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Progressive Multi-channel Fusion Network for Myocardial Pathology Segmentation on Multi-modality CMR Images 1st

Progressive Multi-channel Fusion Network for Myocardial Pat...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th international conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Wang, Jingyan Gong, Xiaojuan Jin, Tangruoyi Gao, Ruikun Zheng, Shaohua Chen, Weisheng College of Physics and Information Engineering Fuzhou University Fuzhou350108 China Maynooth International Engineering College Fuzhou University Fuzhou350108 China Department of Thoracic Surgery Clinical Oncology School of Fujian Medical University Fujian Cancer Hospital FJ Fuzhou350108 China

ISBN: (纸本)9783031870088

Cardiac scarring and edema are critical pathological features of heart diseases. Accurate segmentation of these features in Cardiac Magnetic Resonance (CMR) imaging is crucial for understanding the pathological changes associated with cardiac diseases. In the field of myocardial scar and edema segmentation, it is of significant importance to study the C0, T2, and LGE modalities. These modalities offer different perspectives on myocardial tissue characteristics, aiding in the more accurate diagnosis and assessment of cardiac diseases. However, the high-intensity features of scars and edema cannot be directly obtained from individual CMR imaging sequences, making simultaneous accurate segmentation challenging. To address this, we propose a multi-modal, multi-channel fusion interactive progressive segmentation strategy that leverages the distinctive properties of each modality and the surrounding tissue characteristics for the segmentation of myocardial scars and edema. We have designed a multi-channel fusion interactive progressive segmentation model, suitable for scar and myocardial segmentation, which incorporates an attention mechanism that enhances channel information interaction within a U-Net structure to extract features across different modalities. On the MyoPS++ 2024 public dataset, our method achieved an average Dice score of 0.5486 for scar segmentation and 0.6081 for the segmentation of both scars and edema. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Removing Regional Steering Vectors to Achieve Knowledge Domain Forgetting in Large Language Models 6th

Removing Regional Steering Vectors to Achieve Knowledge Dom...

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6th international conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Wu, Wei Wang, Chen Xu, Qiuhao Kong, Wei Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789819645657

Large language models (LLMs), as a significant direction in the development of artificial intelligence in recent years, are becoming increasingly popular. These models demonstrate significant potential in applications such as personalized services, simulated dialogues, role-playing, and specific compliance requirements. However, with the continuous expansion of LLMs’ practical applications, selective knowledge management of the models—particularly the ability to make the models "forget" certain domain-specific or topic-specific knowledge without impacting their overall performance or knowledge in other areas—has emerged as a critical issue that demands resolution. We proposes an innovative knowledge domain forgetting method designed to enable models to selectively forget specified knowledge without the need for fine-tuning. The method identifies and removes steering vectors associated with specific knowledge domains, thereby achieving effective forgetting of specific knowledge. The proposed approach has been evaluated in several popular open-source LLMs. Experimental results show that this method achieves good knowledge forgetting effects across diverse scenarios and exhibits notable practical value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Domain Knowledge

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Multi-keyword Searchable Data Auditing for Cloud-Based Machine Learning 6th

Multi-keyword Searchable Data Auditing for Cloud-Based Mach...

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6th international conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Yu, Haiyan Ma, Qingru Zhu, Yilu Cui, Yuxin Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789819645657

Machine learning (ML) relies on large amounts of data for training, which often requires outsourcing data to cloud servers. This raises concerns about data integrity and security. Preventing data from being tampered with or lost during storage and transmission has become a critical issue in the ML process. This paper presents a novel data auditing scheme for cloud-based ML, which supports multi-keyword search functionality. It allows searching one or more keywords to ensure the integrity of data in model training. To avoid data privacy leakage, the keywords are encrypted during the auditing process. Only the encrypted keywords of the data required for model training need to be provided to the third-party auditor (TPA). Subsequently, TPA can check and ensure the integrity of the relevant data. Through security analysis and performance evaluations, the results indicate that this scheme can effectively and reliably audit outsourced data while ensuring the security and privacy of model training data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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Advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution 2nd

Advances in smart farming for precision agriculture: Green-I...

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2nd international conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Digra, Monia Rajput, Preeti Sharma, Pooja Model Institute of Engineering and Technology J&K Jammu India School of Computer Science and Engineering Shri Mata Vaishno Devi University J&K Katra India

ISBN: (纸本)9781032738659

Agricultural development is crucial to ending poverty and boosting economic growth in emerging nations. The global population is expected to reach 9.7 billion by 2050. Smart and precise agriculture must enhance agricultural productivity without compromising quality, nutrition, or natural resources. IoT and AI/ML have significant uses in agriculture due to internet technologies. Although IoT has many benefits, the enormous number of networked devices may raise carbon emissions and e-waste due to energy consumption. Inspired by the goal of sustainable smart agriculture, this article addresses major results and research gaps in IoT-based smart agriculture and provides solutions for energy-saving Green-IoT (G-IoT) applications in agriculture. We start with G-IoT’s evolution and smart agriculture applications. We also examine how green architecture may efficiently apply AI/ML in smart agriculture. We propose an AI/ML G-IoT framework for smart agriculture with green sensor cloud and green AI. This unique G-IoT paradigm provides sustainable and energy-efficient agricultural architecture. We investigate statistical and experimental data and emphasise the limitations of adopting G-IoT and AI/ML architecture in agriculture. In conclusion, G-IoT and AI/ML architecture can enable sustainable, energy-efficient, smart precision agriculture. © 2025 The Author(s).

关键词： Energy utilization

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Comparative Analysis of PID and LQR Control for Autonomous Vehicle Platoons 3rd

Comparative Analysis of PID and LQR Control for Autonomous V...

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3rd international conference on Emerging Electronics and automation, E2A 2023

作者： Nandhini, M. Rabik, M. Mohamed Department of Mechatronics Engineering SRM Institute of Science and Technology KTR Tamil Nadu Chennai India

ISBN: (纸本)9789819768011

The advent of autonomous driving technology has spurred interest in autonomous vehicle platooning to enhance traffic efficiency and road safety. This research conducts a comparative analysis of two prominent control techniques, Proportional-Integral-Derivative (PID) control and Linear Quadratic Regulator (LQR) control, in the context of autonomous vehicle platooning. The study aims to assess the significance of these control strategies in achieving and maintaining desired inter-vehicle distances and overall platoon coordination. The analysis considers several critical factors, including control performance, adaptability, robustness, and continuity, to comprehensively evaluate PID and LQR controllers. While PID controllers offer simplicity and ease of tuning, LQR controllers are known for their precision and optimization capabilities. The research investigates how these control methods perform in various scenarios, considering factors such as changing vehicle dynamics and external disturbances. Consequently, this research aims to assess how effective PID and LQR control systems are in achieving and maintaining desired inter-vehicle distances and overall platoon coordination. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Two term control systems

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An Evaluation of Hashing and Other Applied Cryptography Algorithms 5th

An Evaluation of Hashing and Other Applied Cryptography Algo...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Haripriya Sharma, Apurva Thomas, Jobin S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

In the modern digital world, cryptography is critical in protecting personal statistics. Most of the cryptography algorithms used to ease data, hashing, and other implemented cryptography algorithms are extensively used. Hashing refers to transforming plaintext or every other form of statistics into unreadable ciphertext. This type of encryption is used primarily to store passwords, authentication records, and other touchy records. Other applied cryptography algorithms include symmetric encryption, uneven encryption, and key exchanges. In this paper, an assessment of hashing and different implemented cryptography algorithms is conducted. The evaluation will be conscious of each of those algorithms’ distinctive traits, strengths, weaknesses, and safety concerns. The review also appears in the scenarios wherein one-of-a-kind algorithms can be carried out. It is followed by comparing the one-of-a-kind algorithms about their overall performance, protection, and comfort. Numerous pointers are also discussed regarding the application of these algorithms. Finally, the paper concludes with a standard assessment of the multiple algorithms and their suitability in one-of-a-kind contexts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Distributed and Cooperative Security Mechanisms for Networked Applications 5th

Distributed and Cooperative Security Mechanisms for Networke...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kamalraj, R. Singh, Vaishali Bhambu, Pawan Wadhwa, Bhawna Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

Cutting-edge applications rely heavily on conversation via PC networks to provide improved functionality, multiplied interactivity, and more to get admission to sources. Cozy verbal exchange and gain control access should be enforced to shield information and different assets. Distributed and cooperative protection mechanisms enable exceptional components of a networked utility to collaborate for you to get the right of entry to resources responsibly, proportion records securely, and authenticate customers. Such mechanisms are regularly designed to offer automated authorization and authentication, secure conversation channels, and a cozy records garage. In addition, they usually include additives, which have users, roles, and permissions to assist in controlling access. Utility developers and IT directors can ensure that networked packages remain cozy no matter the ever-converting safety landscape by adopting allotted and cooperative safety mechanisms. Distributed and Cooperative security Mechanisms (DCSM) are community security mechanisms designed to provide a layered method to defend in opposition to cyber-attacks on networked applications. The use of DCSM permits a shift from traditional networks to an agile and resilient system structure that can react speedily to a capacity chance, mitigating the damaging effect of malicious attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer viruses

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Research on Face Recognition System Based on RLWE Homomorphic Encryption 6th

Research on Face Recognition System Based on RLWE Homomorphi...

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Wang, YuLin Huang, HaiLin Fang, ZiHao Zhao, YuQi Wang, JinHeng Guangzhou Institute of Science and Technology Guangdong Guangzhou510540 China

ISBN: (纸本)9783031736988

With the development of artificial intelligence face recognition technology is being widely used in various fields. However, the traditional face recognition system has serious security risks, face data is easy to leak, and privacy protection mechanism is insufficient. In order to solve this problem, a secure face recognition system based on RLWE homomorphic encryption algorithm in lattice cryptography is constructed. This method can directly process the face image in the encrypted domain, and realize the recognition function while protecting the security of biometric data. Experiments on CASIA-WebFace data set show that RLWE encryption algorithm is successfully embedded in the face recognition model based on ResNet-34, and the overall recognition accuracy of homomorphic encryption system reaches 97.4%, which is only 2% lower than that of plain text domain recognition. This proves the validity and application prospect of homomorphic encryption technology based on lattice cryptography in protecting the security of biometric information. However, the operation efficiency still needs to be improved. This study provides a valuable exploration path for homomorphic encryption technology to be applied to security biometric identification. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Biometrics

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Analyzing the Role of Time Series Analysis in Early Detection of Pancreatic Cancer 5th

Analyzing the Role of Time Series Analysis in Early Detectio...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kavitha, R. Singh, Yudhveer Madhusudhan, M.V. Mishra, Awakash Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Pancreatic cancer is a vital disorder that is still the number one motive of lack of lifestyle, with a five-12 months survival fee of only 9. Early detection of pancreatic cancers is fundamental to beautifying outcomes. However, it could be tough to stumble on based totally on scientific and imaging outcomes myself. Time collection evaluation offers a progressive technique to sources in the early detection of pancreatic cancer. This paper proposes to research time series analysis for analysis and early detection of pancreatic cancers. Time series evaluation is a powerful series of techniques for analyzing statistics and temporal records, biological alerts, or healthcare facts. It uses a sequence of observations taken through the years to assess patterns and traits in facts, which could tell osteoporotic fracture hazards or provide early sickness signs and symptoms. Previous research has assessed time series evaluation's efficacy in detecting cardiovascular ailments and influenza. However, there may be a shortage of research examining the application of time series evaluation for pancreatic cancer detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN 13

Tomato Leaf Disease Identification and Growth Dataset Augmen...

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13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Li, Xin Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Fruits

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