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IEEE international conference.on Recent advances in Science and Engineering Technology, ICRASET 2024

IEEE International Conference on Recent Advances in Science ...

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2024 IEEE international conference.on Recent advances in Science and Engineering Technology, ICRASET 2024

ISBN: (纸本)9798350388602

The proceedings contain 177 papers. The topics discussed include: weather prediction model using ridge regression;continuous gesture recognition in human-computer interaction through real-time machine learning models;an effective implementation and comparative analysis of mobile robot path planning algorithms;advanced machine learning algorithms for predictive analytics in healthcare to enhance patient outcomes with data-driven insights;blockchain-powered decentralized resource allocation and management for cloud computing;an hand gesture smart translation system for differently abled people;smart iridology: deep learning for predictive health insights;and early detection of stem bleeding in coconut trees using image processing.

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Detection and recognition of Rice Leaf Diseases Using Deep Neural Networks 6

Detection and Recognition of Rice Leaf Diseases Using Deep N...

引用

6th international conference.on image, Video processing, and Artificial Intelligence, IVPAI 2024

作者： Phong, Bui Hai Nguyen, Dang Le Thao Nhan, Nguyen Phuc Bao, Phan Minh Quoc Van Khuong, Pham Faculty of Information Technology Hanoi Architectural University Hanoi Viet Nam High School for the Gifted Ho Chi Minh City National University Viet Nam The Asian International School Viet Nam BK center Hanoi Viet Nam

ISBN: (纸本)9781510681781

Rice plants have shown the crucial importance for our food supply. However, the diseases have significantly affected the rice productivity every year. The early and accurate detection of the diseases allows us to diagnose and treat the diseases efficiently. In the past few years, the advances in artificial intelligence allow us to detect and recognize the rice diseases with high performance. The paper presents a solution for the detection and recognition of rice diseases using deep neural networks. The detection module is performed using the YOLOv8 network. The recognition module is performed using the Denesenet-121 network. We have evaluated the proposed method on two datasets: the Kaggle and Vietnamese rice disease datasets. The obtained accuracy of 93% and 96% for the recognition of diseases of rice plants on Kaggle and Vietnamese datasets have shown the effectiveness of our proposed method. © 2024 SPIE.

关键词： Deep neural networks

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Research on image generation based on VAE and CGAN fusion model

Research on image generation based on VAE and CGAN fusion mo...

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2023 international conference.on image, Signal processing, and pattern recognition, ISPP 2023

作者： Huang, Lingui Li, Huiying Jingdezhen Ceramic Institute Zhiyuan Road Fuliang County Jiangxi Province Jingdezhen City China

ISBN: (纸本)9781510666351

The current image generation research is in full swing, and various image generation technologies emerge in endlessly. Deep learning technology has attracted wide attention since it was proposed. Research on image generation based on deep learning, such as autoencoder network, variational autoencoder network, and generation Adversarial networks occupy an important position in the field of image generation. However, the generated image effect of each individual image generation network is not ideal. In order to generate clear images, this paper combines VAE (Variational Autoencoder Network) and CGAN (Conditional Generative Adversarial Network) to generate high-quality images. This fusion model has both the advantages of VAE network stability and CGAN network clarity. The experimental results prove that the method in this paper can achieve clear and high-quality images. © 2023 SPIE.

关键词： Generative adversarial networks

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6th international conference.on Recent Trends in image processing and pattern recognition, RTIP2R 2023

6th International Conference on Recent Trends in Image Proce...

引用

6th international conference.on Recent Trends in image processing and pattern recognition, RTIP2R 2023

ISBN: (纸本)9783031530814

The proceedings contain 31 papers. The special focus in this conference.is on Recent Trends in image processing and pattern recognition. The topics include: UI/UX for Aerospace Qualification Business Processes;Enhancing Android Malware Detection: CFS Based Texture Feature Selection and Ensembled Classifier for Malware App Analysis;a Comprehensive Analysis of Blockchain Network Security: Attacks and Their Countermeasures;Enhancing CT image Visualization and Analysis Through Rescaling Raw Pixel Values to Hounsfield Units;VAIDS: A Hybrid Deep Learning Model to Detect Intrusions in MQTT Protocol Enabled Networks;leveraging Handwriting Impairment as a Biomarker for Early Parkinson’s Disease Diagnosis;blockchain for Patient Data Integrity: Decentralised Storage and Retrieval in Modern Healthcare Systems;a Residual Learning Approach Towards the Diagnosis of Colorectal Disease Effectively;Large-Language-Models (LLM)-Based AI Chatbots: Architecture, In-Depth Analysis and Their Performance Evaluation;cincoCrypto - A Cryptocurrency Price Forecasting Tool for Everyone;Realistic Skin image Data Generation Leveraging Conditional GAN and Classification Using Deep CNN;Diabetic Retinopathy Blood Vessel Detection Using Deep-CNN-Based Feature Extraction and Classification;analyzing Pulmonary Abnormality with Superpixel Based Graph Neural Network in Chest X-Ray;a Stack Ensemble Approach for Early Alzheimer Classification Using Machine Learning Algorithms;federated Unlearning and Server Right to Forget: Handling Unreliable Client Contributions;optimal Resource Allocation for Energy Harvested Cognitive Radio Networks Based on Learn Heuristic Algorithm;Deep Neural Networks Based Security Solution for ATM Transactions;crypto Bank: Cryptocurrency Wallet Based on Blockchain;radiography and Thermography Based image processing Techniques for Assessment of Lower Back Pain;modified Snapshot Ensemble Algorithm for Skin Lesion Classification;COVID-19 Disease Prediction Using Generative Adversarial

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Simulation of Computer image processing Model Based on Random Forest Algorithm 2

Simulation of Computer Image Processing Model Based on Rando...

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2nd international conference.on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Liu, Haotian University of Edinburgh EdinburghEH8 9JU United Kingdom

ISBN: (纸本)9798350323818

In essence, image recognition, as a pattern classification problem, can also be processed by classical pattern classification algorithms. Under the background of network information technology, it is need to use computer vision algorithm to process the image and realize the three-dimensional reconstruction of the image for the distortion of the true three-dimensional display image of intelligent interactive system. Random Forest (RF) is a new ensemble learning algorithm developed in recent years, which has good classification accuracy. In this article, a computer image processing model based on RF algorithm is proposed to enhance the ability of depth feature learning. The experimental results show that this image processing technology is more accurate than BP neural network and can be widely used in image processing. High-level target feature representation can often improve the recognition accuracy. However, the extraction of high-level target features generally has certain computational complexity. Compared with traditional methods, the accuracy of the proposed method is greatly improved, and the generalization performance is superior, which can identify images that have been beautified and repaired. © 2023 IEEE.

关键词： image recognition

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Power Borehole image recognition Technology Based on Convolution Feature Fine-grained Optimization

Power Borehole Image Recognition Technology Based on Convolu...

引用

2023 international conference.on image, Signal processing, and pattern recognition, ISPP 2023

作者： Zhang, Chen Li, Junjie Zhang, Yi State Grid Shanghai Shibei Electric Power Supply Company Shanghai200072 China

ISBN: (纸本)9781510666351

With the development of power system, the technology of power well is facing many problems. How to not only detect the power well information, but also characterize the measured data in an easy-to-explain way, so that the staff can understand and analyze the power well casing, is of great significance to the detection of pipeline damage and the development of the power industry. Nowadays, the development of informatioan processing and microelectronics technology, especially the rapid development of image processing technology, provides a more theoretical basis for the realization of power well detection technology, and expands more possibilities for the study of detection data. A clutter suppression method based on fine-grained optimization decomposition of the convolution feature is proposed. A noise constraint is added into the non-negative matrix factorization, and a base matrix and a coefficient matrix to be factorized are assumed to be non-negative exponential distributions in probability. The non-negative matrix factorization is carried out by using a convolutional neural network to obtain the approximate inference of the base matrix and the coefficient matrix. The selection of parameter values and the input of horizontal gradients are determined. The low-rank matrix representation of clutter components is obtained so as to separate the clutter from the image. The experimental process is compared with the simulation data, which verifies that the model has good robustness for the image recognition in the power well. © 2023 SPIE.

关键词： Pipelines

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Research on Flame Detection of fire-fighting robot 3

Research on Flame Detection of fire-fighting robot

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3rd international conference.on Computer Vision and pattern Analysis, ICCPA 2023

作者： Tohti, Gulbahar Zhang, Guohui Geni, Mamtimin School of Mechanical Engineering Xinjiang University Xinjiang Urumqi830047 China Company of Baibo Electromechanical Technology Urumqi Xinjiang830011 China

ISBN: (纸本)9781510667563

Aiming at the problems of slow image processing and detection speed of flame recognition method and sensor measurement vulnerable to light environment interference, this paper studies how to improve the speed of flame recognition and overcome light environment interference. To improve the speed of flame recognition, this paper selects convolutional neural network technology, trains a classifier dedicated to flame, improves the preprocessing and post-processing methods of feature map, and develops a new flame recognition model, which greatly improves the speed of flame recognition;To overcome the interference of light environment, this paper proposes a new method by measuring the two output signals of the flame sensor in different light environments. It provides a new method for fast flame recognition of fire-fighting robot. © 2023 SPIE.

关键词： image enhancement

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Asymmetric Learned image Compression Using Fast Residual Channel Attention 27th

Asymmetric Learned Image Compression Using Fast Residual Cha...

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27th international conference.on pattern recognition, ICPR 2024

作者： Hu, Yusong Jung, Cheolkon Liu, Yang Li, Ming School of Electronic Engineering Xidian University Xian710071 China Guangdong OPPO Mobile Telecommunications Corporation Dongguan China

ISBN: (纸本)9783031783944

In recent years, substantial advances have been made in deep learning-based image compression. Most studies have focused on designing accurate and flexible entropy models to predict the distribution of latent features in images. However, the allocation of computing resources and the restoration of decoded images by post-processing are equally important. In this paper, we propose asymmetric learned image compression based on fast residual channel attention. We design an asymmetric image compression network to effectively allocate computational resources into the post-processing of the decoder. Inspired by image super-resolution, we provide a fast residual channel attention module in the post-processing based on depthwise separable convolution. This module can quickly restore the features lost by compression, resulting in image quality enhancement. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods for learned image compression in terms of PSNR, MS-SSIM and runtime. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image compression

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Comprehensive implementation of TextCNN for enhanced collaboration between natural language processing and system recommendation

Comprehensive implementation of TextCNN for enhanced collabo...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Xu, Xiaonan Xu, Zheng Ling, Zhipeng Jin, Zhengyu Du, Shuqian Northern Arizona University Flagstaff United States Stevens Institute of Technology HobokenNJ United States University of Sydney Sydney Australia Univeristy of California Irvine IrvineCA United States Trine University Phoenix ArizonaAZ United States

ISBN: (纸本)9781510680425

Natural Language processing (NLP) constitutes a pivotal domain of artificial intelligence focused on enabling computers to comprehend, process, and generate human language. Text classification, a fundamental NLP task, aims to categorize text into predefined classes. In recent years, deep learning has emerged as a dominant force across various research domains and has become a staple technology within NLP, particularly in text classification tasks. Unlike numerical and visual data, text processing underscores the need for nuanced processing capabilities. Traditional text classification methodologies typically involve preprocessing textual data and annotating samples manually to derive effective feature representations for classification using classical machine learning algorithms. This paper delves into the current landscape of deep learning applications in NLP, specifically in three core areas: text representation, sequence modeling, and knowledge representation. Furthermore, it explores the advancements and synergies facilitated by natural language processing in the realm of text classification, while also addressing challenges posed by adversarial techniques in text generation, classification, and semantic parsing. An empirical investigation into text classification tasks demonstrates the efficacy of interactive integration training, particularly in tandem with TextCNN, underscoring the pivotal role of these advancements in augmenting and refining text classification methodologies. © 2024 SPIE.

关键词： Knowledge representation

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Research on continuous monitoring of subdural hematoma based on optical image mapping feature extraction method

Research on continuous monitoring of subdural hematoma based...

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2024 international conference.on image, Signal processing, and pattern recognition, ISPP 2024

作者： Han, Guang Pan, Zhonghua Liu, Yuqing Wang, Jinhai Zhao, Zhe Wang, Huiquan School of Life Sciences Tiangong University Tianjin300387 China Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices Tianjin300387 China

ISBN: (纸本)9781510680425

Diffuse optical tomography (DOT) guided by medical images can be used to achieve real-time reconstruction of subdural hematoma images for monitoring purposes. However, when the hematoma is irregular and the signal to noise ratio(SNR) of input signal is low, the reconstruction effect of this method is not ideal. In order to alleviate this situation and improve the reconstruction effect of cerebral hematoma images, we proposed a optical image mapping feature extraction method in the paper. The result of experiment shows that the mean relative volume error (VRE) of the hematoma reconstruction model optimized by the optical image mapping feature extraction method is only 0.79%, and the mean value of the average Manhattan distance (AMD) between the reconstructed absorption coefficient and the true absorption coefficient is 0.0062mm-1. Compared with the model directly inputting optical information, the optical image mapping feature extraction method reduces the average VRE of the model by 93.3%, and the average AMD by 27.1%. This method provides a promising method for non-invasive continuous monitoring of clinical cerebral hematoma. © 2024 SPIE.

关键词： Optical tomography

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