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2nd International Conference on intelligent Manufacturing and Robotics, ICIMR 2024

作者： Alebiosu, David Olayemi Folayan, Adeola Chen, Wei Tolulope, Abejide Ajibade, Samuel-Soma M. School of Engineering and Technology Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia School of Medical and Health Science Monash University Petaling Jaya Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China College of Science and Engineering University of Derby Derby United Kingdom Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkey

ISBN: (纸本)9789819639489

Lung segmentation has become a bedrock in the effective diagnosis, and classification of coronavirus (COVID-19) from radiological images such as computed tomography (CT) and X-ray images. Since the coronavirus (COVID-19) discovery, several methods have been employed to segment the COVID-19-infected areas from lung CT images. One of the most popular segmentation methods is the U-Net model. U-Net is a convolutional neural network used for medical image segmentation. U-Net and its variants have become a more reliable architecture used for medical image segmentation. U-Net models have produced outstanding results in segmenting diseases such as COVID-19 from lung CT images. The exceptional results produced by the U-Net model have inspired various researchers to explore the potential of U-Net for various segmentation tasks. This study compares the performances of recently used state-of-the-art U-Net models on lung CT images for tuberculosis segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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MP-GAN: Cyber-Attack Detection and Localization for Cyber-Physical systems with Multi-Process Generative Adversarial Networks*

MP-GAN: Cyber-Attack Detection and Localization for Cyber-Ph...

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2023 International Conference on Artificial Intelligence of Things and systems, AIoTSys 2023

作者： Zhou, Yikui Wang, Jie Tang, Junnan Gou, Chao Jiang, Zigui Li, Dan Ng, See-Kiong Sun-Yat Sen University School of Software Engineering Zhuhai China Sun-Yat Sen University School of Intelligent Systems Engineering Shenzhen China National University of Singapore Institute of Data Science School of Computing Singapore

ISBN: (纸本)9798350312270

Cyber-Physical System (CPS) integrates sensing, computation, cybernetics, and networking to control a hybrid physical system consisting of different functional subsystems, making the production process more intelligent and controllable. However, cyber-attacks during its operation will lead to abnormal system behaviors or even system breakdowns. In recent years, data-driven anomaly detection methods have been adopted to judge whether the CPS system is under cyber-attacks based on rich sensor measurements to avoid further economic losses or safety issues. However, the multi-process essence of CPS has not been adequately addressed in existing works to locate the processes or points being attacked for in-time actions. In this work, we proposed a Multi-Process Generative Adversarial Network (MP-GAN) framework to detect anomalous CPS statuses and locate cyber-attacks. Specifically, the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) was adopted as the base model to capture the temporal correlation of time series distributions, and the data was transferred between latent space and data space in a bi-directional manner, which regulated the generator to generate more realistic samples and thus better grasping the underline principles of the system. Moreover, parallel generators were employed to capture system performances at different physical processes, thus localizing the attacked CPS processes. Experiments on three CPS datasets, two collected from the Secure Water Treatment (SWaT) system and one collected from the water Distribution (WADI) system, showed that the proposed MP-GAN framework effectively reported anomalies caused by various cyber-attacks inserted in these complex multiprocess CPSs, which outperforms the state-of-the-art methods and can also locate most of the detected irregularities at the corresponding stages where the attacks were inserted. © 2023 IEEE.

关键词： Cyber Physical System

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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Towards Depth-Continuous Scene Representation With a Displacement Field for Robust Light Field Depth Estimation

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IEEE Transactions on Multimedia 2025年 27卷 3637-3649页

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

Light field (LF) captures both spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods have typically model surface depth only, while ignoring the continuous nature of depth in 3D scenes. In this paper, we use displacement field (DF) to describe this continuous property, and propose a novel depthcontinuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speed. Specifically, inspired by signed distance field in 3D object description, we aim to exploit the intrinsic depth-continuous property of 3D scenes using DF, and define a novel depth-continuous scene representation. Then, we introduce a simple yet general learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-ofthe-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D Light Field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multi-view stereo depth inference, LF super-resolution, and LF salient object detection. Experiments demonstrate improved performance when the continuous scene representation is applied, suggesting that our framework can potentially bring insights to more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

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Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation

Comparative Study of Parameter Selection for Enhanced Edge I...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Lindamulage, Asiri Kodagoda, Nuwan Reyal, Shyam Samarasinghe, Pradeepa Yogarajah, Pratheepan Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University United Kingdom

Magnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the original multi-output regression model fo... 详细信息

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

关键词： Deep learning

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Comparative Study of Deep Learning Parameter Selection for Multi-Output Regression on Head Pose Estimation

Comparative Study of Deep Learning Parameter Selection for M...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Dhanawansa, Vidushani Samarasinghe, Pradeepa Yogarajah, Pratheepan Gardiner, Bryan Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Engineering and Intelligent Systems Ulster University School of Computing United Kingdom

ISBN: (纸本)9781728119489

This paper presents a systemic analysis on the implications of the loss function, batch size and optimiser function applied within the multi-output regression problem domain of Head Pose estimation. To the best of our knowledge, prior comparative analysis of this calibre has primarily focused on the image classification problem domain, with limited analysis on the optimisation of hyperparameters influencing the performance of a Convolutional Neural Network (CNN) for regression. This motivates the current study which proposes a vanilla regression model based on the EfficientNet models of varying depths between EfficientNet BO and B5, and experiments on multiple protocols incorporating the benchmark datasets of BIWI, 300W-LP and AFLW2000. Therefore, the main contributions of the paper include a study on the impact of: loss functions for models of varying depths;batch sizes for significantly less and more complex models;and different optimiser functions on the model performance. The investigation of the effect of Mean Squared Error (MSE), Mean Absolute Error (MAE) and Huber loss functions on the performance of the models suggested that MAE and Huber loss (d=1.0) yield optimal performance regardless of the model complexity. The comparative analysis based on batch sizes ranging between 8 and 128 evidenced that moderate batch sizes of 16 and 32 yield an optimal performance on less complex models, while large batch training complements more complex models across all tested loss functions, an observation not evident in previous work. Finally, it was proven that the Adam optimiser function generates a minimal loss and is void of overfitting, outperforming other optimiser functions of Stochastic Gradient Descent, RMSProp, AdaGrad and Adam. © 2022 IEEE.

关键词： Optimization

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Machine Learning for Time Series Forecasting Using State Space Models 24th

Machine Learning for Time Series Forecasting Using State Sp...

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24th International Conference on intelligent Data engineering and Automated Learning, IDEAL 2023

作者： Sanchez-Bornot, Jose M. Sotero, Roberto C. School of Computing Engineering and Intelligent Systems Ulster University Derry/Londonderry United Kingdom Department of Radiology and Hotchkiss Brain Institute University of Calgary CalgaryAB Canada

ISBN: (纸本)9783031482311

State-space models (SSMs) are becoming mainstream for time series analysis because their flexibility and increased explainability, as they model observations separately from unobserved dynamics. Critically, using SSMs based on multivariate autoregressive equations enhances understanding of system evolution and its dynamical interactions. However, some challenges remain unsolved, such as estimation in large-scale scenarios, with very noisy data, and model selection. Here, we explore a state-space alternating least squares (SSALS) algorithm for time series forecasting, demonstrating its application with simulated and real data, and how to solve model selection in noisy scenarios with a novel cross-validation technique. Altogether, testing this methodology with time series forecasting is ideal to demonstrate its strengths and weaknesses, and appreciate its advantages compared to current methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios

Evaluating Software Development Agents: Patch Patterns, Code...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Zhi Chen Lingxiao Jiang Centre for Research on Intelligent Software Engineering School of Computing and Information Systems Singapore Management University

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with Software Development Agents representing the next major leap. These agents, capable of reasoning, planning, and interacting with external environments, offer promising solutions to complex software engineering tasks. However, while much research has evaluated code generated by large language models (LLMs), comprehensive studies on agent-generated patches, particularly in real-world settings, are lacking. This study addresses that gap by evaluating 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues from SWE-Bench Verified, focusing on their impact on code quality. Our analysis shows no single agent dominated, with 170 issues unresolved, indicating room for improvement. Even for patches that passed unit tests and resolved issues, agents made different file and function modifications compared to the gold patches from repository developers, revealing limitations in the benchmark's test case coverage. Most agents maintained code reliability and security, avoiding new bugs or vulnerabilities; while some agents increased code complexity, many reduced code duplication and minimized code smells. Finally, agents performed better on simpler codebases, suggesting that breaking complex tasks into smaller sub-tasks could improve effectiveness. This study provides the first comprehensive evaluation of agent-generated patches on real-world GitHub issues, offering insights to advance AI-driven software development.

关键词： Gold Codes Large language models Computer bugs Focusing Complexity theory Security Reliability Software development management Software engineering

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Method for Classification of Cancers with Partial Least Squares Regression as Feature Selector with Kernel SVM

Method for Classification of Cancers with Partial Least Squa...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Koul, Nimrita Manvi, Sunilkumar S Gardiner, Bryan School of Computer Science and Engineering REVA University Bangalore India School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland United Kingdom

ISBN: (纸本)9781665425773

Classification of cancers according to the exact place of origin in the body is an important research problem that is being addressed both clinically and computationally. Application of data science and machine learning to a vast volume of imaging and genomics data regarding cancers has enabled computational researchers to accurately classify the tumor samples according to their place of origin. In current work, we developed a method to classify tumors using partial least squares regression as feature selector and support vector machine classifiers. We have evaluated our approach on three cancer gene expression datasets and found classification accuracies of 100% in some circumstances. The comparison is conducted with standard classification methods like Decision Trees and simple Support Vector Machines with respect to standard performance parameters and the time taken for classification. The comparison in terms of training and testing accuracies and the time taken for classification results show that our method performs consistently better than conventional methods. © 2022 IEEE.

关键词： Support vector machines

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Directed Brain Network Transformer for Psychiatric Diagnosis 27th

Directed Brain Network Transformer for Psychiatric Diagnosis

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhu, Xu Qi, Zhiwei Yue, Kun Su, Yunshan Duan, Liang School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Education Yunnan University Kunming China Department of Radiology Affiliated Hospital of Yunnan University Kunming China

ISBN: (纸本)9783031781940

Human brain is a complex organ that consists of billions of neurons and trillions of connections among the neurons. To describe the correlations among the time series of the brain regions, we model the brain as a functional brain network to diagnose psychosis. However, traditional methods of functional brain network construction are usually noisy and do not consider the causal relationships among brain regions. To obtain the causal relationships and improve diagnosis interpretability, we propose a directed brain network Transformer (DBNT) for psychiatric diagnosis. First, the causal relationships in the blood-oxygen-level-dependent time series of brain regions are extracted to generate a directed brain network. Then, the feature encoding method is proposed to obtain local and global features of the brain networks by using the DBNT. Experimental results demonstrate that the accuracy of DBNT increases by 8.1% and 6.4% compared to state-of-the-art methods on two large-scale brain network datasets. DBNT also highlights the brain regions associated with psychosis and provides interpretation for diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network coding

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