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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

Computer Modeling in Engineering & sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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Advances in Robust Federated Learning: A Survey With Heterogeneity Considerations

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IEEE Transactions on Big Data 2025年第3期11卷 1548-1567页

作者： Chen, Chuan Liao, Tianchi Deng, Xiaojun Wu, Zihou Huang, Sheng Zheng, Zibin Sun Yat-sen University School of Computer Science and Engineering Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Sun Yat-sen University School of Software Engineering Zhuhai519000 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Systems Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Software Engineering Zhuhai Key Laboratory of Trusted Large Language Models Zhuhai519000 China

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and collaboratively train models across multiple clients with different data distributions, model structures, task objectives, computational capabilities, and communication resources. This diversity leads to significant heterogeneity, which increases the complexity of model training. In this paper, we first outline the basic concepts of heterogeneous FL and summarize the research challenges in FL in terms of five aspects: data, model, task, device and communication. In addition, we explore how existing state-of-the-art approaches cope with the heterogeneity of FL, and categorize and review these approaches at three different levels: data-level, model-level, and architecture-level. Subsequently, the paper extensively discusses privacy-preserving strategies in heterogeneous FL environments. Finally, the paper discusses current open issues and directions for future research, aiming to promote the further development of heterogeneous FL. © 2015 IEEE.

关键词： Federated learning

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 IEEE.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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Prediction of New Functional Fluorooxoborates with Expected Anionic Framework:A Case of CaB_(3)O_(5)F

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Chinese Physics Letters 2025年第3期42卷 85-92页

作者： Ke-Wang Zhang Wen-Qi Jin Ke Li Abudukadi Tudi Lin-Lin Liu Cong-Wei Xie Yu Xie Research Center for Crystal Materials CAS Key Laboratory of Functional Materials and Devices for Special Environmental ConditionsXinjiang Key Laboratory of Functional Crystal MaterialsXinjiang Technical Institute of Physics&ChemistryCASUrumqi 830011China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Materials Simulation Methods&Software of Ministry of Education International Center of Future ScienceKey Laboratory of Physics and Technology for Advanced Batteries(Ministry of Education)College of PhysicsJilin UniversityChangchun 130012China

The prediction of new fluorooxoborates as ultraviolet(UV)/deep ultraviolet(DUV)opto-electronic functional materials from a largely unexplored chemical space is a challenging *** has been suggested that the anionic frameworks formed by B–O and B–O–F units significantly determine the physical properties of ***,the rational design of anionic frameworks could facilitate the materials discovery ***,we propose that a candidate anionic framework can be efficiently derived from an existing one by slightly altering its oxygen *** this idea,we hypothesized the existence of a 1D[B_(3)O_(5)F]_(∞)chain from the wellknown 2D[B_(6)O_(9)F_(2_)]_(∞)***,seven CaB_(3)O_(5)F structures with the expected anionic framework were successfully ***-principles calculations show that all these structures have potential in the UV/DUV birefringent or nonlinear optical(NLO)material field,indicating that the 1D[B_(3)O_(5)F]_(∞)chain is indeed a promising anionic framework for achieving UV/DUV birefringent and NLO performance.

关键词： process borate anionic

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DLUE: Benchmarking Document Language Understanding 23rd

DLUE: Benchmarking Document Language Understanding

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23rd China National Conference on computational Linguistics, CCL 2024

作者： Xu, Ruoxi Lin, Hongyu Guan, Xinyan Sun, Yingfei Sun, Le University of Chinese Academy of Sciences Beijing101408 China Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819783663

Understanding documents is central to many real-world tasks but remains a challenging topic. Unfortunately, there is no well-established consensus on how to comprehensively evaluate document understanding abilities, which significantly hinders the fair comparison and measuring the progress of the field. To benchmark document understanding researches, this paper summarizes four representative abilities, i.e., document classification, document structural analysis, document information extraction, and document transcription. Under the new evaluation framework, we propose Document Language Understanding Evaluation – DLUE, a new task suite which covers a wide-range of tasks in various forms, domains and document genres. We also systematically evaluate six well-established transformer models and representative LLMs on DLUE, and find that due to the lengthy content, complicated underlying structure and dispersed knowledge, document understanding is still far from being solved in complex real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information retrieval

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An entropy-based measure of fork diversity and its correlations with open source software projects’ received contributions

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Empirical software Engineering 2025年第4期30卷 1-47页

作者： Wu, Xiangchen Wang, Liang Zheng, Zhiwen Sang, Baihui Zhang, Jierui Tao, Xianping State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The fork-and-pull-based method is an important way for open-source software (OSS) projects to receive contributions. In this study, we introduce a novel metric called fork entropy, inspired by biodiversity, to measure the diversity of OSS projects’ forks beyond their simple counts. Based on Rao’s quadratic entropy, the metric measures the diversity of forks in changing project files. We validate the proposed metric through empirical studies on 102 OSS projects from the Github and Gitlab platforms. The results show significant correlations between the fork entropy of a project and its contributions received with respect to external productivity, acceptance rate of external pull requests, and number of reported bugs. Our findings also reveal significant interactions between fork entropy and other factors, such as the number of forks. Furthermore, the time-shift correlation suggests that the historical impact of the fork entropy, along with other control variables, remains effective for up to twenty months. Based on these insights, we propose to predict a project’s received contributions using fork entropy and other control variables with both a classic linear ARMAX model (Autoregressive Moving Average with Exogenous Variables) and a deep, Transformer-based prediction model. Compared to making predictions using only current data, the models show improved performance in terms of higher prediction accuracy and faster convergence by including historical data. In summary, this work presents a comprehensive study on the correlations and temporal dependencies between the diversity of an OSS project’s forks, measured by the proposed fork entropy, and its received contributions. These findings provide insights for project maintainers and contributors to comprehend and coordinate their forking practices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Correlation analysis Empirical studies Fork diversity measure Open source software

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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COVID-19 Prediction Model Empowered with Fused computational Intelligence Technique

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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DevDynaP: A Dynamic Local Community Detection Algorithm

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SN Computer science 2025年第4期6卷 1-11页

作者： Bakhtar, Sahar Harutyunyan, Hovhannes A. Department of Computer Science and Software Engineering Concordia University Montreal Canada

Recent years have witnessed the rapid growth of social network services. Real-world social networks are huge and changing over time. Consequently, the problems in this area have become more complex. Community detection is one of the most important problems in social networks. A good community can be defined as a group of nodes that are highly connected to each other and loosely connected to the nodes outside the community. Regarding the fact that social networks are huge in size, having complete information about the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. However, the problem of community detection in dynamic networks is well-investigated, the local community detection is not widely addressed by researchers in dynamic networks. In this paper, a dynamic local community detection algorithm is proposed. The purpose of the proposed algorithm is to explore the network as fast as possible and detect communities as well. Experimental results show that the proposed algorithm discovers the unknown parts of networks faster than similar algorithms. Also, the detected communities by the proposed algorithm outperform that of the compared algorithm in different snapshots. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dynamic social network Geodesic distance metric Local community detection Modularity

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Scaling molecular dynamics for large-scale simulation of biological systems on AMD CPU/GPU supercomputers: Lessons from LUMI: Optimizing GENESIS for maximizing the computational efficiency of CPU and GPU kernels on the LUMI supercomputer 25

Scaling molecular dynamics for large-scale simulation of bio...

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8th International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2025

作者： Ugarte La Torre, Diego Jung, Jaewoon Sugita, Yuji Computational Biophysics Research Team Riken R-CCS Kobe Japan Theoretical Molecular Science Laboratory Riken Cpr Wako Japan Laboratory for Biomolecular Function Simulation Riken Bdr Kobe Japan

ISBN: (纸本)9798400713354

This paper presents optimizations of the GENESIS molecular dynamics (MD) software for LUMI. GENESIS is a state-of-the-art MD program capable of simulating large-scale biological systems, yet it requires significant optimizations and investigations to fully utilize the computational resources of the LUMI-G hardware partition of LUMI, a hybrid architecture featuring AMD EPYC processors and AMD Instinct GPUs. Our work focuses on (1) addressing CPU-GPU communication bottlenecks, (2) tuning electrostatic interaction calculations using the Particle Mesh Ewald (PME) method, and (3) adapting GENESIS kernels for the AMD GPUs. A significant improvement in the MD performance is achieved through the NUMA mapping on each LUMI-G node. This improvement is shown in benchmark results for different molecular systems comprising from 2×104 to 1.6×107 atoms. The reciprocal-space calculation in the PME method on LUMI-G is faster than that on FUGAKU. Since the reciprocal-space calculation is carried out using only CPUs on both LUMI-G and FUGAKU, the results highlight the superior network performances on the former. The overall performance of a large system containing 1.6×107 atoms shows 200.8 ns/days when using 1024 nodes on LUMI-G, which is a better performance than previously reported results using general-purpose supercomputers. We conclude by providing recommendations for the optimal configurations of GENESIS on LUMI-G, including guidelines for selecting PME schemes for different system sizes and computational requirements. This optimization allows us to study cellular systems' stability, dynamics, and function relationships using atomistic MD simulations on a large scale. © 2025 Copyright held by the owner/author(s).

关键词： Supercomputers

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