Titlebook: New Technologies, Artificial Intelligence and Smart Data; 10th International C Mohamed Tabaa,Hassan Badir,Fabrice Monteiro Conference proce

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Systematic Mapping Study on Applications of Deep Learning in Stock Market Predictionnomists and scientists, have studied and developed different approaches to predict stock trend or price. Deep learning methods have shown promising results in different areas such as audiovisual recognition and natural language processing. In the same way, researchers are exploring the effectiveness

Abbreviate

Exploring the Knowledge Distillationsize of a model by transferring the knowledge from a large (pre-trained or not) model into a smaller one with minimum loss to the accuracy, for a better fit in edge devices and the devices with low computational power. Furthermore, it can be used also as a performance enhancer for the student model

小淡水鱼

NOCT

BERT for Arabic NLP Applications: Pretraining and Finetuning MSA and Arabic Dialectsing model. It improved the performance of several Natural Language Processing (NLP) Applications [.]. In this paper, following the effectiveness that these models demonstrated, we use the advantages of training Arabic Transformer-based representational language models to create three Arabic NLP appl

Jocose

lacrimal-gland

absolve

Towards a Platform for Higher Education in Virtual Reality of Engineering Sciences are today the catalysts for the development of future teaching tools. They allow students to discover, via their smartphones or headsets, destinations around the world while remaining in their classrooms or even from their homes. These technologies are also used to put into practice, especially for

香料

Evacuate

Multimodal Learning for Road Safety Using Vision Transformer ViTaset. The approach leverages the power of vision transformer (ViT) models, which are based on self-attention layers, to extract high-quality visual features from images. The proposed approach was evaluated on a road safety problem, specifically the identification of high-risk areas for road accident