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Rethinking Distribution Alignment for Inter-class Fairness 1

1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Ye, Jinhuang Wu, Jiawei Li, Zuoyong Zheng, Xianghan College of Computer and Big Data Fuzhou University Fuzhou350108 China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9789819997886

ISBN: (纸本)9789819997879

Semi-supervised learning (SSL) is a successful paradigm that can use unlabelled data to alleviate the labelling cost problem in supervised learning. However, the excellent performance brought by SSL does not transfer well to the task of class imbalance. The reason is that the class bias of pseudo-labelling further misleads the decision boundary. To solve this problem, we propose a new plug-and-play approach to handle the class imbalance problem based on a theoretical extension and analysis of distribution alignment. The method, called Basis Transformation Based Distribution Alignment (BTDA), efficiently aligns class distributions while taking into account inter-class *** implements the basis transformation through a learnable transfer matrix, thereby reducing the performance loss caused by pseudo-labelling biases. Extensive experiments show that our proposed BTDA approach can significantly improve performance in class imbalance tasks in terms of both accuracy and recall metrics when integrated with advanced SSL algorithms. Although the idea of BTDA is not complex, it can show advanced performance on datasets such as CIFAR and SVHN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Time-enhanced Dynamic Graph Network for Next POI Recommendation

Time-enhanced Dynamic Graph Network for Next POI Recommendat...

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2024 International Symposium on Internet of Things and Smart Cities, ISITSC 2024

作者： Liu, Zhao Liu, Wei Chen, Junyang School of Artificial Intelligence Sun Yat-sen University Zhuhai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798331505516

Predicting the next Point-of-Interest (POI) is crucial for location-based services. In this paper, we propose the Time-enhanced Sequence Prediction Model (TSPM) to improve the accuracy of next POI recommendations by incorporating temporal information and dynamic graph *** approach utilizes a Time-enhanced Sequence-based Dynamic Graph (TSDG) that captures both temporal transitions of POIs and sequential dependencies in user behavior. By embedding temporal information directly into the graph structure, TSPM effectively models user movements. We further enhance POI embeddings using knowledge graph techniques and Eigenmap to preserve the topological properties of the *** proposed model integrates these enriched embeddings into a Time-aware Recurrent Neural Network (TiRNN) to capture the influence of past check-ins across different time intervals. Experiments on real-world datasets demonstrate that TSPM significantly outperforms existing methods in prediction accuracy. © 2024 IEEE.

关键词： Graph embeddings

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Automatic Detection and classification of Correct placement of tubes on chest X-rays using deep learning with EfficientNet 2

Automatic Detection and classification of Correct placement ...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Abbas, Moneeb Abdul Salam, Anum Zeb, Jahan College of Electrical and Mechanical Engineering Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

In recent years, the rapid growth of data in healthcare has prompted a lot of interest in artificial intelligence (AI). Powerful AI algorithms are essential for extracting information from medical data and assisting clinicians in establishing quick and accurate diagnoses of a variety of ailments. In the current COVID-19 outbreak, critically ill patients were intubated and various medical tubes, including an endotracheal tube (ETT), were implanted to protect the airways. The Nasogastric tube (NGT) is used for feeding, whereas the Central Venous Catheter (CVC) is utilized for a variety of medical operations. The adoption of medical protocols by doctors to ensure proper tube installation is a major issue. Manual examination of CXR pictures takes time and frequently leads to misinterpretation. This research aims to create an Automated Medical Tube Detection System that can detect misplaced tubes from chest x-rays (CXR) using deep learning. As a result, using chest x-rays to detect poorly positioned tubes can save lives. On CXR the proposed CNN-based EfficientNet architecture efficiently detects and classifies incorrectly positioned tubes. After detailed experimentation, we were able to achieve 0.95 average area under the ROC curve (AUC). © 2022 IEEE.

关键词： Catheters

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Improving multi-class opinion classification in social networks by combining Fuzzy SVM and CNN 28th

Improving multi-class opinion classification in social netwo...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Ghezaiel Hammouda, Nadia Toujani, Radhia College of Computer Science and Software Engineering University of Hail Hail Saudi Arabia ESTMOD Department Higher Institute of management University of Tunis Tunisia

Machine learning techniques, particularly fuzzy Support Vector Machines (fuzzy SVM), have demonstrated effectiveness in sentiment classification within social networks. However, the evolving complexity of social networks content and the necessity to handle diverse opinions prompt ongoing advancements in sentiment analysis methodologies. In this study, we propose an innovative approach by integrating a Convolutional Neural Network (CNN) model with fuzzy SVM. Our key contribution lies in the fusion of these two methodologies, capitalizing on the strengths of fuzzy SVM for opinion mining and CNN for extracting intricate features. Additionally, we extend this method to enable multi-class opinion classification, allowing for a finer understanding of nuances in expressed sentiments. Through evaluation on benchmark datasets, we highlight a notable improvement in performance compared to baseline methods. The results underscore the enhanced generalization capabilities and increased accuracy in opinion classification offered by our hybrid model, rendering it a promising asset for decision-making applications based on multi-opinion analysis. © 2024 The Authors.

关键词： Convolutional neural networks

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A Hybrid Search Method for Accelerating Convolutional Neural Architecture Search 23

A Hybrid Search Method for Accelerating Convolutional Neural...

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15th International Conference on Machine Learning and Computing, ICMLC 2023

作者： Xun, Zhou Songbai, Liu Ka-Chun, Wong Qiuzhen, Lin Kaychen, Tan The Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University KA-CHun Wong China College of Computer Science and Software Engineering Shenzhen University China The Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781450398411

The performance evaluation of candidate architectures is a key step in evolution-based neural architecture search (ENAS). Generally, high-fidelity evaluation is desired for finding the optimal architecture but suffers from expensive computational cost while low-fidelity evaluation can reduce search cost but may influence the search performance. In this work, a hybrid search method, HybridNAS, is proposed by synergizing these two evaluation strategies to improve the search effectiveness of ENAS. This method consists of two stages: global exploration and local exploitation. The former applies low-fidelity evaluation to explore the promising architectures efficiently. As for the latter, it exploits the most promising ones identified by the former to generate the better-performancing architectures. Those two stages are iteratively repeated toward the optimal architecture. Extensive experiments on NAS-Bench-101 and NAS-Bench-201 reflect that HybridNAS can achieve comparable accuracy compared to existing popular NAS but at the expense of only 60%-80% computational cost. © 2023 ACM.

关键词： Iterative methods

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AI Methods Used for Spam Detection in Social Systems - An Overview 10

AI Methods Used for Spam Detection in Social Systems - An Ov...

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10th International Conference on Social Networks Analysis, Management and Security, SNAMS 2023

作者： Odeh, Ayman Hussien Hattab, Mohammad Al College of Engineering Al Ain University Department of Software Engineering and Computer Science Al Ain United Arab Emirates College of Engineering Al Ain University Department of Networok and Communication and Computer Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350318906

Over the past few years, AI has emerged as a formidable asset in the battle against spam. AI approaches have demonstrated their potential in improving spam detection in social systems. The importance of AI methods lies in their flexibility and ability to identify patterns, rendering them highly suitable for addressing the ever-changing characteristics of spam. In this paper, we provide an overview of all AI methods and algorithms used for spam detection in social systems, and evaluation of their performance in terms of used metrics such as accuracy, recall, precision, and F1-score. Also, we provide a comparative analysis of the used AI methods, challenges and limitations, emerging trends and future research directions, and privacy concerns in AI-driven spam detection in social systems. This paper emphasizes the significance of AI Methods in the realm of spam detection and investigates a range of AI-driven strategies that have been suggested and put into practice to combat spam with efficacy. © 2023 IEEE.

关键词： Deep learning

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Interpretable Roadside 2D Object Detection Based on Axial Attention 24

Interpretable Roadside 2D Object Detection Based on Axial At...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Wu, Fan Song, Shaojing Hu, Jinyan Shanghai Polytechnic University Faculty of Intelligent Manufacturing and Control Engineering Shanghai China College of Computer and Informational Engineering Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9798350376647

Environmental perception plays a crucial role in the operation of autonomous vehicles. Recent studies have relied on single vehicle cameras for perception due to the lack of roadside view dataset, but this approach provides limited perception information. To address this limitation, there is a growing interest in studying roadside perception to complement vehicle-side perception information. However, existing methods face challenges in accurately recognizing and locating small objects or objects under occluding conditions in the roadside. And autonomous vehicles require millisecond-level decision-making for safe driving, yet many advanced object detection methods struggle to meet the need for high-speed reasoning. Additionally, deep learning models often make black-box decisions without providing explanations or understanding of their decision-making process. To tackle these issues, this study proposes an interpretable 2D object detection method for roadside based on the YOLO V8 with Axial Attention replacing the CNN network within the network architecture. The trained model is then input into HiResCAM to visualize the decision-making process of the roadside detection model. Experimental results on DAIR-V2X-I and Rope3D demonstrate that our proposed method not only improves mean Average Precision (mAP) but also enhances image processing speed by 1.5 to 2 times compared to existing methods. By visualizing the decision-making process of the roadside detection model, it make autonomous vehicle perception more transparent and credible. © 2024 IEEE.

关键词： Deep learning

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Robust and Reliable Assessment of Preeclampsia Using Statistical Machine Learning 10

Robust and Reliable Assessment of Preeclampsia Using Statist...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Bukhari, Syeda Zainab Raja, Sadia Akram, Muhammad Usman Zeb, Jahan Hassan, Taimur National University of Sciences and Technology Department of Computer and Software Engineering Pakistan Kyber Medical College Department of Gynaecology Pakistan Abu Dhabi University Department of Electrical and Computer Engineering United Arab Emirates

ISBN: (纸本)9798331532895

Artificial intelligence is increasingly prevalent, in today's world. Machine learning techniques play a role, in the healthcare field as a whole and particularly, in the areas of obstetrics and gynecology. More specifically, methods in machine learning can be used to enhance the health and well-being of pregnant women, closely monitoring their health parameters during pregnancy. In this paper, we present a solution for predicting the risk of preeclampsia, a life-threatening disorder affecting women during pregnancy. For this purpose, we collected data from three gynaecologist in Pakistan providing health services in low resource settings in which data has samples of 233 pregnant women using 13 features which are analysed during routine checkups. We adopted model comparison approach where we trained and tested our data on multiple classification models. Performance of each model was tested in terms of accuracy, precision, recall and F1-score. On the basis of these metrics, logistic regression and XGBoost were found to be the best performing models with the accuracy of 85%. © 2024 IEEE.

关键词： Adversarial machine learning

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CRAYM: Neural Field Optimization via Camera RAY Matching 38

CRAYM: Neural Field Optimization via Camera RAY Matching

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lin, Liqiang Wu, Wenpeng Fu, Chi-Wing Zhang, Hao Huang, Hui College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computing Science Simon Fraser University Canada

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives. © 2024 Neural information processing systems foundation. All rights reserved.

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Heterogeneous Image Knowledge Driven Visual Perception

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 255-257页

作者： Lan Yan Wenbo Zheng Fei-Yue Wang IEEE the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous *** the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

关键词： Visual visual knowledge

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