Kimpan, Warangkhana

Preferred name

Kimpan, Warangkhana

Alternative Name

Kimpan, W.

Main Affiliation

School of Science

warangkhana.ki@kmitl.ac.th

Now showing 1 - 10 of 23

Some of the metrics are blocked by your
consent settings
Enhancing of Particle Swarm Optimization Based Method for Multiple Motifs Detection in DNA Sequences Collections
(2020-05-01)
Som-In, Sarawoot
;
Kimpan, Warangkhana
Genome sequence data consists of DNA sequences or input sequences. Each one includes nucleotides with chemical structures presented as characters: 'A ' 'C'' G''A','C','G', and 'T', and groups of motif sequences, called Transcription Factor Binding Sites (TFBSs), which are subsequences of DNA that lead to protein-synthesis. The detection of TFBSs is an important problem for bioinformatics research. With the similar patterns of motif sequences in TFBSs, computational algorithms for TFBSs detection have been improved to reduce resources used in laboratory setting. The metaheuristic algorithm is the important issue that has been continually improved to detect TFBSs with greater precision and recall. This paper proposes PSO_HD by applying Particle Swarm Optimization (PSO) as a pre-process and using Hamming distance to improve the efficiency of detecting TFBSs with more precision and recall. In order to measure its efficiency, the paper compares the TFBSs detection using PSO_HD algorithm with relevant algorithms in eight datasets. F-score is used as a measurement unit and compared to the related algorithms. The experimental results show that PSO_HD algorithm gives the highest average F-score, which can be indicated that the PSO_HD algorithm can improve the efficiency of detecting TFBSs with more precision and recall.
Some of the metrics are blocked by your
consent settings
Simulation model of space optimization in containers arranging and sequencing for logistics
(2013-10-25)
Kimpan, Warangkhana
;
Tantawarak, Nake
This paper proposes the application using 3D Bin Packing to optimize the container space for logistics system. The aim is to reduce logistics costs, arrangement time and delivery time. The application can arrange different sizes of cubical boxes into the container bin of trucks, vans, or wagons considering their distributing sequence by using Best-Fit Decreasing algorithm to optimize the space. It also can be used to reduce the cost of product delivering because the container will be divided into many partitions and arranged by using delivery sequence. The different types of boxes and vehicles can be freely adjusted and arranged. The arranged boxes can be viewed in order to check their position. The experimental results of the application are shown in 3D with freely movement view.
Some of the metrics are blocked by your
consent settings
Using mobile application as an instrument for ptosis diagnosis
(2017-07-02)
Kimpan, Warangkhana
;
Sirivimonsattaya, Pongpicha
This article proposes a novel of mobile application to help ophthalmologists diagnose the Ptosis disease. This application applied digital image processing techniques to identify eye parameters: Marginal Reflex Distance-1, Marginal Reflex Distance-2, and L/M Ratio which doctors can use to diagnose patients besides using naked eyes. The application was built for iOS platform in the first version. The inputs of the application are the images of patient's eyes from a mobile device camera or from a mobile device memory. After using digital image processing techniques, at the end of the process, the eye parameters will be displayed on the mobile device screen for the doctors to be used. The experimental results indicated that the proposed mobile application can perform with 86.36% of accuracy in measuring Marginal Reflex Distance-1 value while it can measure Marginal Reflex Distance-2 value with 63.64% of accuracy.
Some of the metrics are blocked by your
consent settings
Thai food recommendation system using hybrid of particle swarm optimization and K-means algorithm
(2021-04-23)
Puraram, Tanakorn
;
Chaovalit, Pimwadee
;
Peethong, Apatha
;
Tiyanunti, Pongsak
;
Charoensiriwath, Supiya
A food recommendation system is an information filtering tool that helps suggest appropriate food menus to users based on their dietary behavior, nutrition, health, or activity. In this paper, a hybrid method of Particle Swarm Optimization (PSO) and K-Means algorithm is proposed to improve the user's dietary behavior clustering and using Principal Component Analysis (PCA) to reduce the data dimension. Moreover, the User-Based Collaborative Filtering technique is used to predict the rating of relevant Thai food menus and recommendation. The experimental result shows the hybrid method improves the clustering performance from 3 models: Hierarchical Clustering, K-Means, and K-Means with PCA, in terms of silhouette coefficient score. In addition, the hybrid method improves the Davies-Bouldin index score by 44%, 19%, and 17% compared to those models, respectively. The rating prediction result shows the hybrid method outperforms the other methods.
Some of the metrics are blocked by your
consent settings
Water Level Monitoring and Evacuation Guideline Using Ant Colony Optimization on Mobile Application
(2020-08-01)
Kimpan, Warangkhana
;
Kasetvetin, Sirawich
;
Kimpan, Chom
The most natural disasters that have happened in Thailand are storm and flood problems. The people who live near water sources have no warning about the overflowing of water nearby, so they cannot evacuate or get help in time. Thus, there is always a high risk of losing properties or lives. In order to alleviate the losses, this paper proposes water level monitoring on Android application from Internet of Things devices and the guideline for evacuation by applying Ant Colony Optimization which is inspired by the real ant colony. Internet of Things devices are used to monitor the water levels in community for the user who lives near the water sources or near the places which have high risk of flooding. The Hydrostatic level sensors are placed in the water basin near the community to measure the height of the water which can also be observed in real time from mobile application. When the height of the water reaches the critical value that was set in the application, it sends notifications to the user. Moreover, Line bot is used to let the user knows the potential risks from rising water levels. At the critical level, the user needs to evacuate to a safe place located nearby. The application will guide the user to follow the direction to the most safety destination. In case of many people are already evacuated in one place and it reached the maximum amount of limitation, the application will change the recommendation direction to other places nearby using Ant Colony Optimization algorithm for making decisions.
Some of the metrics are blocked by your
consent settings
NexusPSO: A novel algorithm to detect transcription factor binding sites
(2018-08-28)
Som, Sarawoot
;
Kimpan, Warangkhana
The detection of transcription factor binding sites is a major problem in research in Biology. Methods and computer algorithms can be applied to reduce time complexity and cost of detecting transcription factor binding sites in laboratory experiments. One of the well-known methods commonly used is swarm intelligence. However, errors in detection of transcription factor binding sites can be caused by different binding sites in the same genome sequence. The purpose of this research is to improve the effectiveness and accuracy in the detection of transcription factor binding sites by applying the newly developed pre-processing procedure, Nexus, to Particle Swarm Optimization algorithm (NexusPSO). The accuracy of the NexusPSO algorithm was measured in comparison with other algorithms, using information content (IC) as an indicator, with Escherichia coli data. This study found that NexusPSO is the most accurate method being tested. NexusPSO was then tested using consensus sequences on Saccharomyces cerevisiae and Homo sapiens. NexusPSO showed nearly identical results when compared to DNA footprinting methods.
Some of the metrics are blocked by your
consent settings
Feature Selection Method Based on Hybrid Cuckoo Search and Firefly Algorithm for Breast Cancer Prediction
(2025-01-01)
Teerasarn, Chalanwich
;
Kimpan, Warangkhana
This research focuses on developing an efficient feature selection process using a hybrid technique combining Cuckoo Search algorithm and Firefly Algorithm. The Wisconsin Diagnostic Breast Cancer dataset is utilized to evaluate the capability of selecting significant features and eliminating irrelevant ones. The experimental results demonstrate that using the hybrid technique significantly improves the accuracy of machine learning compared to using the Cuckoo Search and Firefly Algorithm individually. Additionally, an analysis was conducted on the impact of splitting the dataset for training and testing, with splits of 70/30, 80/20, and 90/10. The experiments revealed a relationship between the number of selected features and the model accuracy. The findings from this study can serve as a guideline for developing appropriate feature selection process for complex data analysis problems.
Some of the metrics are blocked by your
consent settings
WILDFIRE CLASSIFICATION WITH DEEP LEARNING MODEL
(2026-07-01)
Tankue, Puwanai
;
Kruekaew, Boonhatai
;
Kimpan, Warangkhana
This research aims to develop models for wildfire classification from images. The objective is to provide decision-making support for wildfire control planning, prevention and management. Convolutional Neural Networks techniques are used to analyze a dataset consisting of three image groups: no fire images, fire images that are not wildfires, and wildfire images. The experimental results compared ResNet group, DenseNet group, MobileNet group, and EfficientNet group. The research findings indicate the best-performing model in this study is ResNet152V2, which achieved an accuracy of 92.75%. Furthermore, Precision, Recall, and F1-Score are within a satisfactory range. A web application has also been developed to facilitate users to detect and classify wildfire more conveniently.
Some of the metrics are blocked by your
consent settings
Neural network training model for weather forecasting using fireworks algorithm
(2017-02-21)
Suksri, Saktaya
;
Kimpan, Warangkhana
Weather forecasting is the application of science and technology in order to predict the weather conditions. It is important for agricultural and industrial sectors. Models of Artificial Neural Networks with supervised learning paradigm are suitable for weather forecasting in complexity atmosphere. Training algorithm is required for providing weight and bias values to the model. This research proposed a weather forecasting method using Artificial Neural Networks trained by Fireworks Algorithm. Fireworks Algorithm is a recently developed Swarm Intelligence Algorithm for optimization. The main objective of the method is to predict daily mean temperature based on various measured parameters gained from the Meteorological Station, located in Bangkok. The experimental results indicate that the proposed method is advantageous for weather forecasting.
Some of the metrics are blocked by your
consent settings
Enhancing of artificial bee colony algorithm for virtual machine scheduling and load balancing problem in cloud computing
(2020-01-01)
Kruekaew, Boonhatai
;
Kimpan, Warangkhana
This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system with HABC was compared to that supplemented with other swarm intelligence algorithms: Ant Colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can be concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJF) outperformed those with ACO, PSO, and IPSO.

Kimpan, Warangkhana

Filters

Settings

Sort By

Results per page