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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Nantapanich, Nutyada Mahatthanajatuphat, Pisit Wongsin, Norakamon Lee, Wilaiporn Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani Thailand Yothinburana School Regular Program in Mathematics-Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331543952

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis. © 2025 IEEE.

关键词： Polynomial regression

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Deep-Learning–Based Screening and Ancillary Testing for Thyroid Cytopathology

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American Journal of Pathology 2023年第9期193卷 1185-1194页

作者： Dov, David Elliott Range, Danielle Cohen, Jonathan Bell, Jonathan Rocke, Daniel J. Kahmke, Russel R. Weiss-Meilik, Ahuva Lee, Walter T. Henao, Ricardo Carin, Lawrence Kovalsky, Shahar Z. I-Medata AI Center Tel Aviv Sourasky Medical Center Tel Aviv-Yafo Israel Department of Pathology Duke University Medical Center Durham NC United States Department of Head and Neck Surgery & Communication Sciences Duke University Medical Center Durham NC United States Department of Head and Neck Surgery Kaplan Medical Center Rehovot Israel Biological Environmental Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Biostatistics and Bioinformatics Duke University Durham NC United States Computer Science Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics University of North Carolina at Chapel Hill Chapel Hill NC United States

Thyroid cancer is the most common malignant endocrine tumor. The key test to assess preoperative risk of malignancy is cytologic evaluation of fine-needle aspiration biopsies (FNABs). The evaluation findings can often be indeterminate, leading to unnecessary surgery for benign post-surgical diagnoses. We have developed a deep-learning algorithm to analyze thyroid FNAB whole-slide images (WSIs). We show, on the largest reported data set of thyroid FNAB WSIs, clinical-grade performance in the screening of determinate cases and indications for its use as an ancillary test to disambiguate indeterminate cases. The algorithm screened and definitively classified 45.1% (130/288) of the WSIs as either benign or malignant with risk of malignancy rates of 2.7% and 94.7%, respectively. It reduced the number of indeterminate cases (N = 108) by reclassifying 21.3% (N = 23) as benign with a resultant risk of malignancy rate of 1.8%. Similar results were reproduced using a data set of consecutive FNABs collected during an entire calendar year, achieving clinically acceptable margins of error for thyroid FNAB classification. © 2023 American Society for Investigative Pathology

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Optimal deployment of private 5G multi-access edge computing systems at smart factories: Using hybrid crow search algorithm

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Journal of Network and computer Applications 2024年 227卷

作者： Lin, Chun-Cheng Deng, Der-Jiunn Hsieh, Li-Tsung Pan, Pei-Tzu Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua500 Taiwan

In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities. © 2024 Elsevier Ltd

关键词： 5G mobile communication systems

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Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

Vision-based Semantic Communications for Metaverse Services:...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyuan Liu Hongyang Du Dusit Niyato Jiawen Kang Zehui Xiong Boon Hee Soong Energy Research Institute@NTU Interdisciplinary Graduate Program School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

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Role of deep learning in cognitive healthcare: Wearable signal analysis, algorithms, benefits, and challenges

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Digital Communications and Networks 2025年第3期11卷 642-670页

作者： Md. Sakib Bin Alam Aiman Lameesa Senzuti Sharmin Shaila Afrin Shams Forruque Ahmed Mohammad Reza Nikoo Amir H. Gandomi Department of Information Technology University of Information Technology and Sciences (UITS) Dhaka 1212 Bangladesh Department of Computer Science American International University - Bangladesh (AIUB) Dhaka 1229 Bangladesh Science and Math Program Asian University for Women Chattogram 4000 Bangladesh School of Mathematical Sciences Sunway University Bandar Sunway Petaling Jaya 47500 Selangor Darul Ehsan Malaysia Department of Mathematics and Physics North South University Dhaka 1229 Bangladesh Department of Civil and Architectural Engineering Sultan Qaboos University Muscat Oman Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia University Research and Innovation Center (EKIK) Óbuda University 1034 Budapest Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

Deep Learning (DL) offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders. While previous review studies have explored various aspects of DL in cognitive healthcare, there remains a lack of comprehensive analysis that integrates wearable signals, data processing techniques, and the broader applications, benefits, and challenges of DL methods. Addressing this limitation, our study provides an extensive review of DL's role in cognitive healthcare, with a particular emphasis on wearables, data processing, and the inherent challenges in this field. This review also highlights the considerable promise of DL approaches in addressing a broad spectrum of cognitive issues. By enhancing the understanding and analysis of wearable signal modalities, DL models can achieve remarkable accuracy in cognitive healthcare. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-term Memory (LSTM) networks have demonstrated improved performance and effectiveness in the early diagnosis and progression monitoring of neurological disorders. Beyond cognitive impairment detection, DL has been applied to emotion recognition, sleep analysis, stress monitoring, and neurofeedback. These applications lead to advanced diagnosis, personalized treatment, early intervention, assistive technologies, remote monitoring, and reduced healthcare costs. Nevertheless, the integration of DL and wearable technologies presents several challenges, such as data quality, privacy, interpretability, model generalizability, ethical concerns, and clinical adoption. These challenges emphasize the importance of conducting future research in areas such as multimodal signal analysis and explainable AI. The findings of this review aim to benefit clinicians, healthcare professionals, and society by facilitating better patient outcomes in cognitive healthcare.

关键词： Cognitive healthcare Deep learning Wearable sensor Convolutional neural network Recurrent neural network

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Spam Text Detection using Machine Learning Model

Spam Text Detection using Machine Learning Model

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Mahasak Ketcham Thittaporn Ganokratanaa Patiyuth Pramkeaw Narumol Chumuang Dept. of Information Technology Management King Mongkut's University of Technology North Bangkok Bangkok Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Bangkok Thailand Media Technology Program King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. of Digital Media Technology Muban Chombueng Rajabhat University (MCRU) Thailand

This research presents a classification between spam and non-spam messages by removing duplicate sentences or words. meaningless word and various marks The data is then classified by machine learning techniques and compared by differences between the data sets. Quantitative transformations were performed on each model to find the most efficient model. which can filter spam messages efficiently and quickly By getting the best comparison results in terms of information. quantitative conversion and model use The experimental results showed that Of all the tests, the model that performed best was Random Forest, with an average accuracy of 97

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

引用

International Electrical Engineering Congress (iEECON)

作者： Nutyada Nantapanich Pisit Mahatthanajatuphat Norakamon Wongsin Wilaiporn Lee Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

关键词： Analytical models Adaptation models Correlation Liquids Predictive models Microwave sensors Polynomials Fractals Glucose Predictive analytics

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Analysis of Partial Discharges in Oil-Impregnated Transformer Paper Insulation and PET-G Insulation

IEEE Open Journal of Instrumentation and Measurement

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IEEE Open Journal of Instrumentation and Measurement 2025年 4卷

作者： Kameli, Sayed Mohammad Abuelrub, Abdelaziz AlShaikh Saleh, Mohammad Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Olesz, Marek Texas A&M University at Qatar Electrical and Computer Engineering Department Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering HatfieldAL10 9EU United Kingdom Hamad Bin Khalifa University Qatar Foundation Electrical and Computer Engineering Program in College of Science and Engineering Doha Qatar Gdańsk University of Technology Department of Electrical Power Engineering Gdańsk80-233 Poland

Partial discharge (PD) is a widespread phenomenon instigated in power transformer (PT) insulation systems. PDs are triggered by voids that vary in size and position within the PT insulation. The electrical characteristics of those damaging, PD-causing cavities must be well understood, to accelerate the development of advanced PD detection techniques. Thus, the impact of varying the radius and position of spherical air voids on the characteristics of PDs in PTs is examined using a 3-D finite element analysis (FEA) model designed in COMSOL Multiphysics. The spherical voids are positioned between two windings of a 512 kV, three-phase (3φ ) PT. The peak electric field (EF) and aggregate energy in the FEA model are used in conjunction with laboratory measurements of the apparent discharge magnitude, for detailed analysis of Polyethylene Terephthalate Glycol (PET-G)-based cylindrical voids with different heights. Simulations demonstrate that the inception of PD activity in the PT model occurs for spherical voids with a radius exceeding 1 mm. Furthermore, the most severe PDs occur within the press-board insulation, adjacent to the uppermost part of the innermost windings. Experiments demonstrate that a significant increase in PD activity was observed for PET-G-based cylindrical voids with heights exceeding 1 mm. © 2022 IEEE.

关键词： Partial discharges

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Performance Improvement of Clustering Affinity Propagation Method using Principal Component Analysis 4

Performance Improvement of Clustering Affinity Propagation M...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Simanullang, Jasael Zarlis, Muhammad Muisa Zamzami, Elviawaty Graduate Program of Computer Science Indonesia Faculty of Computer Science and Information Technology Indonesia Universitas Sumatera Utara Medan Indonesia

Affinity Propagation Method it is necessary to modify the algorithm by using Principal Component Analysis (PCA). PCA method is used to reduce the attributes or characteristics that are less influential on the data so that the most influential attributes are obtained to then be carried out the clustering process with Affinity Propagation. The comparison results of the PCA + AP grouping model have better performance than the conventional AP grouping model. This is justified because the number of iterations and clusters produced by the PCA + AP clustering model does not change and converges when there are 8 optimal cluster clusters. While the performance of conventional clustering models produces an optimal number of clusters from 14 clusters with a significant number of iterations. So it can be concluded that the PCA + AP grouping model is suitable for the Air Quality dataset because it produces an optimal number of clusters and iterations of 8 clusters. The comparison results of the PCA + AP grouping model have better performance than the conventional AP grouping model. This is justified because the number of iterations and clusters produced by the PCA + AP clustering model does not change and converges when the optimal number of clusters is 5 clusters. While the performance of conventional clustering models produces a suboptimal number of 10 clusters with a significant number of iterations. So it can be concluded that the PCA + AP grouping model is suitable for the Water Quality Status dataset because it produces an optimal number of clusters and 5 cluster repetitions. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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Deep Learning-Based Automated Segmentation of Fiber Breaks in Advanced Composites 38

Deep Learning-Based Automated Segmentation of Fiber Breaks i...

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38th Technical Conference of the American Society for Composites, ASC 2023

作者： Jain, Sudhir Li, Carina Xiaochen Vuong, Daniel McGllick, Matthew Saifaee, Saeed Furtado, Carolina Kopp, Reed Joseph, Joshua Adjodah, Dhaval Rosini, Sebastian Sinclair, Ian Spearing, S. Mark Wardle, Brian L. Integrated Design and Management Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States MIT Media Lab Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering University of Southampton SouthamptonSO17 1BJ United Kingdom Departments of Aeronautics and Astronautics and Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781605956916

The heterogeneity of the microstructure and the anisotropy of the mechanical properties of carbon fiber reinforced polymer composites (CFRP) are associated with complex damage mechanisms that make failure prediction highly challenging. Typical damage mechanisms for CFRP include: (i) interlaminar cracking (delamination), (ii) matrix cracking, and (iii) fiber breakage. X-ray computed tomography (CT) has been utilized recently to identify the complex damage in progressive failure of CFRP in 3D and 4D (3D spatial and 1D temporal). However, these scans contain a large amount of data (≈10 GB/mm3), which makes it challenging to extract mechanistic insights in a reasonable time frame. In previous work, we presented the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale matrix damage in CFRP. The trained CNN segments complex and sparse composite matrix damage mechanism to ≈ 99.99% agreement, with nearly 100% of human time eliminated, providing higher accuracy compared to human segmentation. In this work, we developed CNN-based method for the automated segmentation of fiber breaks using synchrotron radiation-based CT (SRCT) scans that have been human-labeled. The model employed a U-Net full CNN as the high-level encoder (feature extraction)-decoder (feature localization) structure, and VGG16 as the deep CNN backbone via the publicly available Segmentation Models GitHub repository. An unbiased evaluation was performed comparing the machine vs. the human-labeled ground truth. Finally, active learning was presented for the machine-assisted relabeling of datasets containing fiber breaks. The deep learning-based automated segmentation method demonstrated accurate identification (> 90%) of fiber breakage for CFRP. © 2023 by DEStech Publications, Inc. and American Society for Composites. All rights reserved.

关键词： Complex networks

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