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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66489 条记录，以下是4591-4600 订阅

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Mask Generation with Meta-Learning Classifier Weight Transformer Network for Few-Shot Image Segmentation

Mask Generation with Meta-Learning Classifier Weight Transfo...

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

作者： Jhou, Fong-Ci Liang, Kai-Wen Lo, Chung-Hsun Wang, Chien-Yao Chen, Yung-Fang Wang, Jia-Ching Chang, Pao-Chi National Central University Dept. Communication Engineering Taiwan National Central University Dept. Computer Science and Information Engineering Taiwan Academia Sinica Institute of Information Science Taipei Taiwan

ISBN: (纸本)9798350324174

This paper proposes a meta-learning classification weight transfer network to generate masks as a few-shot image segmentation architecture. It generates good prior masks via a pretrained classification weight transfer architecture, and uses a pretrained feature extraction architecture on query images and support images. The network architecture exploits a top-down path in a feature augmentation module to adaptively transfer information from fine to coarse features for extracting features from query images. Finally, the classification module predicts the segmentation of the query image. The experimental results show that using the mean intersection of joints (mIOU) as the evaluation mechanism, the accuracy of the 1-shot experimental results is 1.7% higher than that of the baseline. In the 5-shot experimental results, the accuracy is also improved by 2.6%. Therefore, compared with the baseline, it clearly shows that the mask generated by the meta-learning classification weight transfer network can effectively help improve the performance of few-shot image segmentation system. © 2023 IEEE.

关键词： Semantic Segmentation

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Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

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Network Traffic Prediction with Decomposition and Multi-Scale Autocorrelation in Large-Scale Cloud Data Centers 21

Network Traffic Prediction with Decomposition and Multi-Scal...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Meijia Yuan, Haitao Kuang, Zhenwei Ma, Hanbo Bi, Jing Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350365221

As the Internet and big data technologies advance, a tremendous amount of data is generated daily. Efficient network operations are essential for handling such data. Accurately predicting future network traffic in real-time enables prompt response from cloud infrastructure and efficient traffic scheduling and allocation, ultimately reducing costs, preventing economic losses, and optimizing the performance of downstream facilities. However, predicting network traffic in large-scale data centers is challenging due to the multidimensional, nonlinear, and high-volatility nature of the time series. Traditional prediction methods, e.g., regression algorithms, struggle to capture non-linear features effectively. Many deep learning models face issues such as gradient explosion or vanishing during their training. Current commonly used prediction methods fail to fully uncover vital information about the frequency domain features in the time series. To do so, this work proposes a novel network traffic prediction model that combines a Savitzky Golay filter, sequence decomposition, multi-scale attention, a temporal convolutional network, an autocorrelation mechanism, and bidirectional long short-term memory. It can accurately predict future network traffic by adaptively extracting important features without requiring excessive feature engineering of the original data. It performs end-to-end sequence prediction, feature selection, and automatic learning of data features and temporal dependencies to achieve accurate time series prediction and avoid redundant data processing. Experiments involving ablation studies and comparisons with advanced prediction models are performed with the Google cluster trace. Experimental results show that the proposed model improves the prediction accuracy by at least 38.52% over the state-of-the-art models. © 2024 IEEE.

关键词： Autocorrelation

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Enhancing 5G Efficiency: Regression Analysis for Resource Allocation Optimization in Wireless Networks

Enhancing 5G Efficiency: Regression Analysis for Resource Al...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Padmageetha B.G Pramod Kumar Naik Mallanagouda Patil Dept. of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer Science and Engineering Dayananda Sagar University Bengaluru India Dept. of Computer Science and Engineering RVITM Bengaluru India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The advent of 5G networks signifies a major breakthrough in mobile communications, providing better data rates, low latency, and improved connectivity. Efficient resource allocation within 5G networks is paramount to fully leverage these capabilities, ensuring optimal performance and user satisfaction. Traditional resource allocation methods often struggle to keep pace with the dynamic and complex nature of 5G environments. The potential benefits of machine learning (ML) is explored in this research to enhance resource allocation in 5G networks. We begin by examining the unique challenges posed by 5G networks, by ML approaches employed for resource allocation, including supervised learning such as Trees, SVM, Neural Networks. Our study we train the model using MATLAB simulator. Analysis on the response and residual plot is conducted for predicted response. Analysis and comparative study is made to find the best performance metric suitable for the predicting the model for resource allocation. The study shows the improvisation in the metrics of regression learner. Simulation results demonstrate significant improvements in network performance. Finally, we have done the comparison between the results of MATLAB simulation. This study gives better decisions to service providers to enhance the overall performance of network

关键词： Measurement Analytical models Accuracy 5G mobile communication Computational modeling Neural networks Predictive models Mathematical models Resource management MATLAB

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A Lightweight CNN for Real-Time Pre-Impact Fall Detection

A Lightweight CNN for Real-Time Pre-Impact Fall Detection

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Design, Automation and Test in Europe Conference and Exhibition

作者： Cristian Turetta Muhammad Toqeer Ali Florenc Demrozi Graziano Pravadelli Dept. of Computer Science University of Verona Italy Dept. of Electrical Engineering and Computer Science University of Stavanger Norway Dept. of Engineering for Innovation Medicine University of Verona Italy

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Falls can have significant and far-reaching effects on various groups, particularly the elderly, workers, and the general population. These effects can impact both physical and psychological well-being, leading to long-term health problems, reduced productivity, and a decreased quality of life. Numerous fall detection systems have been developed to prompt first aid in the event of a fall and reduce its impact on people's lives. However, detecting a fall after it has occurred is insufficient to mitigate its consequences, such as trauma. These effects can be further minimized by activating safety systems (e.g., wearable airbags) during the fall itself—specifically in the pre-impact phase—to reduce the severity of the impact when hitting the ground. Achieving this, however, requires recognizing the fall early enough to provide the necessary time for the safety system to become fully operational before impact. To address this challenge, this paper introduces a novel lightweight convolutional neural network (CNN) designed to detect pre-impact falls. The proposed model overcomes the limitations of current solutions regarding deployability on resource-constrained embedded devices, specifically for controlling the inflation of an airbag jacket. We extensively tested and compared our model, deployed on an STM32F722 microcontroller, against state-of-the-art approaches using two different datasets.

关键词： Productivity Safety devices Microcontrollers Atmospheric modeling Psychology Data models Real-time systems Convolutional neural networks Fall detection Older adults

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Deepfake Detection of Singing Voices With Whisper Encodings

Deepfake Detection of Singing Voices With Whisper Encodings

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sharma, Falguni Gupta, Priyanka Centre for Artificial Intelligence Banasthali Vidyapith Jaipur India Dept. of Communication and Computer Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9798350368741

The deepfake generation of singing vocals is a concerning issue for artists in the music industry. In this work, we propose a singing voice deepfake detection (SVDD) system, which uses noise-variant encodings of open-AI's Whisper model. As counter-intuitive as it may sound, even though the Whisper model is known to be noise-robust, the encodings are rich in non-speech information, and are noise-variant. This leads us to evaluate Whisper encodings as feature representations for the SVDD task. Therefore, in this work, the SVDD task is performed on vocals and mixtures, and the performance is evaluated in %EER over varying Whisper model sizes and two classifiers-CNN and ResNet34, under different testing conditions. © 2025 IEEE.

关键词： anti-spoofing Deepfake Detection Singing voice deepfake detection Transfer Learning Whisper

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The Development of an Autonomous Robotic Scanning System with the Laser Rangefinder

The Development of an Autonomous Robotic Scanning System wit...

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

作者： Ko, Hung-Chin Chen, Syuan-Yi He, Jyun-Hong Chiang, Hsin-Han National Taiwan Normal University Dept. Electrical Engineering Taipei Taiwan National Taipei University of Technology Dept. Computer Science and Information Engineering Taipei City Taiwan National Taipei University of Technology Dept. Vehicle Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

Due to the rise of Industry 4.0, many industries have begun to replace traditional human labor with automated equipment to achieve greater productivity and more accurate inspection results. Due to its advantages of high stability, high accuracy, and low repetition error, many manufacturing industries have chosen to use robotic arms to replace human labor in production and inspection tasks to obtain faster and more accurate work results. Based on the above advantages, this experiment combines a seven-axis robot arm with 2D LiDAR to scan the object to be inspected, and the algorithm is used to analyze, calculate and plan the inspection path of the arm so that the robot arm can automatically scan the surface of the object to be inspected in an equal speed, smooth, and reproducible way. © 2023 IEEE.

关键词： Inspection

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A Comprehensive Review of Deep Learning and IoT in Driver Drowsiness Detection for Safer Roads 3

A Comprehensive Review of Deep Learning and IoT in Driver Dr...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Malik, Ritu Vijarania, Meenu Malik, Meenakshi School of Engineering and Technology K. R. Managalam University Gurugram India SoET Bml Munjal University Dept. of Computer Sc. & Engineering Gurugram India

ISBN: (纸本)9798331518592

This study emphasizes the significance of driver monitoring systems (DMS) for vehicle safety, particularly given their integral role in the Advanced Driver Assistance System (ADAS). It goes over the various components and levels of automation in ADAS, highlighting the importance of precise driver state monitoring for increased traffic safety. It also looks at the role of active safety modules and the possibility of improved detection accuracy and real-time feedback from IoT - based driver drowsiness detection systems. The study highlights the significance of precise measurement instruments and early warning systems while describing the signs and stages of driver drowsiness. It includes a variety of methods for detecting drowsiness, including image-based, hybrid, biological, and vehicle-based strategies. The study also examines Internet of Things (IoT) tools that are commonly used to identify driver fatigue, such as wearables, smart glasses, AI-enabled dashboard cameras, and GPS. Among the difficulties faced by IoT-based drowsiness detection systems are concerns about data reliability, power efficiency, privacy, and sensor accuracy. The conclusion highlights the importance of machine learning techniques with internet of things devices and the opportunities that 5G networks present for enhancing driver fatigue identification systems for roadways with greater safety and smart cities. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Efficient Recognition of Volatile Organic Compounds Using Low-Cost E-Nose Device and Random Forest Classification 10

Efficient Recognition of Volatile Organic Compounds Using Lo...

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Macias-Quijas, Ricardo Velazquez, Ramiro Ghavifekr, Amir Aminzadeh De Fazio, Roberto Visconti, Paolo Universidad Panamericana Facultad de Ingeniería Aguascalientes Mexico University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran University of Salento Dept. of Innovation Engineering Lecce Italy

ISBN: (纸本)9798331509972

This paper introduces a novel method for the efficient recognition of volatile organic compounds (VOCs) using an electronic nose (e-nose) device coupled with advanced signal processing and classification techniques. The proposed e-nose is a cost-effective sensor array designed to detect a broad spectrum of VOCs with applications in several domains including industrial safety. Data collected from the sensors are processed using the Filter Diagonalization Method (FDM), a novel non-Fourier spectral estimation algorithm that provides reliable footprints from the VOCs. The classification stage employs a Random Forest (RF) model to analyze the FDM footprints and predict the VOC present in the e-nose's surroundings. Experimental results show that the FDM-RF approach achieves a 96.4% classification accuracy. © 2024 IEEE.

关键词： Random forests

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Dynamic Portfolio Optimization Using Proximal Gradient Method 15

Dynamic Portfolio Optimization Using Proximal Gradient Metho...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Hareni, M. Krishna, Surya Ayyappan, Swetha Jayan, Sarada Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bangalore India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bangalore India

ISBN: (纸本)9798350370249

The focus of this project will be to develop a general optimization framework for optimal investment portfolios within the proximal gradient method, a suitable algorithm to solve non-smooth constrained optimization problems efficiently. Using historical ADM, Amazon, Apple, and Cisco stock price data, among others, we can derive the optimal allocation of assets that serves to minimize the overall portfolio volatility. While cautiously pre-treating the data on all the stocks through return calculation, our methodology proceeded to use the proximal gradient method in weighing investment optimally, subject to some constraint. In this respect, with our methodology having been applied, the computational advantage and reduction in time usage did become relatively significant. The proximal operator is known to be very effective in dealing with all forms of constraints, be it budgetary, maximum and minimum investment bounds on each asset, or risk constraints, in providing an optimized solution to the dynamic nature of financial markets. © 2024 IEEE.

关键词： dynamic portfolio optimization non-smooth constrained problems proximal gradient

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