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Author: search.filters.author.Kruekaew, Boonhatai

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    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.
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    STRAWBERRY SEEDLING CULTIVATION WITH SMART FARM
    (2025-01-01)
    Kruekaew, Boonhatai
    ;
    Palananda, Attapon
    ;
    Kimpan, Warangkhana
    In Thailand, the main planting areas are in the upper northern region. It is difficult for strawberry to be cultivated in the central region of Thailand due to inappropriate weather conditions. Because the strawberry seedlings are delicate and sensitive to alterations in temperature and weather, they require extra care than mature plants while cultivation before planting. Therefore, modifications to strawberry seedling cultivation may result in more strawberry plantings. Consequently, one of the most beneficial choices is a smart farming. This research focuses on the cultivation of strawberry seedlings in tropical areas of Thailand using an intelligence model and Internet of Things. The prototype system focuses on automatic watering and lighting, and an environmental adaptation system that combines sensors to control water, air, and lighting. The physical characteristics data from all devices in prototype system are collected, and then analyzed using machine learning methods to automatically control the environment within the prototype system to be suitable for growing strawberry seedlings. Moreover, the real-time data will be displayed on a dashboard with various notification systems. The experimental results indicated that using machine learning models can control the suitable temperature and humidity for strawberry seedlings cultivation. The appropriate temperature and soil moisture are between 31-32 degrees Celsius and 70 percent, respectively.
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    Automation 4.0 for Water Level Monitoring System
    (2023-01-01)
    Kimpan, Warangkhana
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    Palananda, Attapon
    ;
    Kruekaew, Boonhatai
    This paper proposed the concept of using automation 4.0 for monitoring the water level. The water level warning system specifications are to measure the water level using ultrasonic sensors and measure the amount of rainfall using a weighing rain gauge. The system automatically controls the measurement of the level of the flood using a Programmable Logic Controller (PLC) via PROFINET. Then the water level is monitored, and the results will be displayed through HMI technology via Web panel trainer, Node-Red dashboard, and transfer data via PROFICLOUD. Moreover, the warning information will be sent via LINE notification on mobile to people who live near water sources or staff in charge of preventing disasters.
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    Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm with Reinforcement Learning
    (2022-01-01)
    Kruekaew, Boonhatai
    ;
    Kimpan, Warangkhana
    Workload balancing in cloud computing is still challenging problem, especially in Infrastructure as a Service (IaaS) in the cloud model. A problem that should not occur during cloud access is a host or server being overloaded or underloaded, which may affect the processing time or may result in a system crash. Therefore, to prevent these problems, an appropriate schedule of access should be considered so that the system can distribute tasks across all available resources, which is called load balancing. The load balancing technique should ensure that all Virtual Machines (VMs) are used appropriately. In this paper, an independent task scheduling approach in cloud computing is proposed using a Multi-objective task scheduling optimization based on the Artificial Bee Colony Algorithm (ABC) with a Q-learning algorithm,which is a reinforcement learning technique that helps the ABC algorithm work faster, called the MOABCQ method. The proposed method aims to optimize scheduling and resource utilization, maximize VM throughput, and create load balancing between VMs based on makespan, cost, and resource utilization, which are limitations of concurrent considerations. Performance analysis of the proposed method was compared using CloudSim with the existing load balancing and scheduling algorithms: Max-Min, FCFS, HABC-LJF, Q-learning, MOPSO, and MOCS algorithms in three datasets: Random, Google Cloud Jobs (GoCJ), and Synthetic workload. The experimental results indicated that the algorithms used MOABCQ approach outperformed the other algorithms in terms of reducing makespan, reducing cost, reducing degree of imbalance, increasing throughput and average resource utilization.
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    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.
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    Heuristic Task Scheduling with Artificial Bee Colony Algorithm for Virtual Machines
    (2016-12-28)
    Kimpan, Warangkhana
    ;
    Kruekaew, Boonhatai
    Cloud computing is one of the Information Technology services which are provided from IT infrastructures to application services. It is the combination of Distributed computing and virtualization technology using virtual machines, an essential component in Cloud computing. Therefore, task scheduling is an important matter to consider for virtual machines to balance load of each machine and to efficiently use the resources in Cloud computing. This paper proposes the use of Heuristic task scheduling with Artificial Bee Colony algorithm for virtual machines in heterogeneous Cloud computing, called HABC. The research aim is to introduce HABC, which is a new task scheduling and load balancing algorithm, for virtual machines in heterogeneous environments to reduce the makespan in the system. In the experiments, CloudSim was simulated to compare various types of the optimization task scheduling in using the virtual machines. The experimental results indicated that using the proposed Artificial Bee Colony algorithm when large job was considered first (HABC-LJF) in virtual machine scheduling, improved the efficiency in task scheduling and load balancing of virtual machines in Cloud computing. In addition, the proposed algorithm can minimize the makespan even if the tasks are increased and the different types of data are distributed.