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检索条件"任意字段=IEEE Conference on Local Computer Networks"

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A Deep Learning-Based Chatbot to Enhance Maternal Health Education 8

A Deep Learning-Based Chatbot to Enhance Maternal Health Edu...

引用

8th conference on Information Communication Technology and Society, ICTAS 2024

作者： Batani, John Mbunge, Elliot Leokana, Lipuo Botho University Faculty of Engineering & Technology Maseru Lesotho University of Eswatini Department of Computer Science Bambane Swaziland

ISBN: (纸本)9798350314892

Maternal mortality remains a global concern, with resource-constrained countries disproportionately affected due to inherent challenges in such countries, like underfunding, distant health facilities, lack of access to maternal health education and inequitable access to maternal health services. Though medical chatbots are gaining popularity, resource-constrained countries lag, and there is a dearth of chatbots specific to maternal health education in local languages. Therefore, this study utilised natural language processing to develop a maternal health education chatbot using a feedforward deep neural network. The model was trained using three local African languages (Sesotho, Shona and Ndebele) and English, and the chatbot was deployed using the Flask server through a web app to present a friendly interface to users. The training and evaluation losses reached zero, while the training and evaluation accuracies reached 100%. © 2024 ieee.

关键词： Deep neural networks

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Food Recognition And Recipe Recommendation For Cebuano Cuisine Using An Ensemble Of Modified CNN And Web Scraping 13

Food Recognition And Recipe Recommendation For Cebuano Cuisi...

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13th ieee International conference on Communication, networks and Satellite, COMNETSAT 2024

作者： Amora, Joseph Aaron L. Manalili, Alleyah Pauline C. Ceniza-Canillo, Angie M. University of San Carlos Department of Computer Information Sciences and Mathematics Cebu Philippines

ISBN: (纸本)9798350368086

Knowledge surrounding Cebuano cuisine, an intangible cultural heritage, continues to diminish due to the effects of globalization and the reliance on imports. Existing food computing solutions that preserve culinary heritage primarily focus on major cuisines such as Japanese, Chinese, and American. Thus, there is a gap for lesser-known cuisines like Cebuano cuisine, which has no existing food image datasets and deep learning models. To address these challenges and simultaneously promote tourism, there is a need to develop a food recognition and recipe recommendation system that shares and preserves local culinary knowledge of Cebuano cuisine culture. To achieve this, the researchers proposed an ensemble of pre-trained Convolutional Neural Network (CNN) models - specifically VGG16, DenseNet201, and InceptionV3 - enhanced by a Self-Attention Mechanism (SAM) to identify Cebuano cuisine. Due to the lack of a dataset for Cebuano cuisine, the researchers created one based on the book 'Hikay: The Culinary Heritage of Cebu' and a web article on famous Cebu delicacies, which is gathered via web scraping and data augmentation techniques such as rotation, flipping, shifting, saturation, and exposure adjustment. The dataset comprised 32,400 images across 27 classes. The ensemble learning attained an accuracy of 96.96%, suggesting a high rate of correct Cebuano cuisine predictions. The finding suggests that the developed system is effective in recognizing and promoting Cebuano culinary heritage. By leveraging advanced CNN models and enhancing them with a Self-Attention Mechanism, the study demonstrates the potential of AI in preserving cultural knowledge. © 2024 ieee.

关键词： Convolutional neural networks

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Wind Speed Estimation Using FRFT-Based Method for mmWave Wind Profiler Radar in Low SNR Conditions

Wind Speed Estimation Using FRFT-Based Method for mmWave Win...

引用

2024 ieee International Students' conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Fan, Yvxin Tan, Zaixing Yang, Guangli Wang, Rui Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai China Suzhou Dufeng Technology Co. Ltd Suzhou China

ISBN: (纸本)9798350348460

When utilizing mmWave wind profiler radar (WPR) to detect wind speed in low signal-to-noise-ratio (SNR) conditions, increasing the No. of coherent accumulation (COACC) is an effective way to increase the SNR, but will also lead to a longer beam period. In a long period, the velocity of wind or clutters cannot be regarded as constant and the acceleration of targets will cause spectrum overlap. In this paper, we propose a wind speed estimation method based on fractional Fourier transform (FRFT) to solve the spectrum overlap problem, estimate the Doppler shift of received signal in the 2-D FRFT domain, and calculate the wind speed of backscattered turbulent echoes. The proposed FRFT-based method is applied to a 24 GHz wind profiler radar in the Zhangbei wind farm of Hebei province, China. In the local wind farm, the results of the proposed method show good match with the meteorology mast in low SNR conditions. © 2024 ieee.

关键词： fractional Fourier transform (FRFT) low signal-to-noise-ratio (SNR) mmWave wind profiler radar (WPR) Wind speed estimation

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Research on PID Control of BP Neural networks Based on PSO Optimization 2

Research on PID Control of BP Neural Networks Based on PSO O...

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2nd ieee International conference on Control, Electronics and computer Technology, ICCECT 2024

作者： Sun, Mingjun Dalian Polytechnic University Dalian China

ISBN: (纸本)9798350380958

In many industrial production fields, the proportional integral derivative (PID) control algorithm is widely applied by the rapid evolution of its control theory. However, conventional PID controllers faced certain difficulties in parameter tuning, real-time online adjustment, and processing of complex nonlinear systems. To solve this problem, the researchers organically combine the PID control algorithm and other mature control strategies to form a new control theory. In this paper, we propose a neural network PID control method based on particle swarm optimization (PSO). This method combines the adaptive learning ability of the BP neural network with the PID controller, and corrects PID control parameters in real time to improve the system stability. In order to overcome the problem that the bp-pid adaptive process is apt to be a problem of the local optimum and the convergence speed slow, optimization is carried out using an improved PSO algorithm. Experimental results show that this method can reduce system overshoot effectively and reduce steady-state error and drastically reduce the steady-state time compared to traditional PID control and bp-pid control. Therefore, the method studied in this paper can greatly improve system control accuracy and stability. © 2024 ieee.

关键词： Three term control systems

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Investigating the Use of Neural networks in Image and Video Processing for Improved Media Quality

Investigating the Use of Neural Networks in Image and Video ...

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2024 OPJU International Technology conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Jaiswal, Manuraj Choubey, Abha Choubey, Siddhartha Jaiswal, Dipti Galgotias University Department of Computer Science and Engineering Uttarpradesh Greater Noida201310 India Shri Shankaracharya Technical Campus Department of Computer Science and Engineering Chhattisgarh Durg491001 India Noida International University Department of Computer Science and Engineering Uttar Pradesh Greater Noida201310 India

ISBN: (纸本)9798350373783

Today, in the digital age, high-quality video and pictures are more important than ever in many areas, from medical images and monitoring to fun and multimedia. This study looks into how neural networks can be used in picture and video processing to make media better. New technologies like deep learning and neural network designs have made it possible for computers to fix and improve video content to a whole new level. Getting rid of image noise, improving sharpness, and denoising videos are all common issues that this study aims to look at how well neural networks deal with them. Part of this research is coming up with new ways to use generative adversarial networks (GANs), reinforcement learning techniques, convolutional and recurrent neural networks, and more. As part of the investigation, we compare the performance of the suggested methods to a group of well-known image and video processing methods. These include Bicubic Interpolation, Histogram Equalization, Wavelet Denoising, Non-local Means Denoising, Sharpening Filters, and Dehazing and Contrast Enhancement. As part of the performance measures, signal-to-noise ratios, picture quality indices, and studies on how people see things are used. The big steps forward made possible by neural networks are shown by our results. Image and video quality, noise reduction, and clarity are all much better when the suggested methods are used instead of more common ones in a number of test cases. There are many ways that neural networks could completely change how picture and video processing is done. This study shows some of these ways. © 2024 ieee.

关键词： Deep reinforcement learning

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Circle Search Algorithm with Convolutional Neural Network for Distributed Denial-of-Service Attack Detection in Software-Defined networks

Circle Search Algorithm with Convolutional Neural Network fo...

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2024 International conference on Distributed Systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Padmakala, S. Al-Jawahry, Hassan M. Palanivel, R. John, Y.M. Mahaboob Madhavi, Arelli Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India Mahendra College of Engineering Department of Ece Salem India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai&ml Hyderabad India

ISBN: (纸本)9798350375442

The Distributed Denial-of-Service (DDoS) attack is a crucial cybersecurity threat for Software-Defined Network (SDN). However, DDoS in SDN severely affect network availability which reduces the classifier performance. Hence, Circle Search Algorithm with Convolutional Neural Network (CSA-CNN) is proposed for detecting DDoS anomaly attacks in SDN. The DDoS anomaly dataset such as CIC-DDoS2019 is preprocessed through label encoding which changes categorical data to arithmetical data types through allocating single numerical labels to every class. Then, CSA is applied for feature selection which helps to efficiently detect malicious traffic patterns by exploring local and global search spaces and improves the detection accuracy. The CNN has ability to detect known and unseen attacks due to handling multidimensional data which increase the security of SDN. The CSA-CNN achieved better performance in terms of 99.97%, 99.95%, 99.94% and 99.98% of precision, sensitivity, f1-score and accuracy for CIC-DDoS2019 dataset when compared to Gated Recurrent Unit (GRU). © 2024 ieee.

关键词： Convolutional neural networks

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Sensor Placement Strategy for Contaminant Detection in Water Distribution Network Using Greedy Algorithm and local Search Technique

Sensor Placement Strategy for Contaminant Detection in Water...

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2024 International conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Sukya, Fadelis Shiddiqi, Ary Mazharuddin Nurhayati, Ervin Kamal, Mustafa Angely, Elshe Erviana Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Malang State of Polytehcnic Departement of Information Technology Malang Indonesia Institut Teknologi Sepuluh Nopember Department of Environmental Engineering Surabaya Indonesia Indonesia

ISBN: (纸本)9798350368802

The increasing risk of contamination from natural and artificial sources requires effective detection strategies to prevent significant health and economic impacts. Optimal sensor placement can enhance early detection capabilities for rapid responses to mitigate the spread of contaminants. This study proposes a sensor placement strategy to optimize sensor place-ment within water distribution networks (WDN) by employing a combination of greedy algorithms and local search techniques. The strategy seeks to maximize detection coverage by considering several variables, including sensor coverage, total detection time, and average detection time. These variables are incorporated into an objective function. Experiment results indicate that our proposed approach yields better sensor placement results by improving sensor coverage capability, total detection time, and average detection time for contaminant detection. © 2024 ieee.

关键词： Health risks

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DRCR Net: Dense Residual Channel Re-calibration Network with Non-local Purification for Spectral Super Resolution

DRCR Net: Dense Residual Channel Re-calibration Network with...

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ieee/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Li, Jiaojiao Du, Songcheng Wu, Chaoxiong Leng, Yihong Song, Rui Li, Yunsong Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Spectral super resolution (SSR) aims to reconstruct the 3D hyperspectral signal from a 2D RGB image, which is prosperous with the proliferation of Convolutional Neural networks (CNNs) and increased access to RGB/hyperspectral datasets. Nevertheless, most CNN-based spectral reconstruction (SR) algorithms can only perform high reconstruction accuracy when the input RGB image is relatively 'clean' with foregone spectral response functions. Unfortunately, in the real world, images are contaminated by mixed noise, bad illumination conditions, compression, artifacts etc. and the existing state-of-the-art (SOTA) methods are no longer working well. To conquer these drawbacks, we propose a novel dense residual channel re-calibration network (DRCR Net) with non-local purification for achieving robust SSR results, which first performs the interference removal through a non-local purification module (NPM) to refine the RGB inputs. To be specific, as the main component of backbone, the dense residual channel re-calibration (DRCR) block is cascaded with an encoder-decoder paradigm through several cross-layer dense residual connections, to capture the deep spatial-spectral interactions, which further improve the generalization ability of the network effectively. Furthermore, we customize dual channel re-calibration modules (CRMs) which are embedded in each DRCR block to adaptively recalibrate channel-wise feature response for pursuing high-fidelity spectral recovery. In the NTIRE 2022 Spectral Reconstruction Challenge, our entry obtained the 3rd ranking. Code will be made available online at https://***/jojolee6513/DRCR-net.

关键词： Three-dimensional displays Image resolution Image coding Purification Customer relationship management Lighting Interference

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Matrix Accelerator Designed for Vision Transformer

Matrix Accelerator Designed for Vision Transformer

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2024 ieee International conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Liu, Mao-Yang Li, Chung-He Lin, Chin-Yu Li, Ding-Wei Wang, Yun-Tzu Xu, Yao-Zhan Fan, Yu-Cheng National Taipei University of Technology Department of Electronic Engineering Taipei Taiwan

ISBN: (纸本)9798331530839

Due to the popularity of large language models such as Generative Pre-Trained Transformer (GPT), many researchers have invested in the research of the Transformer model. Vision Transformer has become one of the most popular research directions in the field of computer vision. Different from the previous Convolutional Neural Network, Vision Transformer blocks the image and adopts Self-Attention. The model can capture the information in the image globally, not just local features. However, a large number of matrix operations consume a lot of GPU resources and memory space. The goal of this paper is to design a dedicated chip to support the digital storage and processing of images. The goal is to design based on the Vision Transformer architecture, focusing on the implementation of matrix multiplication and Softmax of Attention in the Encoder. The completed circuit is simulated running at a clock frequency of 100MHz. Compared with CPU hardware acceleration, the running speed is 51.5 times faster. © 2024 ieee.

关键词： Convolutional neural networks

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MSG-Transformer: Exchanging local Spatial Information by Manipulating Messenger Tokens

MSG-Transformer: Exchanging Local Spatial Information by Man...

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ieee/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Fang, Jiemin Xie, Lingxi Wang, Xinggang Zhang, Xiaopeng Liu, Wenyu Tian, Qi Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan Peoples R China Huazhong Univ Sci & Technol Sch EIC Wuhan Peoples R China Huawei Inc Shenzhen Guangdong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Transformers have offered a new methodology of designing neural networks for visual recognition. Compared to convolutional networks, Transformers enjoy the ability of referring to global features at each stage, yet the attention module brings higher computational overhead that obstructs the application of Transformers to process high-resolution visual data. This paper aims to alleviate the conflict between efficiency and flexibility, for which we propose a specialized token for each region that serves as a messenger (MSG). Hence, by manipulating these MSG tokens, one can flexibly exchange visual information across regions and the computational complexity is reduced. We then integrate the MSG token into a multi-scale architecture named MSG-Transformer. In standard image classification and object detection, MSG-Transformer achieves competitive performance and the inference on both GPU and CPU is accelerated. Code is available at https: //***/hustvl/MSG-Transformer.

关键词： Deep learning Visualization Neural networks Graphics processing units computer architecture Object detection Transformers

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