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Validation of Generative Visual Solutions Using Prompt engineering and Caption based Visual Reasoning Models 2

Validation of Generative Visual Solutions Using Prompt Engin...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Arora, Manali Garg, Chirag Mangla, Deepanshu Samsung Electronics Noida India

ISBN: (纸本)9798331505745

We introduce the novel approach of validation of artificially generated images which helps to validate the images based on the prompt given for the generated image. Existing methods involve generation of images based on the prompts, diffusion of images and modification of images, but fail to determine the correctness of the generated image with respect to the generated content and prompt given at user end. The prompt used for generating the image using generative artificial intelligence solutions can be comprehensive and can hold more than single perspective. To address this issue while validating computer visual solutions, we propose a method for the validation of generative visual solutions using prompt engineering and caption based visual reasoning models. The proposed solution determines the different perspectives and comprehensiveness of the prompts based on entities and attributes and then, multiple test cases are formed considering different perspectives, in more detailed and comprehensive format. Hence, proposed solution validates the generated image based on the text prompt engineered for comprehensive understanding based on the complexity of the prompt suitable for visual reasoning models. © 2025 ieee.

关键词： Case based reasoning

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Optimized Time Window Segmentation for Motor Imagery EEG Classification: An Ant Colony Optimization-based Approach 13

Optimized Time Window Segmentation for Motor Imagery EEG Cla...

引用

13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Agustin, Rosi Indah Raphisak, Pisut Department of Electrical Engineering Kasetsart University Bangkok Thailand

ISBN: (纸本)9798331521929

Motor imagery classification is significantly influenced by the characteristics of individual users. Most state-of-the-art MI-BCI systems rely on fixed time window parameters for EEG data to extract MI features, neglecting individual time latency. In this study, we explore the ant colony optimization (ACO) approach to find the most optimum time window parameters to enhance the feature relevance and improve classification performance. The proposed method comprises a common spatial pattern (CSP) to filter and extract features and linear discriminant analysis (LDA) to classify the subject’s intention. Each subject epoch is trimmed based on ACO’s best solutions. The proposed method is implemented in BCI Competition IV dataset 2a. Experimental results demonstrate that our proposed approach significantly improves classification accuracy, with mean accuracy gains of up to 10.39% in the mu band and 8.97% in the beta band on the validation set over the fixed-time method. Compared to the random search method, the ACO-based approach achieved a 2.41% improvement in the mu band, with convergence occurring faster in most cases. This study highlights the potential of optimizing time segmentation in EEG-based BCIs using the ACO approach to enhance the classification accuracy. © 2025 ieee.

关键词： Ant colony optimization

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Design and Research of a computer Network Experimental Teaching Systembased on SDN 14

Design and Research of a Computer Network Experimental Teach...

引用

14th international conference on Educational and Information Technology, ICEIT 2025

作者： Tao, Jun Yu, Wangfeng Han, Huihui Wang, Xinyu School of computer and software engineering Anhui Institute of information engineering WuHu China

ISBN: (纸本)9798331540883

The importance of computer network experiments in teaching is elaborated, while traditional computer network systems have low efficiency and chaotic management, these shortcomings affect the effectiveness of related teaching heavily. To overcome the shortcomings of traditional experimental systems, a computer network experiment system based on SDN has been constructed, which relies on SDN and AAA to manage network equipment, network simulators, and student experiments. Laboratory managers rely on SDN for device authorization, students rely on SDN for fast and secure computer network experiments, and teachers rely on SDN to obtain corresponding experimental evaluations. Two typical experiments in the experimental system are introduced, and teaching has achieved good results through the experimental system. © 2025 ieee.

关键词： Teaching

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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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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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Road Lane Detection using Region-based Convolutional Neural Network (RCNN) 1

Road Lane Detection using Region-based Convolutional Neural ...

引用

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

作者： Thondralnayagi, A. Nandhakumar, T. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

Road lane markings are one of the main factors that cause traffic accidents and jeopardize the safety of both drivers and pedestrians. Both computer vision systems find it challenging to recognize road lanes. The irregular maintenance, insufficient drainage, and traffic patterns. It negatively affects driving comfort, road safety, and vehicle condition, and it could also lead to a lot of accidents. Recently, several machine learning algorithms have been used, although their accuracy and efficiency have not improved significantly. We suggest applying Region-based Convolutional Neural Networks (RCNN) for path identification to tackle this issue. RCNNs are another kind of neural network that is capable of extracting significant information from picture and time series data. This makes it highly helpful for tasks requiring images, such as shape identification, object classification, and recognition. Image from a dataset with noise removed using a Wiener filter. The next step is to extract features from images using Linear Discriminant Analysis (LDA). Determine which lane line in the RCNN is efficient and provides accuracy, precision, recall, and F1-score as 95.6%, 94.05%, 94.58%, and 94.23%, respectively. © 2025 ieee.

关键词： Convolutional neural networks

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Diffusion Model based Image Reconstruction in Lensless Imaging

Diffusion Model Based Image Reconstruction in Lensless Imagi...

引用

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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Iterative Skill Optimization based Recurrent Network for Air Quality Forecasting 3

Iterative Skill Optimization based Recurrent Network for Air...

引用

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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Research on the Collaborative Mechanism of P4Runtime and SAI in White Box Switches based on the SONiC System 5

Research on the Collaborative Mechanism of P4Runtime and SAI...

引用

5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Pan, Zhuyu Wang, Hongchao Yan, Chen Zhang, Zishi School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9798331533694

With the advancement of Software-Defined Networking (SDN) technology, network programmability and flexibility have been significantly enhanced. However, effectively integrating traditional network systems with emerging programmable platforms remains a critical research challenge. This paper proposes a fusion architecture based on the P4Runtime protocol to achieve deep integration of SONiC white box switches and the P4 platform. By introducing SDN programmability, we design and implement a hybrid model that enables traditional network architectures to achieve advanced functionalities with the assistance of external SDN controllers. This paper provides a detailed discussion of the architecture design and implementation process, including P4 code development, compilation, target configuration file generation, and interaction mechanisms between the control plane and the data plane. Furthermore, we validate the applicability and performance of the architecture in various scenarios, offering robust support for future research and applications in programmable networks. © 2025 ieee.

关键词： Internet protocols

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Autonomous Vehicle Navigation systems: Machine Learning for Real-Time Traffic Prediction

Autonomous Vehicle Navigation Systems: Machine Learning for ...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Hundekari, Sheela Parida, Prasanta Mittal, Tanusha Sehgal, Archana Bhavana, Munugapati LTIMindtree Limited Telangana Hyderabad India School of Computer Applications Pimpri Chinchwad University Pune India School of Rural Management KIIT University Odisha Bhubaneswar India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

By making transportation safer and more efficient, autonomous vehicle navigation systems are transforming the transportation industry. Autonomous cars are able to make route selections based on accurate, up-to-theminute traffic predictions, which help them avoid congestion and maximize their trip time. The use of machine learning algorithms to reliably forecast traffic conditions in real time for use in autonomous vehicle navigation is the focus of this research. Deep learning, support vector machines, and random forests are just a few examples of the machine learning models that can accurately forecast traffic conditions by mining massive datasets collected from GPS data, traffic sensors, and past trends. The suggested method incorporates these models into an autonomous vehicle navigation system that operates in real-time, enabling adaptive modifications according to the flow of traffic. We also look at how things like construction, road accidents, and weather affect the precision of traffic predictions. based on the outcome of decreased trip time, increased safety and efficiency, results demonstrate how navigation systems supplemented with machine learning, far surpass conventional routing approaches. The study suggests that further research needs to be conducted on hybrid algorithms for better traffic prediction, and multi modal data integration to further optimize autonomous vehicle navigation and in general, machine learning presents great promise in this area. © 2025 ieee.

关键词： Traffic congestion

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