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13th International Electrical engineering Congress, iEECON 2025

作者： Popun, Jettasic Lee, Wilaiporn Prayote, Akara Srisomboon, Kanabadee Pipanmekaporn, Luepol 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)9798331543952

Recently, 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. Thai Traditional Medicine (TTM) 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 Thai Traditional Medicine texts into contemporary language, aiding experts in reading and interpreting them. ASR involves converting speech signals into text. In this paper, we propose an end-to-end speech recognition approach for Thai Traditional Medical texts leveraging self-supervised learning (SSL) to overcome the limitations of limited labeled data. We utilize a transformer-based model pre-trained (XLSR-Wav2Vec) on unlabeled TTM texts speech data using SSL techniques and fine-tune it with a small dataset of labeled TTM texts audio-text pairs. Due to the absence of a Thai Traditional Medicine vocabulary repository, a dataset comprising text and speech is developed, consisting of 1,800 training sentences and 200 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 Thai Traditional Medicine prescriptions, ensuring the preservation of this valuable heritage for future generations. © 2025 IEEE.

关键词： Speech enhancement

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A Hypergraph Matching-Based Subchannel Allocation for Multi-Platoon's Communications

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IEEE Access 2023年 11卷 139345-139365页

作者： Huang, Chung-Ming Lam, Dan-Nguyen Dao, Duy-Tuan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan The University of Danang-University of Science and Technology Faculty of Electronics and Telecommunications Engineering Da Nang50000 Viet Nam

In a platoon communication scenario, the Platoon Leader (PL) vehicle interacts with the gNodeB (gNB) to obtain some assigned radio resources and then the obtained radio resources are allocated to the PL vehicle and its Platoon Member (PM) vehicles. The high transmission delay in the platoon network affects the quality of vehicles' connectivity. Therefore, the issues of improving radio resource's spectral efficiency and stability of the vehicle platooning with limited radio resources need to be tackled. This work proposes the 2-STage Resource Allocation (2-STRA) method for multi-platoon communication to reduce the transmission latency of platoon vehicles, i.e., it can improve the data transmission rates of platoon vehicles for the conditions of various speeds and various numbers of subchannels. In the $1^{st}$ stage, a greedy algorithm that jointly considers subchannel allocation and power control for PL vehicles' resource allocation is devised. In the $2^{nd}$ stage, the resource allocation for PM vehicles, which is based on the subchannels that are not allocated to PL vehicles, is resolved. A tripartite hypergraph, in which a tripartite hyperedge links:I 1) cluster of PM vehicles that use the same subchannel;2) individual cellular user;and 3) subchannel, is adopted to devise the PM vehicles' resource allocation algorithm. The simulation results show that the proposed 2-STRA method can have better performance in terms of spectral efficiency and the sum of data transmission rate comparing with the other methods. © 2013 IEEE.

关键词： Quality of service

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Design and fabrication of photovoltaics based on MFS (Ag/BaTiO3/silicon p-type) structure

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Materials science for Energy Technologies 2024年第1期7卷 29-34页

作者： Irzaman Dahrul, M. Rahmani, M. Rukyati, A.M. Samsidar Nurhidayah Deswardani, F. Peslinof, M. Jenie, R.P. Iskandar, J. Wahyuni, Y. Priandana, K. Siskandar, R. Department of Physics Faculty of Mathematics and Natural Sciences IPB University Bogor16680 Indonesia Department of Physics Faculty of Sciences and Technology Jambi University Jambi36361 Indonesia Department of Nutrition Faculty of Health Science and Technology Binawan University East Jakarta13630 Indonesia Department of Computer Engineering Pakuan University Bogor16142 Indonesia Department of Computer Science IPB University Bogor16680 Indonesia Computer Engineering Technology Study Program College of Vocational Studies IPB University West Java Bogor16151 Indonesia

The experiment was carried out by growing BaTiO3 (Undoped or Li-doped) on p-type Si(1 0 0) substrates using the Chemical Solution Deposition (CSD) method and spin coating at a rotational speed of 3000 rpm for 60 s, followed by heating at 850 °C. The characterization results show that the bandgap energy value of the thin film due to lithium doping reduces the bandgap energy value. This is presumably because the donor atom added to a semiconductor causes the allowable energy level to be slightly below the conduction band. The presence of this new band causes the thin film bandgap energy to decrease with a five-valent tantalum dip. The morphological properties showed that the BaTiO3/Si(1 0 0) thin film particles in the deposited lithium had a fairly homogeneous grain. With the addition of lithium acetate as a binder into barium titanate, the grain size is getting smaller because it is suspected that the lithium-ion radius is smaller than the barium-ion radius. Measurement of I-V on the thin film shows that the output voltage value increases with more light intensity hitting the surface of the thin film. The greater the light intensity, the greater the energy of the photons, so the electrons are easier to jump. The three things above (both electrical and morphological properties) conclude that the thin films grown have the potential for photovoltaics. © 2023

关键词： Thin films

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PuzMaty: Supporting Puzzle Mats Design Creation

PuzMaty: Supporting Puzzle Mats Design Creation

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Yamamoto, Sarii Wang, Jiajun Chan, Liwei Sugiura, Yuta Keio University Faculty of Information and Computer Science Kanagawa Japan National Yang Ming Chiao Tung University Department of Computer Science and Engineering Hsinchu Taiwan

ISBN: (纸本)9798331531614

Puzzle mats made of cushioned material are widely used in environments like homes and playrooms to prevent injuries from infants and toddlers falling. The mats feature puzzle-like edges, allowing users to freely adjust their size and shape to fit the space where the mat is placed. This study proposes "PuzMaty,"a design interface for drawing patterns of animals, numbers, letters, etc. using puzzle mats of different colors. This interface is expected to make the puzzle mat function not only as a safety measure but also as an interior decoration. It is also likely to reduce the burden of childcare when placing mats because it makes the number of mats required for a particular space more intuitive. © 2025 IEEE.

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sundararamaiah, Munikrishnaiah Rao Katru, Chandrasekhar Kolli, Chandra Sekhar Sutaria, Kruti Rao, K.B. Venkata Brahma Maranan, Ramya Indian Land SC29707 United States Aditya University Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India Parul institute of Engineering and Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology Gujarat India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

People are making daily purchases and sales through the internet, and most of them require them to use their credit cards. Besides making things easier for the customer, this heightens the probability of online credit card frauds. Credit card fraud detection has increased in several nations in the recent past due to the increased incidence of fraud that requires it to become even more of a security issue than before. In this paper, a new deep-learning-based approach for credit card fraud detection is proposed. It is based on the preprocessing stage, hybrid feature selection, and classification stage. In the first instance, the proposed method is used on the Credit Card Fraud Detection (CCF) data set. A prescreening step that uses Z-score Min-Max normalization is applied to ensure that the ranges of each data set are the same. The Crayfish Optimization Algorithm (COA) and the Emperor Penguin Optimizer (EPO) are integrated and used to identify the better attributes. Next, a similarity-navigated graph neural network (SNGNN) is utilized for classification so that the fraudulent transactions and the transactions that are not fraudulent can be differentiated easily. The overall system is also enhanced with Greylag Goose Optimization (GGO) that maximizes the chances of detection. The proposed method achieves a high accuracy in detecting fraudulent transactions, which means the proposed process has virtually limitless potential to speed up the entire process. Thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. This exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 2025 IEEE.

关键词： Graph neural networks

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A Secure Framework for Blockchain Transactions Protection

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computer Systems science & engineering 2023年第5期45卷 1095-1111页

作者： Wafaa N.Al-Sharu Majdi K.Qabalin Muawya Naser Omar A.Saraerh Department of Electrical Engineering Faculty of EngineeringThe Hashemite UniversityZarqa13133Jordan Department of Computer Science King Hussein School of Computing SciencesPrincess Sumaya University for TechnologyAmman11941Jordan

One of the most extensively used technologies for improving the security of IoT devices is blockchain *** is a new technology that can be utilized to boost the *** is a decentralized peer-to-peer network with no central *** nodes on the network mine or verify the data recorded on the *** is a distributed ledger that may be used to keep track of transactions between several *** one can tamper with the data on the blockchain since it is *** the blocks are connected by hashes,the transaction data is *** is managed by a system that is based on the consensus of network users rather than a central *** immutability and tamper-proof nature of blockchain security is based on asymmetric cryptography and ***,Blockchain has an immutable and tamper-proof smart contract,which is a logic that enforces the Blockchain’s *** is a conflict between the privacy protection needs of cyber-security threat intelligent(CTI)sharing and the necessity to establish a comprehensive attack chain during blockchain *** paper presents a blockchain-based data sharing paradigm that protects the privacy of CTI sharing parties while also preventing unlawful sharing and ensuring the benefit of legitimate sharing *** builds a full attack chain using encrypted threat intelligence and exploits the blockchain’s backtracking capacity to finish the decryption of the threat source in the attack *** contracts are also used to send automatic early warning replies to possible attack *** tests are used to verify the feasibility and efficacy of the suggested model.

关键词： Manuscript preparation typeset format

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Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

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computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

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A secure data hiding approach based on least-significant-bit and nature-inspired optimization techniques

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4639-4657页

作者： Hameed, Mohamed Abdel Abdel-Aleem, Omar A. Hassaballah, M. Department of Computer Science Faculty of Computers and Information Luxor University Luxor Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena83523 Egypt

With remarkable information technology development, information security has become a major concern in the communication environment, where security must be performed for the multimedia messages exchanged between the sender and the intended recipient. Digital multimedia steganography techniques have been developed to attain a security for covert communication and secure data. This paper proposes an approach for image steganography using the Least Significant Bit Substitution (LSB) and Nature-Inspired Harris Hawks Optimization (HHO) algorithm for efficient concealing of the secret data inside a cover image;thus providing high confidentiality. The HHO based data encoding operation uses the PSNR visual quality metric as an objective function. The objective function is used to determine the ideal encoding vector to convert the secret message to its encoded form. The proposed approach performs better than other state-of-the-art methods in terms of standard measures of visual quality with maintaining high embedding capacity. Comparisons with existing LSB or multi-directional PVD embedding methods demonstrate that the proposed method has more optimized and higher embedding capacity with maintaining visual quality. Besides, the proposed approach achieves high security against statistical StegoExpose analysis, ALASKA2 deep learning steganalysis, and image processing attacks. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Steganography

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Data-Filtered Prediction With Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, Mengchu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China University of Melbourne Cloud Computing and Distributed Systems Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing100191 China CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in Quality of Service and cost efficiency. However, these data often exhibit nonlinearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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