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5th technology Innovation management and engineering Science International Conference, TIMES-iCON 2024

作者： Tapaopong, Wachiraporn Charoenphon, Atiphan Raksasri, Jakkapong Samanchuen, Taweesak Technology of Information System Management Division Faculty of Engineering Mahidol University Nakorn Pathom Thailand

ISBN: (纸本)9798350362602

—The proliferation of cyberbullying on social media platforms has led to devastating physical, emotional, and mental consequences for victims, underscoring the need for effective identification and analysis methods. This study leverages Natural Language Processing (NLP) techniques, specifically Transformer models, to enhance cyberbullying detection in harmful social media messages. By employing transfer learning and fine-tuning strategies, we compared the effectiveness of category prediction using five prominent Transformer models: BERT, RoBERTa, ALBERT, DistilBERT, and ConvBERT. Our results demonstrate that DistilBERT outperforms other models, achieving an accuracy of 94.36%, precision of 93.83%, and recall of 93.91% in the 5-Class categorization of cyberbullying severity. This study highlights the significance of automated cyberbullying detection and underscores the potential of NLP techniques in combating the adverse impacts of cyberbullying. The findings of this research have important implications for the development of effective cyberbullying detection systems, which can help mitigate the harmful effects of online harassment. By leveraging the capabilities of DistilBERT, we can create more accurate and efficient systems for detecting and preventing cyberbullying, ultimately contributing to a safer online environment. ©2024 IEEE.

关键词： Phishing

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A Low-Code Platform of Carbon Credit Trading Using Blockchain technology: A Case Study in Nakhon Si Thammarat Province 5

A Low-Code Platform of Carbon Credit Trading Using Blockchai...

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5th technology Innovation management and engineering Science International Conference, TIMES-iCON 2024

作者： Pengna, Piyawadee Leelasantitham, Adisorn Sukamongkol, Yod Technology of Information System Management Division Faculty of Engineering Mahidol University Nakhon Pathom Thailand

ISBN: (纸本)9798350362602

Carbon credit is one of the measures within the Emission Trading Scheme (ETS), which serves as a mechanism to offset the amount of greenhouse gases one emits beyond the allocated rights to emit greenhouse gases. Although there are the existed carbon credit trading platforms, this research aimed to study a carbon credit trading prototype platform by using a low code with blockchain technology (BT). The rubber tree data at Nakhon Si Thammarat province in 3 age groups;namely 6-, 14-, and 22-year-old tree were used as the initial input data along with the area size of rubber tree field. After simulating the developed platform, the results show that the carbon content of the projects that have already been purchased can be sold or re-purchased while the contract for the transaction is recorded on the blockchain. Furthermore, the developed low code platform can save the platform's cost and the developing time, moreover, people without IT experience can use this low-code model to develop platforms of interest. ©2024 IEEE.

关键词： Forestry

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A Social Analysis of Thailand's 2023 Election Through Twitter Feeds 15

A Social Analysis of Thailand's 2023 Election Through Twitte...

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15th International Conference on information technology and Electrical engineering, ICITEE 2023

作者： Sarapugdi, Wanaporn Namkhun, Songphon Rajabhat Mahasarakham University Program in Management of Digital Innovation Faculty of Information Technology Maha Sarakham Thailand Rajabhat Mahasarakham University Program in Computer Engineering Faculty of Engineering Maha Sarakham Thailand

ISBN: (纸本)9798350304466

Nowadays, it cannot be denied that social media has been in many parts of people's lives, especially in entertainment, business and education. Public figures including politicians frequently use social platforms to connect with their audience, for example Facebook, Instagram, Tiktok and etc. One of the most commonly used is a platform called Twitter or also known as X. It is a platform that people can share their opinions in short messages. Since there is an alphabet limitation, people can deliver and receive message rapidly. It is a perfect platform that we could use to update the real-time situations for example election, emergency alert, natural disasters and etc. According to its characteristic, it can be used as a data source for social media analytics, which involves collecting and analyzing data to perform business decisions. The social media analytics has become commonplace because the number of people using social media tends to increase every second. Specifically, Twitter sentiment analysis offers a quick and inexpensive tool for monitoring and predictions. In this work, Twitter is used as a data source to monitor and predict Thailand Election in 2023 because this election highly attracts attention from Thais and international. Moreover, Twitter is a significant platform that is used to debate about political events in Thailand. The result of this election potentially indicates Thailand's future, including economics, public health, environment, international relations and etc. Our work performs sentiment analysis to categorize the data and mainly focuses on extracting sentiment from text without considering semantic word relations and co-occurrences because it is appropriate and functional for Thai sentence analysis. Based on the result, it can be seen that Twitter plays a significant role in politics, with politicians wielding considerable influence over public perception. Comparing of the actual election result and the predicted result, this analysis dem

关键词： Sentiment analysis

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Classification of PCOS Using Convolution Neural Network 12

Classification of PCOS Using Convolution Neural Network

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Kaur, Trishvin Safuan, Syadia Nabilah Mohd Bohari, Azuwien Aida Ahmad, Nor Izzati Katim, Nurul Izzatul Akma Alias, Nor Akmal Management and Science Univeristy Faculty of Information Sciences and Engineering Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Engineering Technology Malaysia

ISBN: (纸本)9798350391398

Polycystic ovary syndrome (PCOS), a complex metabolic disorder affects 15-20% of women of reproductive age. Diagnosing PCOS can be challenging due to factors such as the overlapping of symptoms with other underlying conditions of a patient, the lack of a single definitive test, and the variable presentation of the condition. The introduction of different methods proposed for image classification was not effective in classifying PCOS-infected and non-infected ultrasounds. Consequently, machine learning algorithms are innovated to analyze the Kaggle dataset and improve the accuracy of PCOS diagnosis. Convolutional Neural Network (CNN) can help identify patterns and features that are possibly missed by physicians and it can also provide a more efficient and cost-effective approach to PCOS diagnosis. The best approach for classification of images is CNN. Therefore, it is very crucial to produce a system that can classify the ultrasounds effectively in the 2 categories by being reliable, automatic, and fast with the highest accuracy possible. As for the findings, it shows that the Convolutional Neural Network, to be precise, the Alexnet model showed the highest accuracy among other models proving it to be the best model for image classification. This study is significant as it contributes to the well-being of women and it is greatly useful in early detection of PCOS along with cost-effectiveness in line with United Nation's sustainable development goals for Industry, Innovation, and Infrastructure (SDG 9). © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

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Neural Computing and Applications 2024年第30期36卷 18727-18759页

作者： Kassaymeh, Sofian Al-Betar, Mohammed Azmi Rjoubd, Gaith Fraihat, Salam Abdullah, Salwani Almasri, Ammar Software Engineering Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Artificial Intelligence Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Management Information Systems Department Al-Balqa Applied University Amman Jordan

This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter (c1) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Global optimization

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MSF-Net: Multi-Scale Feedback Reconstruction for Guided Depth Map Super-Resolution

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IEEE Transactions on Circuits and Systems for Video technology 2024年第2期34卷 709-723页

作者： Wang, Jin Li, Chenyang Shi, Yunhui Wang, Dan Wu, Mu-En Ling, Nam Yin, Baocai Beijing University of Technology Faculty of Information Technology Beijing100124 China National Taipei University of Technology Department of Information and Finance Management Taipei10608 Taiwan Santa Clara University Department of Computer Science and Engineering Santa ClaraCA95053 United States

Guided depth map super-resolution (GDSR) is one of the mainstream methods in depth map super-resolution, as high-resolution color images can guide the reconstruction of the depth maps and are often easy to obtain. However, how to make full use of extracted guidance information of the color image to improve the depth map reconstruction remains a challenging problem. In this paper, we first design a multi-scale feedback module (MF) that extracts multi-scale features and alleviates the information loss in network propagation. We further propose a novel multi-scale feedback network (MSF-Net) for guided depth map super-resolution, which can better extract and refine the features by sequentially joining MF blocks. Specifically, our MF block uses parallel sampling layers and feedback links between multiple time steps to better learn information at different scales. Moreover, an inter-scale attention module (IA) is proposed to adaptively select and fuse important features at different scales. Meanwhile, depth features and corresponding color features are interacted using cross-domain attention conciliation module (CAC) after each MF block. We evaluate the performance of our proposed method on both synthetic and real captured datasets. Extensive experimental results validate that the proposed method achieves state-of-the-art performance in both objective and subjective quality. © 1991-2012 IEEE.

关键词： Color

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Knowledge Graph Based System for Community Enterprises Product Enhancement 7

Knowledge Graph Based System for Community Enterprises Produ...

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7th International Conference on information technology, InCIT 2023

作者： Chandoem, Nutjaya Matayong, Sureena Prince of Songkla University Management of Information Technology Faculty of Engineering Songkhla Thailand

ISBN: (纸本)9798350358698

Community enterprises aim to enhance products by standardizing goods and services through the incorporation of knowledge. However, the absence of a centralized knowledge map as a unified database for knowledge integration results in information fragmentation, leading to the challenges of intricate knowledge retrieval, data inaccuracies, resource inefficiencies, and insignificant data accessibility. To address these problems, researchers propose a knowledge graph-based system for community enterprises' product enhancement using a knowledge-guided approach to enhance the efficiency of collecting and retrieving integrated knowledge. This involves developing a data structure based on the knowledge graph concept as well as improving the knowledge retrieval process fostering advanced knowledge incorporation for community enterprises' product enhancement. © 2023 IEEE.

关键词： Knowledge graph

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Formulating Analytical Governance Frameworks: An Integration of Data and AI Governance Approaches 23

Formulating Analytical Governance Frameworks: An Integration...

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13th International Conference on Advances in information technology, IAIT 2023

作者： Kanying, Thanika Thammaboosadee, Sotarat Chuckpaiwong, Rojjalak Technology of Information System Management Division Faculty of Engineering Mahidol University Thailand

ISBN: (纸本)9798400708497

This study underscores the importance of standard data analytics governance within organizations to leverage the potential of increasing volumes of big data. Even though Thailand has operative frameworks for data governance and AI governance, there lacks a concrete framework specifically for analytics governance. To fill this void, the research utilizes the Data Governance Framework from the Digital Government Development Agency (DGA), the Artificial Intelligence Governance for e-Business and Digital Services from the Electronic Transactions Development Agency (ETDA), and the Data management Capability Assessment Model (DCAM) within the Analytics management section. The design and evaluation of the analytics governance framework are carried out using feedback from experts via a questionnaire. The ultimate objective of this study is to pinpoint the relevant components necessary for formulating an analytics governance framework in organizations in Thailand, which have already implemented data governance. © 2023 Owner/Author.

关键词： AI Governance Analytics Governance Analytics management Data Analytics Data Governance

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Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

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information Processing in Agriculture 2023年第3期10卷 289-300页

作者： Inna Novianty Ringga Gilang Baskoro Muhammad Iqbal Nurulhaq Muhammad Achirul Nanda Computer Engineering Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Informatics Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Technology and Plantation Production Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial TechnologyUniversitas PadjadjaranJatinangor 45363Indonesia

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and *** content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product *** on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm ***,350 palm fruits with Tenera varieties(Elaeis guineensis ***),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content ***,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil ***,a robust EMD-ANN model is generated by optimizing the lowest possible *** on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±*** results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally ***,the proposed technique has a promising potential to be applied in the oil palm *** like this will lead to the effective and efficient management of oil palm production.

关键词： Artificial neural network Empirical mode decomposition Oil palm Oil content prediction

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Digital Twins for Construction Project management (DT-CPM): Applications and Future Research Directions

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Journal of The Institution of Engineers (India): Series A 2024年第3期105卷 793-807页

作者： Reja, Varun Kumar Sindhu Pradeep, Megha Varghese, Koshy Building Technology and Construction Management Division Department of Civil Engineering Indian Institute of Technology Madras Sardar Patel Road Adyar Tamil Nadu Chennai600036 India Faculty of Engineering and Information Technology University of Technology Sydney 15 Broadway UltimoNSW2007 Australia

This article explores the underutilised potential of Digital Twins (DTs) beyond their predominant application in facility management, focusing on their promising role in enhancing efficiency across various phases of construction projects. Enabling smooth data transfer between virtual and physical environments, DTs hold considerable potential for enhancing project planning and control, safety and quality management, in addition to procurement and resource management through the construction stage. Positioned at the peak of inflated expectations in the innovation hype cycle, a comprehensive review of the DT concept is required to provide valuable insights for both practitioners and academicians. Furthermore, the study employs a PRISMA based systematic literature review methodology to analyse existing research and identify gaps in the literature. This paper uses the PMI-PMBOK framework to delineate potential applications of DTs in Construction Project management (DT-CPM) across different categories. The key findings of the study include a comprehensive overview of the potential applications of DT-CPM, along with an exploration of current challenges and future research directions. This study benefits practitioners by providing insights into leveraging DTs for improved project management practices and academicians by identifying areas for further research and exploration within the DT domain. © The Institution of Engineers (India) 2024.

关键词： Project management

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