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Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

Computers, Materials & Continua 2023年第10期77卷 583-599页

作者： Fadhil Mukhlif Norafida Ithnin Roobaea Alroobaea Sultan Algarni Wael Y.Alghamdi Ibrahim Hashem Information Assurance and Security Research Group(IASRG) Faculty of ComputingUniversiti Teknologi Malaysia(UTM)Johor BahruJohor81310Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia College of Computing and Informatics Department of Computer ScienceUniversity of SharjahSharjah27272United Arab Emirates

The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several *** every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese *** of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare *** time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will *** prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a *** short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term *** an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for *** provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring *** developed a data analysis methodology,including data preparation and dataset *** examine two popular algorithms,LSTM and Decision tree on the official ***,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the *** issues and challenges are discussed for future *** is expected that the results these methods provide for the Health Scenario would be reliable and credible.

关键词： Healthcare framework AI COVID-19 machine&deep learning LSTM RNN decision tree

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Big Data Analytics for Business Sustainability: Review Study 4

Big Data Analytics for Business Sustainability: Review Study

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4th International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Akour, Iman Elhoseny, Mohamed Al-Amawi, Amaal Akila, Ayman Department of Information Systems College of Computing & Informatics University of Sharjah United Arab Emirates Department of Management Information Systems Isra University Jordan Department of Electrical Engineering University of Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

Recently, many businesses and industries involved sustainability in their strategies. The demand for sustainable development in business has drawn the attention of the government and academia and emerged as a broadly well-known trend. The aim of this project is to develop an integrated approach of the sustainable business from a theoretical perspective integrated with the current wave of big data analytics. To achieve this, aim an advance methodology is employed for the assessment of business sustainability based on the proposed big data analytics model from consumer perception dimension. In addition, this research will quantify the effect of big data analytics on business sustainability. It also proposes a scale for measuring and monitoring sustainability in the industry benchmarking the experiences of big data analytics usage globally and locally. The method used to achieve the mentioned aim is a qualitative approach. Articles published from 2015 to 2022 will be reviewed to get the findings and results of this review. This study contributes to the advancement of knowledge and literature by offering a comprehensive scale based on big data analytics to measure sustainability in the industries. Moreover, this research project is expected to produce research findings that will enhance our understanding of the importance of data analytics in business sustainability. Many literatures showed the impact of data analytics on business performances, but there are less articles that demonstrated the impact of big data analytics on business sustainability which aimed to be explored by this research project. © The Institution of Engineering & Technology 2023.

关键词： Sustainable development goals

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Reinforcement Learning-based Optimization of EBike Charging infrastructure 11

Reinforcement Learning-based Optimization of EBike Charging ...

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11th IEEE/ACM International Conference on Big Data computing, Applications and Technologies, BDCAT 2024

作者： Sharif, Muddsair Seker, Huseyin Birmingham City University Faculty of Computing Engineering and the Built Environment BirminghamB5 5JU United Kingdom The University of Sharjah College of Computing and Informatics Department of Information Systems Sharjah United Arab Emirates

ISBN: (纸本)9798350367300

This paper presents a novel technique for enhancing the allocation of resources in the charging infrastructure for e-bikes by employing deep reinforcement learning (DRL) in a context-specific manner. With the evolving transportation land-scape and the increasing popularity of electric micro-mobility solutions, efficient resource management plays a vital role. The methodology involves utilizing deep Q networks (DQN) in virtual environments to dynamically allocate charging resources based on contextual parameters. Through empirical analysis, we demonstrate the efficacy of our approach in achieving optimal resource allocation while considering the needs of diverse stakeholders. Findings suggest that the DQN model, when integrated with context-awareness and stakeholder objectives, enables efficient resource allocation, maximize fleet utilization, ensures charging station availability, enhances user satisfaction, and promotes environmental sustainability. The findings of this study offer valuable insights for policymakers, urban planners, and industry stakeholders seeking to enhance the sustainability and effectiveness of micro-mobility services in urban settings. Overall, this study contributes to the advancement of sustainable urban mobility solutions and underscores the potential of DRL in addressing complex urban transportation challenges. © 2024 IEEE.

关键词： Federated learning

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Analyzing video footage from security cameras using the Amped FIVE professional tool specifically for the identification of vehicle license plates 4

Analyzing video footage from security cameras using the Ampe...

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4th International Conference on computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2023

作者： Gvozdanovic, Misel Delija, Damir Tulicic, Domagoj Sirovatka, Goran Zagar, Marinko Bilic, Krunoslav Zagreb University of Applied Science Department of Informatics and Computing Zagreb Croatia University of Applied Sciences in Information Technology Department of Computer Systems and Networks Zagreb Croatia

ISBN: (纸本)9798350382754

This paper describes problems of improving security cameras' video footage for forensic investigation. When processing records, one encounters problems with low resolution, poor lighting, or excessive distance of a key object in an image or video. Using the Amped FIVE Professional tool, forensic scientists create videos and analyze and process the record itself using various filters. Record quality demands a choice of video filters. The use and selection of filters show the improvement of the record itself to the level to which useful information can be obtained in this case of vehicle license plates. The presented roadmap can be used as the base for further video forensic research. © 2023 IEEE.

关键词： Image enhancement

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Towards Optimizing the Logistic Transportation Selection in Hajj 1

Towards Optimizing the Logistic Transportation Selection in ...

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1st International Conference on Logistics, ICL 2024

作者： Mohamed, Marwa F. Elsherif, Samiha M. Eltoukhy, Mohamed Meselhy Suez Canal University Faculty of Computers and Informatics Computer Sciences Department Ismailia Egypt Suez Canal University Faculty of Computers and Informatics Information Systems Department Ismailia Egypt College of Computing and Information Technology at Khulais University of Jeddah Department of Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331518844

The Hajj is an international Islamic event that takes place in Mecca, Saudi Arabia, annually and attracts millions of pilgrims. One of the most important services during the Hajj period is pilgrims' transportation services, they transport pilgrims between the holy sites (e.g., walking facilities, traditional buses, shuttle buses, and the holy sites train). The shuttle bus, among these services, is the busiest for Hajj. It operates during the Hajj and transports most of the pilgrims. For example, during the first Muzdalifah holiday, when on the ninth-day pilgrims move from Arafat to Muzdalifah, the transportation serves more than one million pilgrims. Therefore, there are many transportation companies, and choosing an appropriate company is one of the most important challenges facing Hajj organizers. There are many criteria for selecting transportation companies, such as cost and service quality;therefore, this paper presents a framework that uses the NSGA-III optimization algorithm to choose the best companies based on two objectives (e.g., costs and quality of service). The results of NSGA-III have been compared with heuristic algorithms (e.g., best fit and first fit), and NSGA-III outperformed these algorithms. © 2024 IEEE.

关键词： Bus transportation

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Prices and Decentralization of Control 15

Prices and Decentralization of Control

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Gorelov, Mikhail Ereshko, Felix Federal Research Center 'Informatics and Control' of Ras Department of Information Computing Systems Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

The paper considers some models of a two-level hierarchical system for different-degree awareness of the Center and its subsystems. The authors investigate control procedures through the distribution of resources, the output of products, and pricing. They also formulate a problem to design the mechanisms coordinating the decisions of subsystems on voluntary fulfillment of the general restrictions that is fundamentally new for hierarchical games. © 2022 IEEE.

关键词： Hierarchical systems

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A Model of Economic Dynamics with Labor Resources 15

A Model of Economic Dynamics with Labor Resources

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Sytov, Anatoly Bayramov, Orudzh Federal Research Center Computing Systems 'Informatics & Control' of Ras Department of Information Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

The presented model of an economic system is the generalization of the Arrow-Debreu model for the dynamics case. The authors use the description techniques to develop an optimal enterprise by taking into account the dynamics of material products and financial indicators. They present a record of a multi-criterion problem and a procedure for coordinating the interests of industries. © 2022 IEEE.

关键词： Economics Industries Conference management Dynamic scheduling Large-scale systems Labor resources

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Computational Experiments on Back-Testing Complex Using the Forecast of Artificial Neural Network 15

Computational Experiments on Back-Testing Complex Using the ...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Gasanov, Igor Ereshko, Anton Federal Research Center 'Informatics & Control' of Ras Department of Information Computing Systems Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

关键词： Neural networks

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Holistic Cyber Threat Modeling for Machine Learning-Based systems: A Case Study in Healthcare

Holistic Cyber Threat Modeling for Machine Learning-Based Sy...

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18th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Jaal, Janno Bahsi, Hayretdin Department of Software Science Tallinn University of Technology Tallinn Estonia Cybernetica AS Tallinn Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

Considering the immense pace in machine learning (ML) technology and related products, it may be difficult to imagine a software system, including healthcare systems, without any subsystem containing an ML model in the near future. However, ensuring the resiliency of these ML-based systems against cyber attacks is vital for more seamless and widespread technology usage. The secure-by-design principle, considering security from the early stages of development, is a cornerstone to achieving sufficient security at a reasonable cost. The realization of this principle starts with conducting threat modeling to understand the relevant security posture and identify cyber security requirements before system design. Although threat modeling of software systems is widely known, it is unclear how to apply it to software systems with machine learning models. Although adversarial machine learning is a widely studied research topic, it has yet to be thoroughly researched how adversarial and conventional cybersecurity attacks can be holistically considered to identify applicable cyber threats at the early stage of a software development life cycle. This paper adapts STRIDE, a widely-known threat modeling method, for the holistic cyber threat analysis of an ML-based healthcare system. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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