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A Fuzzy C-Means Clustering Algorithm for Real Medical Image Segmentation

A Fuzzy C-Means Clustering Algorithm for Real Medical Image ...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Feifei Zhang Fei Shi Dayong Ren Yue Li School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of medical image processing, the presence of noise can often result in the obfuscation of crucial details, which in turn can have a detrimental impact on the accuracy of clinical diagnoses. In order to effectively remove noise and improve segmentation performance, this paper proposes a refined Fuzzy C-Means (FCM) algorithm, designated as MKL-FCM. The method commences with Poisson denoising, which enables more effective handling of the noise characteristics inherent to medical images. Subsequently, multi-scale Kullback-Leibler divergence is utilised to analyse local image information across varying scales, facilitating the differentiation between tissues and pathological regions. Furthermore, tight wavelet frames are capable of capturing fine image details, while reverse optimisation of the objective function serves to correct errors in feature reconstruction, thereby enhancing the accuracy of the segmentation process. The experimental results demonstrate that MKL-FCM enhances both image clarity and segmentation accuracy, and outperforms existing methods in terms of efficiency.

关键词： Image segmentation Accuracy Noise reduction Noise Signal processing algorithms Clustering algorithms Linear programming Partitioning algorithms Speech processing Medical diagnostic imaging

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Feature Disentangling Dual-stream Network for User Bias Alleviation in Social media Prediction

Feature Disentangling Dual-stream Network for User Bias Alle...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wenhao Hu Weilong Chen Weimin Yuan Xiaolu Chen Han Yang Yanru Zhang Zhu Han College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China University of Houston Houston USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Social media popularity prediction is increasingly crucial for optimizing user engagement and guiding content recommendation systems. However, existing methods suffer from an excessive reliance on user information, which disproportionately influences predictions and leads to the neglect of content diversity. This oversight results in user bias, which adversely impacts the accuracy of predictions. In this paper, an approach named Feature Disentangling Dual-Stream Network (FDDN) is introduced to address this gap. In FDDN, we introduce the Multimodal Extraction Module to extract content features from different modalities. Additionally, the User Popularity Extraction Module helps to analyze the impact of user features on popularity. The Disentangled Adaptation Module distinguishes the impact of user features from content features, thus alleviating user bias and ensuring a more comprehensive prediction. Extensive experiments on a large public dataset demonstrate the robustness and effectiveness of our approach, indicating superior performance compared to existing methods.

关键词： Micromechanical devices Social networking (online) Dams Signal processing media Feature extraction Robustness Cultural differences Speech processing Recommender systems

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Implementation of Morphological Fractional Order Darwinian Operator for Brain Tumour Localization 9th

Implementation of Morphological Fractional Order Darwinian O...

引用

9th international conference on information, Communication and Computing technology, ICICCT 2024

作者： Ansah, Kwabena Adevu, Wisdom Benedictus Mensah, Joseph Agyapong Appati, Justice Kwame Department of Computer Science University of Ghana Accra Ghana Department of Computer Science and Information Systems Ashesi University Berekuso Ghana

ISBN: (纸本)9783031724824

Over time, Magnetic Resonance Imaging (MRI) has played a pivotal role in accurately delineating brain cancers, facilitating diagnostic processes for medical practitioners. However, the inherent subjectivity in human interpretation presents challenges, prompting the exploration of machine learning models to augment diagnostic guidance. Despite their advantages, these models can be susceptible to errors depending on their objectives. This study addresses segmentation errors induced by data capture noise by introducing the morphological fractional order Darwinian particle swarm optimization (M-FODPSO) approach. Leveraging classical discrete wavelet transform and principal component analysis, pertinent features are extracted from M-FODPSO outputs, subsequently trained on an ensemble classifier. Performance evaluation demonstrates an impressive accuracy of 97.03%, achieved with an average processing time of 1.7161 s, thereby showcasing enhanced tumor cell characterization compared to the classical FODPSO method. This approach effectively mitigates segmentation errors attributed to data noise, underscoring its potential for refining MRI-based brain cancer diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Discrete wavelet transforms

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Harnessing AI for Agriculture: Oyster Mushroom Disease Detection with IoT and Web Application on Growing Bags Using Deep Learning

Harnessing AI for Agriculture: Oyster Mushroom Disease Detec...

引用

international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Wongpanya S. Nuankaew Phacharapol Sombutthai Watchrapong Monkhuan Thapanapong Sararat Pratya Nuankaew School of Information and Communicatin Technology University of Phayao Phayao Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This research develops an innovative AI-driven system for detecting oyster mushroom green mold disease using deep learning, Convolutional Neural Networks (CNNs), and IoT technologies. By implementing advanced image processing techniques and data augmentation, the study creates a comprehensive disease monitoring solution with real-time alerts for disease detection, humidity, and temperature variations. The system utilizes the DenseNet201 algorithm, achieving 92.50% accuracy in detecting mushroom diseases. The IoT-enabled and web application enables real-time monitoring, allowing farmers to promptly identify and address potential disease outbreaks, ultimately improving cultivation efficiency and reducing yield losses.

关键词： Deep learning Limiting Accuracy Image processing Green products Data collection Real-time systems Artificial intelligence Monitoring Diseases

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Evaluating Large Language Models: Challenges, Limitations, and Future Directions

Evaluating Large Language Models: Challenges, Limitations, a...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Li-Hua Li Agus Cahyo Nugroho Department of Information Management College of Informatics Chaoyang University of Technology Taichung Taiwan

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Large Language Models (LLMs) have transformed Natural Language processing (NLP) by providing previously unheard-of capabilities in text production, translation, and comprehension. Large Language Models have advanced rapidly. Nevertheless, there are several obstacles and restrictions associated with the implementation and assessment of these models. This study offers a thorough analysis of the situation of LLMs today, with an emphasis on the methods for assessing their effectiveness and significance. Key issues like prejudice, moral dilemmas, scalability, and transparency are noted, and the shortcomings of the current frameworks for evaluation are also covered. Our goal in examining these problems is to identify the areas that require improvement. In addition, we suggest possible direction for future investigation, stressing the importance of inclusive datasets, improved interpretability, and stronger assessment criteria. In addition to guiding the creation of more morally and effectively constructed AI systems, our mission is to promote a greater understanding of LLMs.

关键词： Measurement Ethics Translation Large language models Scalability Training data Production Natural language processing Safety Testing

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Identification of Negative Intent in Social media Posts Using Natural Language processing

Identification of Negative Intent in Social Media Posts Usin...

引用

Computational Intelligence, Communication technology and Networking (CICTN), international conference on

作者： Priti Tiwari Parul Verma HarshDev AIIT Amity University Lucknow India Computer Science and Engineering Department Babu Banarasi Das University Lucknow India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Daily content viewing and/or posting on social media is very common and influences people across all geographies using numerous strategies and ways. A number of daily activities over web platforms like YouTube in terms of posting, watching, and commenting on videos. This is a report that contains a quickly expanding number of online channels where users post, jot down, and comment on a wide range of topics. NLP is applied to quickly take information from online or web content, that will facilitate your business in very different and numerous ways. Customer-specific data can be easily extracted using NLP, and this data can then be used to create advertising campaigns that are specifically targeted to your customers. This assists in introducing your company to the appropriate target market, which is more likely to convert. The paper will perform the critical analysis of versatile methodologies adapted by various researchers for negative intent identification in social networking sites. The paper will also try to observe the challenges associated with the same. This paper will also analyze the usage of NLP in identifying the negative intent in social media posts.

关键词： Geography Sentiment analysis Accuracy Video on demand Text analysis Social networking (online) Machine learning Natural language processing Web sites Videos

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Maintaining Privacy in Contact Tracing Systems for Pandemics Through Edge Computing

Maintaining Privacy in Contact Tracing Systems for Pandemics...

引用

AIML-Applications for Engineering & technology (ICAET), international conference on

作者： Virupakshi Tejaswini NageswaraRao Sirisala Department of Computer Science and Engineering K.S.R.M. College of Engineering Kadapa A.P India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Data processing and publication are crucial today with the advancements in technologies such as edge computing and IoT(Internet of Things). A vast volume of data is being generated by internet-connected devices, often on a continuous basis. The COVID-19 pandemic data exemplifies this trend of constant data flow, as the global medical emergency has necessitated ongoing data collection. This data must be processed and analyzed, and subsequently published, while ensuring personal privacy is upheld. Edge computing devices can handle various tasks like data preprocessing and analysis in any setting. It is crucial to process data before uploading it to the cloud and sharing it with advisories or media outlets, as improper handling could lead to privacy breaches. To protect personal privacy, measures such as differential privacy techniques are implemented to protect data. Edge computing technologies offer several advantages, including efficient data processing close to the origin to minimize internet data transmission capacity use and enhanced security for confidential information on public cloud infrastructures.

关键词： Cloud computing Differential privacy Pandemics Refining media Privacy breach Security Robots Edge computing Testing

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SPRGAN: Streamlined Progressive Refinement for Adversarial Point Cloud Video Upsampling

SPRGAN: Streamlined Progressive Refinement for Adversarial P...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhiyong Zhang Ruyu Liu Chaochao Wang Xianchao Zhang Jianhua Zhang Xiufeng Liu School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Information Science and Technology Hangzhou Normal University Hangzhou China Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Zhejiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Getting dense, uniform, time-series point cloud data is critical for effective rendering. However, due to the limited computational power of edge devices, existing methods cannot achieve real-time results, which affects the visual quality of the consumer experience. To effectively address this issue, this paper presents a self-supervised adversarial upsampling method for point cloud video streams called SPR-GAN. In the generator, we design the Temporal Iterative Graph module to learn local features for each frame and captures long-range spatial information using three iterations of graph convolution operations. Then the Contextual Temporal Fusion module is developed to merge information between different frames, synthesizing temporal information and enriching the dynamic feature representation of the point cloud. Meanwhile, in the discriminator, we introduce the Efficient Shape module. Through dynamic graph convolution operations and stacked learning, it significantly improves the resolution efficiency of global shape information in point clouds. The final experiments show that the proposed method exhibits high practicality and superiority. The model achieves a good result on both the D-FAUST and DeformingThing4D-Animals datasets.

关键词： Point cloud compression Visualization Three-dimensional displays Shape Convolution Streaming media Aerodynamics Rendering (computer graphics) Speech processing Streams

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Harnessing AI for Agriculture: Applying Deep Learning Approaches to Detect and Classify Native Thai Vegetables in Specific Areas of Thailand

Harnessing AI for Agriculture: Applying Deep Learning Approa...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Wongpanya S. Nuankaew Kittiphob Saibunyang Rattanawan Hongmo Thapanapong Sararat Pratya Nuankaew School of Information and Communicatin Technology University of Phayao Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This research applies deep learning to classify native Thai vegetables, focusing on ten species from Northern Thailand. Using a dataset of 2,400 images collected from Phayao Province, six Convolutional Neural Network (CNN) models — MobileNetV1, MobileNetV2, Xception, DenseNet, DenseNet121, and NASNetMobile—were evaluated for performance, with MobileNetV1 achieving the highest accuracy at 98.50%. Its lightweight design and efficiency make it ideal for mobile applications. A prototype app was developed to identify vegetables, share nutritional information, and suggest traditional recipes. This initiative promotes biodiversity conservation, healthier diets, and local economic growth. While challenges such as data collection and model refinement persist, this study highlights the potential of technology in preserving cultural heritage and supporting sustainable agriculture.

关键词： Training Deep learning Accuracy Biological system modeling Image processing Lighting Focusing Agriculture Data models Testing

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OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation

OCTAMamba: A State-Space Model Approach for Precision OCTA V...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shun Zou Zhuo Zhang Guangwei Gao College of Artificial Intelligence Nanjing Agricultural University Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China College of Computer Science and Technology National University of Defense Technology Changsha China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor image quality and eye lesions. In this study, we proposed OCTAMamba, a novel U-shaped network based on the Mamba architecture, designed to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications. Extensive experiments on the OCTA 3M, OCTA 6M, and ROSSA datasets demonstrated that OCTAMamba outperforms state-of-the-art methods, providing a new reference for efficient OCTA segmentation. Code is available at https://***/zs1314/OCTAMamba

关键词： Optical filters Image segmentation Visualization Convolution Noise Streaming media Feature extraction Retina State-space methods Speech processing

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