The parking is one of the biggest problems. One of the emerging features of parking management is congestion in urban centres due to the increase in the number of vehicles on the road, which has added anxiety to drive...
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Urban traffic congestion is still a major problem that causes delays, collisions, and wasteful use of resources. Conventional fixed-schedule systems, which frequently exacerbate problems at junctions, U-turns, and ped...
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Legal Outcome Prediction (LOP) is the process of predicting the possible outcome of a court case based on the case file. The aim of this work is to assess the performance of four deep learning models namely BERT, Lega...
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Fire in skyscrapers and high-rise building is inevitable due to huge volumes of thick gauged wires crawling all over the building, the rise in the number of electronic equipment inside and highly unpredictable acts of...
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Traditional toys often do not adapt to the emotional and physical needs of children, particularly those who may have limited verbal communication abilities. The project’s goal is to create a smart toy that is adaptiv...
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Categorical text generation is an upcoming field within natural language processing focusing on producing text tailored to specific categories or domains. The goal of categorical text production is to produce text tha...
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This is increasingly important as global obesity rates continue to rise: the causes need to be understood, and the ability to predict trends determined. In this study, we use machine learning techniques to predict obe...
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Aspect-based sentiment analysis(ABSA)is a fine-grained *** fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely ***,most existing work...
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Aspect-based sentiment analysis(ABSA)is a fine-grained *** fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely ***,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline *** solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE *** methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of ***,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a *** paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text *** LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its ***,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training *** paper examined the effect of data augmentation on the multi-task model for Arabic *** experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC *** results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation
This study suggests using graph algorithms to detect healthcare fraud, a serious issue that can lead to significant financial losses and compromise patient care. Health care fraud is a representation of deceitful acts...
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Data centers are vital to modern computing but contribute significantly to carbon emissions due to their high energy consumption. To address this, machine learning models can optimize resource allocation by analyzing ...
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