Modeling and sensitivity analysis of NOx emissions and mechanical efficiency for diesel engine

Sharifi, Abbas; Ahmadi, Mohsen; Badfar, Homayoun; Hosseini, Mohsen

doi:10.1007/s11356-019-05613-0

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…Some other methods are fixed-time [39] and finite-time [40] fuzzy method and output-feedback decentralized neural network and fuzzy multiple attribute decisionmaking [41]. Sharifi et al have modeled a sensitivity analysis for predicting NOx emotion and compared it with other methods [42]. The intrusion detection device for the jamming attack was suggested by Soni and Sudhakar.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)

Abdan

Seno

2022

Wireless Communications and Mobile Computing

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A wormhole attack is a type of attack on the network layer that reflects routing protocols. The classification is performed with several methods of machine learning consisting of K -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), naive Bayes (NB), and convolutional neural network (CNN). Moreover, we used nodes’ properties for feature extraction, especially nodes’ speed, in the MANET. We have collected 3997 distinct (normal 3781 and malicious 216) samples that comprise normal and malicious nodes. The classification results show that the accuracy of the KNN, SVM, DT, LDA, NB, and CNN methods are 97.1%, 98.2%, 98.9%, 95.2%, 94.7%, and 96.4%, respectively. Based on our findings, the DT method’s accuracy is 98.9% and higher than other ways. In the next priority, SVM, KNN, CNN, LDA, and NB indicate high accuracy, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)

Abdan

Seno

2022

Wireless Communications and Mobile Computing

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“…e results demonstrate that the proposed strategies outperform a standard LSTM model in terms of accuracy. Sharifi et al [40] have studied a novel model based on gene expression programming for predicting NOx emissions in engine diesel. Ahmadi et al [41] have studied and designed a novel hybrid method for predicting GDP factors using artificial intelligence and statistical methods.…”

Section: Literature Reviewmentioning

confidence: 99%

DCNN-GCM: A Deep CNN and Granger Causality Models for Forecasting Welfare Level of Energy-Producing Countries and Evaluating the Relationship between Energy Consumption and Sustainable Economic Welfare

Hoseinbor

Mousavi

Aminifard

2022

Mathematical Problems in Engineering

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From the beginning of creation, human beings have realized the importance of energy for survival. They have always devoted a significant part of their energy to provide the required energy. Moreover, it can be said that energy resources have an essential role in the life and evolvement of societies. On the contrary, with the depletion of energy resources, severe environmental pollution caused by the consumption of petroleum products, and high cost of energy in the production cycle, allocating these resources must be carried out carefully. This paper aims to look at the connection between energy consumption and sustainable economic welfare in OPEC countries (Iran, Kuwait, Saudi Arabia, UAE, Ecuador, and Venezuela) during 2019–2009, utilizing the panel data method. Finally, we presented a CNN architecture for forecasting welfare levels in the case study countries. As a result, the relationship between energy consumption and economic growth among selected countries from energy-producing countries was compared in this analysis, which used the standard Granger causality test and the Granger causality test in several domains. The findings suggest that economic growth and inflation positively impact energy consumption in the countries studied. In addition, energy consumption positively impacts these countries’ sustainable economic welfare, while inflation has a negative effect. In addition, the findings of the standard Granger causality test indicate a one-way causal association between energy consumption and economic development in Iran and Venezuela, as well as one-way causality from GDP to energy consumption in Ecuador, the United Arab Emirates, Saudi Arabia, and Kuwait. Also, based on the results of the CNN method, the RMSE are 1.75, 3.81, 1.39, 0.52, 0.69, and 1.72 for Ecuador, Emirates, Iran, Saudi Arabia, Kuwait, and Venezuela, respectively.

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“…Studies in this field show two significant challenges in generating large items and multiple database transitions in the basic Apriori algorithm. A review of articles in this field shows that, to resolve these two challenges, several ideas have been proposed to improve the basic Apriori algorithm, many of which, while maintaining the overall structure, have added techniques to increase efficiency [19,21,43,44]. is section has tried to cover some ideas proposed in previous studies regarding association rules mining and frequent patterns mining in different applied areas, especially network topologies and distributed environments.…”

Section: Previous Research Studiesmentioning

confidence: 99%

IAMnet: Presentation of Parallelization Approach for Repetitive Mining on Network Topologies with an Improved Apriori Method

Salehpour

Asghari

Javadi

et al. 2022

Wireless Communications and Mobile Computing

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Recently, the discovery of association rules and the consequent mining frequent patterns have attracted the attention of many researchers to discover unknown relationships in big data, especially in networking and distributed environments. In this research, a parallelization-based approach is proposed to improve the performance of the Apriori algorithm in repetitive mining patterns on network topologies. The proposed approach includes two main features: (1) combining centrality criteria of the node and the Apriori algorithm to identify repetitive patterns and (2) using the mapping/reduction method to create parallel processing and achieve optimal values in the shortest time. This approach also pursues three main objectives: reducing the temporal and spatial complexity of the Apriori algorithm, improving the association rules mining process and identifying repetitive patterns, and comparing the proposed approach’s performance on different network topologies to determine the advantages and disadvantages of each topology. Comparing our proposed method and the basic Apriori algorithm, it is concluded that our approach provides acceptable efficiency in terms of evaluation criteria such as energy consumption, network lifetime, and runtime compared to other methods. Experimental results also show that when using our proposed method compared to the basic Apriori algorithm, network life is increased by 7.1%, the runtime is reduced by 43.2%, and the energy consumption is saved by about 41.2%.

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Modeling and sensitivity analysis of NOx emissions and mechanical efficiency for diesel engine

Cited by 10 publications

References 40 publications

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)

DCNN-GCM: A Deep CNN and Granger Causality Models for Forecasting Welfare Level of Energy-Producing Countries and Evaluating the Relationship between Energy Consumption and Sustainable Economic Welfare

IAMnet: Presentation of Parallelization Approach for Repetitive Mining on Network Topologies with an Improved Apriori Method

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