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A novel lightweight deep learning approach for simultaneousoptic cup and optic disc segmentation in glaucoma detection

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Mathematical Biosciences and Engineering 2024年第4期21卷 5092-5117页

作者： Song, Yantao Zhang, Wenjie Zhang, Yue Institute of Big Data Science and Industry Shanxi University Taiyuan030006 China School of Computer and Information Technology Shanxi University Taiyuan030006 China

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early ***-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it isdetermined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation oftheOC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted ingreat success on this task. However, most existing methods either have unsatisfactory segmentation accuracy orhigh time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation oftheOC and OD, where we haveadoptedfuzzy learning andamulti-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared tomost state-of-the-art approaches in terms of both training time and segmentation accuracy. © 2024the Author(s).

关键词： Multilayer neural networks

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Revocable Hierarchical Identity-based Inner Product Function Encryption in Smart Healthcare

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IEEE Internet of Things Journal 2025年第11期12卷 15319-15332页

作者： Zhu, Yasi Zhou, Yanwei Wang, Jing Yang, Bo Zhang, Mingwu Shaanxi Normal University School of Computer Science Xi'an710119 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hubei University of Technology School of Computer Wuhan430068 China

With the development of cloud computing and the digital transformation of the medical industry, the application scenarios and effects of smart healthcare are constantly expanding and improving. Smart healthcare plays an important role in improving service quality and medical efficiency and reducing medical costs. However, in the process of data collection, storage, and transmission, the risk of patients' privacy being illegally accessed or leaked has become increasingly prominent. These medical data contain sensitive information about patients, and once leaked, it will violate the privacy of patients, cause medical accidents, and seriously damage the legitimate rights and interests of patients. An efficient and reliable privacy protection mechanism is the foundation and key to establishing a harmonious doctor-patient relationship and improving medical quality. Although traditional encryption methods can provide certain information security protection, they cannot balance data protection and data analysis and processing and are not suitable for intelligent medical environments that require more precise medical decisions. Therefore, we propose a hierarchical identity-based inner product functional encryption scheme with a malicious user revocation mechanism aimed at addressing the privacy and security issues of patients in smart healthcare. This solution allows specific access and analysis of data while ensuring that patients' sensitive information is protected from infringement, enabling doctors to diagnose and treat more accurately and optimize the allocation of medical resources to the greatest extent possible. Furthermore, we have formally demonstrated the security of the scheme and conducted a security analysis and performance comparison. The results show that our solution has better performance while ensuring security and is suitable for the efficient and secure data processing requirements of smart healthcare. © 2014 IEEE.

关键词： Differential privacy

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A Credible and Fair Federated Learning Framework Based on Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 301-316页

作者： Chen, Leiming Zhao, Dehai Tao, Liping Wang, Kai Qiao, Sibo Zeng, Xingjie Tan, Chee Wei China University of Petroleum School of Computer Science and Technology East China Qingdao266580 China CSIRO's Data61 Sydney2015 Australia Hefei University of Technology Anhui230009 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Tiangong University School of Software Tianjin300387 China Southwest Petroleum University School of Computer Science Chengdu610500 China

Federated learning (FL) enables cooperative computation between multiple participants while protecting user privacy. Currently, FL algorithms assume that all participants are trustworthy and their systems are secure. However, the following problems arise in real-world scenarios: 1) Malicious clients disrupt FL through model poisoning and data poisoning attacks. Although some research has proposed secure aggregation methods to solve this problem, most methods have limitations. 2) The current method cannot fairly evaluate client contribution in some scenarios. Some clients exhibit free-rider behavior, seeking to cheat the reward system and manipulate global models. Evaluating client contribution and distributing rewards also present challenges. To address these challenges, we design a trustworthy federated framework to ensure secure computing throughout the federated task process. First, we propose a method of detecting malicious models to guarantee secure model aggregation. Then, we propose a fair method of assessing contribution to identify client-side free-riding behavior. Finally, we implement a computation process based on blockchain and smart contracts to guarantee the trustworthiness and fairness of federated tasks. To validate the performance of our framework, we simulate different types of client attacks and contribution evaluation scenarios on several open-source datasets. The experiments show that our framework ensures the credibility of federated tasks and achieves a fair evaluation of client contributions. © 2020 IEEE.

关键词： Blockchain

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Adaptive Feature Selection on Graph 9

Adaptive Feature Selection on Graph

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9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Lin, Yue-Na Zhang, Chun-Yang School of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350342239

Graphs have become a widely-used tool to model data with relationships in real life for a long time. To discover the important contents in the graph, many graph neural networks (GNNs) have been come up with. Nevertheless, these models tend to adopt ReLU as their activation function for its nonlinearity, effectiveness, and efficiency. Owing to its own deficiency, it would cause many generated feature elements to be zero, which would miss part significant features. To overcome this problem, an adaptive weight vector to tune the features was provided. By limiting the elements of the weight vector, it can be a better substitute for ReLU. Besides, such a weight vector adaptively measures the importance of each feature element to work as a feature selection operator. To show the function of the weight vector, we examine a GCN with the weight vecotor in node classification, and it exhibits overall improvements over three well-known citation networks. © 2023 IEEE.

关键词： Feature Selection

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AIGLLM: An Action Instruction Generation Method with Visually Enhanced LLM 7

AIGLLM: An Action Instruction Generation Method with Visuall...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Xia, Yudong Zhao, Sirui Wu, Tong Tang, Huaying Xu, Tong University of Science and Technology of China School of Data Science Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350350890

Action Instruction Generation (AIG) is a key step in semantic-driven action sequence generation, which aims to embed text instructions describing specific actions into the driving text. AIG is a challenging task that requires accurately understanding the potential action semantics contained in the driving text. Besides, the challenge is exacerbated by the lack of paired text-action instructions data. To address these challenges and motivated by the powerful capabilities of LLMs in text generation, we propose AIGLLM, a novel AIG framework with visually enhanced Large Language Model (LLM). Specifically, we perform low-rank adaptation (LoRA) on the off-the-shelf LLM to enable the model understand action semantics and generate action instructions at appropriate positions within the original text sequence. Particularly, to address the current lack of data that can be used to fine-tune LLMs for AIG, we construct a dataset containing 12,000 text-action instruction pairs and 15,000 image-action instruction pairs. Subsequently, a visual module is designed to enable the LLM to learn action knowledge from external visual signals of actions, thereby enhancing the model's understanding of action semantics. Finally, we conduct extensive experiments, where the experimental results and evaluations demonstrate that our method has an impressive key benefit: improved accurate action instruction embedding. © 2024 IEEE.

关键词： Semantics

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Masked-Enhanced Food Segment Anything Model for Automatic Dietary Intake Monitoring 25

Masked-Enhanced Food Segment Anything Model for Automatic Di...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Guo, Zhongsui Javadi, Bahman Singh, Sonit Sowmya, Arcot School of Computer Science and Engineering Unsw Sydney Australia School of Computer Data and Mathematical Sciences Western Sydney University NSW Australia

ISBN: (纸本)9798350379037

Dietary management plays a crucial role in maintaining long-term health, preventing diseases and aiding in recovery, particularly amidst the increasing prevalence of chronic conditions such as hypertension, cardiovascular diseases and diabetes. To achieve best practice in dietary management, providing insights into calorie intake based on the food items that people are eating is pivotal. In this paper, we propose a methodology to achieve this in three phases: instance segmentation, volume estimation and calorie calculation. Building on the foundations of Food Segment Anything Model (FoodSAM), we propose Masked-enhanced FoodSAM (MFoodSAM) focussing on enhancing semantic and instance masks and providing better food identification. MFoodSAM incorporates ResNet50 as a background model, improving its ability to separate non-food elements and reducing dependency on merging thresholds. We follow a coarse-to-fine segmentation approach to create detailed and semantically enriched masks, providing accurate instance and panoptic segmentation masks. After we accurately identify food items, we estimate food volume by calculating the pixel count from the food instance masks and combine this data with information from the USDA National Nutrient database and Nutritiondata to compute the caloric content of the food. We applied the proposed method on both a public and a private food dataset, and obtained state-of-the-art results, with mean IoU of 87.99;mean Accuracy of 95.55;average Acc of 94.85 on MyFood dataset, and mean IoU of 82.38;mean Acc of 92.85;average Acc of 94.66 on UNSW-Food dataset. This intelligent food monitoring system provides users with precise caloric information, facilitating more effective nutrition monitoring and contributing to improved health outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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NeuroProbe: A Performance Profiling Feed Forward Neural Network Simulator for Hardware Interaction Analysis

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SN computer science 2025年第5期6卷 1-22页

作者： Ayyappan, Binu Kumar, G. Santhosh Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence and Data Science Rajagiri School of Engineering & Technology Kochi India

NeuroProbe is a simple neural network simulator designed by authors specifically for educational purposes focusing on simulating inference phase on a computationally capable embedded hardware, aiming to provide a deeper understanding of hardware interactions and performance improvement. This simulator incorporates performance profiling capabilities, allowing students and educators to analyze and optimize the performance of a simulated trained neural network. By simulating the intricate hardware interactions, NeuroProbe enables users to gain insights into how various components impact the overall performance of neural networks. Using this simulator, learners can experiment with different configurations and architectures, identify potential bottlenecks, and explore optimization strategies to enhance neural network performance before deploying the model on an Edge Computing environment. It also enriches computer science education by enabling learners to gain deeper insights into hardware interactions and performance improvement. By identifying performance bottlenecks, learners can refine their understanding of neural networks and develop skills in optimizing their models for enhanced performance. In conclusion, NeuroProbe empowers users to bridge the gap between theoretical knowledge and practical implementation of neural network on an embedded device, fostering a more comprehensive understanding of neural networks and their hardware requirements. Currently, it is focused solely on single-threaded CPU-based computation enabling SSE, AVX. At present, NeuroProbe does not support SIMD extensions such as AVX-FMA, AVX-512, multi-threaded computation, and accelerators such as NPU, TPU, and GPU. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware–software interactions Neural network simulator NeuroProbe Performance bottlenecks Performance profiling Simulations

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Facial Expression Recognition with DToF Sensing

Facial Expression Recognition with DToF Sensing

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

作者： Li, Chengxiao Zhang, Xie Wu, Chenshu Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Facial Expression Recognition (FER) is crucial for understanding human emotions, with applications spanning from mental health assessment to marketing recommendation systems. However, existing camera-based methods raise privacy concerns, while RF-based approaches suffer from limited environmental generalizability and high cost. In this work, we propose ToFace, a FER system leveraging a low-cost (4.8$) Direct Time-of-Flight (DToF) sensor that has been available on commodity smartphones. This sensor provides an extremely low-resolution 8 × 8 depth map and a clear Field of View (FoV), significantly mitigating privacy concerns while avoiding the impact of ambient objects. Despite the benefits, the low-resolution depth map introduces significant challenges for precise expression recognition due to limited facial structure information. We first develop a physical model to extract additional spatial information from the intermediate sensor output, i.e., the transient histograms. We then propose a physics-integrated neural network to reconstruct a facial structure map comprising both depth and orientation for accurate expression recognition. We conduct real-world experiments with 12 users and compare our model with several baselines. The results demonstrate that ToFace achieves the highest recognition accuracy of 75%. © 2025 IEEE.

关键词： direct time-of-flight sensor facial expression recognition internet of things sensing AI

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Importance-Based Pruning for Genetic Programming Based Symbolic Regression 37th

Importance-Based Pruning for Genetic Programming Based Symb...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Rimas, Mohamad Chen, Qi Zhang, Mengjie Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9789819603503

Pruning is a major research field in neural networks, enhancing their efficiency and generalization. The field of pruning approaches in genetic programming (GP) is continually evolving, with researchers actively exploring new techniques and approaches to optimise the performance of GP models. This research introduces a novel pruning algorithm for Genetic Programming-based Symbolic Regression (GPSR). The proposed method employs a weighting mechanism to identify and filter out unimportant subtrees in each generation. To achieve this, the method arranges all subtrees linearly and assigns weights to each subtree and terminal. It then uses Ordinary Least Squares (OLS) to optimize these weights, enabling the identification of unimportant subtrees and terminals for effective pruning. The algorithm’s effectiveness was evaluated using ten regression datasets, including high-dimensional and complex feature sets. Furthermore, comparisons were made with two other algorithms to evaluate its performance. The results indicate that the proposed approach not only achieves better learning and generalisation performance but also generates smaller trees compared to standard GP, thereby improving interpretability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic Programming Ordinary Least Squares Pruning Regression Subtrees

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A data representation method using distance correlation

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Frontiers of computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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