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检索条件"任意字段=5th International Symposium on Image and Signal Processing and Analysis"

共 4483 条记录，以下是31-40 订阅

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Lossless Medical image Compression Using Block-Wise Burrows–Wheeler Transform and Modified Run Length Encoding

Lossless Medical Image Compression Using Block-Wise Burrows...

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5th international symposium on signal and image processing, ISSIP 2024

作者： Malaviya, Khushali Mishra, Pooja Pal, Arup Kumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Dhanbad Jharkhand India

ISBN: (纸本)9789819795147

Lossless compression is preferred for the compact representation of medical images. In general, single compression algorithms are not that effective compared to the hybrid compression algorithm where several compression algorithms are collectively used properly to realize better compression. In this paper, the Burrows–Wheeler Transform (BWT) is applied at a block level for each bit plane of the medical images. thus, this process identifies highly correlated binary streams, and subsequently, the obtained binary streams are further encoded through the modified Run Length Encoding (RLE). the probability of RLE is calculated for each bit plane and used subsequently to perform Huffman coding on each bit plane individually. the DICOM category of medical images and some images that are represented by 8-bit planes are used for the validation of the above approach. It is found that the present approach yields significantly better compression than that obtained with the standalone lossless compression algorithm that is Huffman coding and the conventional BWT-based lossless image compression algorithm. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image compression

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A Comprehensive analysis of the Impact of Demographic, Socioeconomic Factors, and Social Media Advertisements on Consumers’ Online Shopping Behavior

A Comprehensive Analysis of the Impact of Demographic, Socio...

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5th international symposium on signal and image processing, ISSIP 2024

作者： Mukherjee, Ranit Natarajan, Arul Kumar Christ University Bengaluru India Samarkand International University of Technology Samarkand Uzbekistan

ISBN: (纸本)9789819795147

this research comprehensively explores the intricate relationship between social media, demographic factors, socioeconomic status, and online shopping behavior. It investigates how various elements, such as brand, quality, price, and reviews, intersect to influence consumers’ virtual shopping experiences. Recognizing the diversity among consumer populations, the study considers demographic traits like age, gender, and socioeconomic status. It identifies a research gap concerning the moderating effects of these factors on virtual shopping behavior and the influence of different advertisement sources. the research proposes a methodology involving machine learning approaches to address these gaps. Using a dataset collected from 331 respondents in Kolkata, the study employs statistical tests and machine learning models to analyze the correlations and predict online shopping behavior. the results underscore the importance of understanding consumer behavior in the evolving e-commerce landscape. Logistic Regression emerges as the most accurate algorithm for predicting online shopping decisions. Additionally, the research investigates the reasons limiting consumers’ use of online shopping platforms, providing practical insights for tailored marketing strategies. these findings empower businesses to adapt their marketing strategies to the evolving landscape of consumer behavior. Overall, the study enhances our understanding of the multifaceted variables shaping customers’ decisions in online marketplaces. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Marketplaces

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Research on the Method for Testing the signal-to-Noise Ratio at the Output of Microchannel Plate Based on Point source of Electron 10

Research on the Method for Testing the Signal-to-Noise Ratio...

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10th symposium on Novel Optoelectronic Detection Technology and Applications

作者： Chen, KeJin Qian, Yunsheng Liu, Jian Zeng, JinNeng School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China North Night Vision Technology Co. Ltd. Kunming650217 China

ISBN: (数字)9781510688155

ISBN: (纸本)9781510688148

signal-to-noise ratio(SNR) at the output is one of the key parameters for evaluating the noise characteristics of the image intensifier. the noise from the microchannel plate (MCP) is a critical factor affecting the noise characteristics of image intensifier. To obtain an objective result of the SNR at the output of MCP under the actual operating conditions of the intensifier, this paper proposes a testing method with a point MCP as the core of a point source of electron for measuring the SNR at the output of MCP. Based on this, the testing system was improved. A point source of electron, composed of a deuterium lamp, adjustable aperture, optical filter, integrating sphere, and point MCP, is used as the input source. It emits a 0.2mm electron beam to simulate the noise test conditions of the image intensifier. An optical transmission component, primarily consisting of quartz optical fibers, is placed near the phosphor screen surface to minimize loss when transmitting light intensity. the photoelectric current is then collected and processed using a photomultiplier tube and signal processing unit. Finally, the SNR at the output of MCP is tested under different input electron beam densities and MCP voltage conditions. Experimental results show that this testing method is relatively objective and accurate, with a repeatability within 5%. the SNR values are consistent with those of the super second generation image intensifier under corresponding conditions, providing an objective evaluation of the MCP noise characteristics. © 2025 SPIE.

关键词： Photomultipliers

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Data-Driven Strategies for Twitter Engagement: Hashtag Recommendations and API Insights

Data-Driven Strategies for Twitter Engagement: Hashtag Recom...

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5th international symposium on signal and image processing, ISSIP 2024

作者： Ramasamy, Gobi Natarajan, Arul Kumar Rajan, Arokia Paul Bangalore India Samarkand International University of Technology Samarkand Uzbekistan

ISBN: (纸本)9789819795147

Twitter is a great way to connect with people worldwide, and one of the best ways to do that is by using hashtags. A hashtag is a keyword or phrase attached to a particular topic, and users can use it to find related tweets. Using a hashtag relevant to the needs or for business can increase the tweets’ visibility and make it easier for people to see the content they want. It can hugely help content creators who want to increase engagement and influence their tweets. this research introduces TagMate, a hashtag recommender system for Twitter that offers significant benefits. By accessing the tweets using Twitter API and after analyzing and performing algorithms, recommendations for hashtags can be obtained. the Twitter API allows access to various information about the account, including followers, tweets, content, etc. this information can be used to generate recommendations for hashtags related to the business. the system will generate hashtags according to the tweet and recommend trending or popular hashtags to increase their reach or engagement on the Twitter platform. Using the API, a dashboard can be created showing which hashtags are being used most frequently and which are most popular. this information can help create more relevant and engaging tweets, attracting more followers and interest. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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Enhancing Solid-State Drive Security: A Comprehensive analysis of Ransomware Defense and Data Recovery Strategies

Enhancing Solid-State Drive Security: A Comprehensive Analys...

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5th international symposium on signal and image processing, ISSIP 2024

作者： Ganesh Naidu, U. Varshini, Kona Veda B V Raju Institute of Technology Telangana Narsapur India

ISBN: (纸本)9789819795147

this research presents a comprehensive cybersecurity framework to address the escalating threat of ransomware in business and government. Traditional anti-ransomware measures face challenges, with evolved ransomware evading detection and compromising existing backup solutions. To tackle these issues, we introduce two innovative systems. First, AMOEBA offers a device-level backup solution eliminating the need for additional storage. Armed with a hardware accelerator for content-based ransomware detection and a fine-grained backup control mechanism, AMOEBA demonstrates high accuracy and negligible performance overhead. Second, SSD-Insider pioneers a defense mechanism within NAND flash-based solid-state drives (SSDs), using lightweight behavioral features for detection and a perfect recovery algorithm. SSD-Insider achieves remarkable accuracy, detecting ransomware within seconds and recovering instantly with minimal software overhead. In addition, our research introduces QuantumGuard, leveraging quantum-computing principles to fortify SSDs against advanced ransomware threats. Experimental assessments show QuantumGuard’s adaptability to evolving threats, providing resilient protection in quantum computing environments. this threefold contribution not only provides resilient solutions for ransomware defense and data recovery but also pushes the boundaries of innovation in cybersecurity by introducing quantum-resistant technologies. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Qubits

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Data Analyses of Factors Influencing Sustainable Adoption of Online Public Services: An Extended UTAUT analysis in North Macedonia

Data Analyses of Factors Influencing Sustainable Adoption of...

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5th international symposium on signal and image processing, ISSIP 2024

作者： Fetaji, Bekim Baftijari, Burim Fetaji, Majlinda Zdravev, Zoran Mother Teresa University Informatics Skopje Macedonia State University of Tetovo Informatics Tetovo Macedonia South East European University Informatics Tetovo Macedonia University Goce Delcev Informatics Stip Macedonia

ISBN: (纸本)9789819795147

With е-govеrnmеnt adoption lagging dеspitе substantial invеstmеnts, this study is motivatеd by thе nееd to undеrstand factors influеncing citizеns’ accеptancе of onlinе public sеrvicеs. It addrеssеs thе problеm that е-govеrnmеnt initiativеs in dеvеloping countriеs likе North Macеdonia suffеr from unsustainablе adoption. Extеnding thе parsimonious UTAUT modеl with additional contеxtual constructs, thе rеsеarch intеgratеs trust, risk, digital litеracy, and dеmographic factors as kеy barriеrs pеrcеivеd by citizеns. Examining the sustainable adoption of e-government in North Macedonia through the lens of the Unified theory of Acceptance and Use of Technology (UTAUT), while also extending the UTAUT model to better analyze government adoption in this context. In this way, thе study has bееn build on thе UTAUT еxtеnsions from thе analysеs of sustainablе adoption, appliеd into analysеs of е-govеrnmеnt adoption in thе contеxt of North Macеdonia, and addеd additional factors rеlеvant to this sеtting basеd on litеraturе rеviеw analysеs. Using survеy mеthodology and structural еquation modеling, thе analysis tеsts thе еxtеndеd UTAUT modеl’s ability to еxplain е-govеrnmеnt adoption bеhavior in thе undеrstudiеd contеxt of a transition еconomy. thе originality and novеlty liе in adapting a standard tеchnology accеptancе modеl to localizеd rеquirеmеnts and gеnеrating contеxt-spеcific rеcommеndations to promotе sustainablе е-govеrnmеnt assimilation. Ovеrall, thе study makеs timеly contributions both thеorеtically by dеmonstrating UTAUT’s applicability in nеw cultural sеttings and practically by informing policiеs to boost е-govеrnmеnt adoption. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information analysis

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Integrating Deep Learning Frameworks for Automated Medical image Diagnosis 8th

Integrating Deep Learning Frameworks for Automated Medical I...

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8th IFIP TC 12 international Conference on Computer, Communication and signal processing, ICCCSP 2024

作者： Gayatri, K. Kumar, K. M. Anand Padmavathi, B. Shankar Department of Computer Science and Engineering Easwari Engineering College Ramapuram Chennai600089 India

ISBN: (纸本)9783031736162

In today’s healthcare system, medical image analysis is essential for diagnosis, treatment planning, and condition monitoring. In this study, the four well-known tools in the field are 3D Slicer, MONAI, SAMM, and YOLOv8 are examined and contrasted. the study’s scope includes its ability to process a variety of 2D and 4D medical imaging datasets, which contain images, segmentation, annotations, and transformations. the process entails a comprehensive prehensive examination of every tool’s feature, with particular emphasis on their capacity to read and write DICOM images, accommodate multiple file formats, offer interactive 3D Slicer. Key findings show that 3D Slicer performs exceptionally well when using deep learning techniques for segmentation and interactive visualization. While SAM Model exhibits adaptability in managing diverse segmentation prompts, MONAI provides a comprehensive end-to-end medical data processing solution. YOLOv8 shows potential for effective object detection. the study’s implications include the possibility of using these instruments in clinical practices and medical research contexts. the research results provide insight into the current discussion about how to strategically integrate state-of-the-art methods to improve overall healthcare outcomes, diagnostic accuracy, and treatment planning efficiency as medical image analysis continues to advance. © IFIP international Federation for Information processing 2025.

关键词： Clinical research

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Application of AutoML, YOLOv5, and YOLOv8 in brain MRI tumor image recognition

Application of AutoML, YOLOv5, and YOLOv8 in brain MRI tumor...

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5th international Conference on signal processing and Machine Learning (CONF SPML 2025)

作者： Yunhao Song Henan Polytechnic University No. 142 Jiefang Middle Road Jiaozuo City Henan Province People's Republic of China

ISBN: (数字)9781837243143

With the rapid development of machine learning, particularly deep learning, the automatic recognition and classification of brain tumors have become a significant focus of research in medical image analysis. However, due to the complex morphology and indistinct boundaries of brain tumors, traditional manual annotation methods are time-consuming and prone to errors, highlighting the need for more intelligent diagnostic tools. this study compares the effectiveness of applying AutoML and YOLO series models (YOLOv5 and YOLOv8) in brain tumor recognition, aiming to evaluate the performance of each model and provide insights for improving diagnostic accuracy and efficiency. the results indicate that YOLOv8 outperforms the other models in terms of precision, recall, and F1-score, making it highly suitable for high-precision applications. YOLOv5, with its lightweight design, offers advantages in inference speed. While EasyDL performs slightly lower in certain metrics, its high precision and ease of use make it a competitive option in specific *** main contribution of this paper lies in not only verifying the high feasibility of the emerging AutoML technology with weaker requirements on expert knowledge in the field of MRI image recognition for brain tumors, but also providing a detailed comparative analysis that can serve as a reference for medical practitioners to flexibly choose suitable model algorithms based on their cost, demand, etc. conditions.

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An Efficient image Denoising Using Convolutional Neural Network 6th

An Efficient Image Denoising Using Convolutional Neural Netw...

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6th international conference on communication and computational technologies, ICCCT 2024

作者： Bodhale, Vaishali Vijayalakshmi, M. Chopra, Shalu Vivekanand Education Society’s Institute of Technology Sindhi Society Chembur Mumbai India

ISBN: (纸本)9789819774258

Pre-processing plays an essential role in any image processing application. the quality of features is a function of how well the input images are presented to the feature-extracting module or a classifying network. A clean image produces a non-redundant, distinct feature set that assists a classifier system. A variety of noises influence an image due to acquisition systems, interference, and storage. image denoising, thus being an essential and crucial concern, removes unwanted artefacts from significant details of the image. Autoencoder and Decoder constructed using deep-learned convolutional layers can de-noise images with higher performance metrics. the work in this paper proposes a 7-layer convolutional network (Autoencoder-Decoder) for eliminating noise contents from test images. the images are from the CIFAR-10 dataset available with the Kaggle data store. the original image is scrambled, encoded using a random key, and subjected to Gaussian noise at the transmitter. It is further decoded with the same key to obtain a noisy image. the noisy images from the input to the convolutional neural network (CNN) and the original images as the targets. the network is trained using 4000 CIFAR-10 images and tested using 1000 images. Experimental analysis showed remarkable results in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). the average metrics over 10 images showed that the PSNR is about 34.861, and the recovered image retains the original image up to 0.996% (SSIM). © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autoencoder CIFAR-10 Convolutional layers Convolutional network Deep-learned Gaussian noise image denoising PSNR SSIM

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Chaos theory Based Gravitational Search Algorithm For Medical image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

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27th international Conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. the effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. the quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. the findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： signal to noise ratio

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