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Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with Epidemic Intervention—London,UK,2020-2022

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China CDC weekly 2024年第21期6卷 478-486,I0006-I0008页

作者： Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Queen Ethelburga’s Collegiate Thorpe Underwood EstateYorkUK State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao SARChina University College London UCL Faculty of Engineering SciencesLondonUK Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa LisboaPortugal Instituto Superior Técnico Universidade de LisboaLisboaPortugal

Background:This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019(COVID-19)case numbers in London from January 31,2020 to February 24,*** primary objective was to enhance understanding of the interactions among government pandemic responses,viral mutations,and the subsequent changes in COVID-19 case ***:We employed the adaptive Fourier decomposition(AFD)method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three *** restructured components were rigorously evaluated using Pearson correlation,and their effectiveness was compared with other signal analysis *** study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition ***:Analysis of London’s daily COVID-19 data using AFD revealed a strong correlation between the“stay at home”directive and high-frequency components during the first epidemic *** indicates the need for sustained implementation of vaccination policies to maintain their ***:The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).This robust tool has proven invaluable for analyzing COVID-19 pandemic data,offering critical insights that guide the formulation of future preventive and public health strategies.

关键词： offering sustained London

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Handling the Concept Drifts Based on Ensemble Learning with Adaptive Windows

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第3期48卷 1-16页

作者： Myint, Tin Mar Lynn, Khin Thidar Candidate at Data Mining and Machine Learning Lab University of Computer Studies Mandalay Myanmar Professor at Faculty of Information Science University of Computer Studies Mandalay Myanmar

Continuous learning from streaming data is one of the contemporary most challenging topics. Learning algorithms not only need to handle fast-moving big data but should also be able to adapt to future evolving changes. The evolving structure of data streams is the phenomenon referred to as concept drift. Two types of concept drift handling based on ensemble approaches are online ensemble and chunk-based ensemble (window-based ensemble). The main disadvantage of the chunk-based ensembles is the difficulty of tuning the block size to provide a trade-off between fast reactions to drifts. In this paper, we propose Accuracy Updated Ensemble-2 (AUE2) based on the adaptive windowing approach (denoted as A-AUE2) by using Brier Skill Score as sudden and gradual changes detector. Moreover, K-Nearest Neighbors (KNN) based noise filtering method is applied to eliminate the noisy samples from each of the adaptive windows to improve the effectiveness of the ensemble learning approach. This paper's proposed approach has been evaluated on six artificial data sets and four real benchmark data sets by using two base learners. Specially, we illustrate that the proposed approach has outperformed the other state-of-the-art drift detection and handling approaches by using performance metrics and statistical analysis. © 2021. All Rights Reserved.

关键词： Learning algorithms

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Minimal harmonic measure on 2D lattices

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Probability Theory and Related Fields 2025年 1-55页

作者： Cai, Zhenhao Procaccia, Eviatar B. Zhang, Yuan Faculty of Mathematics and Computer Science Weizmann Institute of Science Herzl St 234 Rehovot Israel Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Haifa Israel Center for Applied Statistics and School of Statistics Renmin University of China Beijing China

We study the harmonic measure (i.e. the limit of the hitting distribution of a simple random walk starting from a distant point) on three canonical two-dimensional lattices: the square lattice $$\mathbb {Z}^2$$ , the triangular lattice $$\mathscr {T}$$ and the hexagonal lattice $$\mathscr {H}$$ . In particular, for the least positive value of the harmonic measure of any n-point set, denoted by $$\mathcal {M}_n(\mathscr {G})$$ , we prove in this paper that $$\begin{aligned} [\lambda (\mathscr {G})]^{-n+c\sqrt{n}} \le \mathcal {M}_n(\mathscr {G}) \le [\lambda (\mathscr {G})]^{-n+C\sqrt{n}}, \end{aligned}$$ where $$\lambda (\mathbb {Z}^2)=(2+\sqrt{3})^2$$ , $$\lambda (\mathscr {T})=3+2\sqrt{2}$$ and $$\lambda (\mathscr {H})=(\tfrac{3+\sqrt{5}}{2})^3$$ . Our results confirm a stronger version of the conjecture proposed by Calvert, Ganguly and Hammond (2023) which predicts the asymptotic of the exponent of $$\mathcal {M}_n(\mathbb {Z}^2)$$ . Moreover, these estimates also significantly extend the findings in our previous paper with Kozma (2023) that $$\mathcal {M}_n(\mathscr {G})$$ decays exponentially for a large family of graphs $$\mathscr {G}$$ including $$\mathscr {T}$$ , $$\mathscr {H}$$ and $$\mathbb {Z}^d$$ for all $$d\ge 2$$ .

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Box Facets and Cut Facets of Lifted Multicut Polytopes

arXiv

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arXiv 2024年

作者： Naumann, Lucas Fabian Irmai, Jannik Zhao, Shengxian Andres, Bjoern Faculty of Computer Science TU Dresden Germany Center for Scalable Data Analytics and AI Leipzig Dresden Germany

The lifted multicut problem is a combinatorial optimization problem whose feasible solutions relate one-to-one to the decompositions of a graph G = (V,E). Given an augmentation G = (V,E ∪ F) of G and given costs c ∈ RE∪F , the objective is to minimize the sum of those cuw with uw ∈ E ∪F for which u and w are in distinct components. For F = O, the problem specializes to the multicut problem, and for E = (V/2) to the clique partitioning problem. We study a binary linear program formulation of the lifted multicut problem. More specifically, we contribute to the analysis of the associated lifted multicut polytopes: Firstly, we establish a necessary, sufficient and efficiently decidable condition for a lower box inequality to define a facet. Secondly, we show that deciding whether a cut inequality of the binary linear program defines a facet is np-hard. Copyright © 2024, The Authors. All rights reserved.

关键词： Linear programming

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An Inconsistency-Based Hybrid Feature Selection Approach for Enhancing Medical Classification Modeling 2nd

An Inconsistency-Based Hybrid Feature Selection Approach for...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Zhao, Rong ALDharhani, Ghassan Saleh Ratnavelu, Kuru Thambiayya, Sathiyaraj Jasser, Muhammed Basheer Majeed, Anwar P. P. Abdul Luo, Yang Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Institute of Information Science and Technology Dalian University of Science and Technology Dalian China Faculty of Information Science and Technology UKM University Selangor Bangi43000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur56000 Malaysia Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Artificial Intelligence is an essential tool for early disease recognition and supporting patient condition monitoring in the future. Timely and exact conclusions about the type of disease are significant for treatment and life extension. A combination of classification and feature selection algorithms can effectively handle complex datasets in the medical field and improve the accuracy of disease diagnosis and treatment. In this paper, we propose a new hybrid feature selection algorithm that uses inconsistency metrics as a filter method for the first step, and then feeds the resulting dataset into a wrapper method, which ultimately results in a reduced dataset. The proposed HFSIM algorithm is tested on five datasets in the medical domain from Kaggle. The obtained feature subsets are verified on three classification algorithms, KNN, LR and RF of machine learning to validate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm obtains feature subsets with low dimensionality on most of the datasets and also has high classification accuracy after testing on the classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration 2

Exploring the Effectiveness of Deep Learning in Audio Compre...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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Real-Time data-Driven Maintenance Logistics: A Public-Private Collaboration

Real-Time Data-Driven Maintenance Logistics: A Public-Privat...

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2024 Commit2data, C2D 2024

作者： van Jaarsveld, Willem Bliek, Laurens de Weerdt, Mathijs Kapodistria, Stella Pronk, Verus Verleijsdonk, Peter Voorberg, Simon Akçay, Alp da Costa, Paulo Eshuis, Rik Kaymak, Uzay van Houtum, Geert-Jan Verwer, Sicco Zhang, Yingqian Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Philips Research Laboratories Eindhoven Netherlands Department of Information Systems Supply Chain Management and Decision Analysis NEOMA Business School Rouen France Etsy Inc. Dublin Ireland Jheronimus Academy of Data Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9783959773515

The project "Real-time data-driven maintenance logistics" was initiated with the purpose of bringing innovations in data-driven decision making to maintenance logistics, by bringing problem owners in the form of three innovative companies together with researchers at two leading knowledge institutions. This paper reviews innovations in three related areas: How the innovations were inspired by practice, how they materialized, and how the results impact practice. © Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang.

关键词： Scheduled maintenance

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A machine learning and neural network approach for classifying multidrug-resistant bacterial infections

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Healthcare Analytics 2025年 7卷 100388-100388页

作者： Mengsiri, Preeda Ungcharoen, Ratchadaporn Gertphol, Sethavidh Biomedical Data Science Program Faculty of Science Kasetsart University Thailand Faculty of Public Health Kasetsart University Chalermphrakiat Sakon Nakhon Province Campus Thailand Department of Computer Science Faculty of Science Kasetsart University Thailand

Antimicrobial resistance (AMR) represents a major public health challenge, significantly complicating infection prevention and treatment. This study employs machine learning and neural network techniques to classify multidrug-resistant Gram-negative bacterial (MDR-GNB) infections using electronic health records from 624 patients at Thatphanom Crown Prince Hospital in Thailand. We compared several algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Light Gradient Boosting Machine (LightGBM), with the MLP model exhibiting the highest accuracy and specificity. Performance was further enhanced by integrating feature selection methods such as Sequential Forward Selection (SFS), Recursive Feature Elimination with Cross-Validation (RFE-CV), and SelectKBest with data augmentation techniques, including ADASYN and SMOTE variants. Utilizing SHapley Additive exPlanations (SHAP) provided valuable insights into the most influential predictors for MDR-GNB. Notably, the MLP model achieved an AUC of 0.70, surpassing prior studies and highlighting its potential to advance clinical decision-making in managing MDR-GNB infections. © 2025 The Authors

关键词： Electronic health records Gram-negative bacteria Machine learning Multidrug resistance Neural network

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Price Prediction of Seasonal Items Using Time Series Analysis

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computer Systems science & Engineering 2023年第7期46卷 445-460页

作者： Ahmed Salah Mahmoud Bekhit Esraa Eldesouky Ahmed Ali Ahmed Fathalla Faculty of Computers and Informatics Zagazig UniversitySharkeya44523Egypt Information Technology Department the University of Technology and Applied SciencesIbriOman School of Electrical and Data Engineering University of Technology SydneySydney2007Australia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Computer Science Department Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Higher Future Institute for Specialized Technological Studies Cairo3044Egypt Department of Mathematics Faculty of ScienceSuez Canal UniversityIsmailia41522Egypt

The price prediction task is a well-studied problem due to its impact on the business *** are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal *** lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series *** proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based ***,this research tuned a set of well-known predictive models on a real-life *** models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear ***,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are ***,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined *** obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

关键词： Deep learning price prediction seasonal goods time series analysis

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Improving AI-Based Skin Disease Classification with StyleGAN3 for Minority Skin Tone Generation

Improving AI-Based Skin Disease Classification with StyleGAN...

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Biomedical Engineering International Conference (BMEiCON)

作者： Tanatorn Tanantong Pangon La-or-on Krittakom Srijiranon Department of Computer Science Unit in Data Innovation and Artificial Intelligence Faculty of Science and Technology Thammasat University Research Thammasat University Pathum Thani Thailand

ISBN: (数字)9798331505431

ISBN: (纸本)9798331505448

Skin diseases are prevalent in Thailand due to the hot temperatures, humid climate, and increasing diversity of skin tones, which complicates diagnosis. Artificial intelligence (AI) has improved skin disease detection through deep learning models, aiding faster diagnoses and reducing the workload on dermatologists. However, AI models often struggle with underrepresentation of darker skin tones in training datasets, affecting performance. To address this imbalance, data augmentation techniques like generative models are recommended. This research enhances skin disease classification using StyleGAN3 for data augmentation to balance minority skin tone classes. The Fitzpatrick17k dataset focusing on Acne, Psoriasis, and Vitiligo is used. The Fitzpatrick Skin Type Scale is reduced from six to three levels due to image availability. VGG16 is applied to classify skin tone classes. Two additional datasets, the original and traditionally augmented datasets, are used for comparison against the StyleGAN3-augmented dataset. Results show that the StyleGAN3-augmented dataset achieves the best performance with F1-scores of 0.7193, 0.7446, and 0.7543 and accuracy scores of 0.7200, 0.7368, and 0.7419 for Acne, Psoriasis, and Vitiligo respectively. The obtained results demonstrate that utilizing the StyleGAN3-augmented dataset for skin disease classification in minority skin tone classes yields the highest performance, with F1-scores increasing by 19.25%, 19.77%, and 25.94 % for Acne, Psoriasis, and Vitiligo, respectively. These findings highlight the effectiveness of StyleGAN3 in improving model performance for underrepresented skin tones in skin disease classification.

关键词： Training Accuracy Psoriasis Focusing data augmentation Skin data models Artificial intelligence Diseases Meteorology

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