We introduce a new dataset for Causal Analysis of Mental health issues in Social media posts (CAMS).
In recent days, with increased population and traffic on roadways, vehicle collision is one of the leading causes of death worldwide.
Emotion recognition in conversations is an important step in various virtual chat bots which require opinion-based feedback, like in social media threads, online support and many more applications.
The proposed model uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of LOs and a transformer-based semantic similarity model to assess the semantic similarity of the LOs.
Further, we perform a comparative analysis of the performance of various word embeddings and language models on the existing benchmark datasets and the proposed dataset.
In recent years, machine learning has seen an increasing presencein a large variety of fields, especially in health care and bioinformatics. More specifically, the field where machine learning algorithms have found most applications is Genetic Algorithms. The objective of this paper is to conduct a survey of articles published from 2015 onwards that deal with Genetic Algorithms(GA) and how they are used in bioinformatics. To achieve the objective, a scoping review was conducted that utilized Google Scholar alongside Publish or Perish and the Scimago Journal & CountryRank to search for respectable sources.
Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP).
The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users.
This paper proposes SafeRNet, a safe route computation framework which takes advantage of these technologies to analyze streaming traffic data and historical data to effectively infer safe routes and deliver them back to users in real time.
This research introduces the development of semantic similarity algorithms to calculate the similarity between two learning objectives of the same domain.
To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database.