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检索条件"任意字段=IEEE International Conference and Workshops on Engineering of Computer Based Systems"

共 89044 条记录，以下是141-150 订阅

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High-Throughput Acoustic Sorting of Cellular-Sized Microparticles in 3d Microfluidic Channels 38

High-Throughput Acoustic Sorting of Cellular-Sized Micropart...

引用

38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Roy, Akash Neff, Baptiste Esfahani, Kianoush Sadeghian Sengupta, Anik Kim, Eun S. University of Southern California Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331508890

This manuscript presents high-throughput sorting of cellular-sized microparticles within a three-dimensional microfluidic channel by focused bulk acoustic wave (BAW) produced by a Self-Focusing Acoustic Transducer (SFAT). The focused ultrasound induces a substantially higher acoustic radiation force within the focal region, enabling sorting based on particle size and density. Unlike surface-acoustic-wave-based setups, the BAW-based technique uses a three-dimensional microfluidic channel through which a mixture of particles is transported, while SFAT(s) may be placed at multiple points along the channel for multi-stage sorting. The technique has been successfully used in sorting 50 μm microparticles, which are analogous to cancerous or differentiated Mesenchymal Stem Cells (MSC), from 30 μm microparticles, which are analogous to healthy MSC. The sorting results in 97.5% purity at the smaller microparticle outlet and a 97.2% recovery rate for the smaller particles. The technique allows sorting 650,000 smaller and 142,000 larger microparticles within a mere 10 minutes. © 2025 ieee.

关键词： Stem cells

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Differential Privacy with DP-SGD and PATE for Intrusion Detection: A Comparative Study 4

Differential Privacy with DP-SGD and PATE for Intrusion Dete...

引用

4th ieee international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Machooka, Daniel Yuan, Xiaohong Roy, Kaushik Chen, Guenvere North Carolina A&T State University Department of Computer Science Greensboro United States University of Texas San Antonio Department of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331518882

There is an increase in privacy leakage in the world of Cyber-Physical systems. Membership inference, data reconstruction, memorization, property inference, and model stealing are exacerbated by small physical devices connected to networks where data sharing is common. Deep Learning (DL) based intrusion detection systems may have the risk of leaking sensitive training data. This research compares two privacy frameworks applied to deep learning-based intrusion detection systems for IoT network traffic. Differentially Private Stochastic Gradient Descent (DP-SGD) and Private Aggregation of Teacher Ensemble (PATE) framework are two methods for differential privacy. We compare these two methods applied in intrusion detection systems. We used two deep learning models for intrusion detection: long-short-term memory (LSTM) and multilayer perceptron (MLP). We compare these models trained under the DP-SGD and trained under the PATE framework. The performance of the model (accuracy, precision, recall, and F1) and the privacy budget were compared. The results of this study provide information on selecting differential privacy methods for detecting intrusions in cyber-physical systems. © 2025 ieee.

关键词： Network intrusion

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Machine Learning-Enabled Examination Feedback System 3

Machine Learning-Enabled Examination Feedback System

引用

3rd ieee international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Ragam, Prashanth Sai, Botcha Taraka Rutesha Mekatotti, Anshita Priyadarshini Ojasvi, Nallamala Jahnavi, Adigoppula Danaveni, Pavithra School of Computer Science & Engineering VIT-AP UNIVERSITY Inavolu AP Vijayawada522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science & Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

The project proposes an Examination Feedback System utilizing machine learning to automate the evaluation of subjective answers in educational assessments. The system will analyze student responses and classify them under a number of predefined topics while making sure that the feedback it generates is tailored and specific to the needs of the students. The primary goal is to ensure improved outcomes through enhanced information to students, where improvement is needed, while helping educators identify performance trends. The system is powered by DistilBERT, which is a transformer-based natural language processing model which is known for its efficiency and contextual knowledge. The proposed system differs from previous manual grading systems in that it offers data-driven information while addressing confidentiality issues with a computationally efficient design. Thus, the examination process becomes scalable, accurate, and data-driven in satisfying the demands of upstream education systems with very limited computational infrastructure. © 2025 ieee.

关键词： Natural language processing systems

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Iterative Skill Optimization based Recurrent Network for Air Quality Forecasting 3

Iterative Skill Optimization based Recurrent Network for Air...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Sannasi, Periasamy Subramanian, P. Surendran, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509675

This research introduces a novel air quality prediction model using the optimized deep learning model for enhancing the forecasting accuracy by solving the issue of over-fitting. For this, Iterative Skill Optimization based Recurrent Network (Iter-SkO-RecurNet) model is proposed. The Iter-SkO algorithm is used to optimize the algorithm's hyper-parameters in order to minimize the loss function in the developed model, which uses LSTM for air quality forecasting. The loss function optimization is carried out using the suggested Iter-SkO algorithm, which incorporates the iterative chaotic mapping into the Skill optimization process. The iterative chaos introduces the randomness in the algorithm and assists to acquire global best solution. The analysis of the proposed model based on R2, RMSE, and MAPE and acquired the values of 0.9775, 0.92714775, and 8.174332 respectively. © 2025 ieee.

关键词： Prediction models

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Diffusion Model based Image Reconstruction in Lensless Imaging

Diffusion Model Based Image Reconstruction in Lensless Imagi...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Verma, Ashish Boominathan, Vivek Veeraraghavan, Ashok Seelamantula, Chandra Sekhar Department of Electrical Engineering Indian Institute of Science Bengaluru560 012 India Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

ISBN: (纸本)9798350368741

Lensless imaging systems eliminate the need for lenses by employing an encoding element to multiplex incident light signals, which are then captured directly onto a bare camera sensor. They present a promising alternative to traditional lens-based imaging systems by offering significant advantages in terms of compactness, versatility, and cost. Due to the multiplexed nature of measurements, image reconstruction takes place computationally. However, existing techniques for image reconstruction in lensless imaging fall short of the image quality offered by traditional lens-based imaging. In this work, we consider the application of diffusion models, a class of deep generative models, for image reconstruction in a lensless imaging modality. These models currently achieve state-of-the-art performance in image generation. Specifically, we focus on the PhlatCam lensless system, which consists of a coded phase mask as the encoding element placed close to the camera sensor. We use a ControlNet based diffusion model to improve the perceptual quality of image reconstruction. The performance is measured in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The proposed method improves the performance in these metrics for synthetic measurements. For real measurements, the improvement in image quality comes at the expense of a small bias in color, which is attributed to the generative nature of the diffusion prior itself. © 2025 ieee.

关键词： Computational imaging diffusion model generative model lensless imaging stable diffusion

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Exploring Hand Gesture Recognition: Trends, Technologies, and Application 3

Exploring Hand Gesture Recognition: Trends, Technologies, an...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pohekar, Ayesha Pundge, Ajitkumar Shirsath, Vishal School of Engineering Ajeenkya DY Patil University Maharashtra Pune India

ISBN: (纸本)9798331527549

Hand Gesture Recognition (HGR) systems have garnered significant attention due to their applications in human-computer interaction, virtual reality, and assistive technologies. However, achieving adaptable, accurate, and user-friendly HGR solutions remains a challenge due to issues such as environmental variability and user adaptability. This survey explores state-of-the-art HGR techniques, encompassing traditional methods and advanced deep learning-based methodologies. Classification techniques are analyzed based on sensory-based, vision-based, and hybrid input approaches, highlighting their respective strengths and limitations. The impact of feature extraction methods, dataset quality, and real-time processing on the performance of classification algorithms is discussed. By addressing these challenges, this work identifies potential pathways for advancing HGR systems to enhance their usability and reliability across diverse applications. © 2025 ieee.

关键词： Palmprint recognition

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Analysis of Encryption Techniques for Secure Data in Cloud Environment 1

Analysis of Encryption Techniques for Secure Data in Cloud E...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Kumar, M. Ravi Jeya, I. Jasmine Selvakumari Athira, S. Hindusthan College of Engineering and Technology Department of Computer Science And Engineering Coimbatore India Vit Bhopal University School of Computing Science and Engineering Sehore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331529246

Over the years, Cloud computing is becoming increasingly popular due to the continually changing technology. The primary goal of the cloud computing network is to offer consumers pay-per-use usage of on-demand processing and storage resources, enabling low-cost, efficient infrastructure utilization for smaller businesses. However, because of the feature of cloud resource sharing, data security and privacy create issues, and prevent smart systems from continuously transferring produced data to the cloud computing resources. Many encryption methods were presented to address data security concerns. This article presents the processes of several data protection and encryption systems currently in use and critically analyzes them based on common factors. The purpose of this survey is to compile data encryption methods now in use, often discussed in the survey of smart systems for data security when applied to the cloud platforms. © 2025 ieee.

关键词： Cloud security

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A Certain Investigation of Gesture based Control System in Infotainment of Hybrid Car Using Internet of Things

A Certain Investigation of Gesture Based Control System in I...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Vinoth Kumar, K. Thangamani, R. Rengarajan, V. Achinth, H.S. Agratha, N. Prithviraj, Jaison Joel New Horizon College of Engineering Department of Eee Bengaluru India New Horizon College Department of Bca Bengaluru India Velammal Engineering College Department of Eee Chennai India New Horizon College of Engineering Department of Me Bengaluru India

ISBN: (纸本)9798331509828

Multiple devices while driving can lead to a loss of concentration on the road, which contributes to about a third of accidents. To address this problem, significant research is being devoted to developing human-machine interfaces that allow drivers to control the vehicle's electronic components without distraction. In this paper, a complete system that allows control of the infotainment system with hand gestures. The system focuses on the shift stick area and leverages the car roof with a visible and infrared camera integrated with a novel computer vision algorithm. © 2025 ieee.

关键词： Gesture recognition

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Comparing Collaborative Filtering Algorithms in AI-Powered Recommendation systems for E-Commerce 4

Comparing Collaborative Filtering Algorithms in AI-Powered R...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Aamer, Najmuddin Mittal, Tanusha Muthusamy, Nantha Kumar Battula, R. S. S. Raju Ajith, P.S. Soudagar, ManzooreElahi M. Bharat Ratna Indira Gandhi College of Engineering Department of Computer Science and Engineering Maharashtra Solapur410003 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Maharashtra Dehradun410003 India OntrailInc Dba Uprise San FranciscoCA94104 United States Aditya University Department of Computer Applications Andhra Pradesh Surampalem533432 India Brindavan College of Engineering Department of Mathematics Karnataka Bengaluru560063 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

The quick growth of e-commerce has brought attention to how important efficient recommendation systems are to enhancing user experience and accomplishing business objectives. This paper investigates the effectiveness of collaborative filtering algorithms in AI-powered recommendation systems, with a focus on user-based collaborative filtering (UBCF). A comprehensive method is employed to guarantee consistency across different rating scales, beginning with data normalisation to standardise user-item interaction matrices. To decrease dimensionality and maintain the most significant patterns of user-item interaction, Principal Component Analysis (PCA) is employed for feature selection. The classification system uses similarity measures and UBCF to predict user preferences based on historical data. The algorithms are evaluated on key metrics like precision, recall, and RMSE using publically available datasets. The outcomes demonstrate the impact of preprocessing and dimensionality reduction on UBCF's performance as well as its efficacy in providing customised recommendations. This research contributes to the improvement of collaborative filtering algorithms for e-commerce systems, which will raise customer satisfaction and enable scalability in dynamic online markets. © 2025 ieee.

关键词： Collaborative filtering

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SAF: Local Shape-aware Face-based Garment Collision Handling via Neural SDFs

SAF: Local Shape-aware Face-based Garment Collision Handling...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tang, Minzhe Yuan, Ruisheng Kou, Dongliang Sun, Mingyang Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Institute of Metaverse & Intelligent Medicine Fudan University Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (纸本)9798350368741

Learning-based garment prediction presents an appealing alternative to physics-based methods owing to its high efficiency. However, the predicted garments can exhibit noticeable penetrations into the body. Many collision-handling methods operate in the point domain, which has an inherent limitation in addressing the penetration issues with the mesh faces. In light of this, we propose a local Shape-Aware, Face-based collision handling approach (SAF) that can be applied to garment prediction networks to achieve real-time collision handling. Considering the nature of garments, we design a compact formulation to model the continuity of the garment surface, and utilize neural Signed Distance Fields (SDFs) to accomplish penetration resolution. Recognizing the significant impact of the body's local shape on collision handling, we further propose using the angles between SDF gradients to characterize the sharpness of the body. Our approach can compensate for the inaccuracy of neural SDFs and preserve local smoothness and details. Extensive experiments demonstrate the outstanding performance and generalizability of our method. © 2025 ieee.

关键词： collision handling computer graphics deep learning garment simulation

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