Vertical take-off and landing (VTOL) aircraft pose a challenge in generating reference commands during transition flight. While sparsity between hover and cruise flight modes can be promoted for effective transitions ...
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Uterine cancer is a serious worry for women all over the world, and we have used multi-omics datasets to present a model that predicts the survival rate of uterine cancer patients by combining machine learning approac...
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The advent of new technologies like artificial intelligence, and big data has influenced many cyber attackers to launch their attacks on the network. Hence researchers have already proposed Intrusion Detection Systems...
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The advent of new technologies like artificial intelligence, and big data has influenced many cyber attackers to launch their attacks on the network. Hence researchers have already proposed Intrusion Detection Systems by incorporating machine learning as well. Building an effective IDS is still a challenging task because of low accuracy. Managing high dimensional data is another major problem that occurs in IDS. Hence in this paper, an efficient Machine Learning based Intrusion Detection System is developed by means of a novel stable feature selection strategy called IV-RFE. The proposed methodology aims to select only the relevant features that contribute to the attack, which is purely based on relative variance, and weight factor in combination with RFE. This methodology increases the performance in terms of accuracy and maintains a stable set of features. Previous studies only focussed on the feature selection strategy and their performance. The feature stability also has to be considered which is an equally important metric, especially in the field of Intrusion Detection Systems. Hence in the current study, an efficient ML based IDS is proposed which selects only the relevant and stable features. Experimental results also revealed that the proposed IV-RFE outperformed well for three attacks with respect to accuracy and stability metrics also. The results show that stability is also an important indicator in selecting the features in the field of Intrusion Detection Systems.
Multiview fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices has driven the emergence of new challenge in MV...
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Image color saliency has been popularly exploited to detect airports from remote sensing images (RSIs). However, in a complex environment, many non-airport image regions could also produce high saliency, leading to po...
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In many fields, such as healthcare, finance, and scientific research, data sharing and collaboration are critical to achieving better outcomes. However, the sharing of personal data often involves privacy risks, so pr...
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Blockchain technology, implemented via smart contracts, provides significant improvements in data security, transparency, and automation within healthcare applications. Smart contracts are inherently unsuitable for cr...
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Radio surveys of the red-shifted 21 cm emission line from neutral hydrogen provide a means to measure statistical cosmological signals that cannot be measured through other means. Many past experiments have shown that...
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In recent years, water quality Ph detection has gradually become a hot spot. However, the existing classical models in the field of water quality prediction, such as machine learning and deep learning, have insufficie...
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The deregulation of the electricity market led to an increase in competition, which has compelled many strategies makers to opt for forecasting electricity prices because the more accuracy of prediction better the str...
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