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FedLRes: enhancing lung cancer detection using federated learning with convolution neural network (ResNet50)

Neural Computing and Applications 2025年第14期37卷 8273-8284页

作者： Usharani, C. Selvapandian, A. Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering Gnanamani College of Technology Namakkal India

Lung cancer remains a leading global cause of mortality, necessitating efficient early detection. Lung cancer image analysis plays a pivotal role, yet current manual segmentation by oncologists is laborious. Our innovative (FedLRes) approach proposes a comprehensive system for automated diagnoses of lung cancer using federated learning with ResNet50. The lung cancer dataset from the Iraq-Oncology Teaching Hospital/National Centre for Cancer Diseases was gathered by the IQ-OTH/NCCD. The dataset consists of 1097 images, split into training (822 images) and validation (275 images) sets. The training set includes 312 normal, 420 malignant, and 90 benign cases. The training set's data are further enhanced by using data augmentation techniques, which are then applied to normalise images differences before being fed into the federated learning with ResNet50 architecture. This approach combines deep learning models trained on different datasets allowing for improvising accuracy and generalisation. The federated learning approach enables the use of distributed data while ensuring data privacy and security. The proposed approach compared with different state of art algorithms. Through rigorous experimentation, our system showcases remarkable advancements a classification accuracy of 99.40%. This innovative approach, utilising 3D input CT scan data, offers a potent and precise tool for early detection and effective treatment strategies against the scourge of lung cancer. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Revisiting SGX-Based Encrypted Deduplication via PoW-Before-Encryption and Eliminating Redundant Computations

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2037-2053页

作者： Ha, Guanxiong Ge, Xiaowei Jia, Chunfu Chen, Yuchen Su, Zhen Nankai University College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Dissec Tianjin300350 China

Encrypted deduplication is attractive for outsourced storage as it provides both data confidentiality and storage savings. Conventional encrypted deduplication schemes protect data confidentiality based on expensive cryptographic primitives, leading to performance degradation. Recently, several SGX-based schemes have been proposed to accelerate encrypted deduplication. However, these schemes have limitations in both security and performance aspects. This paper presents a SGX-based basic scheme to address these limitations, which first performs proof of ownership (PoW), followed by key generation and data encryption, realizing a new paradigm known as PoW-before-encryption (PbE) to solve the security issue in existing schemes. Additionally, the basic scheme implements deduplication-before-encryption (DbE) to reduce redundant computations, thus improving performance. Despite these improvements, the duplicate detection and key generation in the basic scheme still involve redundant computations. Consequently, we propose an epoch-based enhanced scheme that utilizes data locality and computation deduplication, which caches fresh computations in an epoch and reuses them to enhance performance. We provide a security analysis and evaluate the performance of our schemes using both synthetic and real-world workloads. The results demonstrate that our schemes offer stronger security guarantees while outperforming state-of-the-art schemes in terms of performance. © 2024 IEEE.

关键词： Cryptography

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Prompt Learning for Few-Shot Question Answering via Self-Context data Augmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 589-603页

作者： Qiu, Jian-Qiang Zhang, Chun-Yang Chen, C. L. Philip Fuzhou University College of Computer and Data Science Fuzhou350108 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China

Pretrained language models (PLMs) have shown remarkable performance on question answering (QA) tasks, but they usually require fine-tuning (FT) that depends on a substantial quantity of QA pairs. Therefore, improving the performance of PLMs in scenarios with only a small number of training examples, also known as a few-shot setting, is of great practical significance. Current mitigation strategies for the few-shot QA task largely rely on pretraining a QA task-specific language model from scratch, overlooking the potential of foundational PLMs to generate QA pairs, particularly in the few-shot setting. To address this issue, we propose a prompt-based QA data augmentation method aimed at automating the creation of high-quality QA pairs. It employs the PFT method, adapting the question generation process of PLMs to the few-shot setting. Additionally, we introduce a dynamic text filling training strategy. This strategy simulates the progressive human learning process, thereby alleviating overfitting of PLMs in the few-shot setting and enhancing their reasoning capability to tackle complex questions. Extensive experiments demonstrate that the proposed method outperforms existing approaches across various few-shot configurations. 2691-4581 © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://***/publications/rights/*** for more information.

关键词： Zero-shot learning

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kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

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Computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

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Moving Average-Based Variable Projection for Separable Nonlinear Problems

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IEEE Signal Processing Letters 2025年 32卷 1900-1904页

作者： Xue, Peng Gan, Min Yuan, Fang Chen, Guang-Yong Chen, C. L. Philip Fuzhou University College of Computer Science and Big Data Fuzhou350116 China College of Computer Science and Technology Qing dao University Qingdao266071 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China

The identification of separable nonlinear models, prevalent in tasks such as signal analysis, image processing, time series analysis, and machine learning, presents a non-convex optimization challenge that necessitates the development of efficient identification algorithms. The Variable Projection (VP) algorithm has been proven to be quite effective for addressing these problems;however, traditional VP relying on the Hessian matrix and its inverse are highly time-consuming and unsuitable for complex, large-scale applications. This letter introduces a novel approach that employs the exponential moving average of gradient and gradient estimation bias to indirectly estimate the curvature of the objective landscape, proposing a Moving Average-based Variable Projection method (MAVP). The proposed algorithm utilizes only gradient information and can properly tackle the coupling relationships between different parameters during the optimization process, thereby achieving faster convergence. Numerical results on nonlinear time series analysis and image reconstruction demonstrate that the MAVP algorithm exhibits significant efficiency and effectiveness. © 1994-2012 IEEE.

关键词： Convex optimization

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Peephole Optimization Strategies for Intermediate Code in the Compilation Process 1

Peephole Optimization Strategies for Intermediate Code in th...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Guan, Yunxin Yun, Jing Sun, Pengfei Su, Xiaoming College of Data Science and Application Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9781643685779

In the design of modern compilers, the generation and optimization of intermediate code play a crucial role. Serving as a bridge between source code and target machine code, intermediate code provides ample opportunities for optimization. This paper focuses on porthole optimization techniques, proposing a new optimization strategy aimed at reducing unnecessary computations and memory accesses by analyzing control and data flows within intermediate code, thereby enhancing program execution efficiency. We first outline the various stages of the compilation process, followed by a detailed exploration of the basic principles and implementation methods of porthole optimization. Finally, we validate the effectiveness and advantages of this optimization strategy through experiments. The experimental results indicate significant improvements in the execution performance of intermediate code after the adoption of porthole optimization, providing new insights and directions for compiler optimization. © 2025 The Authors.

关键词： Program compilers

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Sun, Yaqi Yun, Jing Ma, Zhuoqun College of Data Science and Application Inner Mongolia University of Technology Huhhot China

ISBN: (纸本)9789819783663

There is a continuous and explosive growth of multimodal data. Efficient cross-modal hashing retrieval is of significant importance in conserving computational *** further enhance the attention to informative data within modalities and capture the semantic correlations in cross-modal data, we propose an enhanced deep Joint-Semantics Reconstructing Hashing algorithm, which is the Joint Similarity Guidance Hash Coding Based on Adaptive Weight Mixing Strategy(JSGHCA). The algorithm focuses on delving deeper into the correlations of the data in cross-modal. We introduce the adaptive weight mixing strategy to construct the semantic affinity matrix, so that the matrix can identify each modal data with specific weight in each batch. At the same time, in the process of the hash code generation, we introduce collaborative attention mechanism. It helps the model to pay more attention to the local information of each modality, thereby capturing the semantic features within each modality more accurately. Additionally, it enables the model to jointly process the attention across different modalities and extract shared semantic features more precisely. Experimental results show that the proposed model is significantly better than the deep joint semantic reconstruction hash algorithm on multiple benchmark datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Matrix algebra

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Cross-chain Transaction of Notaries Based on Two-stage Improved PageRank Algorithm

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International Journal of Network Security 2025年第1期27卷 95-103页

作者： Fan, Yuqing Li, Juyan College of Data Science and Technology Heilongjiang University No. 74 Xuefu Road Nangang Heilongjiang Harbin150080 China

The rapid advancement of blockchain has influenced numerous industries, playing a pivotal role. However, its drawbacks have gradually emerged. Different blockchains cannot interoperate, leading to "data islands." This article first optimized the PageRank sorting notary election method and then proposed a two-stage transaction process (Prepare and formal phases), using incentive mechanisms to maintain node vitality, achieving interoperability between different blockchains, and solving the problem of "data island."Finally, experiments have shown that compared to the other algorithms, the improved PageRank algorithm reduces the score difference between high-scoring old nodes and other nodes, and the score of new nodes is also significantly improved. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

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Research in Astronomy and Astrophysics 2025年第3期25卷 236-248页

作者： Zhen-Hong Shang Long Chen Zhen-Ping Qiang Yi Bi Run-Xin Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Observatories Chinese Academy of Sciences

Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.

关键词： methods: data analysis Sun: fundamental parameters techniques: image processing

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Clinically Robust Polyp Segmentation: Enhanced Generalization and Perturbation Resistance

Clinically Robust Polyp Segmentation: Enhanced Generalizatio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Shanchuan Yuan, Fang Su, Jian-Nan Gan, Min College of Computer Science and Technology Qingdao University Qingdao China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350368741

Colonoscopy is vital for detecting colorectal polyps, which are closely linked to colorectal cancer. Accurate segmentation of polyps in colonoscopic images is essential for diagnosis and surgical planning but is challenging due to variability in polyp size, shape, and unclear boundaries. The Segment Anything Model (SAM) has shown promise in polyp segmentation but relies heavily on user-provided prompts and involves a large number of parameters, limiting its practicality in clinical settings. To address the limitations of SAM in clinical practice, we introduced Low Rank and Perturbation Segment Anything Model (LP-SAM) to improve segmentation accuracy and generalization ability while reducing the parameter count and complexity of user input. LP-SAM showed enhanced generalization and a lightweight design, making it more suitable for clinical applications where precise user input may not always be feasible. Comparative evaluations demonstrate that LP-SAM outperforms state-of-the-art methods on datasets such as CVC-ColonDB, CVC-300, and ETIS. © 2025 IEEE.

关键词： Low-rank Adaptation Perturbation Resistance polyp segmentation Segment Anything Model

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