SCOPUS 2024

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Analytical Validation and Integration of CIC-Bell-DNS-EXF-2021 Dataset on Security Information and Event Management
(Institute of Electrical and Electronics Engineers Inc., 2024) Gyana Ranjana Panigrahi; Prabira Kumar Sethy; Santi Kumari Behera; Manoj Gupta; Farhan A. Alenizi; Pannee Suanpang; Aziz Nanthaamornphong; P.K. Sethy; Sambalpur University, Department of Electronics, Sambalpur, Odisha, 768019, India; email: prabirsethy.05@gmail.com; A. Nanthaamornphong; Prince of Songkla University, College of Computing, Phuket, 83120, Thailand; email: aziz.n@phuket.psu.ac.th
Contemporary culture presents a substantial obstacle for cyber security experts in the shape of software vulnerabilities, which, if taken advantage of, can jeopardize the Confidentiality, Integrity, and Availability (CIA) of any system. Data-driven and modern threat intelligence tools can enhance cyber security, bolster resilience, and foster innovation across cloud, multi-cloud, and hybrid platforms. As a result, performance evaluation and accuracy verification have become essential for Security Information and Event Management (SIEM) to prevent cyber threats. The SIEM system offers threat intelligence, reporting, and security incident management through the collection and analysis of event logs and other data sources that are specific to events and their context. We propose a hybrid strategy to address threat intelligence, reporting, and security incident management consisting of two layers that utilize a predefined set of characteristics. Here, we use RStudio to assess how well a hybrid intrusion detection system (HIDS) handles the CIC-Bell-DNS-EXF-2021 dataset. Furthermore, we have incorporated our developed model into Multi-Criteria Decision Analysis Methods (MCDM) to enhance the methods' ability to identify complex DNS exfiltration attacks using machine learning algorithms: RF-AHP (RA), KNN-TOPSIS (KT), GBT-VIKOR (GV), and DT-Entropy-TOPSIS (DET). We consider several factors during the work, including accuracy, absolute error, weighted average recall, weighted average precision, kappa value, logistic loss, and root mean square deviation (RMSD). We use the Machine-Automated Model function to integrate and validate the models. According to the findings, GV has the highest level of accuracy, with a rate of 99.52%, while KT has the lowest level of authenticity, with a rate of 93.65%. Furthermore, these findings illustrate enhanced performance metrics for multiclass classification in comparison to previous approaches. © 2013 IEEE.
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.com
This 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.
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.th
The 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)
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.com
All 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.
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.com
PCOS 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.
SPSO-EFVM: A Particle Swarm Optimization- Based Ensemble Fusion Voting Model for Sentence-Level Sentiment Analysis
(Institute of Electrical and Electronics Engineers Inc., 2024) Dimple Tiwari; Bharti Nagpal; Bhoopesh Singh Bhati; Manoj Gupta; Pannee Suanpang; Sujin Butdisuwan; Aziz Nanthaamornphong; M. Gupta; Guru Ghasidas Vishwavidyalaya Bilaspur, Department of Electrical Engineering, Bilaspur, Koni, Chhattisgarh, 495009, India; email: manojgupta35@yahoo.co.in; A. Nanthaamornphong; Prince of Songkla University, College of Computing, Phuket, 83120, Thailand; email: aziz.n@phuket.psu.ac.th
Sentiment analysis has received incremental growth in recent years for emerging applications, including human-robot integration, social platforms monitoring, and decision-support systems. Several neural or transformer model-based solutions have been provided in the field of sentiment analysis that relies on the decision of a single classifier or neural model. These are erroneous to encode contextual information into appropriate dialogues and increase extra computational cost and time. Hence, we proposed a compact and parameter-effective Particle Swarm Optimization-based Ensemble Fusion Voting Model (PSO-EFVM) that exploited the combined properties of four ensemble techniques, namely Adaptive-Boost, Gradient-Boost, Random-Forest, and Extremely-Randomized Tree with Particle Swarm Optimization (PSO)-based hyperparameter selection. The proposed model is investigated on five cross-domain datasets after applying the foremost initialization and feature extraction using Information Gain (IG). It employs adaptive and gradient learning to incorporate the automatic attribute selection with the arbitrary loss function optimization. In short, a generalized two-block composite classifier is designed to perform context compositionality and sentiment classification. A population-based meta-heuristic optimization PSO is applied to each base ensemble learner that calculates weights for the best parameter selection. Comprehensive investigations of different domains reveal the superiority of the proposed PSO-EFVM over established baselines and the latest state-of-the-art models. © 2013 IEEE.
Super-Resolution Methods for Endoscopic Imaging: A Review
(Institute of Electrical and Electronics Engineers Inc., 2024) Mansoor Hayat; Manoj Gupta; Pannee Suanpang; Aziz Nanthaamornphong; M. Hayat; Dept. of Electrical Engineering, Chulalongkorn University, Bangkok, Thailand; email: 6471015721@student.chula.ac.th
This review paper presents a comprehensive analysis of recent advancements in super-resolution applications in endoscopic imaging. It synthesizes findings from multiple cutting-edge research papers, each contributing unique methodologies and results. The review highlights the progression from traditional techniques to deep learning models, and attention mechanisms. Emphasis is placed on the practical application of these advancements in enhancing the quality of images for minimally invasive surgery, ultimately contributing to improved surgical outcomes. This synthesis not only showcases the current state of the field but also identifies potential areas for future research and development. © 2024 IEEE.
Virtual Learning Environment - Evaluation of LearnerÕs Behavior Using Topic Models
(Institute of Electrical and Electronics Engineers Inc., 2024) N.A. Deepak; Gyanendra Kumar; Aunkrisa Sangchumnong; R.S. Chaithra; Sur Singh Rawat; Aziz Nanthaamornphong; Girish B.C. Kumar; Manoj Gupta; N.A. Deepak; RV Institute of Technology and Management, Bengaluru, India; email: deepakna.rvitm@rvei.edu.in
Online learning platforms come with a number of difficulties. To identify the student who does not do the given assignment within the allotted time. Researchers have been attempting to solve this issue in the literature of late, however most algorithms are unable to produce linearly separable learner clusters and correctly classify the input documents. In an attempt to overcome these problems, the suggested methodology builds clusters of linearly separable learners by applying topic models such as Latent Dirichlet Allocation (LDA). First, the necessary features are retrieved and converted into an appropriate LDA of words and phrases. The topic-modeling algorithm (LDA) is then fed the words to create clusters of related content or learners. A number of experiments were carried out to assess how well various predictive models performed. The results show the topic-modeling algorithm LDA attains significant clustering of documents over the other state-of-art. © 2024 IEEE.

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