A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network

Khan, Junaid; Lee, Eun-Kyu; Kim, Kyungsup

doi:10.1049/cit2.12148

Cited by 38 publications

(15 citation statements)

References 37 publications

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“…Many numerical and analytical methods were used to modeling and simulation the industrial applications [ [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] ]. In the following, the conductive HTC of the GNSs-WO 3 /LP107160 hybrid nanofluid was considered as a function of temperature and mass concentrations of the nanopowders in the base fluid.…”

Section: Resultsmentioning

confidence: 99%

Experimental study of thermal conductivity coefficient of GNSs-WO3/LP107160 hybrid nanofluid and development of a practical ANN modeling for estimating thermal conductivity

Dehkordi

Alizadeh

Zekri

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Experimental study of thermal conductivity coefficient of GNSs-WO3/LP107160 hybrid nanofluid and development of a practical ANN modeling for estimating thermal conductivity

Dehkordi

Alizadeh

Zekri

et al. 2023

Heliyon

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“…First, as a robust nonlinear model, ANN could effectively process datasets with intricate nonlinear features, making it advantageous in capturing diverse and abstract characteristics, particularly in medical imaging domains ( 40 ). Second, the adaptive learning capacity of ANN, achieved using the backpropagation algorithm, optimized model parameters, gradually adapting to data features and enhancing predictive accuracy ( 41 ). Additionally, ANN excelled in processing large-scale data, especially in deep learning, where its multi-layered network structure efficiently handled high-dimensional complex data, thereby bolstering model performance ( 42 ).…”

Section: Discussionmentioning

confidence: 99%

Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms

Peng,

Wang,

Wen

et al. 2024

Front. Neurol.

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ObjectiveThe objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function.MethodsThis retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman correlation analysis, minimum redundancy maximum relevance algorithm and Lasso regression, to identify features most correlated with postoperative Hunt-Hess grading. In the model construction phase, three distinct models were constructed: radiomics feature-based model (RSM), deep learning feature-based model (DLM), and deep learning-radiomics feature fusion model (DLRSCM). The study also calculated the radiomics score and combined it with clinical data to construct a Nomogram for predictive modeling. DLM, RSM and DLRSCM model was constructed by 9 base algorithms and 1 ensemble learning algorithm – Stacking ensemble model. Model performance was evaluated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), Matthews Correlation Coefficient (MCC), calibration curves, and decision curves analysis.Results5 significant radiomic feature and 4 significant deep learning features were obtained through the feature selection process. These features were utilized for model construction. Bootstrap resampling method was used for internal validation of the models. In terms of model evaluation, the DLM model, the stacking ensemble algorithm results achieved an AUC of 0.959 and MCC of 0.815. In the RSM model, the stacking ensemble model AUC was 0.935 and MCC was 0.793. The stacking ensemble model in DLRSCM outperformed others, with an AUC of 0.968 and MCC of 0.820. Results indicated that the ANN performed optimally among all base models, while the stacked ensemble learning model exhibited the highest predictive performance.ConclusionThis study demonstrates that the combination of radiomics and deep learning is an effective approach to predict the postoperative Hunt-Hess grade in patients with intracranial aneurysms. This holds significant value in the early identification of postoperative neurological complications and in enhancing clinical decision-making.

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“…Figure 6 illustrates the process of five-fold cross-validation. The performance metrics are calculated as the average of the five experiments’ results (five folds) [ 47 ].…”

Section: Doh Identification Architecturementioning

confidence: 99%

A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach

Al‐Haija

Alohaly

Odeh

2023

Sensors

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The Domain Name System (DNS) protocol essentially translates domain names to IP addresses, enabling browsers to load and utilize Internet resources. Despite its major role, DNS is vulnerable to various security loopholes that attackers have continually abused. Therefore, delivering secure DNS traffic has become challenging since attackers use advanced and fast malicious information-stealing approaches. To overcome DNS vulnerabilities, the DNS over HTTPS (DoH) protocol was introduced to improve the security of the DNS protocol by encrypting the DNS traffic and communicating it over a covert network channel. This paper proposes a lightweight, double-stage scheme to identify malicious DoH traffic using a hybrid learning approach. The system comprises two layers. At the first layer, the traffic is examined using random fine trees (RF) and identified as DoH traffic or non-DoH traffic. At the second layer, the DoH traffic is further investigated using Adaboost trees (ADT) and identified as benign DoH or malicious DoH. Specifically, the proposed system is lightweight since it works with the least number of features (using only six out of thirty-three features) selected using principal component analysis (PCA) and minimizes the number of samples produced using a random under-sampling (RUS) approach. The experiential evaluation reported a high-performance system with a predictive accuracy of 99.4% and 100% and a predictive overhead of 0.83 µs and 2.27 µs for layer one and layer two, respectively. Hence, the reported results are superior and surpass existing models, given that our proposed model uses only 18% of the feature set and 17% of the sample set, distributed in balanced classes.

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A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network

Cited by 38 publications

References 37 publications

Experimental study of thermal conductivity coefficient of GNSs-WO3/LP107160 hybrid nanofluid and development of a practical ANN modeling for estimating thermal conductivity

Experimental study of thermal conductivity coefficient of GNSs-WO3/LP107160 hybrid nanofluid and development of a practical ANN modeling for estimating thermal conductivity

Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms

A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach

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