5 research outputs found
#REVAL: a semantic evaluation framework for hashtag recommendation
Automatic evaluation of hashtag recommendation models is a fundamental task
in many online social network systems. In the traditional evaluation method,
the recommended hashtags from an algorithm are firstly compared with the ground
truth hashtags for exact correspondences. The number of exact matches is then
used to calculate the hit rate, hit ratio, precision, recall, or F1-score. This
way of evaluating hashtag similarities is inadequate as it ignores the semantic
correlation between the recommended and ground truth hashtags. To tackle this
problem, we propose a novel semantic evaluation framework for hashtag
recommendation, called #REval. This framework includes an internal module
referred to as BERTag, which automatically learns the hashtag embeddings. We
investigate on how the #REval framework performs under different word embedding
methods and different numbers of synonyms and hashtags in the recommendation
using our proposed #REval-hit-ratio measure. Our experiments of the proposed
framework on three large datasets show that #REval gave more meaningful hashtag
synonyms for hashtag recommendation evaluation. Our analysis also highlights
the sensitivity of the framework to the word embedding technique, with #REval
based on BERTag more superior over #REval based on FastText and Word2Vec.Comment: 18 pages, 4 figure
Road Damage Detection Using the Hunger Games Search with Elman Neural Network on High-Resolution Remote Sensing Images
Roads can be significant traffic lifelines that can be damaged by collapsed tree branches, landslide rubble, and buildings debris. Thus, road damage detection and evaluation by utilizing High-Resolution Remote Sensing Images (RSI) are highly important to maintain routes in optimal conditions and execute rescue operations. Detecting damaged road areas through high-resolution aerial images could promote faster and effectual disaster management and decision making. Several techniques for the prediction and detection of road damage caused by earthquakes are available. Recently, computer vision (CV) techniques have appeared as an optimal solution for road damage automated inspection. This article presents a new Road Damage Detection modality using the Hunger Games Search with Elman Neural Network (RDD–HGSENN) on High-Resolution RSIs. The presented RDD–HGSENN technique mainly aims to determine road damages using RSIs. In the presented RDD–HGSENN technique, the RetinaNet model was applied for damage detection on a road. In addition, the RDD–HGSENN technique can perform road damage classification using the ENN model. To tune the ENN parameters automatically, the HGS algorithm was exploited in this work. To examine the enhanced outcomes of the presented RDD–HGSENN technique, a comprehensive set of simulations were conducted. The experimental outcomes demonstrated the improved performance of the RDD–HGSENN technique with respect to recent approaches in relation to several measures