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Home healthcare: particle swarm optimization for human resource planning under uncertainty

Multimedia Tools and Applications 2024年 1-34页

作者： Zarrouk, Rim Mahmoudi, Ramzi Bedoui, Mouhamed Hedi Hu, Yu-Chen Medical Imaging Technology Laboratory Faculty of Medicine LTIM-LR12ES06 University of Monastir Monastir5019 Tunisia Gaspard-Monge Computer-science Laboratory Paris-Est University Mixed Unit CNRS-UMLV-ESIEE UMR8049 ESIEE Noisy Le Grand Paris City Descartes93162 France Department of Computer Science Tunghai University Taichung City Taiwan

The home healthcare (HHC) staff scheduling problem involves allocating care tasks to healthcare staff at a minimal cost, subject to healthcare service requirements, labor law, organizational requirements, staff preferences, and other restrictions. Healthcare service providers strive to meet the time window restrictions specified by patients to improve their service quality. Nevertheless, many of those models are deterministic and neglect to take into account the system’s dynamic and uncertain characteristics, as well as the unpredictability of particular patient data. This study proposes a Two-Level particle swarm optimization variant (2LPSO) for solving the HHC staff’s scheduling problem with Staff Workload Equalization and Uncertainty management while avoiding overtime work. This paper provides a solution for good planning within reasonable computation times with the treatment of two cases: patient cancellation and new patient requests. The computational results obtained in this study demonstrate the efficiency and effectiveness of our approach. Overall, the proposed optimization structure achieves robust scheduling performance so that staff work schedules are balanced to meet the time window restrictions specified by patients to improve their quality of service by taking into account a new patient request or an incoming cancellation request. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Particle swarm optimization (PSO)

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse

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Digital Communications and Networks 2022年第4期8卷 498-507页

作者： Yihong Yang Sheng Ding Yuwen Liu Shunmei Meng Xiaoxiao Chi Rui Ma Chao Yan School of Computer Science Qufu Normal UniversityRizhao276826China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguangWeifang262700China Department of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210000China General Education Department Shandong First Medical University(Shandong Academy Of Medical Sciences)Taian271000China

Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things(IoT)technology,which can monitor the environmental information in real-time and employ the information to contribute to intelligent *** the process,anomaly detection for wireless sensor data plays an important ***,the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness,correlations,and concept drift,which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and ***,the data stream is usually generated quickly,which means that the data stream is infinite and ***,any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory ***,there exist correlations among different data streams,and traditional algorithms hardly consider these ***,the underlying data generation process or distribution may change over ***,traditional anomaly detection algorithms with no model update will lose their *** these issues,a novel method(called DLSHiForest)based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient *** experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our *** results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency.

关键词： Anomaly detection Data stream DLSHiForest Smart greenhouse Edge computing

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TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment 29th

TX-Phase: Secure Phasing of Private Genomes in a Trusted ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Dokmai, Natnatee Zhu, Kaiyuan Sahinalp, S. Cenk Cho, Hyunghoon Department of Biomedical Informatics and Data Science Yale School of Medicine New HavenCT United States Department of Computer Science and Engineering UC San Diego La JollaCA United States Cancer Data Science Laboratory National Cancer Institute National Institutes of Health BethesdaMD United States Department of Computer Science Yale University New HavenCT United States

ISBN: (纸本)9783031902512

Genotype imputation servers enable researchers with limited resources to extract valuable insights from their data with enhanced accuracy and ease. However, the utility of these services is limited for those with sensitive study cohorts or those in restrictive regulatory environments due to data privacy concerns. Although privacy-preserving analysis tools have been developed to broaden access to these servers, none of the existing methods support haplotype phasing, a critical component of the imputation workflow. The complexity of phasing algorithms poses a significant challenge in maintaining practical performance under privacy constraints. Here, we introduce TX-Phase, a secure haplotype phasing method based on the framework of Trusted Execution Environments (TEEs). TX-Phase allows users’ private genomic data to be phased while ensuring data confidentiality and integrity of the computation. We introduce novel data-oblivious algorithmic techniques based on compressed reference panels and dynamic fixed-point arithmetic that comprehensively mitigate side-channel leakages in TEEs to provide robust protection of users’ genomic data throughout the analysis. Our experiments on a range of datasets from the UK Biobank and Haplotype Reference Consortium demonstrate the state-of-the-art phasing accuracy and practical runtimes of TX-Phase. Our work enables secure phasing of private genomes, opening access to large reference genomic datasets for a broader scientific community. Our open-source software is available at https://***/hcholab/txphase. The full manuscript is available at https://***/10.1101/2024.09.16.613301. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sensitive data

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A label noise filtering and label missing supplement framework based on game theory

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Digital Communications and Networks 2023年第4期9卷 887-895页

作者： Yuwen Liu Rongju Yao Song Jia Fan Wang Ruili Wang Rui Ma Lianyong Qi College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Weifang Key Laboratory of Blockchain on Agricultural Vegetables Weifang University of Science and TechnologyShouguangChina China Unicom Taian Branch China School of Natural and Computational Sciences Massey UniversityNew Zealand General Education Department Shandong First Medical UniversityShandong Academy of Medical SciencesTai'an271000China College of Computer Science and Technology Zhejiang UniversityHangzhouChina

Labeled data is widely used in various classification ***,there is a huge challenge that labels are often added *** labels added by malicious users will affect the training effect of the *** unreliability of labeled data has hindered the *** order to solve the above problems,we propose a framework of Label Noise Filtering and Missing Label Supplement(LNFS).And we take location labels in Location-Based Social Networks(LBSN)as an example to implement our *** the problem of label noise filtering,we first use FastText to transform the restaurant's labels into vectors,and then based on the assumption that the label most similar to all other labels in the location is most *** use cosine similarity to judge and select the most representative *** the problem of label missing,we use simple common word similarity to judge the similarity of users'comments,and then use the label of the similar restaurant to supplement the missing *** optimize the performance of the model,we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the ***,a case study is given to illustrate the effectiveness and reliability of LNFS.

关键词： Label noise FastText Cosine similarity Game theory LSTM

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Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control

Cooperative Multi-Agent Deep Reinforcement Learning for Adap...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Zhang, Ying Yue, Meng Montana State University Department of Electrical and Computer Engineering BozemanMT United States Brookhaven National Laboratory Interdisciplinary Science Department UptonNY United States

ISBN: (纸本)9798350313604

Under voltage load shedding (UVLS) for power grid emergency control builds the last defensive perimeter to prevent cascade outages and blackouts in case of contingencies. This letter proposes a novel cooperative multi-Agent deep reinforcement learning (MADRL)-based UVLS algorithm in an adaptive decentralized way. With well-designed input signals reflecting the voltage deviation, newly structured neural networks are developed as intelligent agents to obtain control actions and their probabilities to accommodate high uncertainties in volatile power system operations. Moreover, the interaction among the agents for coordinated control is implemented and refined by a state-of-The-Art attention mechanism, which helps agents concentratively learn effective interacted information. The proposed method realizes decentralized coordinated control, adapting to extremely high uncertainties. Case studies on an IEEE benchmark system indicate the superior performance of the proposed algorithm. © 2024 IEEE.

关键词： Reinforcement learning

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Morocco's COVID-19 instance as a potential epidemiological scenario 3

Morocco's COVID-19 instance as a potential epidemiological s...

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3rd International Conference on Innovative Research in Applied science, Engineering and Technology, IRASET 2023

作者： Chouit, El Mehdi Rachdi, Mohamed Bellafkih, Mostafa Raouyane, Brahim Raiss Laboratory Department of Mathematics and Computer Science Rabat Morocco Ben m'Sik Ensad Hassan Ii University Tim Laboratory Faculty of Sciences Casablanca Morocco Ain Chock University Hassan Ii Faculty of Sciences Department of Mathematics and Computer Science Casablanca Morocco

ISBN: (纸本)9798350398366

Machine learning is widely used in all industries. In order to speed up decision-making on the most likely course of action, machine learning have shown their effectiveness in analyzing perioperative effects. In several technological sectors, ML models have been utilized for a while to specify and rank dangerous threat variables. To handle forecasting problems, several different prediction approaches are regularly used. The work shows how ML models can forecast the frequency of COVID-19 instances in the future, which is now considered to be a serious threat to civilisation. In this study, we carried out a comparison analysis of two widely used machine learning models: SVM and linear regression. Each model projects three variables: the cumulative count of confirmed cases, the cumulative count of fatalities, and the cumulative count of recoveries over the next 30 days. The study's conclusions demonstrate that applying these tactics to the current COVID-19 pandemic scenario is a viable choice. To boost accuracy, we used two ML models. The results of the experiment indicate that when it comes to COVID-19 prediction, Linear Regression yields the best results, while SVM yields the worst. © 2023 IEEE.

关键词： COVID-19

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Advancing blood cell detection and classification: performance evaluation of modern deep learning models

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BMC Medical Informatics and Decision Making 2025年第1期25卷 1-22页

作者： Choudhary, Shilpa Kumar, Sandeep Siddhaarth, Pammi Sri Charitasri, Guntu Gulhane, Monali Rakesh, Nitin Mon, Feslin Anish Al-Rasheed, Amal Getahun, Masresha Soufiene, Ben Othman Department of CSE (AIML) Neil Gogte Institute of Technology Hyderabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal 506371 India Department of Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering (AIML) Keshav Memorial Engineering College Hyderabad India Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Faculty in Department of Information technology University of Technology and Applied Sciences Ibri Oman Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh Saudi Arabia Department of Computer Science and Information Technology College of Engineering and Technology Kebri Dehar University Kebri Dehar Ethiopia PRINCE Laboratory Research ISITcom Hammam Sousse University of Sousse Sousse Tunisia

The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leukemia, and infection, all of which may cause significant mortality. Accurate blood cell identification has a high clinical relevance in these patients because this would help to prevent false-negative diagnosis and to treat them in a timely and effective manner, thus reducing their clinical *** research aims to automate the process and eliminate manual efforts in blood cell counting. While our primary focus is on detection and classification, the output generated by our approach can be useful for disease prediction. This follows a two-step approach, where YOLO-based detection is first performed to locate blood cells, followed by classification using a hybrid CNN model to ensure accurate identification. We conducted a thorough and extensive comparison with other state-of-the-art models, including MobileNetV2, ShuffleNetV2, and DarkNet, for blood cell detection and classification. In terms of real-time performance, YOLOv10 outperforms other object detection models with better detection rates and classification accuracy. But MobileNetV2 and ShuffleNetV2 are more computationally efficient, which becomes more appropriate for resource-constrained environments. In contrast, DarkNet outperformed in terms of feature extraction performance, and the fine blood cell type classification. Additionally, an annotated blood cell data set was generated for this study. A diverse set of blood cell images with fine-grained annotations is contained in this dataset to make it useful for deep learning models training and evaluation. Because the present dataset will be an important resource for researchers and developers working on automatic blood cell detection and classification systems, we will make it publicly available under the open-access nature in order to accelerate the collaboration and progress in this field. © The Author(s) 2

关键词： Blood cell detection Classification DarkNet MobileNetV2 Precision medicine Real-time detection ShuffleNetV2 YOLOv10

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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Cyber Deception Using NLP

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Journal of Information Security 2024年第2期15卷 279-297页

作者： Igor Godefroy Kouam Kamdem Marcellin Nkenlifack Department of Mathematics and Computer Science URIFIA Laboratory Faculty of Science University of Dschang Dschang Cameroon

Cyber security addresses the protection of information systems in cyberspace. These systems face multiple attacks on a daily basis, with the level of complication getting increasingly challenging. Despite the existence of multiple solutions, attackers are still quite successful at identifying vulnerabilities to exploit. This is why cyber deception is increasingly being used to divert attackers’ attention and, therefore, enhance the security of information systems. To be effective, deception environments need fake data. This is where Natural Language (NLP) Processing comes in. Many cyber security models have used NLP for vulnerability detection in information systems, email classification, fake citation detection, and many others. Although it is used for text generation, existing models seem to be unsuitable for data generation in a deception environment. Our goal is to use text generation in NLP to generate data in the deception context that will be used to build multi-level deception in information systems. Our model consists of three (3) components, including the connection component, the deception component, composed of several states in which an attacker may be, depending on whether he is malicious or not, and the text generation component. The text generation component considers as input the real data of the information system and allows the production of several texts as output, which are usable at different deception levels.

关键词： Cyber Deception Cybersecurity Natural Language Processing Text Generation

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