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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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A Heterogeneous Multi-Model Integration Approach for Unbalanced Direct Marketing 2

A Heterogeneous Multi-Model Integration Approach for Unbalan...

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2nd International conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Mao, Jianghua Hu, Longxing He, Yueming Che, Jie Liu, Jian Jiang, Ye Dong, Feibiao Jiang, Zhaoneng HeFei University of Technology Hefei China HeFei University of Technology University of Chinese Academy of Sciences Hefei China HeFei University of Technology Xi'An Jiaotong University Xi'an China HeFei University of Technology Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Anhui Province Key Laboratory of Industry Safety and Emergency Technology Xuancheng China

ISBN: (纸本)9798350328530

The crucial factor for enterprises to succeed in direct marketing with the help of Internet technology is to deal with massive unbalanced data and extract useful information. Traditional classification algorithms tend to favor majority categories and ignore minority categories. Therefore, solving the unbalance of direct marketing data becomes the key to precision marketing. Here, we propose a heterogeneous multi-model integration approach from the data pre-processing level, feature level and classification algorithm level to improve the prediction effect of unbalanced direct marketing. Using multiple evaluation indicators such as Weighted-Fl and AUC, the experimental evaluation on two publicly-available data set shows that the proposed heterogeneous multi-model integration approach achieves the best recognition performance. This approach is effectively used to explore whether users will respond to promotional activities, so as to help enterprises carry out targeted marketing plans and save redundant expenses. © 2023 IEEE.

关键词： Commerce

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12th International conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

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12th International conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819788354

The proceedings contain 34 papers. The special focus in this conference is on Recent Trends in Computing. The topics include: Deep Learning-Based Prediction of Human Ageing Related Proteins;the Insight of Clustering algorithms for Emerging Networking Paradigms;Revolutionizing Cardiovascular Health: Exploring the Potential of AI-Driven Approaches in Diagnosing Arrhythmia Disorders;humor Detection in Telugu Social Media Text Using Cost-Sensitive Learning and Indian Language Embeddings;empirical Comparison of Sleep Disorder Prediction Using Machine Learning Techniques;securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks;machine Learning-Powered Intrusion Detection for Network Security;secure Chat System: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality;deep Neural Networks for Early Diabetes Mellitus Prediction;advancements in image Deblurring and Performance Metrics Using Deep Learning Technique;emotion-Levitating Music Recommendation System Through Real-Time Facial Recognition;smartVoice TrashMate: The Intelligent Voice-Controlled Trash Bin;an Innovative image Enhancement Algorithm for Color Filter Array images: A Novel Approach;Enhancing Secure and Efficient Communication in Free-Space Optical Networks Using Spectral Amplitude Coding - OCDMA Technology;enhancing Citrus Fruit Classification and Quality Assessment Through Advanced Machine Learning Techniques;connecting Health for a Better Tomorrow Through Internet of Medical Things;image processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency;Leaf Checker: AI-Based Plant Diagnostic Tool Using Alexnet and Mobilenet;ioT-Supported Indoor Navigation System Using Machine Learning Technique;a Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities;An Unequal Cluster-Based Routing Mechanism Using Lyrebird Optimization Algorithm to Achieve Network Longevity in WSNs.

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Infrared small target detection based on long-short term frame difference algorithm(LSTFD)

Infrared small target detection based on long-short term fra...

引用

Intelligent Control, Measurement and Signal processing and Intelligent Oil Field (ICMSP), International conference on

作者： Bing Zhu Jiahui Shi Ruixing Yu Weixin Gao Siyu Wang School of Electronic Engineering Xi'an Shiyou University Xi'an China School of Astronautics Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

According to the characteristic that the position change of small infrared target in sequence images is more obvious than background and noise, an infrared small target detection algorithm based on multi-frame time domain information is proposed, called the Long-Short Term Frame Difference (LSTFD) algorithm. The algorithm first adopts the SURF (Speeded Up Robust Feature) alignment strategy to eliminate the relative motion of background pixels caused by camera shake and achieve global motion compensation. Then, the image sequence is constructed based on the current frame, and the computational results of long-interval difference and short-interval difference are combined to detect the infrared small targets. The experimental results show that the LSTFD algorithm can accurately detect moving targets with high detection accuracy and low false alarm rate under the condition of low signal-to-noise ratio. And the average detection rate can reach over 90% on five sets of experimental data.

关键词： Accuracy Signal processing algorithms Object detection Cameras Motion compensation Trajectory image sequences Motion measurement Time-domain analysis Signal to noise ratio

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Medical image Noise Reduction Methods Based on Cutting-Edge Hybrid Filtering Designs

Medical Image Noise Reduction Methods Based on Cutting-Edge ...

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International conference on Smart systems and Wireless Communication, SSWC 2024

作者： Saha, Tanusree Vishal, Kumar Magadh University Bodh Gaya India Department of Information Technology JIS College of Engineering Kalyani India Department of Mathematics Magadh University Bodh Gaya India

ISBN: (纸本)9789819613472

A high-resolution noise free medical image is an essential requirement for accurate disease detection. Medical image processing and analysis techniques have become utmost important in recent decades as a supplementary competence for medical professionals in the fields of diagnosis and prevention. Obtaining high-quality images without any interference is a crucial requirement for effectively managing therapeutic images. Medical images may contain many types of noises for involvement of simultaneous operation of multiple equipment involved in data capturing and transmission. Various filters have been suggested for eliminating different types of noises, each having its own merits and drawbacks depending on its capabilities and projected benefits. Our research depicts a technique for removing noises from medical images using a hybrid filtering approach based on ensemble adaptive mean filtering and Block-matching-3D filtering. The proposed methodologies specifically aim to reduce the speckle noise in brain MRI images obtained from ultrasonic therapy by employing hybrid filtering techniques. This proposed hybrid filtering algorithms integrate the most effective aspects of high-performance filters to achieve a de-noised image that retains fine details, edges, and textures. The statistical performance measurement metrics like Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) are employed to evaluate the improved visual quality of the images. The results indicate that our proposed hybrid filtering technique functioning efficiently and provide better quality de-noised image compare with other existing de-noising techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image enhancement

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12th International conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

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12th International conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819789450

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Quantum Variational Singular Value Decomposition: A new hybrid approach towards quantum image compression

Quantum Variational Singular Value Decomposition: A new hybr...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Britant Abhishek Tiwari CDAC Noida Uttar Pradesh India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

This research paper explores the application of singular value decomposition (SVD) in quantum image processing (QIP), specifically focusing on the computation of eigenvalues using variational quantum algorithms. SVD is a powerful mathematical tool in image processing, used for tasks such as image compression, noise reduction, and feature extraction. In this study, we propose quantum variational singular value decomposition (QVSVD) based on the variational quantum deflation (VQD) algorithm to determine the eigenvalues that contributes in calculating the singular values of the image matrix and eventually doing the SVD. This facilitates the extraction of eigenvalues with exponential speedup on real hardware compared to classical methods. We detail the implementation of this quantum algorithm within the framework of QIP, highlighting its advantages in terms of computational efficiency and accuracy on a Fault-Tolerant Quantum Computing (FTQC). Furthermore, we present a comparative analysis of the quantum and the classical method of SVD, demonstrating the accuracy of the image data. The simulation results validate the theoretical advantages of using quantum algorithms for SVD in image processing. This work not only underscores the potential of quantum computing in enhancing image processing techniques but also sets the stage for future research in the field, exploring more complex image processing tasks and other quantum algorithms. Our findings suggest that quantum image processing can offer unprecedented capabilities, paving the way for advancements in various applications such as medical imaging, remote sensing, and multimedia processing.

关键词： Training Quantum computing Quantum algorithm image coding Accuracy Feature extraction Eigenvalues and eigenfunctions Matrix decomposition Time complexity Singular value decomposition

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Diffusion models as plug-and-play priors 36

Diffusion models as plug-and-play priors

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36th conference on Neural Information processing systems (NeurIPS)

作者： Graikos, Alexandros Malkin, Nikolay Jojic, Nebojsa Samaras, Dimitris SUNY Stony Brook Stony Brook NY 11794 USA Univ Montreal Mila Montreal PQ Canada Microsoft Res Redmond WA USA

ISBN: (纸本)9781713871088

We consider the problem of inferring high-dimensional data x in a model that consists of a prior p(x) and an auxiliary differentiable constraint c(x, y) on x given some additional information y. In this paper, the prior is an independently trained denoising diffusion generative model. The auxiliary constraint is expected to have a differentiable form, but can come from diverse sources. The possibility of such inference turns diffusion models into plug-and-play modules, thereby allowing a range of potential applications in adapting models to new domains and tasks, such as conditional generation or image segmentation. The structure of diffusion models allows us to perform approximate inference by iterating differentiation through the fixed denoising network enriched with different amounts of noise at each step. Considering many noised versions of x in evaluation of its fitness is a novel search mechanism that may lead to new algorithms for solving combinatorial optimization problems. The code is available at https://***/AlexGraikos/diffusion_priors.

关键词： image segmentation

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Exploring Data as Quantum Bits: Analysis of Quantum image Encoding Patterns for NISQ era

Exploring Data as Quantum Bits: Analysis of Quantum Image En...

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2023 IEEE International conference on Quantum Technologies, Communications, Computing, Hardware and Embedded systems Security, iQ-CCHESS 2023

作者： Gopakumar, Parvathy Rubell Marion Lincy, G. Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kerala Pala Kottayam India

ISBN: (纸本)9798350314946

The generall efficiency of a computational system, be it simple or complex, presentation of the data plays a crucial role in getting the most out of it. Classical information theory, quantum mechanics, and computer science collectively influence Quantum Information in comprehending how information can be encoded, transmitted, and modified using quantum systems. Quantum mechanics allows quantum information to incorporate a variety of properties, such as quantum encoding of classical information, storing coherent details in quantum superpositions, quantum computations and entanglement, which occurs when two or more qubits become highly correlated and share an unbreakable quantum state. There are numerous barriers and constraints in the broad field of quantum computation, ranging from dealing with Decoherence to ensuring error correction. Moreover, the qubit, a fundamental computational unit, is challenging to create, manipulate, and store. To ensure the quality of the Quantum algorithms, the area of quantum data encoding has been thoroughly researched and explored as relevant results can be obtained by adequate encoding. In this paper, we reviewed the terminology and physical modeling of qubits, as well as the encoding approaches used to transform data from classical to quantum models. Furthermore, different encoding schemes for Quantum image processing were explored to determine the efficiency of quantum pixel representation(QPIXL) framework for effective application in NISQ era. © 2023 IEEE.

关键词： Qubits

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Deep Learning-Based Diagnosis of Rice Sheath Rot Disease: A Wavelet-Filtered Approach for Feature Extraction and Analysis

Deep Learning-Based Diagnosis of Rice Sheath Rot Disease: A ...

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2024 International conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Atheeswaran, Athiraja Maheshkumar, K. Jayareka, K.S. Janani, B. Lakshmanan, K. Sobiyaa, P. Malla Reddy College of Engineering Department of Cse Telangana Hyderabad India Bannari Amman Institute of Technonology Dept of Computer Science and Design Tamilnadu Erode India Sona College of Technology Dept of Computer Science and Engineering Tamilnadu Salem India Dr.NGP Institute of Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Hindusthan College of Engineering and Technology Dept of Cse Tamilnadu Coimbatore India Bannari Amman Institute of Technonology Dept of Information Technology Tamilnadu Erode India

ISBN: (纸本)9798350359688

A major danger to rice farming, rice sheath rot disease affects the quantity and quality of one of the main food crops grown worldwide. This research uses a large dataset gathered from many sources, such as agricultural universities, research centres, and first hand field observations, to provide a unique method of diagnosing Rice Sheath Rot disease using cutting-edge deep learning algorithms. Our work is primarily focused on creating a dependable and effective diagnostic tool using state-of-the-art image processing and machine learning methods. Wavelet filters are applied in the first step of our approach to pre-process photos. This method is essential for eliminating noise and improving the quality of the dataset, which is made up of high-definition photos of rice plants with the disease Sheath Rot. The colour, shape, and texture of the sick spots are then analyzed using advanced feature extraction techniques. When separating plant tissues that are diseased from those that are not, these characteristics are essential. Convolutional Neural Networks (CNN), our primary diagnostic model's foundation, were selected due to their shown efficacy in picture recognition and classification applications. The preprocessed and feature-enhanced dataset is used to train the CNN model, giving it the ability to recognize the complex patterns and traits connected to Sheath Rot illness. The architecture of the model is built to maximize efficiency and accuracy, guaranteeing quick and accurate illness identification. By utilizing AI and deep learning, the proposed method seeks to not only help farmers and agronomists identify diseases early on but also make a substantial contribution to the area of agricultural disease diagnostics. Combine that our method, which combines CNN-based analysis with wavelet-filtered pre-processing establishes a new standard for the identification and treatment of Rice Sheath Rot disease. This might result in improved crop management techniques and more pot

关键词： image enhancement

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Comparative image processing and Object Detection in Aerial Search and Rescue

Comparative Image Processing and Object Detection in Aerial ...

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Cognitive Robotics and Intelligent systems (ICC - ROBINS), International conference on

作者： Sushil Rawat Abhay Singh Rawat Vikas Singh Mewad Himadri Vaidya Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Aerial search and response plays an important role in finding and rescuing persons in need. Unmanned Aerial Vehicle (UAV) -acquired aerial images provide an intensive profile search area and facilitate identification of potential targets. To improve the effectiveness of aircraft Search and Rescue missions, this study compares many object detection algorithms and image processing methods. The investigated object detection methods are Mask R-CNN, SSD, and YOLOv8, and image processing techniques are contrast improvement and image brightness control. The effectiveness of these strategies has been evaluated by using real aerial images that include different targets such as humans, vehicles, and fire. The results show how well the object detection algorithms work to accurately distinguish targets, and how an attractive image processing approach improves the image performance of the best object detection model, as well as how the performance of the best object detection model with image processing can improve the performance of model. This research shows that combining image processing (contrast and brightness) with an optimal object detection algorithm (Mask R-CNN) significantly improves target detection in aerial search and rescue. While only Mask R-CNN performs best, adding image processing increases its accuracy to 0.92, mAP to 0.89, and makes it more reliable in finding people in need. This shows the potential of this joint approach to save people in air rescue missions.

关键词： image quality image processing Brightness Object detection Search problems Autonomous aerial vehicles Real-time systems

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