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Detecting personal protective equipment (PPE) utilising YOLOv8 in a federated learning environment

AIP Conference Proceedings 2024年第1期3220卷

作者： Ioannis Makris Anastasios Lytos Konstantinos Kyranou Vasileios Argyriou Thomas Lagkas Konstantinos-Filippos Kollias George F. Fragoulis Panagiotis Sarigianndis METAMIND INNOVATIONS IKE KILA - Administration Building of Univ. of Western Macedonia Kozani Greece SIDROCO Holdings LTD Karyatis 8 Leoforo Kyriakou Matsi 23 4th Floor Office 401 Nicosia Cyprus Dept. of Computing & Information Systems Penrhyn Road Kingston Univ. London London United Kingdom Dept. of Computer Science School of Science International Hellenic University Kavala Campus Kavala Greece Dept. of Electrical And Computer Engineering ZEP Campus University of Western Macedonia Kozani Greece

In the construction industry, where worker safety is a paramount concern, the effective use of PPE is crucial. This study delves into an innovative approach by exploring the integration of Federated Learning (FL) with the YOLOv8 architecture for enhanced PPE object detection. A specialized construction safety dataset was utilized to conduct the corresponding experiments, comparing FL against conventional local training methodologies. The results have clearly shown the superiority of FL in terms of accuracy improvement of PPE object detection which opens a lot of paths for creative solutions leading to the progress of safety performance at construction sites, at the same time, while observing the strictest privacy and security issues for personal data that is a primary consideration in our data-driven era. The proposed solution achieved an average mAP of 79.52%, and an average Recall of 78.49%, proving its effectiveness over the centralized training.

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Corrigendum to “Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems” Appl. Soft Comput. 132 (2023) 109909

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Applied Soft Computing 2025年 169卷

作者： Hafiz Tayyab Rauf Jiechao Gao Ahmad Almadhor Ali Haider Yu-Dong Zhang Fadi Al-Turjman Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Department of Computer Science University of Virginia USA Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka 72388 Saudi Arabia Department of Computer Science University of Gujrat Gujrat Pakistan School of Informatics University of Leicester Leicester LE1 7RH UK Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Mersin 10 Turkey

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Search@Home: A commercial off-the-shelf environment for investigating optimization problems

arXiv

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arXiv 2020年

作者： Fredericks, Erik M. Moore, Jared M. Dept. of Computer Science & Engineering Oakland University RochesterMI48309 United States School of Computing and Information Systems Grand Valley State University AllendaleMI49401 United States

Search heuristics, particularly those that are evaluation-driven (e.g., evolutionary computation), are often performed in simulation, enabling exploration of large solution spaces. Yet simulation may not truly replicate real-world conditions. However, search heuristics have been proven to be successful when executed in real-world constrained environments that limit searching ability even with broad solution spaces. Moreover, searching in situ provides the added benefit of exposing the search heuristic to the exact conditions and uncertainties that the deployed application will face. software engineering problems can benefit from in situ search via instantiation and analysis in real-world environments. This paper introduces Search@Home, an environment comprising heterogeneous commercial off-the-shelf devices enabling rapid prototyping of optimization strategies for real-world problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Heuristic algorithms

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IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II)

IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive...

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IEEE International Conference on Cognitive Informatics

作者： Witold Kinsner Haibin Zhu George Baciu Guiming Luo Fernando Rubio Jie Sui Runhe Huang Hironori Hiraishi Jun Peng Liang Chen FI2CICC Dept of ECE Univ. of Manitoba Winnipeg Canada FI2CICC Nipissing University ON Canada FI2CICC Dept. of Computing Hong Kong Polytechnic University Hong Kong FI2CICC Software Institute Tsinghua University Beijing China FI2CICC Dept. of Computer Systems &#x0026 Computation Complutense Univ. of Madrid Spain FI2CICC Aberdeen University Aberdeen UK FI2CICC Faculty of Computer and Information Sciences Hosei University Japan Faculty of Engineering Ashikaga University Tochigi Japan FI2CICC School of AI Chongqing Univ. of Science and Technology Chongqing China Dept. of Computer Science Univ. of Northern British Columbia BC Canada

ISBN: (纸本)9781665421201

Cognitive Computing (CC) is a contemporary field of fundamental intelligence theories and general AI technologies triggered by the transdisciplinary development in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part II) on the theoretical foundations of CI/CC and recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, cognitive robots, autonomous systems, intelligent vehicles, and safety-and-mission-critical systems.

关键词： Knowledge engineering Intelligent vehicles Autonomous systems Conferences Cognitive informatics Cognitive robotics Cognitive systems

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Cell quantization tool for anterior chamber OCT images

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Transactions of the Korean Institute of Electrical Engineers 2020年第7期69卷 1073-1080页

作者： Lee, Yeongseop Kang, Taeseen Han, Yongseop Kim, Jinhyun Kim, Kyong Hoon Lee, Seongjin Dept. of Aerospace and Software Engineering Gyeongsang National University Korea Republic of Dept. of Informatics Gyeongsang National University Korea Republic of Dept of Ophthalmology Gyeongsang National University Changwon Hospital Changwon Korea Republic of Dept of Ophthalmology College of Medicine Gyeongsang National University Jinju Korea Republic of Dept of Information and Communication Engineering Gyeongsang University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of

In general, ophthalmologists visually grade the state of a patient by counting the cells within the anterior chamber OCT image. The manual cell counting method is highly inaccurate and spends a lot of time to determine the progress of the patient. In this work, we develop a new tool to count cells in anterior chamber OCT images to aid doctors in analyzing the state of patients. We exploit image processing to remove noises from images, segment the anterior chamber, and quantize the cells in OCT images. We also provide statistics to aid the doctors in determining the progress of the patients. © Korean Institute of Electrical Engineers

关键词： Cells

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Communication Management in Global software Development Projects 4

Communication Management in Global Software Development Proj...

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4th International Conference on information Retrieval and Knowledge Management: Diving into Data sciences, CAMP 2018

作者： Jusoh, Yusmadi Yah Haizan Nor, Rozi Nor Mahmood, Bashar Amir Wafeeq, Mustafa Thamir Ali, Mohamed Abdullahi Baihaqi Jusoh, Muhammad Nur Dept. of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor43400 Malaysia

ISBN: (纸本)9781538638125

Many organizations that have the global software development (GSD) projects use communication technologies to connect their virtual teams. However, the virtual team faces various challenges and issues in the process of the GSD. One of the significant challenges is obtaining an efficient communication among team members. This study focuses on the communication factors among the virtual teams highlighted in the literature. The communication factors are related to the temporal distances, geographical distances, socio-cultural, access to training, technological problems that hindering communications, the communication within the status of development process, personal communication skills and language differences. The objective of this study is to examine the communication factors and identify the related issues which are commonly occurring between the virtual teams in the global software development. A survey was conducted in different sectors. The findings indicate that some of important points related to the communication factors contribute to the success of the GSD. © 2018 IEEE.

关键词： Project management

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Bayesian Neural Networks Uncertainty Quantification with Cubature Rules

Bayesian Neural Networks Uncertainty Quantification with Cub...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peng Wang Nidhal C. Bouaynaya Lyudmila Mihaylova Jikai Wang Qibin Zhang Renke He Automatic Control & Systems Engineering Dept. The University of Sheffield Information and Navigation College Air Force Engineering University The 32nd Research Institute of CETC Department of Automation University of Science and Technology of China Department of Electrical and Computer Engineering Rowan University

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

关键词： Neural networks Uncertainty Bayes methods Computational modeling Gaussian distribution Two dimensional displays Weather forecasting

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Disruptive IoT-Based Healthcare Insurance Business Model

Disruptive IoT-Based Healthcare Insurance Business Model

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IEEE International Conference on Computational science and engineering, CSE

作者： Maryam Al Thawadi Farag Sallabi Mamoun Awad Khaled Shuaib Dept. of Information Systems and Security UAE University Al Ain UAE Dept. of Computer and Network Engineering UAE University Al Ain UAE Dept. of Computer Science and Software Engineering UAE University Al Ain UAE

information revolution and technology growth have made a considerable contribution to restraining the cost expansion and empowering the customer. They disrupted most business models in different industries. The customer-centric business model has pervaded the different sectors. Smart healthcare has made an enormous shift in patient life and raised their expectation of healthcare services quality. Healthcare insurance is an essential business in the healthcare sector; patients expect a new business model to meet their needs and enhance their wellness. This paper presents a smart healthcare architecture based on the recent development of information and communications technology. Then develops a disruptive healthcare insurance business model that adapts to this architecture. The paper presents a use case to show in part the application of this business model.

关键词： Insurance Temperature sensors Diseases Solid modeling

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Correction to: LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model

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Remote Sensing in Earth systems sciences 2025年第2期8卷 473-473页

作者： Krushnasamy, V. S. Al-Omari, Omaia Sundaram, Ajith Varghese, Issac K. Muniyandy, Elangovan Rao, M. V. A. L. Narasimha Dept. of EIE Dayananda Sagar College of Engineering Bangalore India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of MBA Koneru Lakshmaiah Education Foundation Guntur India

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Sharing of vulnerability information among companies - A survey of Swedish companies

arXiv

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arXiv 2019年

作者： Olsson, Thomas Hell, Martin Host, Martin Franke, Ulrik Borg, Markus Software and Systems Engineering Lab RISE Research Institutes of Sweden Lund Dept. of Electrical and Information Technology Lund University Lund Dept. of Computer Science Lund University Lund Software and Systems Engineering Lab RISE Research Institutes of Sweden Kista

software products are rarely developed from scratch and vulnerabilities in such products might reside in parts that are either open source software or provided by another organization. Hence, the total cybersecurity of a product often depends on cooperation, explicit or implicit, between several organizations. We study the attitudes and practices of companies in software ecosystems towards sharing vulnerability information. Furthermore, we compare these practices to contemporary cybersecurity recommendations. This is performed through a questionnairebased qualitative survey. The questionnaire is divided into two parts: the providers' perspective and the acquirers' perspective. The results show that companies are willing to share information with each other regarding vulnerabilities. Sharing is not considered to be harmful neither to the cybersecurity nor their business, even though a majority of the respondents consider vulnerability information sensitive. However, the companies, despite being open to sharing, are less inclined to proactively sharing vulnerability information. Furthermore, the providers do not perceive that there is a large interest in vulnerability information from their customers. Hence, the companies' overall attitude to sharing vulnerability information is passive but open. In contrast, contemporary cybersecurity guidelines recommend active disclosure and sharing among actors in an ecosystem. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

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