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Road Network Detection from Aerial Imagery of Urban Areas Using Deep ResUNet in Combination with the B-snake Algorithm

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Human-Centric Intelligent Systems 2023年第1期3卷 37-46页

作者： Munawar, Hafiz Suliman Hammad, Ahmed W. A. Waller, S. Travis Shahzad, Danish Islam, Md. Rafiqul School of the Built Environment University of New South Wales Sydney Australia Lighthouse Professor and Chair of Transport Modelling and Simulation “Friedrich List” Faculty of Transport and Traffic Sciences Technische Universität Dresden Dresden Germany Department of Visual Computing University of Saarland Saarbrücken Germany Data Science Institute (DSI) University of Technology Sydney (UTS) Sydney Australia

Road network detection is critical to enhance disaster response and detecting a safe evacuation route. Due to expanding computational capacity, road extraction from aerial imagery has been investigated extensively in the literature, specifically in the last decade. Previous studies have mainly proposed methods based on pixel classification or image segmentation as road/non-road images, such as thresholding, edge-based segmentation, k-means clustering, histogram-based segmentation, etc. However, these methods have limitations of over-segmentation, sensitivity to noise, and distortion in images. This study considers the case study of Hawkesbury Nepean valley, NSW, Australia, which is prone to flood and has been selected for road network extraction. For road area extraction, the application of semantic segmentation along with residual learning and U-Net is suggested. Public road datasets were used for training and testing purposes. The study suggested a framework to train and test datasets with the application of the deep ResUnet architecture. Based on maximal similarity, the regions were merged, and the road network was extracted with the B-snake algorithm application. The proposed framework (baseline + region merging + B-snake) improved performance when evaluated on the synthetically modified dataset. It was evident that in comparison with the baseline, region merging and addition of the B-snake algorithm improved significantly, achieving a value of 0.92% for precision and 0.89% for recall.

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

A Deep Learning Approach Towards Student Performance Predict...

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Information Technology (ACIT)

作者： Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Electrical and Computer Engineering Department Western University London Ontario Canada Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

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Incremental Feature Selection for Dynamic Incomplete data Using Sub-Tolerance Relation

SSRN

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SSRN 2023年

作者： Zhao, Jie Ling, Yun Huang, Faliang Wang, Jiahai See-To, Eric Wing kuen Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

TRS theory is widely used for feature selection with incomplete data. However, the traditional positive region-based measure ignores uncertainty, resulting in redundant features and poor classification accuracy. In addition, the computation of the positive region incurs significant time and space costs. To address these limitations, we extend the tolerance class to a Subrelation Tolerance Class (STC) by decomposing the tolerance relation into two subrelations. STC transforms feature selection into a two-stage certainty measurement by partitioning the universe into certainty and uncertainty regions. By progressively filtering out certain regions, the computational space is reduced. Furthermore, we propose a new significance measure that incorporates both certain and uncertain information. This measure effectively reduces redundant features, resulting in improved computational efficiency and classification accuracy. Using STC and our measure, we develop an incremental feature selection algorithm for dealing with incomplete streaming data, which is validated through extensive experiments and comparisons. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International Conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling Image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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Consistency Approximation: Incremental Feature Selection Based on Fuzzy Rough Set Theory

SSRN

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SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin Ye, Wenhao Huang, Faliang Wang, Jiahai See-To, Wing-Kuen Eric Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5888-5903页

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Qilu University of Technology Shandong Academy of Sciences Faculty of Computer Science and Technology Jinan 250014 China National University of Singapore School of Computing 21 Lower Kent Ridge Rd 119077 Singapore Great Bay University School of Computing and Information Technology Dongguan 523000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Qilu University of Technology (Shandong Academy of Sciences) Shandong Artificial Intelligence Institute Jinan 250014 China Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin 300384 China Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Shenzhen 518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. © 2005-2012 IEEE.

关键词： CLIP Diffusion face forgery detection Image-noise fusion Sample pair attention Transformer

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A Review of Breast Cancer Histopathological Image Classification 1

A Review of Breast Cancer Histopathological Image Classifica...

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1st International Conference on Material Processing and Technology, ICMProTech 2021

作者： Huang, Lei Zain, Azlan Mohd Zhou, Kai-Qing Chen, Chang-Feng School of Computing Faculty of Engineering Universiti Teknologi Malaysia UTM Skudai Johor Johor Bahru Malaysia UTM Big Data Centre School of Computing Universiti Teknologi Malaysia UTM Skudai Johor Johor Bahru Malaysia College of Information Science and Engineering Jishou University Jishou Hunan China

Breast Cancer (BC) is the most common malignant tumor for women in the world. Histopathological examination serves as basis for breast cancer diagnosis. Due to the low accuracy of histopathological images through manual judgment, the classification of histopathological images of breast cancer has become a research hotspot in the field of medical image processing. Accurate classification of images can help doctors to properly diagnoses and improve the survival rate of patients. This paper reviews the existing works on histopathological image classification of breast cancer and analysis the advantages and disadvantages of related algorithms. Findings of the histopathological image classification of the Breast Cancer study are drawn, and the possible future directions are also discussed. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Image classification

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Sensitive vPSA - Exploring Sensitivity in Visual Parameter Space Analysis

arXiv

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arXiv 2022年

作者： Fröhler, Bernhard Elberfeld, Tim Möller, Torsten Hege, Hans-Christian Maurer, Julia Heinzl, Christoph the Research Group X-Ray Computed Tomography University of Applied Sciences Upper Austria Austria the imec-Vision Lab University of Antwerp Belgium Data Science @ Uni Vienna the Faculty of Computer Science University of Vienna Austria Visual and Data-Centric Computing Department Zuse Institut Berlin Germany

The sensitivity of parameters in computational science problems is difficult to assess, especially for algorithms with multiple input parameters and diverse outputs. This work seeks to explore sensitivity analysis in the visualization domain, introducing novel techniques for respective visual analyses of parameter sensitivity in multi-dimensional algorithms. First, the sensitivity analysis background is revisited, highlighting the definition of sensitivity analysis and approaches analyzing global and local sensitivity as well as the differences of sensitivity analysis to the more common uncertainty analysis. We introduce and explore parameter sensitivity using visualization techniques from overviews to details on demand, covering the analysis of all aspects of sensitivity in a prototypical implementation. The respective visualization techniques outline the algorithmic in- and outputs including indications, on how sensitive an input is with regard to the outputs. The detailed sensitivity information is communicated through constellation plots for the exploration of input and output spaces. A matrix view is discussed for localized information on the sensitivity of specific outputs to specific inputs. A 3D view provides the link of the parameter sensitivity to the spatial domain, in which the results of the multi-dimensional algorithms are embedded. The proposed sensitivity analysis techniques are implemented and evaluated in a prototype called Sensitivity Explorer. We show that Sensitivity Explorer reliably identifies the most influential parameters and provides insights into which of the output characteristics these affect as well as to which extent. © 2022, CC BY-NC-SA.

关键词： Sensitivity analysis

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Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance Systems

arXiv

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arXiv 2023年

作者： Guan, Jiwei Pan, Lei Wang, Chen Yu, Shui Gao, Longxiang Zheng, Xi The School of Computing Macquarie University Sydney Australia The Data61 CSIRO Sydney Australia The School of Information Technology Deakin University Waurn Ponds Australia The Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia The Shangdong Computer Science Center Qilu University of Technology China

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this paper, we propose the Multimodal Fusion Framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio-vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio-vision tasks reveal the model's uncertainty. Using Expected Calibration Errors, we measure calibration errors and Monte-Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for Advanced Driver Assistance Systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Advanced driver assistance systems

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Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives

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Informatics in Medicine Unlocked 2023年 38卷

作者： Kanyongo, Wellington Ezugwu, Absalom E. Department of Computer Science Faculty of Science Engineering Bindura University of Science Education Bindura 0771 603 900 Zimbabwe School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg Campus Pietermaritzburg 3201 South Africa Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom 2520 South Africa

Medication nonadherence is a significant public health concern that leads to ineffective treatment, which in turn engenders complications such as increased morbidity risks, unnecessary hospitalisations, and premature mortality. Technologies of the Fourth Industrial Revolution, such as machine learning, provide breakthroughs in identifying the most important features for building predictive models of medication adherence. Due to the diversity and complexity of medication adherence, it is crucial to leverage machine learning to determine significant medication adherence factors. This study systematically reviewed articles exhibiting feature selection and feature importance in research utilising machine learning to analyse medication adherence among Non-communicable diseases (NCD) patients. The articles were retrieved using Google Scholar, Research4Life, IEEE Xplore, and PubMed. The requirements for inclusion were met by 27 papers published between 2010 and 2022. The publications reviewed incorporate machine learning while also demonstrating feature selection and the importance of predictors of medication adherence in NCD patients with hypertension (n = 6), cardiovascular diseases (n = 6), diabetes (n = 4), opioid use disorder (n = 3), and other NCDs (n = 8). The findings demonstrate that medication adherence is a multifactorial issue influenced by various features such as sociodemographic and economic characteristics, medication information, behavioural, disease-related, and healthcare system-related factors. Some of these features, such as the patient's age, gender and race, cannot be modified. Once the patients with nonmodifiable features have been identified, they must be proactively monitored for medication adherence. On the other hand, adjustable risk features, such as self-efficacy and medication knowledge, can be modified and improved through medication education or medication adherence awareness. Various techniques for selecting and ranking features have emerg

关键词： Chronic disease Feature importance Feature selection (FS) Machine learning (ML) Medication adherence (MA) Non-communicable disease (NCD) Predictors of MA

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