Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive d...
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Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in Io...
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Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.
Model-based testing is a substantial approach that is based on and involving models. It is well known for achieving test coverage and for generating and executing test cases automatically. The main and core activity o...
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The integration of renewable energy sources inherently reduces power system inertia, which can result in a higher rate of change of frequency. Moreover, reduced inertia also affects the small-signal stability and cont...
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ISBN:
(数字)9798350390421
ISBN:
(纸本)9798350390438
The integration of renewable energy sources inherently reduces power system inertia, which can result in a higher rate of change of frequency. Moreover, reduced inertia also affects the small-signal stability and controllability of the interconnected synchronous generators (SGs). In this paper, we use the linear state-space model to show the impact of SGs’ inertia constants on eigenvalues, participation factors, and Gramian-based controllability metrics. Both a single-machine and an IEEE 9-bus system were analyzed. The results for the IEEE 9-bus system with three SGs show an optimal range of inertia constants to increase the damping of a specific mode and increase controllability. Thus, a framework for multi-objective optimization combining large-signal stability, small-signal stability, and controllability is proposed that can be applied to grid-forming converters.
Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis o...
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Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.
The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's pha...
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This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...
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This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
Feature selection (FS) analyzes the most important features to enhance classification accuracy during preprocessing. The objective of FS techniques is to reduce the number of input variables and computational load to ...
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ISBN:
(数字)9798350365351
ISBN:
(纸本)9798350365368
Feature selection (FS) analyzes the most important features to enhance classification accuracy during preprocessing. The objective of FS techniques is to reduce the number of input variables and computational load to achieve optimal classification performance. However, finding the best features is challenging due to high dimensionality, with many features but limited data samples. Metaheuristic optimization algorithms (MOAs) are crucial in creating the best subset of features through exploration and exploitation phases. This study experiments with Teaching-Learning-based Optimization (TLO) as MOA from several diverse binary medical datasets. TLO draws attention to its excellence in improving performance with minimal features. TLO is a fairly straightforward algorithm without algorithm-specific parameters, with rapid convergence and implementation yet effectiveness. In this study, we have also conducted four classifiers to obtain the best model for minimizing the features and maximizing the performance of the model, i.e., decision tree (DT), random forest (RF), k-Nearest Neighbour (KNN), and support vector machine (SVM) with the default parameter of each classifier. As the result, TLO shows its excellence in achieving minimal features, but still outstanding performance with 100% accuracy, sensitivity, specificity, and precision. TLO is successfully applied to varying diverse medical datasets with varying number of features.
This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc...
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This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and *** articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility *** study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network *** findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network *** paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.
Oil palm plantations in Indonesia still have many challenges, especially in terms of monitoring and mapping technology. One of the important aspects in the monitoring stage of oil palm plantations is monitoring the pr...
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