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