Titlebook: Computer, Communication, and Signal Processing. AI, Knowledge Engineering and IoT for Smart Systems; 7th IFIP TC 12 Inter Eunika Mercier-La

nonradioactive

https://doi.org/10.1007/978-3-030-42295-0categorize the sentiment of sentences from the rotten tomato’s movie review dataset. In order to achieve best performance three machine learning and three deep learning algorithms were estimated: logistic regression, linear support vector classifier, and Multinomial Naive Bayes (MNB), Recurrent Neur

Throttle

https://doi.org/10.1007/978-3-030-35005-5ent. With the increase of such hostile content in social media, there has arisen a need to effectively detect such posts so as take appropriate measures against them. Hence, extensive studies have been performed in this field for the English language, however, similar work is relatively sparse in th

NICHE

Electrolysis

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Law, Governance and Technology Seriesor satellite. Accurate object detection in remote sensing images is crucial since security, transportation, environmental monitoring, and rescue applications in the military and civilian sectors demand comprehensive, precise analysis and utilization of these images. Despite recent advances, small ob

EVADE

Hangar

Joshua Woodyatt,Ottavio Quiriconing Vector Quantization (MGLVQ) for selecting important features. We apply knearest neighbor (kNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Classification and Regression Trees (CART), and Multilayer Perceptron (MLP) classification machine learning algorithms. The performan

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Joshua Woodyatt,Ottavio Quirico vessel. The performance of these three algorithms is compared against the available ground truth average diameter for both CLRIS and VDIS. It was found that the estimated diameter is closer to the available ground truth using morphological-based and center line-based, respectively. However, the est

ESO

Ottavio Quirico,Katarzyna Kwapisz Williamss is proposed. Results show that our method can efficiently detect abnormal frames and is robust to the variations in the frames. The proposed CNN architecture outperforms the other classification models with an accuracy of 0.9698 with less number of parameters.

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