Victor Kwaku Agbesi

Orcid: 0000-0003-0723-5008

According to our database1, Victor Kwaku Agbesi authored at least 11 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2024
Attention-enriched deeper UNet (ADU-NET) for disease diagnosis in breast ultrasound and retina fundus images.
Prog. Artif. Intell., December, 2024

Low resource Twi-English parallel corpus for machine translation in multiple domains (Twi-2-ENG).
Discov. Comput., December, 2024

Pre-Trained Transformer-Based Models for Text Classification Using Low-Resourced Ewe Language.
Syst., 2024

2023
Predicting students' continuance use of learning management system at a technical university using machine learning algorithms.
Interact. Technol. Smart Educ., 2023

Efficient Adaptive Convolutional Model Based on Label Embedding for Text Classification Using Low Resource Languages.
Proceedings of the ISMSI 2023: Proceedings of the 7th International Conference on Intelligent Systems, 2023

Attention Based BiGRU-2DCNN with Hunger Game Search Technique for Low-Resource Document-Level Sentiment Classification.
Proceedings of the 5th International Conference on Image Processing and Machine Vision, 2023

2022
Transfer Learning for Autonomous Cell Activation Based on Relational Reinforcement Learning With Adaptive Reward.
IEEE Syst. J., 2022

LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images.
Comput. Biol. Medicine, 2022

Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images.
Comput. Biol. Medicine, 2022

Multi-Topic Categorization in a Low-Resource Ewe Language: A Modern Transformer Approach.
Proceedings of the 7th International Conference on Computer and Communication Systems, 2022

2021
Algorithmic Generation of Positive Samples for Compound-Target Interaction Prediction.
Proceedings of the ICMLC 2021: 13th International Conference on Machine Learning and Computing, 2021


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