Browsing by Author "Chanyanan Somthawinpongsai"
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Item Brain Cancer Tumor Detection by U-Net Deep Learning Algorithm from MRI Images(Institute of Electrical and Electronics Engineers Inc., 2024) Utpal Chandra Das; Watit Benjapolakul; Manoj Gupta; Timporn Vitoonpong; Pannee Suanpang; Chanyanan Somthawinpongsai; Sujin Butdisuwan; Aziz Nanthaamornphong; U.C. Das; Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand; email: dasutpal999@gmail.comThis research looks at the genomic subtypes of low-grade glioma tumors and their shape characteristics by deep learning magnetic resonance image (MRI) segmentation. We analyzed preoperative imaging and genetic data from 110 patients with low-grade glioma from the Cancer Genome Atlas. Three shape features were recovered to quantify the two- and three-dimensional aspects of the malignancies. Based on gene expression, DNA copy number, IDH mutation, 1p/19q co-deletion, DNA methylation, and microRNA, previously identified clusters were found in genomic data. We used the exact trait test to investigate the connection between chromosomal clusters and imaging traits. Our findings show a significant correlation between the margin fluctuation-bounding ellipsoid volume ratio and the RNA Seq clusters. Furthermore, a correlation was discovered between RNA-seq clusters and angular standard deviation. The U-net deep learning algorithm demonstrated a test accuracy of 94\% and a mean Dice coefficient of 90\%. These findings suggest that tumor shape characteristics derived from MRI can be projected through genomic subtypes in lower-grade gliomas. © 2024 IEEE.Item Development of automatic CNC machine with versatile applications in art, design, and engineering(Elsevier B.V., 2024) Utpal Chandra Das; Nagoor Basha Shaik; Pannee Suanpang; Rajib Chandra Nath; Kedar Mallik Mantrala; Watit Benjapolakul; Manoj Gupta; Chanyanan Somthawinpongsai; Aziz Nanthaamornphong; U.C. Das; Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Faculty of Engineering, Chulalongkorn University, Bangkok, Pathum Wan District, 10330, Thailand; email: utpal597@gmail.com; A. Nanthaamornphong; College of Computing, Prince of Songkla University, Phuket, Phuket Campus, Thailand; email: aziz.n@phuket.psu.ac.thThe area of computer numerical control (CNC) machines has grown fast, and their use has risen significantly in recent years. This article presents the design and development of a CNC writing machine that uses an Arduino, a motor driver, a stepper motor, and a servo motor. The machine is meant to create 2D designs and write in numerous input languages using 3-axis simultaneous interpolated operations. The suggested machine is low-cost, simple to build, and can be operated with merely G codes. The performance of the CNC writing machine was assessed by testing it on a range of solid surfaces, including paper, cardboard, and wood. The results reveal that the machine can generate high-quality text and images with great accuracy and consistency. The proposed machine's ability to write in several input languages makes it appropriate for various applications, including art, design, and engineering. © 2024 The Author(s)Item Framework for evaluation and providing Security in the tourism industry for a Trustworthy Rating System(Institute of Electrical and Electronics Engineers Inc., 2024) Girish B.C. Kumar; Gyanendra Kumar; Aunkrisa Sangchumnong; Parma Nand; Manoj Gupta; Chanyanan Somthawinpongsai; Vikram Bali; Aziz Nanthaamornphong; G.B.C. Kumar; Department of CSE, Sharda University, Greater Noida, UP, India; email: girishshekar.89@gmail.comAll the customers are well enabled for online transactions including the purchase of cosmetics, hospitalization, booking of airways, and the like, here the service in form can be centrally done, and the means of display of satisfaction by the customer in as much as feedback for the same is concerned is provided. By using this rating system, other customers can be attracted to seek the services of the firm in the future. They can also convey interest in the same transactions. Nevertheless, the data statistics of the customers collected can be modified, or removed by the management people or some authorized individual if they tend to provoke unfavorable feedback on their service, therefore to obtain reliable amicable in the context of rating using, which is based on Blockchain technology, here nodes are decentralized and are largely disseminated over the network, In the proposed system the data collected cannot be manipulated by the management or some other unauthorized individual, The Here, the participants traded directly with each other with no middlemen or a central authority to regulate the process. The Test Net is another technology that has been employed to construct rating systems. This is a blockchain instance that is employed alongside the same or maybe the most current version of the software as can be utilized without posing a risk to the primary chain or actual money. © 2024 IEEE.Item Performance Evaluation of various ML Algorithms for PCOS Diagnosis(Institute of Electrical and Electronics Engineers Inc., 2024) Sonam Juneja; Pannee Suanpang; Manoj Gupta; Nitesh Kumar Bhati; Bhoopesh Singh Bhati; Chanyanan Somthawinpongsai; Aziz Nanthaamornphong; S. Juneja; Department of CSE, Chandigarh University, Gharuan, India; email: sonam.december@gmail.comPCOS is a common endocrine disturbance leading to anovulation and subsequent severe health disorders such as cardiovascular events, type 2 diabetes, and infertility. An early and correct diagnosis is crucial to managing the disease and optimizing clinical outcomes. Traditional methods used to diagnose PCOS involve physical examinations and hormone testing but are not always conclusive, particularly at an initial stage. One technology that could substitute exploring large datasets and identifying magnified patterns that might assist in predict disease is machine learning. In this paper, we examine whether numerous ML algorithms can recommend the possibility for women to have PCOS. We use a dataset from a Google Collaboratory in which our features differ from the traditional diagnostic criteria for PCOS. This is the distinctive part of the study that enables us to test the possible advantages and disadvantages of including this extra information in the forecasting models. We will test the efficacy of such characteristics in determining the precise PCOS patients across a variety of classification models. The result of the current research will contribute to the growing body of evidence suggesting that machine learning may be used to identify diseases sooner, and promote women better manage their health. © 2024 IEEE.