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Found 8 papers, 2 papers with code
Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language
The latest advancements in machine learning and deep learning have brought forth the concept of semantic similarity, which has proven immensely beneficial in multiple applications and has largely replaced keyword search.
Modelling of a DC-DC Buck Converter Using Long-Short-Term-Memory (LSTM)
This approach employs an algorithm for training a neural network using the inputs and outputs (currents and voltages) of a Buck converter.
Arabic Text-To-Speech (TTS) Data Preparation
The purpose of this work is to offer light on how ground-truth utterances may influence the evolution of speech systems in terms of naturalness, intelligibility, and understanding.
Online Gradient Descent for Flexible Power Point Tracking Under a Highly Fluctuating Weather and Load
The increasing electricity demand and the need for clean and renewable energy resources to satisfy this demand in a cost-effective manner, imposes new challenges on researchers and developers to maximize the output of these renewable resources at all times.
Bench-Marking And Improving Arabic Automatic Image Captioning Through The Use Of Multi-Task Learning Paradigm
The results showed that the use of multi-task learning and pre-trained word embeddings noticeably enhanced the quality of image captioning, however the presented results shows that Arabic captioning still lags behind when compared to the English language.
sarcasm detection and quantification in arabic tweets
The role of predicting sarcasm in the text is known as automatic sarcasm detection.
SPARTA: Speaker Profiling for ARabic TAlk
LSTM and CNN networks were implemented using raw features: MFCC and MEL, where FCNN was explored on the pre-trained vectors while varying the hyper-parameters of these networks to obtain the best results for each dataset and task.
Multi-Dialect Arabic BERT for Country-Level Dialect Identification
Our winning solution itself came in the form of an ensemble of different training iterations of our pre-trained BERT model, which achieved a micro-averaged F1-score of 26. 78% on the subtask at hand.
