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检索条件"任意字段=10th International Conference on Computer Analysis of Images and Patterns"

共 4187 条记录，以下是11-20 订阅

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Data Mining and Cloud Computing for Customer Pattern analysis and Value Maximization 10

Data Mining and Cloud Computing for Customer Pattern Analysi...

引用

10th international conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Sirjani, Mohammad Sadegh Mousavi, Seyed Amir Sadeghi, Mostafa Department of Computer Ferdowsi University of Mashhad Mashhad Iran Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran

ISBN: (纸本)9798350348873

In the contemporary business landscape, companies are seeking efficient methods to analyze customer behavior and extract actionable insights to foster customer relationships and drive business growth. this paper presents a novel approach that combines the strengths of cloud computing, software engineering, and data mining techniques to analyze customer patterns and optimize the value of the customer life cycle. By employing data mining techniques, including clustering, classification, association analysis, and predictive modeling, businesses can identify customer patterns and behaviors. through the extensive analysis of customer data, organizations can uncover concealed patterns, preferences, and trends, which facilitate informed decisions regarding customer acquisition, retention, upselling, and personalized marketing strategies, ultimately maximizing the value of the customer life cycle. the integration of cloud computing, software engineering, and data mining enables businesses to leverage advanced analytics and extract valuable insights from customer data. this approach enables organizations to gain a comprehensive understanding of customer patterns and behaviors, thereby facilitating targeted marketing campaigns, personalized customer experiences, and improved customer satisfaction. ©2024 IEEE.

关键词： Data mining

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Exploring Convolutional Autoencoder Efficacy in Noise Removal for Image Processing and computer Vision: A Study Using the MNIST Dataset 10

Exploring Convolutional Autoencoder Efficacy in Noise Remova...

引用

10th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Kandic, Elma Akagic, Amila Bohlouli, Mandi Univ Pecs Fac Engn & Informat Technol Boszorkany Ut 2 H-7624 Pecs Hungary Univ Sarajevo Dept Comp Sci & Informat Zmaja Bosne BbUniv Campus Sarajevo 71000 Bosnia & Herceg Petanux GmbH Res & Innovat Dept Dottendorfer Str 86 D-53129 Bonn Germany

ISBN: (纸本)9798350373981;9798350373974

Noise removal in image processing and computer vision is a crucial preprocessing step employing a spectrum of techniques. In recent years, autoencoders exhibit remarkable efficacy in mapping noisy images to clean counterparts, capturing intricate relationships for effective noise removal. Motivated by the challenges posed by noise in real-world images, this research focuses on the denoising preprocessing step, crucial for tasks like object detection and segmentation. the study explores the application of autoencoders in removing artificially added noise from images within the MNIST dataset. the MNIST dataset's simplicity and historical significance facilitate focused examinations on specific aspects, such as the impact of different types and levels of noise. the efficacy of autoencoders for noise removal is assessed through the evaluation of results using various metrics, including SSIM, PSNR, MSE, and RMSE. In one remarkable instance, the reconstruction process achieved an impressive peak SSIM score of 99.06%, showcasing the efficacy of the method in preserving image fidelity despite the challenging presence of noise. this comprehensive analysis provides valuable insights into the performance and effectiveness of autoencoders in the context of noise reduction in various domains.

关键词： Object detection

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Selecting Attractive images from 3D Captures of Buddhist Statues Using Grad-CAM plus 10

Selecting Attractive Images from 3D Captures of Buddhist Sta...

引用

IEEE 10th international conference on Big Data Computing Service and Machine Learning Applications (IEEE BigDataService)

作者： Shimoe, Hirofumi Fujioka, Hiroyuki Fukuoka Inst Technol Grad Sch Engn Fukuoka 8110295 Japan Fukuoka Inst Technol Dept Informat Management Fukuoka 8110295 Japan

ISBN: (纸本)9798350366396;9798350366389

this study considers the problem of selecting images that enhance the perception of attractiveness from a series of 3D captures of Buddhist statues, utilizing the advanced visualization capabilities of Grad-CAM++, an enhancement over traditional Gradient-weighted Class Activation Mapping (Grad-CAM). By leveraging convolutional neural networks (CNNs), Grad-CAM++ enables the identification of features that contribute to attractiveness in images, as perceived by observers. To validate the effectiveness of this approach, the study incorporates a comprehensive survey involving human participants to assess their reactions to the images selected by our method. this combination of computational analysis with human feedback not only showcases the capabilities of Grad-CAM++ in selecting appealing images from complex 3D datasets but also validates its utility through empirical evidence. the findings provide significant insights into the intersection of computer vision technology and cultural heritage, proposing a novel method for appreciating and evaluating the aesthetic qualities of cultural artifacts.

关键词： Image Selection attractive factors deep learning approach Grad-CAM plus Buddist statue images

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An Exploration of Diffusion Models in the Context of Data Augmentation 10

An Exploration of Diffusion Models in the Context of Data Au...

引用

10th international conference on Applied System Innovation, ICASI 2024

作者： Goel, Aaditya Rao, Prateek Bhat, Pratham Ramesh, Rahul Natarajan, S. Pes University Department of Computer Science and Engineering 100 Feet Ring Road Banashankari Stage III Dwaraka Nagar Banashankari Karnataka Bengaluru560085 India

ISBN: (纸本)9798350394924

this survey paper examines the transformative role of medical imaging in healthcare and the integration of AI systems for diagnosis and treatment. the emergence of HealthTech startups and AI algorithms highlights the industry's rapid evolution. However, challenges persist in obtaining sensitive medical data due to consent and privacy concerns. the survey paper orchestrates an exhaustive exploration of potential remedies, harnessing the capabilities of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and the innovative Stable Diffusion architecture. these cutting-edge frameworks collectively facilitate the synthesis of highly authentic medical images, adeptly alleviating the challenges tied to data procurement. In parallel, the survey paper undertakes a meticulous comparative analysis underpinned by three pivotal benchmarks: sampling efficiency, image diversity, and the overarching quality of the generated images. © 2024 IEEE.

关键词： computer vision

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A Deep Learning and Image Content analysis enabled approach for Sustainable Transportation 10

A Deep Learning and Image Content Analysis enabled approach ...

引用

10th IEEE international conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Garg, Chirag Bansal, Vaasu Gera, Nandini Bansal, Hemang Goel, Kanu Punjab Engn Coll Dept Comp Sci & Engn Chandigarh India

ISBN: (纸本)9798350385939;9798350385922

Traffic accidents and congested roads constitute a pervasive and concerning global issue, contributing substantially to numerous fatal incidents on a worldwide scale. Moreover, alleviating traffic congestion is essential for improving the efficiency and safety of transportation systems. To address these challenges, modern technologies such as video surveillance and intelligent traffic systems have been widely adopted. the proposed approach leverages the power of Deep Learning to devise an advanced strategy that can automatically identify and classify different traffic scenarios based on image content. the strategy involves training a Deep Learning model on a diverse dataset that covers various traffic conditions, enabling it to learn and generalize the patterns of different scenarios. the model will be designed to recognize factors such as vehicle types, road conditions, and the presence of pedestrians or obstacles. this sustainable approach achieved an accuracy of 81% on a test set of traffic images, aiming to provide accurate and real-time evaluations of traffic situations, which can help facilitate prompt interventions and transport management strategies.

关键词： Artificial Intelligence Deep Learning Machine Learning Road Safety Accident CNN Image Content analysis

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Digital Red Blood Cell Morphology analysis and Multi-Class Classification powered by Hierarchical AI 10

Digital Red Blood Cell Morphology Analysis and Multi-Class C...

引用

10th IEEE international conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Parayil, Shivani Debnath, Ayan Perumal, Varalakshmi Narayanan, Aarthi Sathya Natarajan, Bharathi Kannan Bose, S. Sree Niranjanaa Bosch Global Software Technol Engn Healthcare Bangalore Karnataka India

ISBN: (纸本)9798350385939;9798350385922

Blood Cell morphology identification is a very crucial step in tracing and identifying any abnormality related to hematological disorders. While the traditional method of examining blood smears under a microscope has been prevalent over the years, there is a shift towards leveraging modern computer-edge technologies to assist pathologists in interpreting digitized images of blood smears. However, challenges persist in ensuring the robustness and efficiency of Artificial Intelligence technology utilized in this process. In this paper, we have performed the detail feature extraction and hierarchical Artificial Intelligence models to get accurate results in the classification of 18 types of Red Blood Cells.

关键词： Blood Cell Morphology Feature Extraction Hierarchical AI models RBC Peripheral smears

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Dual Encoder-Decoder U-Net Architecture for Polyp Segmentation in Colonoscopy images with Shuffle Attention and Conditional Random Fields 10

Dual Encoder-Decoder U-Net Architecture for Polyp Segmentati...

引用

10th IEEE international conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Lijin, P. Santhosh, Kumar G. Nair, Madhu S. Cochin Univ Sci & Technol Dept Comp Sci Artificial Intelligence & Comp Vis Lab Kochi 682022 Kerala India

ISBN: (纸本)9798350385939;9798350385922

Colonoscopy plays a pivotal role in detecting and diagnosing colorectal diseases, with polyp segmentation being a critical step for accurate diagnosis. In this study, we propose a novel approach for polyp segmentation in colonoscopy images, leveraging the Shuffle attention mechanism within the proposed architecture. Our method is rigorously evaluated across three diverse colonoscopy datasets, demonstrating promising results with an mean dice score of 0.93. Furthermore, to enhance segmentation accuracy, we employ a Conditional Random Field (CRF) post-processing method to refine the segmentation results. through extensive experimentation and analysis, we showcase the effectiveness of our approach in achieving highly accurate polyp segmentation, thereby contributing to improved diagnostic outcomes in colorectal healthcare. Our method holds significant potential for enhancing computer-aided detection systems in clinical practice, facilitating early detection and treatment of colorectal abnormalities.

关键词： segmentation shuffle attention conditional random field

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Hierarchical AI-Powered Digital White Blood Cell and Platelet Morphology analysis using Multi-Class Classification 10

Hierarchical AI-Powered Digital White Blood Cell and Platele...

引用

10th IEEE international conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Perumal, Varalakshmi Narayanan, Aarthi Sathya Parayil, Shivani Debnath, Ayan Natarajan, Bharathi Kannan Bose, S. Sree Niranjanaa Bosch Global Software Technol Engn Healthcare Bangalore India

ISBN: (纸本)9798350385939;9798350385922

Identifying white blood cell morphology plays a pivotal role in detecting and diagnosing abnormalities associated with hematological disorders especially in cases such as leukemia and other allergies. Although examining blood smears under a microscope has been the conventional approach for years, there's a growing trend towards harnessing cutting-edge computer technologies to aid pathologists in analyzing digitized blood smear images. Yet, ensuring the reliability and effectiveness of Artificial Intelligence technology used in this context remains a challenge. In this study, we conducted comprehensive feature extraction and implemented hierarchical Artificial Intelligence models to achieve precise classification of five types of white blood cells and three types of platelets.

关键词： Blood Cell Morphology Feature Extraction Hierarchical AI models WBC Platelets

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Supervised Classification Problem: Searching for Maximum patterns 10

Supervised Classification Problem: Searching for Maximum Pat...

引用

10th international conference on Information Technology and Nanotechnology, ITNT 2024

作者： Anisimova, Diana Djukova, Elena Djukova, Anastasia Lomonosov Moscow State University Moscow Russia Federal Research Center 'Computer Science And Control' Of The Russian Academy Of Sciences Moscow Russia

ISBN: (纸本)9798350372939

A logical approach to the supervised classification problem is considered. A well-known classifier model is studied, in which the analysis of initial integer data is reduced to the search for certain fragments in the feature descriptions of precedents, called maximum patterns. the main disadvantage of this model is the high number of classification refusals. In addition, the search for maximum patterns is a computationally complex discrete problem. In the presented work, a modification of the model learning stage is proposed based on a new, more effective way of enumerating maximum patterns. It is experimentally established that due to special linear ordering of feature values a higher quality of classification can be achieved. © 2024 IEEE.

关键词： Supervised learning

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Gallbladder Cancer Detection in Ultrasound images based on YOLO and Faster R-CNN 10

Gallbladder Cancer Detection in Ultrasound Images based on Y...

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10th international conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Dadjouy, Sara Sajedi, Hedieh Department of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran Iran

ISBN: (纸本)9798350348873

Medical image analysis is a significant application of artificial intelligence for disease diagnosis. A crucial step in this process is the identification of regions of interest within the images. this task can be automated using object detection algorithms. YOLO and Faster R-CNN are renowned for such algorithms, each with its own strengths and weaknesses. this study aims to explore the advantages of both techniques to select more accurate bounding boxes for gallbladder detection from ultrasound images, thereby enhancing gallbladder cancer classification. A fusion method that leverages the benefits of both techniques is presented in this study. the proposed method demonstrated superior classification performance, with an accuracy of 92.62%, compared to the individual use of Faster R-CNN and YOLOv8, which yielded accuracies of 90.16% and 82.79%, respectively. ©2024 IEEE.

关键词： Object detection

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