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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Ahmad, Imam Tri Prastowo, Agung Indra Borman, Rohmat Tonggiroh, Mursalim Jusman, Yessi Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Yapis Papua Faculty of Computer Science Jayapura Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Faculty of Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350379839

Lung cancer is the leading cause of cancer-related fatalities in Indonesia, primarily due to late-stage diagnoses. This study aims to develop a model that employs image processing to classify lung cancer from CT scan images. The system combines the Learning Vector Quantization (LVQ) algorithm with the Gray Level Co-occurrence Matrix (GLCM) feature extraction method and utilizes spatial filtering techniques such as low pass, median, and high pass filters. The LVQ algorithm effectively manages non-linear data and forms complex decision boundaries through the distribution of prototype vectors. The findings show that the median filtering technique achieved the highest accuracy of 90.14% at a GLCM angle of 0°, demonstrating its effectiveness in reducing noise while maintaining crucial details for accurate classification. This suggests that the horizontal texture orientation may be more critical for analyzing lung images in this dataset. © 2024 IEEE.

关键词： Lung cancer

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Bilateral Coupled Epsilon Negative Metamaterial for Dual Band Wireless Communications

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computers, Materials & Continua 2022年第4期71卷 1263-1281页

作者： Md Mhedi Hasan Mohammad Tariqul Islam Md Moniruzzaman Mohd Hafiz Uddin Norsuzlin Binti Mohd Sahar Md Samsuzzaman Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaSelangor43600Malaysia Space Science Centre Institute of Climate ChangeUniversiti Kebangsaan MalaysiaSelangor43600Malaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityPatuakhali8602Bangladesh

This work presents a dual band epsilon negative(ENG)metamaterial with a bilateral coupled split ring resonator(SRR)for use in C and X band wireless communication *** traditional split-ring resonator(SRR)has been amended with this engineered *** proposed metamaterial unit cell is realized on the 1.6 mm thick FR-4 printed media with a dimension of 10×10 *** resonating patch built with a square split outer *** interlinked inner rings are coupled vertically to the outer ring to extend its electrical length as well as to tune the resonance *** simulation is performed using CST studio suite 2019 to design and performance *** transmission coefficient(S21)of the proposed unit cell and array configuration exhibits two resonances at 6.7 and 10.5 GHz with wide bandwidth extended from 4.86 to 8.06 GHz and 10.1 to 11.2 GHz,*** permittivity is noted at frequencies between 6.76–9.5 GHz and 10.5–12 GHz,consecutively,with near-zero refractive index and *** optimal EMR value depicts the compactness of the proposed *** 1×2,2×2 and 4×4 arrays are analyzed that shows similar response compared to the unit *** results of the 2×2 array shows the close similarity of S21 response as compared to *** observed properties of the proposed unit cell ascertain its suitability for wireless communications by enhancing the gain and directivity of the antenna system.

关键词： Metamaterial epsilon negative near-zero index split ring resonator wireless communications

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Development of Dementia Care Training System Using AR and Large Language Model

Development of Dementia Care Training System Using AR and La...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Miyawaki, Tomoya Nishiura, Yuuki Fukuda, Ryouta Nakashima, Kazuto Kurazume, Ryo Graduate School of Information Science and Electrical Engineering Kyushu University Japan Kyushu University Faculty of Engineering Department of Electrical Engineering and Computer Science Japan Kyushu University Faculty of Information Science and Electrical Engineering Japan

ISBN: (纸本)9781665410205

We have developed HEARTS, a dementia care training system using augmented reality based on Humanitude. Humanitude is a multimodal comprehensive care technique for dementia, and has attracted attention as a method to reduce the burden on both caregivers and patients. However, the HEARTS developed so far could not evaluate 'speaking' skills based on the content of conversations among 'seeing,' 'touching,' and 'speaking,' all of which are fundamental skills in Humanitude. Therefore, we attempted a new quantitative evaluation of trainees' 'speaking' skills by estimating the emotional value of conversational content using GPT-4 named HEARTS 5. A survey of caregivers was conducted using the developed system and was well received by the participants. We also developed a HEARTS 5 conversational version based on HEARTS 5, with the addition of GPT-4 conversation generation and Azure Text-to-Speech. © 2024 IEEE.

关键词： Augmented reality

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EEG emotion recognition framework based on invariant wavelet scattering convolution network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2181-2199页

作者： Elrefaiy, Ahmed Tawfik, Nahed Zayed, Nourhan Elhenawy, Ibrahim Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computers and Systems Department Electronics Research Institute Cairo Egypt Mechanical Engineering Department The British University in Egypt Cairo Egypt

EEG signals for real-time emotion identification are crucial for affective computing and human-computer interaction. The current emotion recognition models, which rely on a small number of emotion classes and stimuli like music and images in controlled lab conditions, have poor ecological validity. Furthermore, identifying relevant EEG signal features is crucial for efficient emotion identification. According to the complexity, non-stationarity, and variation nature of EEG signals, which make it challenging to identify relevant features to categorize and identify emotions, a novel approach for feature extraction and classification concerning EEG signals is suggested based on invariant wavelet scattering transform (WST) and support vector machine algorithm (SVM). The WST is a new time-frequency domain equivalent to a deep convolutional network. It produces scattering feature matrix representations that are stable against time-warping deformations, noise-resistant, and time-shift invariant existing in EEG signals. So, small, difficult-to-measure variations in the amplitude and duration of EEG signals can be captured. As a result, it addresses the limitations of the previous feature extraction approaches, which are unstable and sensitive to time-shift variations. In this paper, the zero, first, and second order features from DEAP datasets are obtained by performing the WST with two deep layers. Then, the PCA method is used for dimensionality reduction. Finally, the extracted features are fed as inputs for different classifiers. In the classification step, the SVM classifier is utilized with different classification algorithms such as k-nearest neighbours (KNN), random forest (RF), and AdaBoost classifier. This research employs a principal component analysis (PCA) approach to reduce the high dimensionality of scattering characteristics and increase the computational efficiency of our classifiers. The proposed method is performed across four different emotional classific

关键词： Support vector machines

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Towards Seamless Glucose Monitoring: A Smart Non-Invasive System for Diabetes Patients 2

Towards Seamless Glucose Monitoring: A Smart Non-Invasive Sy...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Pawar, Adesh Kelzarkar, Sanket G. Choudhary, Vishal Kumar Daburkar, Ashwini Kumar, Praveen Hore, Rutuja Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Blood Glucose Monitoring levels are essential to the treatment of diabetes and must be done continuously. Patients often don't comply with conventional glucose monitoring techniques since they cause discomfort and are invasive. An Arduino microcontroller and infrared (I.R.) sensor are used in this work to provide a robust, non-invasive glucose monitoring system. I.R. light is transmitted through the skin to measure the quantity of light reflected, and this relationship exists between the amount of light and the amount of glucose in the interstitial fluid. In real-time, the Arduino analyses blood glucose levels by analyzing IR Spectroscopy and using machine learning techniques. The device features wireless connectivity, enabling it to send glucose readings to a mobile application for continuous monitoring, historical data analysis, and customized alerts. Initial trials indicate that this non-invasive system provides accurate glucose measurements comparable to traditional methods, significantly improving user comfort and compliance. This innovative approach promises a pain-free, user-friendly, and cost-effective solution for diabetes management, paving the way for more widespread adoption of continuous glucose monitoring. © 2024 IEEE.

关键词： Glucose sensors

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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

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Exploration of soliton structures and modulation instability analysis for the highly dispersive perturbed NLSE with sextic-power law refractive index

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Optical and Quantum Electronics 2025年第1期57卷 1-18页

作者： Abdullah, Eman H. M. Ahmed, Hamdy M. Zaghrout, Afaf A. S. Bahnasy, Amal Ibrahim Ahmed Rabie, Wafaa B. Hashemi, M.S. Bayram, M. Department of Physics and Engineering Mathematics Higher Institute of Engineering El Shorouk Academy Cairo Egypt Department of Mathematics Faculty of Science Al Azhar University Cairo Egypt Department of Physics and Engineering Mathematics Higher Institute of Engineering and Technology Tanta Egypt Department of Mathematics Basic Science Faculty University of Bonab Bonab Iran Computer Engineering Biruni University Istanbul Turkey

This study introduces some novel soliton solutions and other analytic wave solutions for the highly dispersive perturbed nonlinear Schrödinger equation with generalized nonlocal laws and sextic-power law refractive index. This equation models the behavior of complex wave propagation. The improved modified extended tanh function governs the transmission of various solitons with different effects. New optical solitons are constructed as bright solitons, dark solitons, combo bright-dark solitons and singular solitons. Furthermore, exponential solutions, triangular solutions and rational wave solutions are shown. The findings conferred in this article show a crucial advancement in understanding the propagation of waves in non-linear media. The study also includes modulation instability analysis to examine the stability of the derived wave solutions, supplemented by graphical simulations that depict their distinctive features. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Solitons

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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A Systematic Review of Federated Learning Mechanism for The Internet of Medical Things 1

A Systematic Review of Federated Learning Mechanism for The ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Aravind, P. Jerlin, M Asha Devarajan, Ganesh Gopal Nagarajan, Senthil Murugan School of Computer Science and Engineering Vellore Institute of Technology Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India Medicine University of Luxembourg Faculty of Science Technology Department of Mathematics Luxembourg

ISBN: (纸本)9798350353648

The rapid evolution of healthcare technology, no-tably the integration of the Internet of Medical Things (loMT), has revolutionized patient care, diagnosis, and treatment method-ologies. However, this progress introduces substantial challenges, primarily concerning the security, privacy, and interoperability of sensitive medical data. Federated Learning, a decentralized ma-chine learning approach, emerges as a possible solution to address these difficulties since it allows for collaborative model training without requiring raw patient data exchange. This systematic review explores and evaluates various FL mechanisms tailored for 10MT applications. The review examines existing literature, encompassing the amalgamation of loT and healthcare, the challenges faced in traditional health care systems, and the role of FL in mitigating these challenges. It highlights applications, privacy-preserving techniques, security measures, advantages, limitations, and potential enhancements in utilizing FL within 10MT environments. The review also presents a structured methodology for evaluating FL paradigms, encompassing performance metrics, privacy/security analysis, advantages/limitations assessment, and comparative analysis. Moreover, the paper pro-poses an innovative framework for federated learning in 10MT, detailing its architecture, modules, algorithms, functionalities, ethical considerations, and expected impact. Through a comprehensive analysis and synthesis of existing literature and proposed strategies, this review aims to provide insights for health care stakeholders in selecting and implementing optimal FL strategies for collaborative medical advancements while upholding stringent data privacy and security standards in 10MT. © 2024 IEEE.

关键词： Federated learning

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EAFL: Edge-Assisted Federated Learning for real-time disease prediction using privacy-preserving AI

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Iran Journal of computer science 2025年 1-11页

作者： Rawas, Soha Samala, Agariadne Dwinggo Faculty of Science Department of Mathematics and Computer Science Beirut Arab University Beirut Lebanon Faculty of Engineering Universitas Negeri Padang West Sumatra Padang Indonesia

The growing demand for real-time disease prediction in healthcare necessitates advanced AI frameworks capable of ensuring both computational efficiency and patient privacy. This study introduces an Edge-Assisted Federated Learning (EAFL) system designed for real-time disease prediction while preserving sensitive data using privacy-preserving AI techniques. Leveraging lightweight convolutional neural networks and federated learning, the framework decentralizes model training across simulated edge nodes, reducing reliance on centralized data storage. Differential privacy mechanisms are integrated to secure inter-node communications, enhancing data protection. The proposed framework was evaluated on publicly available datasets, including CheXpert and COVID-19 radiography, achieving an accuracy of 94.2% and 97.5%, respectively, with minimal latency (0.85 s per prediction). Comparative analysis reveals that the EAFL system matches the performance of centralized AI models while significantly reducing privacy risks. To demonstrate scalability, additional evaluation was conducted across different dataset distributions, revealing consistent performance across varying data characteristics and medical scenarios. This research offers a scalable, privacy-focused solution that bridges the gap between real-time disease prediction and secure data handling, paving the way for its adoption in modern healthcare applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Disease prediction Edge AI Federated learning GDPR compliance in AI Healthcare data security

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