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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

Soft computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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Real-Time Interactive Face Beautification

SSRN

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SSRN 2024年

作者： Lin, Luojun Chen, Wang Chen, Peizhen Zheng, Xiawu Jin, Lianwen College of Computer and Data Science Fuzhou University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University China School of Electronic and Information Engineering South China University of Technology China

The increasing popularity of sharing visually appealing self-portraits on social media platforms accentuates the demand for advanced face beautification technologies that can provide immediate results tailored to individual aesthetic preferences. To this end, we develop a Real-time Interactive Face Beautification task that can adaptively and promptly enhance facial images while preserving identity information. Specifically, we propose an aesthetic hyperplane by using a standard facial beauty prediction model, and subsequently develop an optimization-free Latent Space Beauty Interpolation method to manipulate faces through the aesthetic hyperplane. This method involves encoding facial representations into latent codes, which are then deftly manipulated within the latent space to align with a user-specified aesthetic score, effectively drawing them closer to or further from the aesthetic hyperplane as desired. These tailored latent codes are subsequently employed to generate the refined facial images via a pre-trained generative network (e.g., StyleGAN), facilitating swift and precise adherence to user-prescribed aesthetic standards. We conduct extensive experiments on the CelebA-HQ dataset with various attributes, calculating the time required to retouch each face and compare it with state-of-the-arts. The experimental results demonstrate that our method achieves very promising performance in terms of both effectiveness and visual quality. © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Deep Learning for Emoji Generation: From Faces and Gestures to Digital Emotion

Deep Learning for Emoji Generation: From Faces and Gestures ...

引用

Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： R. Nareshkumar K. Nimala M. K. Mohamed Faizal P. Sasikumar S. Karthigeyan Srikanth Cherukuvada Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu India Department of Artificial Intelligence and Data Science K. Ramakrishnan College of Engineering Samayapuram Tamil Nadu India Department of AIML Sphoorthy Engineering College Hyderabad Tamilnadu India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

The objective of this project is to create a reliable and effective system for identifying facial expressions and hand movements and translating them into suitable emojis. Emotions are vital in human communication, and precisely understanding them may significantly improve user experiences in several applications, such as social media, virtual assistants, and human-computer interaction. Through the creation of an extensive dataset and the use of a robust CNN, the system is capable of precisely categorizing facial expressions, even when faced with different lighting conditions and a wide range of facial characteristics. The suggested methodology entails the acquisition of live video data, which is then subjected to preprocessing to enhance the extraction of relevant features. Subsequently, CNN models are used to accurately recognize and interpret facial emotions and movements. These acknowledged phrases are then converted into equivalent emojis, offering an instinctive and smooth method to communicate emotions via digital communication. Initial findings indicate that the system attains a notable level of precision in identifying a diverse array of facial emotions, showcasing its capacity for practical use in real-life scenarios. This study enhances the field of human-computer interaction by facilitating a more authentic and articulate form of communication via emojis. It effectively narrows the divide between human emotions and digital interfaces. Subsequent efforts will concentrate on augmenting the system's functionalities to include a wider array of motions and improving its efficiency in more demanding settings.

关键词： Human computer interaction Hands Deep learning Emotion recognition Social networking (online) Virtual assistants Feature extraction Digital communication Real-time systems Emojis

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data-Driven Intersection Safety Evaluation Methodology and Analysis 23

Data-Driven Intersection Safety Evaluation Methodology and A...

引用

23rd COTA International Conference of Transportation Professionals: Emerging data-Driven Sustainable Technological Innovation in Transportation, CICTP 2023

作者： Wu, Pingping Lu, Guangquan Liu, Miaomiao Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Southeast Univ. Nanjing China

ISBN: (数字)9780784485040

ISBN: (纸本)9780784485040

With the development of information and sensing technologies, a wide variety of intersection traffic data is available. The purpose of this paper is to make full use of video and commercial vehicle trajectory data to deeply explore the intersection safety status. In this paper, based on different sample sizes, different sampling frequencies, and different accuracies of data, the safety analysis of intersections is carried out from micro and macro aspects based on post-encroachment time (PET) and vehicle deceleration behavior, respectively. On the one hand, we use the full-sample and high-precision trajectory data extracted from video to analyze the safety of each region of intersection based on PET. On the other hand, using the trajectory data with low penetration rate and low sampling frequency extracted from the positioning data of the online car-hailing, a distribution fit, and non-parametric test are conducted for the vehicle deceleration behavior, and then the overall safety state of the intersection is evaluated. The results can provide ideas for the identification of intersection risk points and the application of different kinds of data on intersection safety evaluation. © ASCE.

关键词： Commercial vehicles

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Swarm Dynamic Spectrum Access for Internet-of-Things

Swarm Dynamic Spectrum Access for Internet-of-Things

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IEEE Conference on Wireless Communications and Networking

作者： Yingjie Wang Bowen Shen Feng Li Kwok-Yan Lam School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China College of Computing and Data Science Nanyang Technological University Singapore Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rapid advancement of wireless communication technologies, the scarcity of available spectrum resources has become increasingly pronounced. Dynamic Spectrum Access (DSA) emerges as a promising solution to address this challenge. Traditional DSA methods based on Q-learning emphasize autonomous learning by individual nodes, whereas more recent approaches incorporating Federated Learning (FL) introduce collaborative learning among nodes but remain reliant on a central server. In this paper, we propose a novel DSA scheme based on Swarm Learning (SL), which enables a fully decentralized, distributed machine learning paradigm by establishing a blockchain-based peer-to-peer network. This approach capitalizes on the strengths of SL, facilitating cooperative learning among multiple nodes to enhance DSA performance. By allowing IoT terminals to share model parameters within a blockchain framework, the proposed scheme mitigates the vulnerabilities associated with centralized servers. Simulation results demonstrate that the SL-based DSA scheme not only surpasses the access efficiency of FL-based methods but also obviates the necessity of a central aggregation server. Furthermore, the fully decentralized architecture enhances the auditability of system data, thereby bolstering user privacy protection.

关键词： Wireless communication Q-learning Federated learning Simulation Dynamic spectrum access Deep reinforcement learning Peer-to-peer computing Servers Internet of Things Protection

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SAH: Shifting-aware Asymmetric Hashing for Reverse k-Maximum Inner Product Search

arXiv

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arXiv 2022年

作者： Huang, Qiang Wang, Yanhao Tung, Anthony K.H. School of Computing National University of Singapore Singapore Singapore School of Data Science and Engineering East China Normal University Shanghai China

This paper investigates a new yet challenging problem called Reverse k-Maximum Inner Product Search (RkMIPS). Given a query (item) vector, a set of item vectors, and a set of user vectors, the problem of RkMIPS aims to find a set of user vectors whose inner products with the query vector are one of the k largest among the query and item vectors. We propose the first subquadratic-time algorithm, i.e., Shiftingaware Asymmetric Hashing (SAH), to tackle the RkMIPS problem. To speed up the Maximum Inner Product Search (MIPS) on item vectors, we design a shifting-invariant asymmetric transformation and develop a novel sublinear-time Shifting-Aware Asymmetric Locality Sensitive Hashing (SAALSH) scheme. Furthermore, we devise a new blocking strategy based on the Cone-Tree to effectively prune user vectors (in a batch). We prove that SAH achieves a theoretical guarantee for solving the RMIPS problem. Experimental results on five real-world datasets show that SAH runs 4∼8× faster than the state-of-the-art methods for RkMIPS while achieving F1-scores of over 90%. The code is available at https://***/HuangQiang/SAH. © 2022, CC BY-NC-SA.

关键词： Vectors

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PP-Grading: Hierarchical Authorization using Polarization Pruning

PP-Grading: Hierarchical Authorization using Polarization Pr...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Yujia Zhu Ruoxi Wang Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the popularity of neural networks, more and more service providers are providing us with convenient neural network services. In this situation, it is necessary for service providers to grant hierarchical authorization of the networks to meet the service requirements of different users. Pruning technology, as a mainstream method for modifying neural network structures, can also be applied in the hierarchical authorization of neural networks. This article uses the method of polarization pruning to decentralize and grade neural networks. Firstly, we extract the scale factors of the batch normalization(BN) layer as a measure of the importance of each channel. Then, sparse methods are used to screen for scale factors. In order to make the selected scale factors have more obvious and reasonable truncation intervals, we also use polarization methods to induce clustering of scale factors. According to different grading requirements, we prune the channels corresponding to different quantities of scale factors. The experiment proves that our method has good grading performance on different datasets and different neural networks. Moreover, good grading results can be achieved by pruning only a small portion of the channels.

关键词： Authorization Accuracy Neural networks Education Information technology

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Robust Optical Quantum Imaging Framework with Entangled Photons in Oceanic Turbulent Environments

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IEEE Transactions on Communications 2025年

作者： Cao, Jingyang Zhou, Mu Zhang, Ruichen Niyato, Dusit Nie, Wei Han, Zhu Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing China Nanyang Technological University College of Computing and Data Science Nanyang China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important part of underwater optical technology, underwater imaging plays a crucial role in accurately capturing underwater targets and environmental features. Facing the challenges of complex ocean environments and severe photon attenuation, quantum imaging breaks through the limitations of traditional optical imaging technology by utilizing the characteristics of two-photon entanglement and time-space correlation, thus offering a new perspective on ocean turbulence. To this end, we propose a new underwater entangled-photon quantum imaging system. Specifically, we first construct an entangled photon quantum imaging physical model through ocean long-exposure turbulence and then exploit an entangled light co-incidence imaging reconstruction method to image the target object. Furthermore, in response to the problem that ocean environment has a great impact on photons, we develop a photon capture probability method based on entangled photon pairs to reduce the impact of the ocean turbulence noise on imaging and improve the underwater target imaging resolution. We experimentally demonstrate the effectiveness of our method by showing that even in harsh ocean environments, quantum imaging performs superior resolution capabilities over traditional light source imaging techniques. © 1972-2012 IEEE.

关键词： Quantum entanglement

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Improving Compositional Generalization in Math Word Problem Solving

arXiv

引用

arXiv 2022年

作者： Lan, Yunshi Wang, Lei Jiang, Jing Lim, Ee-Peng School of Data Science and Engineering East China Normal University China School of Computing and Information System Singapore Management University Singapore

Compositional generalization refers to a model's capability to generalize to newly composed input data based on the data components observed during training. It has triggered a series of compositional generalization analysis on different tasks as generalization is an important aspect of language and problem solving skills. However, the similar discussion on math word problems (MWPs) is limited. In this manuscript, we study compositional generalization in MWP solving. Specifically, we first introduce a data splitting method to create compositional splits from existing MWP datasets. Meanwhile, we synthesize data to isolate the effect of compositions. To improve the compositional generalization in MWP solving, we propose an iterative data augmentation method that includes diverse compositional variation into training data and could collaborate with MWP methods. During the evaluation, we examine a set of methods and find all of them encounter severe performance loss on the evaluated datasets. We also find our data augmentation method could significantly improve the compositional generalization of general MWP methods. Code is available at https://***/demoleiwang/CGMWP. © 2022, CC BY.

关键词： Iterative methods

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Rethinking CLIP-based Video Learners in Cross-Domain Open-Vocabulary Action Recognition

arXiv

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arXiv 2024年

作者： Lin, Kun-Yu Ding, Henghui Zhou, Jiaming Tang, Yu-Ming Peng, Yi-Xing Zhao, Zhilin Loy, Chen Change Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China S-Lab Nanyang Technological University Singapore Institute of Big Data Fudan University China AI Thrust Hong Kong University of Science and Technology Guangzhou China Data Science Lab School of Computing & DataX Research Centre Macquarie University Australia

Building upon the impressive success of CLIP (Contrastive Language-Image Pretraining), recent pioneer works have proposed to adapt the powerful CLIP to video data, leading to efficient and effective video learners for open-vocabulary action recognition. Inspired by that humans perform actions in diverse environments, our work delves into an intriguing question: Can CLIP-based video learners effectively generalize to video domains they have not encountered during training? To answer this, we establish a CROSS-domain Open-Vocabulary Action recognition benchmark named XOV-Action, and conduct a comprehensive evaluation of five state-of-the-art CLIP-based video learners under various types of domain gaps. The evaluation demonstrates that previous methods exhibit limited action recognition performance in unseen video domains, revealing potential challenges of the cross-domain open-vocabulary action recognition task. In this paper, we focus on one critical challenge of the task, namely scene bias, and accordingly contribute a novel scene-aware video-text alignment method. Our key idea is to distinguish video representations apart from scene-encoded text representations, aiming to learn scene-agnostic video representations for recognizing actions across domains. Extensive experiments demonstrate the effectiveness of our method. The benchmark and code will be available at https://***/KunyuLin/XOV-Action/. Copyright © 2024, The Authors. All rights reserved.

关键词： Video recording

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