Kudisthalert, Wasu

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

Settings

Now showing 1 - 6 of 6
  • 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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    SELM: Siamese extreme learning machine with application to face biometrics
    (2022-07-01)
    Kudisthalert, Wasu
    ;
    Pasupa, Kitsuchart
    ;
    Morales, Aythami
    ;
    Fierrez, Julian
    Extreme learning machine (ELM) is a powerful classification method and is very competitive among existing classification methods. It is speedy at training. Nevertheless, it cannot perform face verification tasks properly because face verification tasks require the comparison of facial images of two individuals simultaneously and decide whether the two faces identify the same person. The ELM structure was not designed to feed two input data streams simultaneously. Thus, in 2-input scenarios, ELM methods are typically applied using concatenated inputs. However, this setup consumes two times more computational resources, and it is not optimized for recognition tasks where learning a separable distance metric is critical. For these reasons, we propose and develop a Siamese extreme learning machine (SELM). SELM was designed to be fed with two data streams in parallel simultaneously. It utilizes a dual-stream Siamese condition in the extra Siamese layer to transform the data before passing it to the hidden layer. Moreover, we propose a Gender-Ethnicity-dependent triplet feature exclusively trained on various specific demographic groups. This feature enables learning and extracting useful facial features of each group. Experiments were conducted to evaluate and compare the performances of SELM, ELM, and deep convolutional neural network (DCNN). The experimental results showed that the proposed feature could perform correct classification at 97.87 % accuracy and 99.45 % area under the curve (AUC). They also showed that using SELM in conjunction with the proposed feature provided 98.31 % accuracy and 99.72 % AUC. SELM outperformed the robust performances over the well-known DCNN and ELM methods.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Counting and classification of malarial parasite from giemsa-stained thin film images
    (2020-01-01)
    Kudisthalert, Wasu
    ;
    Pasupa, Kitsuchart
    ;
    Tongsima, Sissades
    Malaria is a life-threatening disease causing by an infection of the protozoan parasite Plasmodium. Plasmodium falciparum is the deadliest and most common human infected parasites hosted by anopheles mosquito vector. To cure a malaria infected patient and prevent further spreading, malaria diagnosis using microscopy to visualize Giemsa-stained parasites is commonly done. The microscopy diagnosis is somewhat time consuming and requires well-trained malaria experts to interpret what they see under the microscope. To address this limitation, an automated malaria infected diagnosis is needed. This work proposed a computer-aided automated diagnosis system that can perform remote field diagnosis with high accuracy while requiring less computational demands. The proposed framework consists of two main parts that are red blood cell counting and parasite life-cycle stage classification. The counting process is performed by computer vision techniques, namely Hough transform. Different machine learning techniques, i.e., Multilayer Perceptron, Linear Discriminant Analysis, Support Vector Machine, and Weighted Similarity Extreme Learning Machine, are employed in the classification task. We also demonstrated that combining hand-crafted and deep-learned features can enhance the overall performance of the framework. The experimental results showed that the proposed methods could correctly count and classify at 97.94% and 98.12% accuracy, respectively. The overall proposal system can achieve at 96.18% accuracy. This is achieved by WELM in conjunction with deep-learned (AlexNet_FC7) and the hand-crafted (color) features.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Virtual screening by a new clustering-based weighted similarity extreme learning machine approach
    (2018-04-01)
    Pasupa, Kitsuchart
    ;
    Kudisthalert, Wasu
    Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6.