检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Xiong, Mingfu Xiong, Jiefan Wang, Zhongyuan Chen, Jia Hu, Ruimin Muhammad, Khan Xiong, Zixiang School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan430200 China Wuhan University Wuhan430072 China Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of The Department of Electrical and Computer Engineering Texas A&M University College StationTX7784 United States

Recently, person re-identification (person Re-ID) equipped with attention mechanism (AM) has gradually emerged as one of the prevalent study highlights. In fact, the existing attention-based methods still remain the challenges for recognizing the different persons with similar appearances in the unpredictable realistic environments: (1) the adaptability of the learned a single and local attention to the hostile scenes cannot be guaranteed;(2) most existing methods originated from the attention features usually try to find a simple linear transformation, which is easy to lost the function of distinguishing different pedestrians with similar appearance attributes. To tackle these issues, this paper proposed a spatial adaptive cooperative graph convolution attention (SACGCA) method to excavate the relevances of individual body local regions for each person. The proposed framework encompasses a dual-attention mechanism model (DAMM) and a spatial adaptive graph convolution model (SAGCM). In particular, the DAMM consists of the multi-region (local and global) association structure and a feature channel attention network to extract person features. Then, the SAGCM is primed for constructing the spatial nonlinear transformation relationships among the various body regions for each person by the virtue of graph optimization. Extensive experiments have conducted on the person Re-ID public datasets (Market-1501, DukeMTMC-reid and CUHK-03), which results indicate that the performance of our proposed algorithm is better than existing methods by 2%, 2%, 10% at Rank-1 or mAP respectively. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Hierarchical Multi-Modal Fusion on Dynamic Heterogeneous Graph for Health Insurance Fraud Detection

Hierarchical Multi-Modal Fusion on Dynamic Heterogeneous Gra...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhang, Jian Yang, Fan Lin, Kaibiao Lai, Yongxuan Xiamen University Department of Automation Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Shenzhen Research Institute of Xiamen University Shenzhen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Informatics Xiamen University Xiamen China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In this paper, we devote to aggregating longitudinal and multi-modal information from heterogeneous neighbors to obtain an accurate node embedding on the dynamic heterogeneous graph. Recently, Heterogeneous Graph Neural Networks (GNNs) have attracted extensive attention in fraud detection. However, when faced with longitudinal and multi-modal data such as health insurance records, existing GNN-based fraud detectors always discard the multi-modal information (e.g., medication and treatment) of heterogeneous neighbors and ignore the inconsistent claimer behavior in the longitudinal records. To fully utilize the information, we represent the records in the form of a dynamic heterogeneous graph, and propose Hierarchical Multi-modal Fusion Graph Neural Network (HMF-GNN) which learns not only topological information, but also embeddings of longitudinal and multi-modal entities to improve the performance of fraud detection. Experimental results on two real-world health insurance datasets demonstrate that HMF-GNN outperforms state-of-the-art graph embedding methods and GNN-based fraud detectors. © 2022 IEEE.

关键词： Health insurance

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Classifying Cognitive Workload Using Machine Learning Techniques and Non-Intrusive Wearable Devices

Classifying Cognitive Workload Using Machine Learning Techni...

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IEEE International Conference on Human-Machine systems

作者： Yunmei Liu Nicolas S. Grimaldi Niosh Basnet David Wozniak Eric Chen Maryam Zahabi David Kaber Jaime Ruiz Department of Industrial Engineering University of Louisville Louisville KY USA Department of Industrial & Systems Engineering University of Florida Gainesville FL USA Department of Industrial & Systems Engineering Wm Michael Barnes ’64 Texas A&M University College Station TX USA Department of Computer & Information Science & Engineering University of Florida Gainesville FL USA

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

Effective management of cognitive workload is essential to ensure user performance and minimize mental fatigue. This study aimed to evaluate the effectiveness of three machine learning (ML) algorithms, including a Support Vector Machine (SVM), a Random Forest (RF) and Random Fourier Features (RFF) models, for predicting instances of high cognitive workload based on physiological data collected from non-intrusive wearable devices. The study included scenarios to induce high workload. After processing data from 30 human participants, and extracting cognitive workload-related features, we evaluated the ML algorithms in terms of accuracy, Area Under the Curve (AUC), and Average Precision (AP) responses. The RFF model emerged as a top performer, achieving 97% accuracy, 99.5% AUC, and 99% AP, demonstrating the capability to detect high workload scenarios and robustness to unbalanced data in workload predictions. The most significant physiological features in the RFF model included skin conductance level (SCL) change, SCL mean, and percent change in pupil size. Future studies should enhance the present binary classification model to support multi-class or continuous workload predictions and implement real-time analysis using trained algorithms.

关键词： Support vector machines Performance evaluation Machine learning algorithms Accuracy Predictive models Prediction algorithms Feature extraction

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Mitosis detection and classification for breast cancer diagnosis: What we know and what is next

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computers in Biology and Medicine 2025年 191卷 110057-110057页

作者： Khalil, Rafi Ullah Sajjad, Muhammad Dhahbi, Sami Bourouis, Sami Hijji, Mohammad Muhammad, Khan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Peshawar25000 Pakistan Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine a physical sample for the presence of mitotic cells, or dividing cells. However, the high resolution of histopathology images and the difficulty of manually detecting tiny mitotic nuclei make it particularly challenging to differentiate mitotic cells from other types of cells. Numerous studies have addressed the detection and classification of mitosis, owing to increasing capacity and developments in automated approaches. The combination of machine learning and deep learning techniques has greatly revolutionized the process of identifying mitotic cells by offering automated, precise, and efficient solutions. In the last ten years, several pioneering methods have been presented, advancing towards practical applications in clinical settings. Unlike other forms of cancer, breast cancer and gliomas are categorized according to the number of mitotic divisions. Numerous papers have been published on techniques for identifying mitosis due to easy access to datasets and open competitions. Convolutional neural networks and other deep learning architectures can precisely identify mitotic cells, significantly decreasing the amount of labor that pathologists must perform. This article examines the techniques used over the past decade to identify and classify mitotic cells in histologically stained breast cancer hematoxylin and eosin images. Furthermore, we examine the benefits of current research techniques and predict forthcoming developments in the investigation of breast cancer mitosis, specifically highlighting machine learning and deep learning. © 2025 Elsevier Ltd

关键词： Lung cancer

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions

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Procedia computer science 2024年 235卷 3438-3446页

作者： Seifedine Kadry Laith Abualigah Rubén González Crespo Elena Verdú Robertas Damasevicius Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays.

关键词： Covid19 X-ray Vision-transformer Patch-size Classification

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Risk-Sensitive Reinforcement Learning with Exponential Criteria

arXiv

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arXiv 2022年

作者： Noorani, Erfaun Mavridis, Christos N. Baras, John S. Department of Electrical and Computer Engineering the Institute for Systems Research University of Maryland College Park United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

While reinforcement learning has shown experimental success in a number of applications, it is known to be sensitive to noise and perturbations in the parameters of the system, leading to high variability in the total reward amongst different episodes on slightly different environments. To introduce robustness, as well as sample efficiency, risk-sensitive reinforcement learning methods are being thoroughly studied. In this work, we provide a definition of robust reinforcement learning policies and formulate a risk-sensitive reinforcement learning problem to approximate them, by solving an optimization problem with respect to a modified objective based on exponential criteria. In particular, we study a model-free risk-sensitive variation of the widely-used Monte Carlo Policy Gradient algorithm, and introduce a novel risk-sensitive online Actor-Critic algorithm based on solving a multiplicative Bellman equation using stochastic approximation updates. Analytical results suggest that the use of exponential criteria generalizes commonly used ad-hoc regularization approaches, improves sample efficiency, and introduces robustness with respect to perturbations in the model parameters and the environment. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments. © 2022, CC BY.

关键词： Contrastive Learning

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Enhancing Internet of Things Safety with AI-Powered Intrusion Avoidance systems

Enhancing Internet of Things Safety with AI-Powered Intrusio...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Sachin Ramchandra Misal M. Monica Bhavani Chandra Rao Merla Ganieva Shodiya Azizovna Jumisree Sarmah Pathak Praveen Kumar International Institute of Management Science Chinchwad Pune India Data Science & Business Systems SRM University Kattankulathur Department of Computer Science & Engineering Malla Reddy Engineering College for Women Hyderabad Department of Methodology of Primary education Fergana State University Department of Physics Indian Institute of Teacher Education Gandhinagar Gujarat Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The rapid proliferation of Internet of Things (IoT) devices has ushered in unprecedented levels of connectivity, offering enhanced automation and data sharing across diverse domains. However, this surge in interconnectivity has brought about a significant challenge regarding cyber security threats and intrusions. To overcome this problem, we propose a Hybrid Bayesian Tree Fusion (HBTF) for predicting the detection system. These systems leverage the capabilities of Artificial Intelligence to predict and proactively mitigate potential security breaches. We gather the NSL-KDD data set for evaluating the proposed method for forecasting intrusion detection. The collected data are preprocessed using Z score normalization to eliminate the noise. Extract the feature from the preprocessed data using kernel-based independent component analysis (KICA). The experimental findings of the proposed method are evaluated by recall, precision, f1-score, and accuracy from the result. The HBTF is greater than the existing method. The integration of AI augments the capability of IoT networks to counter emerging threats, ultimately fostering a safer and more secure interconnected world.

关键词： Automation Intrusion detection Independent component analysis Feature extraction Bayes methods Internet of Things Security Data mining Artificial intelligence computer crime

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Accurate Prior-centric Monocular Positioning with Offline LiDAR Fusion

Accurate Prior-centric Monocular Positioning with Offline Li...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jinhao He Huaiyang Huang Shuyang Zhang Jianhao Jiao Chengju Liu Ming Liu Thrust of Robotics and Autonomous Systems The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Baidu Autonomous Driving Technology Department (ADT) Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Computer Science University College London London UK School of Electronics and Information Engineering Tongji University Shanghai China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Unmanned vehicles usually rely on Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) sensors to achieve high-precision localization results for navigation purpose. However, this combination with their associated costs and infrastructure demands, poses challenges for widespread adoption in mass-market applications. In this paper, we aim to use only a monocular camera to achieve comparable onboard localization performance by tracking deep-learning visual features on a LiDAR-enhanced visual prior map. Experiments show that the proposed algorithm can provide centimeter-level global positioning results with scale, which is effortlessly integrated and favorable for low-cost robot system deployment in real-world applications.

关键词： Location awareness Visualization Laser radar Robot vision systems Cameras Sensor systems Robustness

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Development of A Surveillance System to Detect Forest Fire and Smoke Using Deep Neural Networks 3

Development of A Surveillance System to Detect Forest Fire a...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Subramanian, Malliga Shanmugavadivel, Kogilavani Muthusamy, Suresh Bajaj, Mohit Rubanenko, Olena Danylchenko, Dmytro Kongu Engineering College Department of Computer Science and Engineering Erode India Department of Electronics and Communication Engineering Tamil Nadu Erode India Department of Electrical Engineering Dehradun248002 India Vinnytsia National Technical University Department of Electrical Stations and Systems Vinnytsya Ukraine National Technical University «kharkiv Polytechnic Institute» Department of Electric Power Transmission Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

Forest fires are a major environmental hazard, causing economic, ecological, and human harm. Forest fires are natural flames that occur in forests, shrubs, and grasslands. Wildfires are usually caused by lightning or human error and occur during dry hot summers. Predicting such an environmental issue is crucial to reducing the threat. WSN is one of the innovative technologies and approaches developed to anticipate and detect forest fires. The accuracy of such fire detection technologies ranges from 80% to 91%. In this work, deep learning identification systems based on Convolutional Neural Networks (CNN) namely VGG16 and Mobile Net via Teachable Machine are presented to automatically detect fire in its early phases. Fire, No Fire, Smoke, and Smoke Fire are the four classes of images of forest fires identified in this work. With 98.5% accuracy on the Fire Image dataset, Mobile Net via Teachable machine outperforms the literature when it comes to predicting the fire. © 2022 IEEE.

关键词： Fires

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Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description

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computers, Materials & Continua 2021年第3期66卷 2691-2708页

作者： Zhengbo Luo Hamïd Parvïn Harish Garg Sultan Noman Qasem Kim-Hung Pho Zulkefli Mansor Graduate School of Information Production and SystemsWaseda UniversityTokyoJapan Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatialaPunjab147004India Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaizYemen Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsaan MalaysiaSelangorMalaysia

These days,imbalanced datasets,denoted throughout the paper by ID,(a dataset that contains some(usually two)classes where one contains considerably smaller number of samples than the other(s))emerge in many real world problems(like health care systems or disease diagnosis systems,anomaly detection,fraud detection,stream based malware detection systems,and so on)and these datasets cause some problems(like under-training of minority class(es)and over-training of majority class(es),bias towards majority class(es),and so on)in classification process and ***,these datasets take the focus of many researchers in any science and there are several solutions for dealing with this *** main aim of this study for dealing with IDs is to resample the borderline samples discovered by Support Vector Data Description(SVDD).There are naturally two kinds of resampling:Under-sampling(U-S)and oversampling(O-S).The O-S may cause the occurrence of over-fitting(the occurrence of over-fitting is its main drawback).The U-S can cause the occurrence of significant information loss(the occurrence of significant information loss is its main drawback).In this study,to avoid the drawbacks of the sampling techniques,we focus on the samples that may be *** data points that can be misclassified are considered to be the borderline data points which are on border(s)between the majority class(es)and minority class(es).First by SVDD,we find the borderline examples;then,the data resampling is applied over *** the next step,the base classifier is trained on the newly created ***,we compare the result of our method in terms of Area Under Curve(AUC)and F-measure and G-mean with the other state-of-the-art *** show that our method has betterresults than the other state-of-the-art methods on our experimental study.

关键词： Imbalanced learning classification borderline examples

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