Loyalty initiatives refer to the rewards offered by a business to customers who make recurring purchases. Traditional loyalty programmes, on the other hand, have numerous disadvantages, including low redemption rates,...
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Aquaculture stands as a flourishing industry, serving as a significant financial resource for many. However, it encounters various challenges such as environmental degradation and disease outbreaks. Consequently, main...
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To address the limitations of current methods in detecting small objects, such as pedestrians and cyclists, within autonomous driving scenarios, we propose a novel 3D object detection algorithm based on an improved Pi...
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Recent advances in machine learning have made forged video and audio more convincing. This poses a threat to the security of individuals, societies and nations. To address this threat, the ASVspoof initiative was conc...
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Based on the characteristics of synthetic aperture radar (SAR) images, this paper designs a lightweight generative adversarial network (GAN) model that uses dual-channel mode to simultaneously read two SAR images from...
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In today's modern world, efficient programming is a necessity. To speed up code generation nowadays, programming code is generated using different graphical tools. Although efficient, this technology is scarcely u...
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This technical abstract makes a specialty of an Iota-based strategy to enhance the performance of livestock monitoring and management. The proposed answer involves the usage of wireless sensors and mobile gadgets to g...
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Efficient resource allocation is critical to improve the quality of service in wireless networks. The problem of resource allocation is usually non-convex and non-deterministic polynomial-hard. Meta-heuristic algorith...
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Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated...
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The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine...
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The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI
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