Kudisthalert, Wasu

Loading...
Thumbnail Image
Preferred name
Kudisthalert, Wasu
Main Affiliation
Email
wasu.ku@kmitl.ac.th

Settings

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    TrafficInternVL: Spatially-Guided Fine-Tuning with Caption Refinement for Fine-Grained Traffic Safety Captioning and Visual Question Answering
    (2025-01-01)
    Phimsiri, Sasin
    ;
    Sunpawatr, Sarut
    ;
    Cherdchusakulchai, Riu
    ;
    Kiawjak, Pornprom
    ;
    Tosawadi, Teepakorn
    Fine-grained traffic understanding requires both detailed visual descriptions and precise answers to safety-critical questions. We present TrafficInternVl, a framework for fine-grained traffic safety description and question answering, developed for AI City Challenge 2025 Track 2. Our approach is based on the InternVL3-38B vision-language model and integrates four key components: (1) spatially guided visual prompting via bounding-box-based cropping and rendering; (2) Adaptive view selection protocols; (3) low-rank adaptation (LoRA) fine-tuning, updating only 1% of model parameters; and (4) caption refinement for intra-scene consistency. Our model achieves a Caption Score of 32.75 (BLEU-4, METEOR, ROUGE-L, CIDEr averaged) and a VQA accuracy of 83.08 %. Code, prompts, and LoRA weights are released at https://github.com/ARV-MLCORE/TrafficInternVL
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A coefficient comparison of weighted similarity extreme learning machine for drug screening
    (2016-03-23)
    Kudisthalert, Wasu
    ;
    Pasupa, Kitsuchart
    Machine learning techniques are becoming popular in drug discovery process. It can be used to predict the biological activities of compounds. This paper focuses on virtual screening task. We proposed the Weighted Similarity Extreme Learning Machine algorithm (WELM). It is based on Single Layer Feedforward Neural Network. The algorithm is powerful, iteratively free, and easy to program. In this work, we compared the performance of 17 different types of coefficients with WELM on a well-known dataset in the area of virtual screening named Maximum Unbiased Validation dataset. Moreover, the WELM with different types of coefficients were also compared with the conventional technique-similarity searching. WELM together with Jaccard/Tanimoto were able to achieve the best results on average in most of the activity classes.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Clustering-based weighted extreme learning machine for classification in drug discovery process
    (2016-01-01)
    Kudisthalert, Wasu
    ;
    Pasupa, Kitsuchart
    Extreme Learning Machine (ELM) is a universal approximation method that is extremely fast and easy to implement, but the weights of the model are normally randomly selected so they can lead to poor prediction performance. In this work, we applied Weighted Similarity Extreme Learning Machine in combination with Jaccard/Tanimoto (WELM-JT) and cluster analysis (namely, k-means clustering and Support Vector Clustering) on similarity and distance measures (i.e., Jaccard/ Tanimoto and Euclidean) in order to predict which compounds with not-so-different chemical structures have an activity for treating a certain symptom or disease. The proposed method was experimented on one of the most challenging datasets named Maximum Unbiased Validation (MUV) dataset with 4 different types of fingerprints (i.e. ECFP 4, ECFP 6, FCFP 4 and FCFP 6). The experimental results show that WELM-JT in combination with k-means-ED gave the best performance. It retrieved the highest number of active molecules and used the lowest number of nodes. Meanwhile, WELM-JT with k-means-JT and ECFP 6 encoding proved to be a robust contender for most of the activity classes.