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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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Predictive Modeling for Disease Progression in Chronic Conditions Using Machine Learning

Predictive Modeling for Disease Progression in Chronic Condi...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： T. S. Umamaheswari Amol Dattatray Dhaygude Omprakash Dewangan T. Krishnan Poonam Yerpude Suman Kumar Swarnkar Department of Computer Applications Jayagovind Harigopal Agarwal Agarsen College Chennai India Department of Data Science University of Washington Seattle USA Department of CS & IT Kalinga University Raipur Chhattisgarh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada Andhra Pradesh India Department of Computer Science & Engineering Indian Institute Of Technology Bhilai India Department of Computer Science and Engineering Shri Shankaracharya Institute of Professional Management and Technology Raipur India

Predictive modeling of disease progression in chronic conditions is a crucial task in healthcare, as it enables early identification and personalized intervention for patients at risk of adverse outcomes. In recent times, there has been a notable advancement in the field of machine learning techniques, which has demonstrated considerable potential in accurately forecasting the evolution of diseases. This development has proven to be highly beneficial for healthcare professionals, as it equips them with vital insights that can significantly enhance the management of patients. This research paper provides a thorough examination and comparative evaluation of different machine learning algorithms in the context of forecasting the course of diseases in chronic situations. We explore the use of diverse datasets containing longitudinal patient records, clinical variables, and disease-specific markers to train and evaluate predictive models. The study also investigates the impact of feature selection and data preprocessing techniques on the model's performance. Moreover, we assess the generalizability and robustness of the models across different chronic diseases to determine their potential for broader clinical applications. The results demonstrate that machine learning models can effectively predict disease progression, with certain algorithms outperforming others in specific scenarios. The research findings presented in this study offer significant contributions to the understanding and implementation of machine learning techniques for predicting disease development. The aforementioned realizations have the potential to be of substantial assistance to medical professionals in terms of making well-informed decisions, which will ultimately lead to better outcomes for patients.

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Enhancing Cybersecurity in IoT-Enabled Healthcare Systems: Challenges and Future Directions

Enhancing Cybersecurity in IoT-Enabled Healthcare Systems: C...

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International Conference on computer and Communications Technologies (ICCCT)

作者： M. Husain Bathushaw S. Nagasundaram G. Puthilibai S Chitradevi Darshan. R Department of Computer Applications VELS institute of science technology and advanced studies Chennai India Department of Chemistry Sri Sairam Engineering College (Autonomous) Chennai Tamilnadu India Department of Chemistry DMI College of Engineering Chennai Tamilnadu India Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College(Autonomous) Chennai Tamilnadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Even within the health care industry, the IoT has lowered patient outcomes via realism, analytical estimates, and data sharing. However, it brings great threats of cyber security in terms of patients and data privacy, data accuracy, and availability of such systems. These challenges include Architecture and device heterogeneity constraint, poor resource availability as well as IoT and its associated security threats. They also analyze existing security measures by providing insights for further research to strengthen the existing features of such systems. It stresses machine learning, blockchain, and zero-trust architecture as probable solutions to the existing security deficits.

关键词： Hands Industries Prevention and mitigation Medical services computer architecture Machine learning Blockchains Internet of Things Stakeholders Stress

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Automated Detection of Language Disorders in Children Using NLP and Machine Learning

Automated Detection of Language Disorders in Children Using ...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： P. Malathi N. Legapriyadharshini Shruthi S Nair M. P. Sujatha Sadaieswaran R T.Thirumalaikumari Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Application Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Applications Tagore College of Arts and Science Chennai India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

The purpose of the study is to address the essential need for a more efficient and objective technique for detecting language disorders in children, overcoming the constraints of standard manual evaluation systems. Existing systems, which utilize subjective evaluations and manual scoring, frequently result in lengthy processes and possible errors. To solve these issues, the proposed system automates the identification process by combining Natural Language Processing (NLP) and Machine Learning (ML). The system achieves a breakthrough in accuracy by normalizing text and extracting a comprehensive collection of linguistic features using advanced NLP techniques. The implementation involves difficult data preprocessing, extensive feature extraction, and the application of a cutting-edge Support Vector Machine (SVM) classifier. The proposed system achieves a 15% increase in accuracy, a 12% increase in precision, a 10% increase in recall, and an 11% increase in F1 score over existing systems. The confusion matrices show improved prediction skills, while accuracy across varied linguistic samples demonstrates the system's adaptability. Finally, the proposed system not only speeds up the detection of language disorders but also provides the groundwork for deeper insights into linguistic development, with revolutionary potential in educational and therapeutic settings.

关键词： Support vector machines Adaptation models Technological innovation Accuracy Confusion matrices Manuals Linguistics Feature extraction Natural language processing Standards

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Predicting the Impacts of the Environment on the Probability and Severity of Electrical Injuries by AIML

Predicting the Impacts of the Environment on the Probability...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Deshmukh, Araddhana Arvind Kumar, K Parish Venkata Kumar, B.Anjanee Patro, Pramoda Singh, Pavitar Parkash Ashokkumar, N. Marathwada Mitra Mandal College of Engineering Department of Artificial Intelligence and Data Science Pune India Department of Computer Applications Vijayawada India Dadi Institute of Engineering and Technology Electronics and Communication Engineering Visakhapatnam India Koneru Lakshmaiah Education Foundation Department of Mathematics Hyderabad India Lovely Professional University Department of Management Phagwara India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Department of Ece Tirupati India

ISBN: (纸本)9798350317060

The acceleration of global warming and environmental degradation has been attributed to industrial growth and fossil fuel utilization. Consequently, this phenomenon has resulted in a rise in the frequency and intensity of natural disasters. Due to their predominantly outdoor installation, generators are subject to significant impacts. The occurrence of natural disasters has led to an increase in accidents associated with electrical equipment. This paper analyses statistical data on electrical fires, electric shock accidents, and electricity-related accidents caused by climate change in domestic settings to determine the associated risk *** this study, we propose a risk assessment model for electrical fires, explicitly focusing on every month. Our approach involves analysing data on electrical hazards in different regional areas, particularly metropolitan cities, considering climatic conditions such as temperature and humidity. Additionally, we aim to evaluate the accident risk rating associated with natural disasters, specifically those related to low and high-voltage equipment. In this study, a risk analysis model has been developed to assess electrical hazards in different regions and equipment types. The model presented herein serves as a foundational predictive tool. Hence, offering electrical security amenities in the forthcoming period is feasible by giving a risk estimation map delineating potential dangers specific to each geographical area and wiring category via web platforms or mobile applications utilizing the investigation data provided. Additionally, it is imperative to enhance the resilience and dependability of electrical components as a proactive measure against electrocute ions resulting from climate change-induced emergencies. © 2023 IEEE.

关键词： Global warming

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ML Models and Big data Analytics to Predict the Cricket Match

ML Models and Big Data Analytics to Predict the Cricket Matc...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： V. Asha S. Sreekanth Boddu Rajasekhar Ginni Nijhawan Tamam Ali Abd Ulabbas Abedi Ashish Parmar Department of Computer Applications New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India LPU Phagwara India Medical Technology The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

artificial intelligence (AI)and Machine learning have greatly improved the accuracy of cricket, one of the most popular, well-supported, and exhilarating sports of today. The quantity of matches played throughout time has led to an exponential growth in the data about cricket matches and individual players. Furthermore, big data analytics are in high demand, and this data has many potential applications. Some examples of its potential applications include team player selection and prediction utilizing machine learning models and significant data approaches. A machine learning linear reversion model was used to forecast the team's performance in the absence of massive data and the comprehensive Spark ML framework. Following the application of linear regression in Spark ML, the untried outcomes are evaluated using accuracy, mean square error (MSE) and mean absolute error (MAE) root mean square error (RMSE), which are reported as 95%, 30.2, 1350.34, and 28.2 correspondingly. More than that, may use this method for other kinds of sports.

关键词： Analytical models Accuracy Linear regression Urban areas Machine learning Predictive models Big data data models Sparks Sports

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Time series forecast of sales volume based on XGBoost 2

Time series forecast of sales volume based on XGBoost

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2021 2nd International Workshop on Electronic communication and Artificial Intelligence, IWECAI 2021

作者： Zhang, Lingyu Bian, Wenjie Qu, Wenyi Tuo, Liheng Wang, Yunhai School of Computer Science and Technology Shandong University Qingdao China Didi AI Labs Didi Chuxing Beijing China Smart Transportation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing China

Some problems such as the decline of new labor force, the increase of retired labor force emerge because of the complex and changeable market environment, consequently exacerbating the staffing problem in the retail industry. Also, the unreasonable distribution of personnel, there are few people in busy hours and too many people in idle hours, which causes waste of labor. To address this issue, we analyzed the time series of sales volume in the retail industry in detail, and processed the data with feature engineering for predicting the in-store sales volume in the future. At the same time, other features such as weather and temperature are added to improve the accuracy of the model. Considering the characteristics of the data, we choose XGBoost as the prediction model. The experiments on real-world datasets verified better performance of proposed model compared with other state-of-art models. © Published under licence by IOP Publishing Ltd.

关键词： Time series

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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT)

Exploring Sensor-Based Smart Farming Technologies in the Int...

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Computing and data science (ICCDS), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Thenmozhi K C. Balakumar T Sujatha Wilfred Blessing N. R Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India Department of Compute Science Kristu Jayanti College (Autonomous) Bengaluru India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field.

关键词： Smart agriculture Soil measurements Soil moisture Sociology Phosphorus Internet of Things Statistics

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Design of novel circular lattice photonic crystal fiber suitable for transporting 48 OAM modes

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Optoelectronics Letters 2021年第8期17卷 501-506页

作者： Alaaeddine Rjeb Habib Fathallah Saleh Chebaane Mohsen Machhout Laboratory of Electronic and Microelectronic Physics DepartmentFaculty of Sciences of MonastirUniversity of MonastirMonastir 5000Tunisia Laboratory of Artificial Intelligence and Data Engineering Applications Computer DepartmentFaculty of Sciences of BizerteUniversity of CarthageBizerte 7000Tunisia Physics Department College of ScienceUniversity of Hai'ilKingdom of Saudi Arabia

In this paper,we propose and numerically investigate a novel circular lattice photonic crystal fiber(CL-PCF)using controllable GeO_(2) doped silica,suitable for modes carrying quantized orbital angular momentum(OAM).Large effective index separations between 25 supported vector modes(≥10^(-4))are confirmed over large bandwidth(C+L bands)leading to 48 OAM modes bearing *** simulations show that the modes in the proposed CLPCF have good features including low and flat dispersion(within 51.82 ps/km/nm),low confinement loss(lower than 0.002 d B/m),high effective mode area(88.5μm^(2))and low nonlinearity(1.22 W^(-1)·km^(-1)).These promising results show that the proposed CL-PCF could be an arguably candidate in fiber-based OAM multiplexing or other applications using OAM states.

关键词： fiber OAM circular

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Efficient Detection of DDoS Attacks in E-Government Clouds Using Sparse Neural Networks

Efficient Detection of DDoS Attacks in E-Government Clouds U...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： A. Bixapathi H. Mohammed Ali R Maranan Sudhakar AVV Beschi I S M. Ramya Department of Computer Science Engineering -Data Science Geetanjali College of Engineering and Technology Department Mechanical Engineering SRM Institute of Science and Technology-Ramapuram Campus Chennai Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Electrical and Electronics Engineering SR University Warangal Telangana Department of Computer Applications St. Joseph's College (Arts & Science) Chennai Division of Research and Development Lovely Professional University Jalandhar Punjab India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

E-government (Electronic Government) refers to the use of information and communication technology (ICT) by governmental bodies to provide public services, manage government operations, and facilitate communication with citizens, businesses, and other government entities. E-government platforms have expanded significantly in recent years, offering a range of services such as online tax filing, license renewals, healthcare services, and more. These services, often hosted on cloud-based infrastructures, benefit from improved scalability, efficiency, and accessibility through the use of cloud computing technologies. However, the growing volume of legitimate traffic from a diverse range of users on e-government platforms makes distinguishing between normal and malicious traffic, such as DDoS (Distributed Denial of Service) attacks, increasingly difficult. To address this challenge, this manuscript proposes the use of Sparse Neural Networks (SNN) for DDoS attack detection in cloud-based e-government systems. Initially, data from the CSE-CIC-IDS2018 dataset is collected and processed through a feature selection phase. The selected features are selected using the Energy Valley Optimizer. These optimized features are fed into the Sparse Neural Network, which categorizes traffic as benign, DDoS, or DoS. To further enhance the accuracy of DDoS detection, the Ladybug Beetle Optimization (LBOA) is employed to optimize the Sparse Neural Network's parameters, ensuring optimal performance in classifying attacks. This approach aims to safeguard cloud-based e-government systems from cyber threats while maintaining reliable public service delivery. In comparison to current methods such as data clustering for the detection of DDoS attacks in the cloud for e-government (DBSCAN), e-government impact factor analysis cleverly using enhanced machine learning (ML-EGOV), and Problems with Machine Learning in Cloud Computing E-Government Models in Developing Nations, the proposed DCNN-QSA-PDO

关键词： Cloud computing Neural networks Machine learning Denial-of-service attack Feature extraction Information and communication technology Reliability computer crime Optimization Electronic government

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