Identifying hot topic trends in streaming text data using sequential evolution model based on distributed representations

ZA Khan, Y Xia, S Ali, JA Khan, SS Askar… - IEEE …, 2023 - ieeexplore.ieee.org
Hot topic trends have become increasingly important in the era of social media, as these
trends can spread rapidly through online platforms and significantly impact public discourse …

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach

M Harun-Or-Roshid, K Maeda, B Manavalan… - Computers in Biology …, 2024 - Elsevier
Dihydrouridine (DHU, D) is one of the most abundant post-transcriptional uridine
modifications found in tRNA, mRNA, and snoRNA, closely associated with disease …

Fusing sequence and structural knowledge by heterogeneous models to accurately and interpretively predict drug–target affinity

X Zeng, KY Zhong, B Jiang, Y Li - Molecules, 2023 - mdpi.com
Drug–target affinity (DTA) prediction is crucial for understanding molecular interactions and
aiding drug discovery and development. While various computational methods have been …

Meta-2OM: a multi-classifier meta-model for the accurate prediction of RNA 2′-O-methylation sites in human RNA

M Harun-Or-Roshid, NT Pham, B Manavalan… - PloS One, 2024 - journals.plos.org
2′-O-methylation (2-OM or Nm) is a widespread RNA modification observed in various
RNA types like tRNA, mRNA, rRNA, miRNA, piRNA, and snRNA, which plays a crucial role …

Adoption of machine learning algorithm for predicting the length of stay of patients (construction workers) during COVID pandemic

SS Samy, S Karthick, M Ghosal, S Singh… - International Journal of …, 2023 - Springer
The construction sector in a rapidly developing country like India is a very unorganized
sector. A large number of workers were affected and hospitalized during the pandemic. This …

A comparative study on word embedding techniques for suicide prediction on COVID-19 tweets using deep learning models

R Kancharapu, SN A Ayyagari - International Journal of Information …, 2023 - Springer
COVID-19 caused a pathetic situation worldwide which led to public health crises, economic
crises, employment losses, and mental anxiety. Social media websites are being inundated …

Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study

AG Er, DY Ding, B Er, M Uzun, M Cakmak… - NPJ Digital …, 2024 - nature.com
Through technological innovations, patient cohorts can be examined from multiple views
with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict …

Leveraging contextual features to enhanced machine learning models in detecting COVID-19 fake news

AE Qasem, M Sajid - International Journal of Information Technology, 2024 - Springer
The proliferation of fake news on online social networks, particularly Twitter, has become a
major issue in recent years. False and potentially harmful information can spread quickly …

Leveraging attention layer in improving deep learning models performance for sentiment analysis

MY Salmony, AR Faridi, F Masood - International Journal of Information …, 2023 - Springer
Sentiment analysis (SA) is a rapidly expanding research field, making it difficult to keep up
with all of its activities. It aims to examine people's feelings about events and individuals as …

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID‐19 from Chest Radiographical Images: A Survey

D Sinwar, VS Dhaka, BA Tesfaye… - Contrast Media & …, 2022 - Wiley Online Library
Artificial Intelligence (AI) has been applied successfully in many real‐life domains for solving
complex problems. With the invention of Machine Learning (ML) paradigms, it becomes …