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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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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

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.

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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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Interconnected Through Robotic System in English Foreign Language Education 5th

Interconnected Through Robotic System in English Foreign Lan...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Muthmainnah, M. de Barros Cardoso, Luís Miguel Oliveira Kaur, Dalwinder Burhamzah, Muftihaturrahmah Aeni, Nur Al Yakin, Ahmad Sharma, Aditi Teacher Training and Education Faculty Indonesian Language Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Portugal Department of Languages and Communication Sciences Polytechnic Institute of Portalegre Portalegre Portugal Manipal Globalnxt University Kuala Lumpur Malaysia English Study Program Universitas Negeri Makassar Makassar Indonesia Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International [Deemed University Pune India

ISBN: (纸本)9789819761050

Robots have become increasingly important in a variety of life domains as a result of the rapid advancement of technology. Additionally, robots are employed in education as teaching assistants in science, art, and language courses. This research aims to explore the potential of robotic systems as a multipurpose tool for increasing cultural awareness, language proficiency, and communicative competence in the GenAI era. Language games, conversation simulations, and real-time feedback mechanisms are just a few examples of interactive scenarios enabled by virtual robot systems or tutor robots that enable immersive language experiences that go beyond the limitations of conventional schooling. This research contributes knowledge that explores how AI can be used to personalize language teaching according to the needs and learning styles of each student in higher education. The total population of undergraduate students involved in this research was 350 for the 2023 academic year, with 24 students selected purposefully based on sample characteristics from this group who were exposed to using AI for one semester. The research method used is a mix method with quantitative analysis of the results of surveys and interviews with students who are known to have given positive responses and proposes a comprehensive strategy for using AI robot technology in EFL classrooms, which can significantly improve effective learning outcomes. The survey results also show that if educators have a solid understanding of the relationship between linguistic acquisition and technological advances, they will be better prepared to design engaging, individualized lessons that prepare their students to use the language effectively in real-world contexts. The robot-assisted language learning (RALL) approach used in this study, with the integration of robots in the classroom, has the potential to change the way people learn languages to make it more interesting, fun, and motivating, increase engagement

关键词： Students

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Comments on "relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems"

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IEEE Transactions on Automatic Control 2025年第6期70卷 4233-4236页

作者： Şahan, Gökhan Trenn, Stephan Izmir Institute of Technology Department of Mathematics and Computational Science and Engineering Program Urla Izmir Turkey University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Groningen9722BA Netherlands

This study addresses the deficiencies in the assumptions of the results in Chen and Yang, 2017 [1] due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then we prove why they are wrong in that form and show the errors in the proof of the main result of [1]. Next, we compare the assumptions and related results of [1] with similar works in the literature. Lastly, we give suggestions to complement the shortcomings of the hypotheses and thus correct them. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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A Self-Healing WDM Access Network with Protected Fiber and FSO Link Paths Effective Against Fiber Breaks

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Photonics 2025年第4期12卷 323-323页

作者： Wu, Tsu-Hsin Liao, Chien-Yu Yeh, Chien-Hung Chen, Yuan-Wen Kao, Yu-Hsin Lin, Sung-Yi Lin, Yu-Heng Liaw, Shien-Kuei Department of Photonics Feng Chia University Taichung40724 Taiwan Master’s Program of Space System Engineering Feng Chia University Taichung407802 Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan

In this article, an additional protected fiber and free-space optical (FSO) link path is proposed, to provide self-healing capabilities for protection against fiber faults in wavelength division multiplexed passive optical network (WDM-PON) systems. The new optical line terminal (OLT), remote node (RN), and optical network unit (ONU) in the presented PON architecture result in self-protective function against fiber breakpoints. In the measurement, 25 Gbit/s on-off keying (OOK) modulation was applied on each WDM channel to assess the downstream and upstream signals after 25 km single-mode fiber (SMF) and 25 km SMF + 2 m FSO connections, respectively. In addition to using protected fiber paths for self-healing operations. This PON system can also apply the FSO link method. The measured bit error rate (BER) for all downstream and upstream traffic was maintained below 3.8 × 10−3 with forward error correction (FEC). The detected optical power sensitivity of the proposed self-restorative fiber- and FSO-based WDM-PON for downstream and upstream WDM signals ranged from −33.5 to −28.5 dBm and from −33 to −28.5 dBm, respectively, and the corresponding power budgets of the downstream and upstream WDM signals were between 29.5 and 30.5 dB and 33 and 38 dB, respectively. © 2025 by the authors.

关键词： Forward error correction

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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Enhancement of Antenna Gain With M&W Slot Model on EBG Reflector Structure Utilizing Reflection Phase Diagram Analysis

Enhancement of Antenna Gain With M&W Slot Model on EBG Refle...

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International Electrical engineering Congress (iEECON)

作者： Payu Chakunna Nopphadon Phusam Pisit Mahatthanajatuphat Wilaiporn Lee Thanakarn Suangun School of Engineering University of Phayao Phayao Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This research demonstrates the improvement in antenna gain by utilizing an EBG reflector. The EBG reflector includes a unit cell with grooves shaped like the letters M and W to mitigate surface waves at a frequency of 2.45 GHz. The suggested EBG reflector is constructed on an FR-4 substrate exhibiting a uniform dielectric constant of 4.3, a loss tangent of 0.025, and a thickness of 1.6 mm. Furthermore, the impact of boosting the antenna gain can be analyzed by the reflection phase diagram, which exhibits values ranging from +90° to -90° at the resonant frequency of the reflector. The proposed EBG reflector comprises 5x5 unit cells. The testing results of the reflector's performance, when integrated with a general dipole antenna, indicate that it can effectively respond to the resonant frequency of 2.45 GHz, with a |S 11 | level below -10 dB within the frequency range of 2.2 GHz to 2.6 GHz, thereby adequately covering the IEEE 802.11 b/g/n band. The suggested EBG reflector exhibits a gain of around 6.5 dBi at 2.45 GHz, indicating its efficacy in enhancing gain relative to the standard PEC reflector. Moreover, the radiation pattern is also directional.

关键词： Slot antennas Dipole antennas Resonant frequency Reflector antennas Metamaterials Reflection Gain Standards Periodic structures Antenna radiation patterns

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Evaluation of an artificial intelligence-based model in diagnosing periodontal radiographic bone loss

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Clinical Oral Investigations 2025年第4期29卷 1-8页

作者： Cassiano, Luanny de Brito Avelino da Silva, Jordão Paulino Cassiano Martins, Agnes Andrade Barbosa, Matheus Targino Rodrigues, Katryne Targino Barbosa, Ádylla Rominne Lima da Silva Gomes, Gabriela Ellen Maia, Paulo Raphael Leite de Oliveira, Patrícia Teixeira de Sousa Lopes, Maria Luiza Diniz da Silva, Ivanovitch Medeiros Dantas de Aquino Martins, Ana Rafaela Luz Department of Dentistry Federal University of Rio Grande do Norte - UFRN Rio Grande do Norte Natal Brazil Postgraduate Program in Electrical and Computer Engineering Center of Technology Rio Grande do Norte Natal Brazil Department of Dentistry Federal University of Rio Grande do Norte Av. Senador Salgado Filho 1787 Rio Grande do Norte Natal 59056-000 Brazil

Objective: To develop an artificial intelligence model based on convolutional neural network for detecting and measuring periodontal radiographic bone loss (RBL). Materials and methods: Keypoint annotations were carried out in 595 digital bitewing radiographic images using a computer Vision Annotation Tool. The dataset was splitted: 416 of these images were trained using the You Only Look Once version 8 architecture with pose estimation (YOLO-v8-pose), 119 images were destined for the validation set, and 60 images were used for the test set, resulting in a model capable of detecting keypoints related to the cementoenamel junction (CEJ) and alveolar bone crest (ABC). In order to evaluate the performance of the obtained model, the following metrics were analyzed: F1-Score, precision, sensitivity and mean average precision (mAP). Then, an algorithm was implemented to measure the RBL by calculating the Euclidean distance between CEJ and ABC. Results: The model achieved an F1-Score of 66,89%, precision of 61,1%, a sensitivity of 73,9% and an mAP of 73.8%. Conclusions: The developed model and its algorithm for identifying and measuring periodontal radiographic bone loss demonstrated promising performance, thereby presenting a potential tool for assisting in periodontal diagnosis. Further studies comparing the developed model with manual measurements performed by specialists are necessary for its validation. Clinical relevance: Applying artificial intelligence in clinical dental practice can support diagnosis, streamline clinical workflows, and inform treatment planning, representing a significant advancement in dental automation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Alveolar bone loss computer neural networks Deep learning Periodontitis Radiography

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Mixotrophic Chlorella pyrenoidosa biofilm with enhanced biomass production, microalgal activity, and nutrient removal from nutrient-rich wastewater

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Journal of Environmental Sciences (China) 2025年 157卷 366-377页

作者： Wu, Xiongwei Jin, Cong Zhang, Chiqian Li, Ping Huang, Jim Junhui Wu, Jing Wu, Jinhua Hu, Zhiqiang School of Environment and Energy South China University of Technology Guangzhou510006 China Zhongshan Institute of Modern Industrial Technology of South China University of Technology Zhongshan528400 China Civil Engineering Program College of Engineering & Computer Science Arkansas State University JonesboroAR72467 United States Environmental Research Institute National University of Singapore Singapore117411 Singapore Department of Civil and Environmental Engineering University of Missouri-Columbia 65211 United States

Microalgae possess significant advantages in nitrogen and phosphorus removal from nutrient-rich wastewater that are highly efficient and independent of the C/N ratio. However, challenges such as low biomass productivity, high variability in nutrient removal under different trophic types, and difficulty in harvesting biomass limits the large-scale application of microalgae wastewater treatment. This study attempted to employ mixotrophic microalgae biofilm to address these issues. The biomass production, microalgal activity, and nutrient removal of Chlorella pyrenoidosa biofilms with different trophic types were compared for nutrient-rich wastewater treatment. The results showed that the biomass productivity of the mixotrophic microalgal biofilm (0.215 g/(L·d)) was 2.3, 8.6, and 6.0 times higher than that of photoautotrophic microalgal biofilm, heterotrophic microalgal biofilm, and photoautotrophic suspended microalga, respectively. Additionally, the dehydrogenase activity (DHA), indicating microalgal activity, of the mixotrophic biofilm was 2.3 and 16.5 times higher than that of photoautotrophic and heterotrophic biofilms, respectively. Meanwhile, the mixotrophic biofilm removed 96.0 % of NH4+-N and 99.2 % of PO43–-P, more efficient than that with other types of biofilms and suspended microalgae. In an open-ended air-lift photobioreactor, the mixotrophic microalgal biofilm produced biomass at 0.12 g/(L·d) and removed 90.0 % of NH4+-N and 97.6 % of PO43–-P. This study suggests that the mixotrophic microalgal biofilm shows promise in treating nutrient-rich wastewater and producing microalgal biomass for value-added products. © 2024

关键词： Microalgae

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