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7th International Conference on information technology, InCIT 2023

作者： Bundasak, Supaporn Chomchan, Iya Kasetsart University Sriracha Campus Faculty of Science at Sriracha Computer Science and Information Technology Department Chonburi Thailand

ISBN: (纸本)9798350358698

The faculty of science, Sriracha Campus is in the Eastern Economic Corridor (EEC) to recognize the importance of the development of Industry4.0, Therefore it has been developed a control and data analysis program based on a controlled automatic sorting simulation system to disseminate the technology of PLC and data analysis to develop into a smart factory. By developing personnel to have knowledge and developing the capabilities of machines for Industry4.0. Automated machines for smart factories, such as production systems, can be adjusted according to the situation by responding in real time. Moreover, by creating intelligent machines, trackers and forecasters can analyze production data by receiving information from machines to prevent errors from occurring. Being able to develop a machine status monitoring program can help extend the working life of machines. Therefore, developing a training set to program and study data analysis in factory machinery. It is important to develop technological knowledge and understanding in developing smart factory systems such as automatic control. Machine maintenance through data analysis from various sensors using production data to analyze production rates or detecting work controls through sensors that have complex functions from various research developments through studies on this simulation set in the future. © 2023 IEEE.

关键词： Data Analytics

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Unseen Named Entity Recognition for Thai Language 21

Unseen Named Entity Recognition for Thai Language

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21st International system-on-Chip Design Conference, ISOCC 2024

作者： Kuntichod, Watcharet Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Most common challenges in Named-entity recognition (NER) require suitable and annotated datasets, tailored to the specific domain of the documents under analysis, for example, legal, medical, and financial documents each require distinct datasets. Reliable identification of named entities within the training set is crucial. Annotations is a vital role in building an effective mechanism. However, creating training sets demands significant human effort, contributing to the high costs associated with NER. High quality training materials are indispensable for evolving machine-learning algorithms, underscoring the importance of dependable training data. Thus, the primary challenge lies in acquiring and managing data rather than in the algorithms themselves. To overcome most common situations of finding new unseen names, this paper proposes a mechanism to detect a new undiscovered name in Thai language based on sentence contexts. The method uses BI-LSTM model to learn sentence structures and predict a new unseen name from its context. Our framework comprises two main tasks. The first one is word entity extraction. The second task is using the model to identify unrecognized named entities. Evaluation of the proposed method is described with a successful rate of accuracy in discovering newly identified entities. This contributes to the expansion of entity recognition capabilities in Thai language processing. © 2024 IEEE.

关键词： BI-LSTM Herb Entity Unseen Named Entity

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A Study on the Vulnerabilities of Openid Connect Based Federated Authentication system for Registration Data Access Protocol 23

A Study on the Vulnerabilities of Openid Connect Based Feder...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Hsu, Che-Hao Teng, Wei-Chung National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106335 Taiwan

ISBN: (纸本)9798331528041

Registration Data Access Protocol (RDAP) is designed to replace the classic WHOIS protocol by providing Representational State Transfer (RESTful) web services and tiered access control, enabling more secure and controlled data access based on the client's authorization level. Despite these improvements, the de-centralized management of registration metadata by various internet registries and registrars presents a challenge for users needing to obtain login credentials from multiple RDAP servers. This study examines the federated authentication mechanism proposed by RFC 9560 [8], identifying vulnerabilities within the current WHOIS/RDAP architecture and proposing countermeasures to enhance security and efficiency in the domain registration ecosystem. © 2024 IEEE.

关键词： Authentication Protocol

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Audiovisual speech recognition based on a deep convolutional neural network

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Data science and Management 2024年第1期7卷 25-34页

作者： Shashidhar Rudregowda Sudarshan Patilkulkarni Vinayakumar Ravi Gururaj H.L. Moez Krichen Department of Electronics and Communication Engineering JSS Science and Technology UniversityMysuru570006India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobar34754Saudi Arabia Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal560064India Department of Information Technology Faculty of Computer Science and Information Technology(FCSIT)Al-Baha UniversityAlaqiq65779-7738Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia

Audiovisual speech recognition is an emerging research *** is the recognition of what someone is saying using visual information,primarily lip *** this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual *** features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural *** feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural ***,integration was performed using a deep convolutional *** audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 *** training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 *** integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively.

关键词： Audiovisual speech recognition,Custom dataset 1D Convolution neural network(CNN) Deep CNN(DCNN) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)

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Integrating IoT and Blockchain-Enabled Device-to-Device Communication to Improve Privacy in Intelligent Quarantine Environments 7

Integrating IoT and Blockchain-Enabled Device-to-Device Comm...

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7th IEEE International Conference on Computational systems and information technology for Sustainable Solutions, CSITSS 2023

作者： Siddiqui, Shams Tabrez College of Computer Science and Information Technology Jazan University Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798350343144

The exponential progress of Internet of Things (IoT) technology has resulted in the emergence of groundbreaking solutions across diverse industries, such as healthcare and quarantine management. In the realm of global health crises, such as the ongoing COVID-19 pandemic, the successful execution of efficient quarantine protocols assumes paramount importance. The objective of this research paper is to examine the potential integration of Internet of Things (IoT) devices, wireless sensors, and blockchain technology in order to establish a Smart Quarantine Environment (SQE). The SQE aims to improve privacy, security, and efficiency in the management of quarantine protocols. By analyzing the existing literature and exploring relevant case studies, this paper seeks to provide insights into the feasibility and benefits of implementing such a system. The utilization of IoT devices and wireless sensors enables the facilitation of real-time monitoring and data collection. This approach is supported by the integration of blockchain technology, which ensures data integrity, transparency, and user control. By addressing concerns related to privacy and security, this combination of technologies enhances the overall effectiveness of the system. © 2023 IEEE.

关键词： Internet of things

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Advancements in Skin Cancer Detection: A Comprehensive Review of Convolutional Neural Network Approaches 14

Advancements in Skin Cancer Detection: A Comprehensive Revie...

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14th International Conference on Software technology and Engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Ganga, Krishna INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Skin cancer is one of the most prevalent types of cancer globally, with its incidence steadily rising over the past decades. Early and accurate detection of skin cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated image analysis and classification, offering promising results in various medical imaging tasks, including skin cancer detection. This paper provides a comprehensive review and analysis of CNN-based approaches for skin cancer classification. We discuss the challenges associated with traditional methods of skin cancer diagnosis and highlight the potential of CNNs in addressing these challenges. Furthermore, we survey state-of-the-art CNN architectures, dataset collections, preprocessing techniques, and evaluation metrics commonly used in skin cancer classification tasks. We also delve into the transfer learning and data augmentation strategies employed to improve model generalization and robustness. Additionally, we review recent advancements in interpretability techniques for CNNs, which enhance the transparency and trustworthiness of automated skin cancer diagnosis systems. Finally, we discuss the limitations and future directions of CNN-based approaches in skin cancer classification, emphasizing the need for large-scale, diverse datasets, standardized evaluation protocols, and interdisciplinary collaborations to facilitate the clinical translation and deployment of CNN-based diagnostic tool. © 2024 IEEE.

关键词： Lung cancer

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Explainable Software Fault Localization Model: From Blackbox to Whitebox

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computers, Materials & Continua 2022年第10期73卷 1463-1482页

作者： Abdulaziz Alhumam Department of Computer Science College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia

The most resource-intensive and laborious part of debugging is finding the exact location of the fault from the more significant number of code *** of machine intelligence models has offered the effective localization of *** models can precisely locate the faulty with more than 95%accuracy,resulting in demand for trustworthy models in fault *** and trustworthiness within machine intelligencebased software models can only be achieved via explainable artificial intelligence in Fault Localization(XFL).The current study presents a model for generating counterfactual interpretations for the fault localization model’s *** system approximations and disseminated presentation of input information may be achieved by building a nonlinear neural network *** demonstrates a high level of proficiency in transfer learning,even with minimal training *** proposed XFL would make the decisionmaking transparent simultaneously without impacting the model’s *** proposed XFL ranks the software program statements based on the possible vulnerability score approximated from the training *** model’s performance is further evaluated using various metrics like the number of assessed statements,confidence level of fault localization,and TopN evaluation strategies.

关键词： Software fault localization explainable artificial intelligence statement ranking vulnerability detection

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computer Generated Imagery Animation and Visual Effects Education in India during the Covid-19 pandemic: A significant assessment 2

Computer Generated Imagery Animation and Visual Effects Educ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Raturi, Vinod Yadav, Rakesh Kumar Department of Computer Science and Engineering Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Maharishi Maharishi University of Information Technology Lucknow India

ISBN: (纸本)9798350305418

The COVID-19 outbreak has been designated an epidemic in India. Lockdown was implemented on March 25, 2020 to fight COVID-19, affecting the country's school system. It has transformed conventional education into an Ed Tech paradigm where teaching and evaluation are done online. This research tries to highlight the challenges Animation and Visual Effects (VFX) educators experience while teaching and assessing online in India. This study used interpretive phenomenological analysis (IPA). Educators from public and private universities were surveyed. Animation and VFX Online instructors and evaluators were semi-structured. The research identified different types of difficulties experience while teaching and assessing online. Lack of basic amenities, external distractions, family disruptions during instruction and evaluation were key difficulties mentioned. Institutional support hurdles included training, technical assistance, and clarity, lack of understanding of online teaching platforms, In addition to a lack of technical understanding was came out from the research. That's good news for institutions that plan to frequently utilize online computer-Generated Imagery (CGI) Animation and Visual Effects (VFX) education and evaluation in the future. © 2023 IEEE.

关键词： animation computer generated imagery (CGI) COVID-19 educator online education outbreak visual effects (VFX)

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Advance Path Loss Model for Distance Estimation Using LoRaWAN Network's Received Signal Strength Indicator (RSSI)

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IEEE Access 2024年 12卷 83205-83216页

作者： Vo, Hoang Hoang Long Nguyen, Van Tran, Van Lic Ferrero, Fabien Lee, Fang-Yi Tsai, Meng-Hsun The University of Danang-University of Science and Technology Faculty of Advance Science and Technology Da Nang550000 Viet Nam The University of Danang-University of Science and Technology Department of Electronic and Telecommunication Engineering Da Nang550000 Viet Nam LEAT CNRS Universite Cote d'Azur Sophia Antipolis06903 France National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan

This work introduces a novel approach to improve the precision of distance estimation in localization systems by using existing LoRaWAN and RSSI-based techniques. Despite the benefits of range and power efficiency, these systems exhibit limited accuracy in practical situations. To address the limitation, this study provides an innovative technique that greatly improves the precision of distance estimations, particularly in urban environments. The fundamental basis of this approach lies in the use of a dynamic path loss model. An additional element is to accommodate the varied and dynamic conditions of signal transmission in metropolitan areas. A better Kalman filter is also used in the study. This is important because it reduces the effects of multipath fading and environmental noise that often make RSSI-based localization in LoRaWAN networks less accurate. The study further examines the influence of the environmental exponent, also known as the path loss exponent, on the RSSI results and the precision of the distance measurements. This methodology achieves the average error under 1 meters for indoor environments and under 7 meters for outdoor environments. Finally, the Cumulative Density Function (CDF) shows 90 % of the distance estimation algorithm error for indoor environment is lower than 1.08 meters while for outdoor environment is lower than 7.55 meters. Based on these improvements, the introduced methodology not only enhances and improves existing approaches but also optimizes the precision and dependability of urban localization technologies, with substantial implications for a variety of practical applications. © 2013 IEEE.

关键词： Kalman filters

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Beyond administrative reports: a deep learning framework for classifying and monitoring crime and accidents leveraging large-scale online news

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Neural Computing and Applications 2025年第10期37卷 7183-7205页

作者： Tuarob, Suppawong Tatiyamaneekul, Phonarnun Pongpaichet, Siripen Tawichsri, Tanisa Noraset, Thanapon Faculty of Information and Communication Technology Mahidol University Nakhon Pathom Thailand Department of Computer Science University College London London United Kingdom Puey Ungphakorn Institute for Economic Research Bank of Thailand Bangkok Thailand

The escalating prevalence of violent crimes and accidents underscores the urgent need for efficient and timely monitoring systems. Traditional methods reliant on administrative reports often suffer from significant delays. This paper proposes CRIMSON, a novel framework that leverages large-scale online news to provide real-time insights into crime and accident trends. CRIMSON utilizes a multi-label classification technique that leverages a fine-tuned, pre-trained, cross-lingual language model to accurately categorize news articles. Our experimental results, conducted on a substantial dataset of Thai news articles, demonstrate superior performance, achieving an average F1 score of 86%. Beyond classification, CRIMSON aggregates categorized news into real-time statistics, revealing strong correlations between news-reported incidents and official crime data. This study pioneers online news as a reliable and timely crime and accident monitoring source, offering valuable insights for law enforcement, policymakers, and researchers. © The Author(s) 2025.

关键词： Deep learning

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