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

作者： Popun, Jettasic 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

Currently, Automatic Speech Recognition (ASR) technology is widely used for communication convenience in converting speech to text. Initial surveys reveal that existing studies on ASR, both in Thai and foreign languages, focus on contemporary language communication, lacking support for specialized vocabulary. Traditional Thai Medicine contain ancient medical terminology recorded in various media such as palm leaves, parchment, notebooks, and inscriptions. Centuries of textual preservation have led to text degradation. Given the suitability of ASR study to accommodate medical terminology and ancient languages, it is proposed to apply it to convert original Traditional Thai Medicine texts into contemporary language, aiding experts in reading and interpreting them. ASR involves converting speech signals into text. In this study, Hidden Markov Model techniques using Kaldi are employed. Due to the absence of a Thai Traditional Medicine vocabulary repository, a dataset comprising text and speech is developed, consisting of 900 training sentences and 100 test sentences randomly selected from recordings of 10 Thai Traditional Medicine experts. Offline evaluation methods are employed, resulting in improved accuracy rates. Future efforts will focus on expanding the speech dataset to enhance recognition accuracy further. This study lays the groundwork for efficiently extracting knowledge from Traditional Thai Medicine prescriptions, ensuring the preservation of this valuable heritage for future generations. © 2024 IEEE.

关键词： Hidden Markov models

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Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

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computers, Materials & Continua 2023年第3期74卷 6587-6604页

作者： Mohammed Maray Haya Mesfer Alshahrani Khalid A.Alissa Najm Alotaibi Abdulbaset Gaddah AliMeree Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Warwick UniversityCovetryUK Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent years,wireless networks are widely used in different *** phenomenon has increased the number of Internet of Things(IoT)devices and their *** IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks *** scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic *** Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT *** Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this *** current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT *** presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature ***,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT ***,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the *** process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s *** proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

关键词： Internet of things SDN controller feature selection hyperparameter tuning autoencoder

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Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates 17

Feed-Forward Probabilistic Error Cancellation with Noisy Rec...

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Kurosawa, Leo Saito, Yoshiyuki Lee, Xinwei Yan, Xinjian Xie, Ningyi Cai, Dongsheng Shin, Jungpil Asai, Nobuyoshi University of Aizu School of Computer Science and Engineering Aizu-Wakamatsu Fukushima Japan Singapore Management University School of Computing and Information Systems Singapore University of Tsukuba School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9798331530471

Probabilistic Error Cancellation (PEC) aims to improve the accuracy of expectation values for observables. This is accomplished using the probabilistic insertion of recovery gates, which correspond to the inverse of errors. However, the inserted recovery gates also induce errors. Thus, it is difficult to obtain accurate expectation values with PEC since the estimator of PEC has a bias due to noise induced by recovery gates. To address this challenge, we propose an improved version of PEC that considers the noise resulting from gate insertion, called Feed-Forward PEC (FFPEC). FFPEC provides an unbiased estimator of expectation values by cancelling out the noise induced by recovery gates. We demonstrate that FFPEC yields more accurate expectation values compared to the conventional PEC method through analytical evaluations. Numerical experiments are used to evaluate analytical results. © 2024 IEEE.

关键词： Inverse problems

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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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Machine Learning Classification for Intrusion Detection on computer Networks 9

Machine Learning Classification for Intrusion Detection on C...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Tachaapornchai, Abhibhu Kosolsombat, Somkiat Ratanavilisagul, Chiabwoot Data Science and Innovation College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Thailand

ISBN: (纸本)9798350352214

To build an intelligent intrusion detection system, it is essential to have a suitable and high-quality dataset with a sufficiently large quantity to simulate real-world scenarios. The NSL-KDD dataset is an improved version derived from the previous KDD 99 dataset. In this article, an analysis of the NSL-KDD dataset was conducted to study the efficiency of classification machine learning in detecting abnormalities in network data transmission patterns. The study achieved the highest accuracy by XGBoost method using 14 features important and grid search for hyperparameter tuning and revealing many interesting insights. This research has significant implications and can be further applied in the field of cybersecurity by leveraging machine learning for network intrusion detection systems. © 2024 IEEE.

关键词： Cybersecurity

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Enhancing Cultural Tourism with VR: A Virtual Tour of Wat Pho 10

Enhancing Cultural Tourism with VR: A Virtual Tour of Wat Ph...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Seripanichkarn, Pitcha Piyasoranart, Chananart Ratanasanya, San Visutsak, Porawat KMUTNB Faculty of Applied Science Department of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798331543273

This paper presents the development of an interactive virtual reality (VR) web application for exploring Wat Pho, a UNESCO Memory of the World heritage site in Bangkok, Thailand. Aimed at enhancing cultural tourism and accessibility, the application provides a 360° virtual tour of key locations within the temple grounds. Developed using Unity and Firebase, the application integrates interactive elements such as navigational arrows, Thai audio narration, textual information, and a chatbot for user queries. This immersive VR experience offers a novel way to engage with Wat Pho's rich history and cultural significance for both local and remote audiences, overcoming barriers of physical access. © 2025 IEEE.

关键词： Virtual reality

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SNSVM:SqueezeNet-Guided SVM for Breast Cancer Diagnosis

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computers, Materials & Continua 2023年第8期76卷 2201-2216页

作者： Jiaji Wang Muhammad Attique Khan Shuihua Wang Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Breast cancer is a major public health concern that affects women *** is a leading cause of cancer-related deaths among women,and early detection is crucial for successful ***,breast cancer can often go undetected until it has reached advanced stages,making it more difficult to ***,there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early *** proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography *** extracted features are then utilized to train a support vector machine(SVM)for mammography image *** SqueezeNet-guided SVMmodel,known as SNSVM,achieved promising results,with an accuracy of 94.10% and a sensitivity of 94.30%.A 10-fold cross-validation was performed to ensure the robustness of the results,and the mean and standard deviation of various performance indicators were calculated across multiple *** model also outperforms state-of-the-art models in all performance indicators,indicating its superior *** demonstrates the effectiveness of the proposed approach for breast cancer diagnosis using mammography *** superior performance of the proposed model across all indicators makes it a promising tool for early breast cancer *** may have significant implications for reducing breast cancer mortality rates.

关键词： SqueezeNet support vector machine breast cancer diagnosis

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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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computers, Materials & Continua 2024年第10期81卷 643-661页

作者： Stephen Ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

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PDF Malware Detection Using Machine Learning 9

PDF Malware Detection Using Machine Learning

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Tantikulvichit, Naragorn Kosolsombat, Somkiat Ratanavilisagul, Chiabwoot College of Interdisciplinary Studies Thammasat University Data Science and Innovation Pathum Thani Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Thailand

ISBN: (纸本)9798350352214

In today's world, cyber-attacks are on the rise, and PDF files are commonly used as a means of attack. One common type of attack through PDF files is the covert embedding of dangerous malware and tricking users into clicking on malicious links. The victims may have their important data stolen and suffer various forms of damage. Detecting malware embedded in PDF files can help mitigate the harm to users. This project aims to study methods for detecting malware embedded in PDF files using machine learning techniques, with the best-performing model being Gradient Boosting, achieving an accuracy of 99% in 5-fold cross-validation and 97% using 10 features. Using Feature Selection Technique Voting from 3 Models consist of Logistic Regression, Random Forest, Lasso Regression, Each model will choose matching features to use for machine learning analysis. This project approach reduce feature for machine learning to detect PDFMalware. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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computer systems science & Engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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