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IEEE Education engineering (EDUCON)

作者： Francesca Fiore Giulia Paludo Alberto Montresor Dept. of Computer Science and Engineering University of Trento Trento Italy

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

In the rapidly evolving landscape of STEAM disciplines, the demand for multidisciplinary communication, problem-solving skills, and creativity has become increasingly critical. Traditional educational curricula often fall short in preparing students with the competencies needed to navigate these complex, interdisciplinary challenges in their professional careers. This study explores the outcomes of a three-day co-design spring school involving 27 students from diverse STEAM fields. The focus is on how participation in community-driven, real-world design activities influenced their self-efficacy, growth mindset, and creative problem-solving abilities. To achieve this, we employed a mixed-methods approach, incorporating pre- and post-questionnaires, student diaries, design artifacts, and observational notes. The pre-questionnaire aimed to establish a baseline for creative problem-solving styles and mindset using a reduced version of the Basadur Creative Problem Solving Profile (CPSP), as well as self-efficacy using selected items from Limeri's mindset scale. The post-program assessments measured changes in these dimensions and further explored participants' perceptions of the experience. The results indicate that students reported experimenting with a different creative approach than their usual one. Additionally, their involvement in a co-design activity targeting the local community and a real world problem significantly influenced the way they approached tasks. Creative style “contamination” was particularly observed in engineering students, who shifted from evaluative to ideative and thinking styles. While mindsets, due to their nature, exhibited limited and non-significant shifts, participants expressed a stronger belief in their ability to con-tribute to meaningful, real-world projects in terms of a growth mindset. Self-efficacy showed significant improvement, but only in terms of increased confidence in performing diverse tasks. The findings emphasize the impor

关键词： Navigation engineering profession Particle measurements Teamwork Pollution measurement Problem-solving Springs engineering students Contamination Creativity

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User Authentication on Smart Speakers Leveraging Acoustic Imaging

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IEEE Transactions on Mobile Computing 2025年

作者： Ren, Yanzhi Xia, Zhiliang Li, Siyi Liu, Hongbo Yu, Jiadi Li, Shuai Li, Hongwei University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China Shanghai Jiao Tong University Dept. of Computer Science and Engineering Shanghai200240 China University of Oulu Finland

The user authentication has drawn increasingly attention as the smart speaker becomes more prevalent. For example, smart speakers that can verify who is sending voice commands can mitigate various types of attacks such as replay attack or impersonation attack. Existing user authentication solutions either cannot be applicable to smart speakers directly or require certain additional user-device interaction or pre-installed infrastructure, which may severely affect the user experience and create extra burdens to users. In this work, we propose a user authentication system utilizing acoustic images, which are derived from the smart speaker by emitting beep signals and sensing echoes from the user's body with its microphone array, as the proof for user authentication. Given the acoustic samplings of the reflected beep signal, our system designs a distance estimation component by applying a correlation based technique on the beamformed signal to estimate the distance between the user and microphone array. Our image construction component then constructs a virtual imaging plane using the estimated distance and steers the array towards each grid of the plane to generate an acoustic image of the user. We also propose a transfer learning-based method to derive efficient features from the constructed images, and employ SVM classifiers for accurate user authentication. Moreover, to ensure the accuracy of our system, our user direction estimation scheme could further estimate the DoA of the user's voice command to make sure that the user stands in front of the smart speaker during the authentication process. Our extensive experiments demonstrate that our system is robust and accurate across various scenarios. © 2002-2012 IEEE.

关键词： Acoustic imaging

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Detectiona and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learing

arXiv

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arXiv 2025年

作者： Mollick, Abu Ahnaf Tamim, Abdullah Rahman, Mahfujur Dristi, Nosin Anjum Asadujjaman, D.M. Hossen, Takbir Dept. Of Computer Science and Engineering Varendra University Rajshahi Bangladesh

A mutation in the DNA of a single cell that compromises its function initiates leukemia. This leads to the overproduction of immature white blood cells, which encroach upon the space required for the generation of healthy blood cells. Leukemia is treatable if identified in its initial stages. Nonetheless, its diagnosis is both arduous and time-consuming. In this study, a novel approach for diagnosing leukemia across four stages—Benign, Early, Pre, and Pro—utilizing deep learning techniques. We employed two Convolutional Neural Network (CNN) models: MobileNetV2 with an altered head and a bespoke model. The custom model has multiple convolutional layers, each paired with corresponding max pooling layers. We utilized MobileNetV2 with ImageNet weights, and the head was adjusted to integrate the final results. The utilized dataset is a publicly available collection of blood cell smear images titled "Acute Lymphoblastic Leukemia (ALL) image dataset", and then used the Synthetic Minority Oversampling Technique (SMOTE) to augment and balance the training dataset. Which attained an accuracy of 98.6% with the custom model, while MobileNetV2 achieved a superior accuracy of 99.69%. The pretrained model exhibited encouraging results and an increased likelihood of real-world *** Codes 68T07 © 2025, CC BY.

关键词： Convolutional neural networks

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Securing Microservice Architecture: Load Balancing and Role-Based Access Control

Securing Microservice Architecture: Load Balancing and Role-...

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IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)

作者： Payal Thakur Navjot Singh Talwandi Shanu Khare Dept of Computer Science Engineering Chandigarh University Punjab India

ISBN: (数字)9798350387193

ISBN: (纸本)9798350387209

This study focuses on the security aspects within microservice architecture, particularly addressing load balancing and role-based access control (RBAC). Exploring the intersection of load balancing techniques and RBAC mechanisms, the research aims to enhance the security posture of microservices. By evaluating strategies for efficient load distribution and implementing RBAC protocols, the study seeks to fortify the architecture against potential vulnerabilities. The integration of load balancing and RBAC not only ensures optimized resource utilization but also strengthens access control measures, bolstering the overall security framework in microservice-based systems.

关键词： Access control Electric potential Protocols Microservice architectures Load management Power electronics Resource management Intelligent control

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AI Driven Brain Tumor Classification and Localization Using Faster R-CNN

AI Driven Brain Tumor Classification and Localization Using ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Gospelin Reena E T. Kavitha Dept. of Computer Science and Engineering Karunya University Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The increasing prevalence of brain tumors necessitates the development of accurate, automated methods for early diagnosis and localization. Traditional approaches, which rely on manual MRI interpretation by radiologists, are prone to errors due to varying expertise levels and limited resources. To overcome these limitations, this study proposes an AI-driven solution for brain tumor classification and localization using Faster R-CNN, a deep learning architecture known for its robust object detection capabilities. The proposed model utilizes public MRI datasets to train a Faster R-CNN model, which effectively identifies and locates brain tumors with high precision. The model's performance is evaluated using standard metrics such as accuracy, precision, recall, F1-score, and Intersection over Union (IoU), demonstrating superior results compared to conventional methods. A user-friendly web-based interface developed using Streamlit allows radiologists to upload MRI images and receive real-time diagnostic results, enhancing clinical workflows. Despite its high performance, the study acknowledges the need for improvements in image resolution and model interpretability. Future work will explore the integration of 3D MRI data and explainable AI techniques to further enhance the model's transparency and clinical applicability.

关键词： Location awareness Deep learning YOLO Accuracy Magnetic resonance imaging Brain tumors Brain modeling Real-time systems Proposals Biomedical imaging

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Deep Reinforcement Learning in Healthcare and Bio-Medical Applications

Deep Reinforcement Learning in Healthcare and Bio-Medical Ap...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Payal Thakur Navjot Singh Talwandi Dept of Computer Science Engineering Chandigarh University Punjab India

Deep reinforcement learning (DRL), which learns a set of behaviors that maximize the projected reward, combines the representational power of deep neural networks with the reinforcement learning paradigm. DRL holds great promise for the future of healthcare and medicine, according to recent *** overview of the research on DRL in medical imaging is provided in this article. We start with a comprehensive DRL course that covers both the most recent model-based and model-free approaches. The tasks covered in the next section of this article are loosely divided into three main categories: (i) parametric medical image analysis tasks like landmark detection,object/lesion detection, registration, and view plane localization; optimization tasks like hyperparameter tuning, augmentation strategy selection, and neural architecture search; and (iii) other applications like surgical gesture segmentation, person tracking, and perso The study finishes with thoughts of potential future directions.

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EHRGPT: Leveraging language models for generating synthetic health records

EHRGPT: Leveraging language models for generating synthetic ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Pamisetty, Giridhar Batreddy, Subbareddy Verma, Priya Babu, Ch Sobhan Indian Institute of Technology Dept. of Electrical Engineering Hyderabad India Administrative Staff College of India Centre for Management Studies Hyderabad India Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350362480

Electronic health records (EHRs) are the comprehensive digital records containing patient health information, which help in various domains like public health monitoring, predictive modeling, data-driven research, etc. However, there are strict regulations about data sharing due to the sensitivity of the EHR data. So, there is an increasing demand for generating high-quality synthetic EHR data that mitigates privacy concerns. Most of the existing approaches to EHR generation are limited as they predominantly generate binary decisions about affecting a disease or count-based summaries of disease occurrences for a patient, failing to capture the full patient trajectory contained in the real data and hence reducing the downstream applications. With the success of language models in generative approaches, we propose a language modeling approach using transformers to generate the complete health record of a patient for each admission. We propose to group the patient's EHRs as it enhances the model to capture temporal coherence and the medical history of the corresponding patient. The proposed model can generate high-fidelity privacy preserved EHR data with the statistical properties of the real data. © 2024 IEEE.

关键词： Electronic health record

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Affordable Navigation Aid: A Smart Walking Stick for the Visually Impaired

Affordable Navigation Aid: A Smart Walking Stick for the Vis...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Muhammed Suhair K Shanid Malayil Deepak AV Faslu Rahman TP Marwan Pookatil Abdul Kabeer Dept: Computer Science MEA Engineering College Perinthalmanna India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

An innovative effort to improve mobility and freedom is the Low-Cost Smart Walking Stick, which is intended for those with vision impairments. This inventive walking stick offers an effective navigation solution for a variety of locations by integrating affordable technologies like the ESP8266, ultrasonic sensor, buzzer, and vibrator. Users are greatly enhanced by being instantly notified about barriers, changes in topography, and potential risks through real-time notifications and haptic feedback. With the help of this affordable smart walking stick, people who are blind or visually impaired can now confidently and independently traverse their daily lives. It is a major technological advancement.

关键词： Legged locomotion Navigation Visual impairment Surfaces Real-time systems Acoustics Haptic interfaces

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Machine Learning Approaches for Predicting Lung Cancer Risk and Early Diagnosis

Machine Learning Approaches for Predicting Lung Cancer Risk ...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Jegan K Infant Nikesh A. Pravin Dept of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Using a healthcare dataset, this study applies a variety of ML techniques to analyze and forecast the likelihood of lung cancer. This dataset will be initialized by cleaning, encoding categorical variables, and removing duplicates to ensure quality and efficiency. Correlation analysis is carried out to find important characteristics that raise the risk of lung cancer. engineering including interaction terms enhances the predictive efficiency of the dataset. The various machine learning models are introduced they are Logistic Regression, SVC, Random Forests, GBM, and XGB. Over-sampling with ADASYN addresses class imbalance to improve model training. The logistic regression machine learning model has attained 97% accuracy. It will ensure consistency and reliability. Comprehensive visualizations such as heatmaps and bar plots, explore feature correlations and class distributions. The study represents the efficiency of ensemble methods like Random Forests and XGBoost, achieving superior predictive performance compared to baseline models. This system predicts whether they might have lung cancer, helping doctors to diagnose or early intervention.

关键词： Training Visualization Logistic regression Correlation Accuracy Lung cancer Machine learning Medical services Predictive models Random forests

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Design of QR Code Tracking Car based on Monocular Vision

Design of QR Code Tracking Car based on Monocular Vision

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Shen, RuiBing Chang, Chih-Yung Hu, ZhiJie Wu, Shih-Jung Hou, Di Xi'an Eurasia University School of Information Engineering Xi'an China Dept. of Computer Science and Information Engineering Tamkang University Taiwan Xi'an ShiYou University Dept. of Electronic Engineering China

ISBN: (纸本)9798350324174

This paper designs an automatic tracking car that uses machine vision technology to realize QR code recognition. The car uses the MSP430F5529 chip as the main controller. It uses the OpenMV camera as the image acquisition sensor and the OpenCV library to process the acquired images. It can locate the target of the QR code by monoculture visual positioning technology. The car can obtain the distance between it and the leading vehicle, and compare the actual distance with the expected distance. The main controller can control the motor operation according to the distance deviation obtained, aiming to realize the automatic following function of the car. © 2023 IEEE.

关键词： Controllers

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