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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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Big data analytics for Quality Control in The Pharmaceutical Industry 10

Big Data Analytics for Quality Control in The Pharmaceutical...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Malathi, P. Jansi, S. Prabhakaran, M.V. Senbagam, K. Sujatha, M.P. Sadaieswaran, R. Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Acharya college of Engineering and Technology Department of Computer science and Engineering Achariyapuram Puducherry India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Agni College of Technology Department of Computer Science and Engineering Chennai Thalambur India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India

ISBN: (纸本)9798350384369

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns. © 2024 IEEE.

关键词： data quality

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The Impact of Social Media on Caregiver's Mental Well-Being: An Empirical Study

The Impact of Social Media on Caregiver's Mental Well-Being:...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Bijun Wang Yuze Zhang Department of Business Analytics and Data Science Florida Polytechnic University Lakeland FL Department of Chemical Engineering and Material Science Stevens Institute of Technology Hoboken NJ

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

This study investigates the impact of social media engagement on health management confidence and mental well-being among 664 caregivers. Using Structural Equation Modeling, we found that while social media engagement positively influences caregivers' confidence in finding online health resources and enhances mental well-being, it does not significantly affect their confidence in personal health management. Interestingly, using social media resources for decision-making was observed to improve resource-finding ability but reduce confidence in personal health management. Additionally, higher confidence in health management correlates with better mental well-being, but confidence in finding online health resources does not directly impact mental health. These results emphasize the complex role of social media in caregiving and underscore the importance of accessible, adapted digital health resources with mental well-being support.

关键词： Social networking (online) Decision making Mental health Mathematical models Electronic healthcare Informatics

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Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass

Improving Heart Disease Prediction with Stacked Ensemble Lea...

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Women in Computing (InCoWoCo), International Conference for

作者： Amirthaa M Gerardine Immaculate Mary Anitha Julian Anto Lourdu Xavier Raj Selvarathinam School of Electronics Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Saveetha Engineering College Chennai Tamilnadu India Data Science and Analytics Grand Valley State University Michigan USA

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Heart disease prediction remains a critical area of research due to its significant impact on public health. This paper, titled “Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass,” presents a comprehensive study comparing the performance of binary and multiclass classification models for heart disease prediction. Utilizing two distinct datasets, one for binary classification and one for multiclass classification. The study evaluates various machine learning and deep learning models, with a focus on stacked ensemble methods. The Binary Classification Results leveraging the Preprocessing-and-PCA-on-Heart-Disease-dataset, the study evaluated several models including Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, Deep Learning model, and Logistic Regression. The results indicate that the Stacking Ensemble model achieved the highest accuracy of 90.2%, outperforming individual models where Random Forest (88.5%), SVM (89.1%), XGBoost (89.7%), Gradient Boosting (88.0%) and Deep Learning (89.1%). The Multiclass Classification study using the UCI Heart Disease dataset, when applied the same models, the Stacking Ensemble model achieved an accuracy of 64.1%, which, although superior to other individual models such as Random Forest (58.1%), SVM (54.3%), Gradient Boosting (59.2%) and Deep Learning model (57.6%) fell short compared to XGBoost, which attained an accuracy of 65.8%. This outcome highlights the challenges associated with multiclass classification in heart disease prediction.

关键词： Heart Support vector machines Deep learning Accuracy Stacking Predictive models Boosting Ensemble learning Random forests Diseases

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Approximations to Isentropic Planar Magneto-Hydrodynamics Equations by Relaxed Euler-Type Systems

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Chinese Annals of Mathematics,Series B 2024年第3期45卷 413-440页

作者： Yachun LI Zhaoyang SHANG Chenmu WANG Liang ZHAO School of Mathematical Sciences CMA-ShanghaiMOE-LSC and SHL-MACShanghai Jiao Tong UniversityShanghai 200240China School of Finance Shanghai Lixin University of Accounting and FinanceShanghai 201209China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China School of Mathematical Sciences Harbin Engineering UniversityHarbin 150001China Mathematical Modelling&Data Analytics Center Oxford Suzhou Centre for Advanced ResearchSuzhou 215123JiangsuChina

In this paper, the authors consider an approximation to the isentropic planar Magneto-hydrodynamics(MHD for short) equations by a kind of relaxed Euler-type system. The approximation is based on the generalization of the Maxwell law for nonNewtonian fluids together with the Maxwell correction for the Ampe`re law, hence the approximate system becomes a first-order quasilinear symmetrizable hyperbolic systems with partial dissipation. They establish the global-in-time smooth solutions to the approximate Euler-type equations in a small neighbourhood of constant equilibrium states and obtain the global-in-time convergence towards the isentropic planar MHD equations. In addition, they also establish the global-in-time error estimates of the limit based on stream function techniques and energy estimates for error variables.

关键词： Planar MHD equations Relaxation limits Global convergence Stream function

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Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Recognition Framework 1

Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Reco...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Bhaskar, Seelam Ch Vijaya Kalyani, Gedela Al-Fatlawy, Ramy Alsalamy, Fatima Kurdi, Waleed Hadi Madhloom Al-Aboudy, H.M. Sujatha, G. MVSR Engineering College Department of IT Telangana Hyderabad India Karpaga Vinayaga College of Engineering and Technology Big Data Analytics Tamil Nadu Chengalpattu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Altoosi University College Department of Computer Science Iraq Mazaya University College Department of Computer Techniques Engineering DhiQar Iraq Arunai Engineering College Department of ECE Tamil Nadu Tiruvannamalai India

ISBN: (纸本)9798350367003

Applications such as industrial automation, healthcare, and environmental monitoring need the use of wireless sensor networks (WSNs). However, due to their dispersed organizational makeup, they have become vulnerable to security risks, particularly clone assaults. To protect confidentiality, availability, and confidentiality, several attacks must be recognized and prevented. This project aims to offer an effective method for identifying and averting clone assaults. To identify cloned both nationally and internationally use a low-cost verification process. In this study, we offer a new adaptive sea-horse optimized light gradient boosting machine (ASHO-LGBM) technique for protecting the network against node identity duplicates. The ASHO approach is used in the ASHO-LGBM framework to improve the recognition accuracy of the light gradient boosting machine (LGBM) characteristics. The replications with the nodes intrusion detection (ID) are used to choose a most trustworthy communication mode. The procedure is intended to be implemented and used for gathering data through an internet component. Using a Python tool, the suggested technique is simulated and its delay, packet delivery ratio, packet drop, and energy are evaluated. When compared to other approaches, the study's results show that the ASHO-LGBM strategy's performance analysis achieves the highest accuracy rate. © 2024 IEEE.

关键词： Sensor nodes

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Machine Learning Techniques for Autism Spectrum Disorder: current trends and future directions

Machine Learning Techniques for Autism Spectrum Disorder: cu...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Kainat Khan Rahul Katarya Department of Computer Science & Engineering Big Data Analytics and Web Intelligence Laboratory Delhi Technological University New Delhi India

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD.

关键词： Autism Medical treatment Machine learning Market research Medical tests Biology data mining

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Automation Through Machine Learning: Reinvigorating Cyber Security 15

Automation Through Machine Learning: Reinvigorating Cyber Se...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bagade, Jayashree V. Dari, Sukhvinder Singh Dhivrani, Rekha Chowhan, Santosh Ravivarman, G. Janakiraman, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Symbiosis International Symbiosis Law School Nagpur Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India

ISBN: (纸本)9798350370249

Cyber security is predicated heavily on manual efforts to fight malicious assaults, but such processes are increasingly turning into overwhelmed by means of the quantity of threats - a hassle worsened in the course of the global COVID-19 pandemic. This has highlighted the importance of leveraging device studying (ML) to automate cyber protection methods. In this article, we speak how ML may be used to optimize the detection, prevention, and remediations of malicious assaults in an extensive kind of applications and offerings. We discover the various procedures being evolved that may assist make the identity of threats greater accurate and greater effortlessly to be had. We additionally recollect the capacity challenges associated with the software of ML strategies, along with the potential for unfair bias in opposition to positive instructions of customers, and become aware of methods for addressing those issues. Our end emphasizes the significance of leveraging ML in order to enable automation in cyber safety, as well as to make sure the detection of state-of-the-art new threats. © 2024 IEEE.

关键词： Cyber Instructions Malicious Procedures Threats

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Real-Time Fatigue Monitoring System in Diverse Driving Scenarios

Real-Time Fatigue Monitoring System in Diverse Driving Scena...

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Thasnimol Valuthottiyil Shajahan Babji Srinivasan Rajagopalan Srinivasan Dept. Applied Mechanics & Biomedical Engineering Indian Institute of Technology Madras Chennai India Dept. Applied Mechanics & Biomedical Engineering American Express Lab for Data Analytics Risk and Technology Indian Institute of Technology Madras Chennai India Dept. Chemical Engineering American Express Lab for Data Analytics Risk and Technology Indian Institute of Technology Madras Chennai India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

Monitoring driver fatigue is critical in safety-critical domains like defence vehicles, aircraft, and commercial transportation to ensure safety and efficiency. Operational errors due to cognitive impairment, physical fatigue, and distractions are major causes of accidents. Our real-time fatigue monitoring system assesses users’ physiological and behavioural states, including head movements, to detect fatigue and distraction. Human studies validate its effectiveness in capturing facial features and detecting fatigue with minimal data loss. This non-intrusive solution can be customised and deployed across diverse driving scenarios to enhance vehicle crew performance and safety.

关键词： Space vehicles Measurement Fatigue Real-time systems Hardware Safety Biomedical monitoring

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Monitoring Recombinant Protein Titer in Escherichia Coli Fermentations Using Filtering Techniques

Monitoring Recombinant Protein Titer in Escherichia Coli Fer...

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IEEE International Conference on Big data

作者： Paul Okafor Zahed Siddique Talayeh Razzaghi Data Science and Analytics Institute University of Oklahoma Norman USA School of Aerospace and Mechanical Engineering University of Oklahoma Norman USA School of Industrial and Systems Engineering University of Oklahoma Norman USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Recombinant protein production in Escherichia coli (E. coli) is crucial for biotechnological applications, but optimizing protein titer is challenging and often requires iterative experimentation and expert guidance. This study utilizes advanced filtering techniques—Kalman, particle, bilateral, and extended Kalman filters—to enhance titer estimation accuracy using fermentation data provided by Cytovance Biologics. The proposed techniques effectively impute missing titer values and are validated on a secondary dataset from Chinese Hamster Ovary (CHO) cell lines, demonstrating robustness and accuracy. By reducing the need for extensive sampling and iterative experimentation, these filtering techniques enable predictive control over fermentation conditions. One significant cost-saving advantage is identifying the protein titer plateau, allowing lab scientists to conclude the fermentation process at the optimal time. This approach ensures maximum protein production while preventing unnecessary resource consumption. As a result, this method significantly lowers the overall cost of recombinant protein production in E. coli while enhancing process efficiency.

关键词： Proteins Accuracy Costs Filtering Escherichia coli Estimation Production Fermentation Kalman filters Iterative methods

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