Titlebook: Advances on Smart and Soft Computing; Proceedings of ICAC Faisal Saeed,Tawfik Al-Hadhrami,Errais Mohammed Conference proceedings 2021 The

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Ensemble Feature Selection Method Based on Bio-inspired Algorithms for Multi-objective Classificatiofeature selection method as they tend to work individually and cause incorrect feature selection, which in turn affect the classification accuracy. The objective of this research was to utilise the potential of ensemble methods (boosting) with bio-inspired techniques in improving the performance of

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Feature Selection and Classification Using CatBoost Method for Improving the Performance of Predicti network, Naïve Bayes and K-nearest neighbor. In addition, different ensemble methods were used such as bagging, random forest and boosting. On the other hand, different feature ranking methods have been used to reduce the data dimensionality by selecting the most important features. In this paper,

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Context modeling for decision support,ributed computing platform by using Apache Hadoop framework and a real-time distributed storage system using HBase. In fact, the amount of multimedia data is growing exponentially. Most of this data is available in image and video models. Analyzing huge data involves complex algorithms, which leads

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Context modeling for decision support,f unhealthy lifestyles and the number of diabetic patients is rising more rapidly, there is a growing need for an automated system for early diagnosis and treatment to avoid blindness. With the development of different technologies [e.g., smart devices, cloud computing and the Internet of Things (Io

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https://doi.org/10.1057/9780230286825chine learning methods for classifying the text as hate speech. However, the performance of machine learning method differs when using different parameters settings. Selecting the best values of parameters for machine learning method yields directly in the performance of the method. It is very time