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Partial label learning via label-specific features enhancement and semi-disambiguation

Partial label learning via label-specific features enhanceme...

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2024 International Conference on Automation Control, Algorithm, and intelligent Bionics, ACAIB 2024

作者： Hu, Jinhua Chai, Jing Sun, Xianran School of information Science and Engineering Yunnan University Kunming650504 China Yunnan Key Laboratory of Intelligent Systems and Computing China

ISBN: (纸本)9781510682597

Partial Label Learning (PLL) represents a common multi-class weakly supervised learning challenge, where each instance is linked with a set of possible labels, only one of which is the ground-truth label, and it remains unknown during the training. A common strategy for solving this multi-class problems is to decompose it into a series of binary classification problems based on certain coding rules (e.g., OvO, ECOC), and then process each binary classification problem separately using the original features. To address the deficiency of discriminative information in the original features, a feature enhancement mechanism is proposed to introduce label-specific features to improve the discriminative performance of features. Firstly, label-specific features are generated for each decomposed binary classification problem, and then fused with the original features. The fused features are used to design corresponding binary classifiers. Additionally, due to the presence of a large number of false positive labels in the training examples, a semi-disambiguation strategy can be employed to remove some false positive labels, thereby "slimming down" the candidate label set as a preprocessing step to reduce its ambiguity. The proposed partial label learning algorithm based on features enhancement and semi-disambiguation proves its effectiveness on synthetic and real-world data sets through extensive experiments. © 2024 SPIE.

关键词： Contrastive Learning

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A Utility-Adjustable Reverse Auction Mechanism for UAV Data Collection Task Allocation 20th

A Utility-Adjustable Reverse Auction Mechanism for UAV Data...

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20th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2024

作者： Chen, Peng Zhang, Jixian School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9789819628636

Due to their low cost, easy deployment, and high flexibility, unmanned aerial vehicles(UAV) have become widely utilized for various data collection tasks across different fields. In particular, virtual service providers(VSP) recruit UAVs to collect data in their regions of interest(ROI), and leverage this data to offer a range of services, such as metaverse, traffic management, etc. To attract UAV owners to participate in the recruitment activities, VSP need to provide participating users with payments as rewards to motivate their participation. However, the recruitment demands of different VSPs for UAVs can vary significantly. For instance, time-sensitive tasks require timely recruitment of sufficient UAVs to ensure prompt completion of data collection tasks, necessitating higher payments to increase user enthusiasm. Conversely, in other types of tasks, the VSP’s objective might be to maximize its own utility. However, designing a general incentive mechanism for VSPs with different tasks that satisfies incentive compatibility(IC) and individual rationality(IR) to address the issues of UAV task allocation and payment is challenging. If VSP offer insufficient payments to users, it will be difficult to attract enough users to share their UAVs;conversely, offering excessive payments to users will harm the VSP’ profits. To address this problem, we propose a utility-adjustable reverse auction mechanism based on reverse Vickrey-Clarke-Groves(VCG) auction and affine maximizer auction(AMA) theory to cope with different task scenarios. This incentive mechanism can maximize social welfare while flexibly adjusting the utility allocation between VSP and users, ensuring non-negative utility for both parties. Experimental results show that the mechanism has excellent performance. © IFIP International Federation for Information Processing 2025.

关键词： Unmanned aerial vehicles (UAV)

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EnsembleCAM: Unified Visualization for Explainable Cervical Cancer Identification

EnsembleCAM: Unified Visualization for Explainable Cervical ...

引用

2024 International Research Conference on Smart computing and systems engineering, SCSE 2024

作者： Sritharan, Niruthikka Gnanavel, Nishaanthini Inparaj, Prathushan Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka Ulster University School of Computing Engineering and Intelligent Systems Londonderry United Kingdom

ISBN: (纸本)9798350375688

Cervical cancer, which is ranked fourth among cancers affecting women, is highly treatable when detected early through the pap smear test. Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), analyze pap smear images, yet their Black-Box nature raises transparency concerns in medical diagnostics. This paper introduces a solution named EnsembleCAM to enhance interpretability by unifying visual explanations through the combination of diverse Class Activation Maps (CAMs). Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques, develop an XceptionNet based binary classification model with an accuracy of 89% and apply GradCAM, GradCAM++, Score-CAM, Eigen-CAM and LayerCAM on this classifier. Then, the novel EnsembleCAM is constructed taking the median of activation maps from the five individual CAM methods. The analysis of activation maps of each CAM method and EnsembleCAM confirmed that in activation maps of EnsembleCAM, higher activation values were more concentrated around the nucleus which is the most important region in indicating cervical malignancy. The evaluation using pixel flipping performance metric also proved that the EnsembleCAM effectively recognises regions vital to the model's decision-making through its steepest drop in the mean prediction score when the pixels in the region contributing most to the model's decision were flipped. © 2024 IEEE.

关键词： Visualization

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Interpretable Cervical Cell Classification: A Comparative Analysis 4

Interpretable Cervical Cell Classification: A Comparative An...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Gnanavel, Nishaanthini Inparaj, Prathushan Sritharan, Niruthikka Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (纸本)9798350384864

Cervical cancer is a significant global health issue, and traditional screening methods like Pap smears are labor-intensive and may miss some cases. Automation is needed, but it faces challenges in terms of interpretability and data availability. To address this, the paper proposes using Explainable Artificial Intelligence (XAI) techniques like GradCAM, GradCAM++, and LRP to improve the transparency and interpretability of a cervical cell classification model, making it a novel contribution to enhancing the trustworthiness of automated cervical cancer detection. Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques and develop a binary classification model, achieving a 91.94% accuracy with VGG16. The qualitative analysis of XAI methods confirmed that the model relied on nucleus and cytoplasm features, key indicators of malignancy. The least mean image entropy of 2.4849 and steep prediction confidence drop with perturbations quantitatively proved Layer-wise Relevance Propagation (LRP) to be the most effective XAI technique for cervical cell classification. © 2024 IEEE.

关键词： Artificial intelligence

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Enhancing Skin Cancer Detection Through Automated Classification Using Convolutional Neural Networks 1

引用

7th International Conference on intelligent computing and Optimization, ICO 2023

作者： Hossain, Md Jakir Bhuiyan, M. D. Wahidul Islam Raisa, Sadia Afrin Tomal, Minhazul Amin Jennifer, Sanjeda Sara Reza, Ahmed Wasif Siddique, Nazmul Department of Computer Science and Engineering East West University Dhaka Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Skin cancer presents a significant public health concern, necessitating early detection and intervention to mitigate life-threatening consequences. Despite its prevalence, skin cancer is not always given the attention it deserves. In this paper, we propose an automated procedure for distinguishing skin cancers using several neural network algorithms. We used the MobileNet architecture whose accuracy is not very high and this is why we have also used other algorithms such as Sequential CNN (Convolution Neural Network), VGG16, and Xception to improve the accuracy of our model. By comparing these algorithms, we found that the Sequential CNN performed best with an accuracy of 94%. The proposed model utilizes the Sequential CNN algorithm to achieve a high level of accuracy, which is of utmost importance for the early detection of skin cancer. The high accuracy of the proposed model makes it a promising tool for detecting skin cancer early. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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Dynamic Event-Triggered Leader-Following Consensus of Multiagent systems under DoS Attacks 43

Dynamic Event-Triggered Leader-Following Consensus of Multia...

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43rd Chinese Control Conference, CCC 2024

作者： Ma, Ji Chen, Xiangyong Wu, Yue Sun, Ji Liu, Jianhua Linyi University The Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong School of Automation and Electrical Engineering Linyi276005 China

ISBN: (纸本)9789887581581

In this paper, the leader-following consensus of multi-agent systems (MASs) under denial-of-service (DoS) attacks over undirected networks is studies. First, introduce an estimator under DoS attacks to design a dynamic event-triggered communication protocol, which avoids continuous communication between agents. Then, with the presence of the estimator between the triggering instants can reduce the state shift. In contrast to the conventional method, the existence of dynamic threshold need fewer triggering times. Under this control strategy, the systems only need the states in triggering instants according to estimated states. Finally, verify the effectiveness of the proposed approach by a simulation example. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Denial-of-service attack

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Strong Co-Location Pattern Mining Incorporating Multi-Path and Distance Decay Effects 7

Strong Co-Location Pattern Mining Incorporating Multi-Path a...

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7th International Conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： He, Yonggui Yang, Peizhong Wang, Lizhen Chen, Hongmei Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Kunming China

ISBN: (纸本)9798331529314

Spatial co-location pattern mining (SCPM) is a sub-field of data mining, which aims to discover the subset of spatial features whose instances are frequently located in proximate areas. SCPM has broad prospects in many applications, such as ecology, public health, smart cities, etc. In recent years, improving and applying SCPM technology with the constraints of road networks has emerged as a prominent research focus. However, existing studies solely focus on the shortest path between instances when assessing the proximity relationships, neglecting other proximity paths, and thus may overlook co-location patterns with strong associations. To address this issue, this paper introduces a novel metric called Strong Proximity Score, which integrates both multi-path proximity and distance decay effects to measure the strength of proximity relationships. Additionally, the Minimum First Search (MFS) algorithm is presented, which utilizes the strategy of minimum instance pair search to accelerate the calculation of Strong Proximity Score. Extensive experiments conducted on real datasets of points of interest demonstrate the superiority of the method based on Strong Proximity Score over traditional SCPM methods and confirm the efficiency of MFS algorithm. © 2024 IEEE.

关键词： Smart city

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Comparison of Segmentation Algorithms for Extraction of Stone from Kidney X-ray Image 4th

Comparison of Segmentation Algorithms for Extraction of Ston...

引用

4th International Conference on Smart IoT systems: Innovations and computing, SSIC 2023

作者： Nisha, N. Dhaka, Vijaypal Singh Sinwar, Deepak Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India Director of School of Computing and Intelligent Systems Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819751990

Healthcare systems are gaining popularity because of the advancement of new technology aiding healthcare professional for diagnosis of disease from medical modalities. The detection and classification of disease using AI and its technologies are active research areas in the last decade. Radiologist usually detects stone from the kidney using different imaging like CT scan, MRI scan, and X-ray. For the detection of stone in kidney X-ray images, various segmentation and classification methods are used. These different classification and segmentation methods are used to reduce errors and time of healthcare professional in diagnosis of stone in X-ray image. In this paper, comparison of different algorithms like thresholding algorithm, contour-based algorithm, deep learning algorithms like U-Net, U-Net++, V-Net, V-Net++, and Transformer Net are used for segmentation of stone from kidney X-ray images. The thorough comparison of different deep learning architectures in diagnosing stone from kidney X-ray images is also made in this paper. Most of the papers work on classification of stone from different medical modalities before segmentation which cause data leakage problem. In this research paper, comparisons of different segmentation algorithms help in achieving higher accuracy and reduce the data leakage problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Temperature Control Method for Batch Reactors Based on Adaptive Delay Compensation

Temperature Control Method for Batch Reactors Based on Adapt...

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2024 China Automation Congress, CAC 2024

作者： Mao, Dehui Li, Peng Ai, Mingxi Wu, Jiande Yang, Chuangyan Wang, Hancheng School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350368604

To address the instability issue caused by time-delay and nonlinear characteristics of intermittent reactions in batch reactors, a temperature control method for batch reactors based on adaptive delay compensation was proposed. By integrating the chemical reaction mechanism inside the reactor with graphical analysis and actual production data, a time-varying second-order delayed transfer function model for the reactor is established. The adaptive rate for controlling the time-varying delayed system is designed based on the control error between the actual controlled object and the nominal model. Furthermore, standard Smith predictor is employed to compensate for the delay in the error term. When applied to temperature control in batch reactors, experimental results demonstrate that this method effectively reduces the temperature control errors caused by time-varying delays, significantly accelerates the convergence speed and diminishes the oscillation amplitude of the temperature curve. © 2024 IEEE.

关键词： Batch reactors

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Graph Neural Network with Heterogeneous Attributes for Node Classification 4

Graph Neural Network with Heterogeneous Attributes for Node ...

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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Jiang, Bohou Wang, Jiahui Yue, Kun Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (纸本)9798350394375

Node classification on heterogeneous graphs with multiple node types and relationships has been widely used in commodity classification, friend recommendation, and so on. Most of the existing models need to define the meta paths manually with incomplete attribute information, leading to the low accuracy of classification. To improve the accuracy of node classification task, we propose a novel graph neural network with heterogeneous attributes (GNNHA) by introducing the heterogeneous attributes of nodes and edges without defining meta paths. First, we design a spatial mapping for heterogeneous edges by mapping and fusing the source and target nodes to a hidden layer space. Then, we make a unique linear projection for each node to obtain the embeddings containing the attributes of nodes and edges by aggregating the information of neighbor nodes. Finally, we provide an improved loss function by leveraging the information loss of mapping and node class imbalance. Our model can capture more complete heterogeneous graph attributes and obtain more objective and accurate classification results. Experimental results show that our proposed model outperforms state-of-the-art ones. © 2024 IEEE.

关键词： Classification (of information)

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