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5th College of Science International Conference on Recent Trends in Information Technology, CSCTIT 2022

作者： Al-Ibadi, Samer Emad Neama Cevik, Mesut Altinbas University Department of Electrical and Computer Engineering Istanbul Turkey Information and Telecom Public Company Ministry of Communications Baghdad Iraq Altinbas University Istanbul Turkey Electrical and Computer Engineering Department Istanbul Turkey

ISBN: (纸本)9798350334920

Difficulty in detecting social media bots - software-controlled accounts that seem as human - has serious consequences. Bots have been used to affect the financial market, political elections, and spreading conspiracy theories. Most approaches detect bots at the account level by studying social media posts and using network structure, system dynamics, content analytics, etc. In this study, we compare the performance of the recurrent neural network (RNN), convolutional neural network (CNN), and feed forward neural network (FNN) in detecting Twitter bots by evaluating the textual content of 10,416,240 tweets from humans and bots. Deep learning models utilize sample data to gauge performance. The CNN model identified recent social bot samples most accurately (97.67 %). In comparison, the RNN model achieved great accuracy (97.04 %) with a longer training time. RNN and CNN algorithms perform well when compared to other deep learning classifiers. © 2022 IEEE.

关键词： Recurrent neural networks

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Operational Modeling of the Nexus Between Water-Distribution and Electricity Systems

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Current Sustainable/Renewable Energy Reports 2025年第1期12卷 1-16页

作者： Gu, Weiying Sioshansi, Ramteen Department of Integrated Systems Engineering The Ohio State University 1971 Neil Avenue ColumbusOH43210-1271 United States Department of Engineering and Public Policy Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States Carnegie Mellon Electricity Industry Center Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States Wilton E. Scott Institute for Energy Innovation Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States

Purpose of Review: We survey operational models of water-distribution systems. Although such modeling is important in its own right, our focus is motivated by the growing desire to examine and manage the nexus between water-distribution and electricity systems. As such, our survey discusses computational challenges in modeling water-distribution systems, co-ordination dynamics between water-distribution and electricity systems, and gaps in the literature. Recent Findings: Modeling water-distribution systems is made difficult by their highly non-linear and non-convex physical properties. Co-ordinating water-distribution and electricity systems, especially with the growing supply and demand uncertainties of the latter, requires fast optimization techniques for real-time system management. Although many works suggest means of co-ordinating the two systems, practical applications are limited, due to the systems having separate and autonomous management and ownership. Nonetheless, recent works are navigating this challenge, by seeking methods to foster improved co-ordination of the two systems while respecting their autonomy. Additionally, with the backdrop of increased security threats, there is a growing need to bolster infrastructure protection, which is complicated by the intertwined nature of the two systems. Summary: By providing a steady supply of potable water to satisfy residential, commercial, agricultural, and industrial demands, water-distribution systems are pivotal components of modern society and infrastructure. The extant literature presents many models and optimization strategies that are tailored for operating water-distribution systems. Yet, there remain unexplored problems, particularly related to simplifying model computation, capturing the flexibility of water-distribution systems, and capturing interdependencies between water-distribution and other systems and infrastructures. Future research that addresses these gaps will allow greater operational

关键词： Water distribution systems

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Carbon-neutral pathway to mitigating transport-power grid cross-sector effects

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The Innovation 2024年第3期5卷 27-29页

作者： Jing Ma Huiwen Kong Jianxiao Wang Haiwang Zhong Bo Li Jie Song Daniel M.Kammen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic EngineeringNorth China Electric Power UniversityBeijing 102206China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China Peking University Ordos Research Institute of Energy Ordos 017000China State Key Laboratory of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China School of Electrical Engineering Guangxi UniversityNanning 530004China Department of Industrial Engineering and Management College of EngineeringPeking UniversityBeijing 100871China Energy and Resources Group and Goldman School of Public PolicyUniversity of CaliforniaBerkeleyBerkeleyCA 94720USA

Dear Editor,The growing demand for transportation energy has brought increasing challenges to reducing greenhouse gas ***,many countries or regions have proposed solutions to achieve carbon-neutral transportation such as the rapid expansion of the global electric vehicle(EV)***,these benefits are not *** the goal of decarbonization in electricity is achieved,the“pseudo net zero emissions”effect of the transportation sector will inevitably be accompanied by a quiet shifting of carbon responsibility.1 This shift is undoubtedly fatal for both net zero emissions at the national level and carbon responsibility at the industry level;however,the corresponding effects have not yet been clarified.

关键词： neutral doubt shifting

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From Lab to Field: Real-World Evaluation of an Ai-Driven Smart Video Solution to Enhance Community Safety

SSRN

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SSRN 2024年

作者： Yao, Shanle Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bourque, Lauren Tabkhi, Hamed University of North Carolina at Charlotte Department of Electrical and Computer Engineering United States University of North Carolina at Charlotte Department of Public Policy United States

This article adopts and evaluates an AI-enabled Smart Video Solution (SVS) designed to enhance safety in the real world. The system integrates with existing infrastructure camera networks, leveraging recent advancements in AI for easy adoption. Prioritizing privacy and ethical standards, pose based data is used for downstream AI tasks such as anomaly detection. Cloud-based infrastructure and mobile app are deployed, enabling real-time alerts within communities. The SVS employs innovative data representation and visualization techniques, such as the Occupancy Indicator, Statistical Anomaly Detection, Bird's Eye View, and Heatmaps, to understand pedestrian behaviors and enhance public safety. Evaluation of the SVS demonstrates its capacity to convert complex computer vision outputs into actionable insights for stakeholders, community partners, law enforcement, urban planners, and social scientists. This article presents a comprehensive real-world deployment and evaluation of the SVS, implemented in a community college environment across 16 cameras. The system integrates AI-driven visual processing, supported by statistical analysis, database management, cloud communication, and user notifications. Additionally, the article evaluates the end-to-end latency from the moment an AI algorithm detects anomalous behavior in real-time at the camera level to the time stakeholders receive a notification. The results demonstrate the system's robustness, effectively managing 16 CCTV cameras with a consistent throughput of 16.5 frames per second (FPS) over a 21-hour period and an average end-to-end latency of 26.76 seconds between anomaly detection and alert issuance. © 2024, The Authors. All rights reserved.

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Ethical Issues in Near-Future Socially Supportive Smart Assistants for Older Adults

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2023年第4期4卷 291-301页

作者： London, Alex John Razin, Yosef S. Borenstein, Jason Eslami, Motahhare Perkins, Russell Robinette, Paul Carnegie Mellon University Department of Philosophy PittsburghPA15213 United States Georgia Institute of Technology School of Aerospace Engineering AtlantaGA30332 United States Georgia Institute of Technology School of Public Policy and Office of Graduate Education AtlantaGA30332 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States University of Massachusetts Lowell Department of Electrical and Computer Engineering LowellMA01854 United States

This paper considers novel ethical issues pertaining to near-future artificial intelligence (AI) systems that seek to support, maintain, or enhance the capabilities of older adults as they age and experience cognitive decline. In particular, we focus on smart assistants (SAs) that would seek to provide proactive assistance and mediate social interactions between users and other members of their social or support networks. Such systems would potentially have significant utility for users and their caregivers if they could reduce the cognitive load for tasks that help older adults maintain their autonomy and independence. However, proactively supporting even simple tasks, such as providing the user with a summary of a meeting or a conversation, would require a future SA to engage with ethical aspects of human interactions which computational systems currently have difficulty identifying, tracking, and navigating. If SAs fail to perceive ethically relevant aspects of social interactions, the resulting deficit in moral discernment would threaten important aspects of user autonomy and well-being. After describing the dynamic that generates these ethical challenges, we note how simple strategies for prompting user oversight of such systems might also undermine their utility. We conclude by considering how near-future SAs could exacerbate current worries about privacy, commodification of users, trust calibration and injustice. © 2020 IEEE.

关键词： Cognitive systems

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Empowering Healthcare with AI: Brain Tumor Detection Using MRI and Multiple Algorithms

Empowering Healthcare with AI: Brain Tumor Detection Using M...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Arowolo, Micheal Olaolu Oluwatobiloba Ajayi, Williams Olawoye, Prisca Babajide, Oluwatomilola Aigbogun, Happiness Eric Damola Salawu, Mukhtar Adebiyi, Marion Olubunmi Adebiyi, Ayodele University of Missouri Department of Electrical Engineering and Computer Science Columbia65201 United States Landmark University Department of Computer Science Omu-Aran Nigeria Western Illinois University Department of Public Health 61455 United States Stowers Institute for Medical Research Kansas CityMO United States Kwara State University Department of Computer Science Malete Nigeria

ISBN: (纸本)9798350358155

Artificial intelligence (AI) has revolutionized neuroimaging, especially in the identification of brain cancers, by giving patients greater control and providing highly accurate diagnostic imaging. utilizing MRI data, this study investigates the feasibility of AI-driven brain tumor diagnosis utilizing methods including K-nearest neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). Careful data preprocessing allowed us to access Kaggle's brain MRI datasets labeled with cancer information, which allowed us to obtain accurate results. While Naive Bayes achieved 60% accuracy and 84% sensitivity, SVM achieved 82% sensitivity and 62% accuracy. Enhanced sensitivity and precision, in addition to 63% accuracy, were shared by Decision Tree and KNN. These findings suggest that AI could be helpful in the multi-method detection strategy that is essential for the correct diagnosis of brain tumors. This study provides clinicians with flexible diagnostic tools powered by artificial intelligence, opening a new chapter in the fight against neurological disorders and for better patient outcomes. The combination of artificial intelligence with magnetic resonance imaging (MRI) holds great promise for the better early detection and treatment planning of brain cancers. As AI keeps changing the healthcare landscape, these studies show how AI can revolutionize diagnostics and the fight against neurological illnesses. © 2024 IEEE.

关键词： Support vector machines

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Analysis of Maintenance Events in Medical Equipment in the Largest Trauma Hospital in Natal/RN 9th

Analysis of Maintenance Events in Medical Equipment in th...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Paz, Lyssandra Moura, Lucas Barreto, Tiago Lima, Marcelo Suassuna, Alice Federal Institute of Education Science and Technology of Rio Grande do Norte RN Natal Brazil Department of Electrical and Computer Engineering Federal University of Rio Grande do Norte RN Natal Brazil Secretary of State for Public Health RN Natal Brazil Department of Electrical Engineering Federal University of Uberlândia MG Uberlândia Brazil

ISBN: (纸本)9783031494093

The Advancements in biomedical technologies have led to the need for a sector responsible for management of all medical equipment in health establishments, thus coming to clinical engineering. There are three basic types of causes of failures that occur in these equipment: human error, technology failure, and external phenomena. Monitoring maintenance events is essential to understand what leads to these failures and, thus, seek solutions to mitigate them. Thus, the present work aims to analyze the requests for maintenance in the largest trauma hospital in Natal, Rio Grande do Norte, categorizing them in an attempt to understand the maintenance occurrences and carelessness with the equipment. For this, a survey of maintenance calls for biomedical equipment was performed, in the period of 3 months (February to May 2022), at the Monsenhor Walfredo Gurgel Hospital in Natal, Rio Grande do Norte. After data collection, it was found that 51.18% of the calls for maintenance corresponded to human error, and that a large part of the occurrences due to human error were related to problems with dirt and the others to false calls arising from the lack of information about correct operation of this equipment. From the classification, it is understood in a process of listening and observation of these professionals, that errors are caused, above all, by the lack of periodic training of the hospital’s medical team. The implementation of a training routine would be a solution to alleviate the occurrences of maintenance in the hospital in question. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hospitals

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Strengthening Capacity in Industrial Revolution 4.0 (IR 4.0): A Case Study on Training Product-service Systems Between the US and Vietnam

Strengthening Capacity in Industrial Revolution 4.0 (IR 4.0)...

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2024 Portland International Conference on Management of engineering and Technology, PICMET 2024

作者： Yip, Man Hang Santarnaria, Ana Sofia Castellanos Babcock, Julia University of Cambridge Centre for Technology Management Department of Engineering United Kingdom National Policy Consensus Center College of Urban and Public Affairs Portland State University Portland United States Maseeh College of Engineering and Computer Science Portland State University Portland United States

ISBN: (纸本)9781890843458

This case study analyzes how value is created in the development process of a collaborative training product-service system (PSS) designed for provincial government officials in Vietnam. This initiative, part of the USAID Strengthening Provincial Capacity (USAID SPC) project, encapsulates a series of training solutions designed to empower officials with key skills in engineering and technology management, including the knowledge to improve global value chains, upgrade technology infrastructure and sustainable production models. A primary driver for the 'four-day course delivery', the PSS analyzed in this study, was to bolster economic competitiveness at the provincial level in the context of Industrial Revolution 4.0 (IR 4.0). This case study offers lessons on stakeholder-value mapping and recommendations for future approaches to training courses development that enhances capacity development. These trainings aligned with the demands of balancing organizational development and public value creation in the context of developing countries transitioning to a knowledge economy, such as Vietnam. The learnings from this case study may provide a blueprint for similar endeavors in other countries navigating the complexities of digital transformation of public services. © 2024 PICMET.

关键词： Product development

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Robust Frequency Estimation Under Additive Mixture Noise

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computers, Materials & Continua 2022年第7期72卷 1671-1684页

作者： Yuan Chen Yulu Tian Dingfan Zhang Longting Huang Jingxin Xu School of Computer and Communication Engineering University of Science&Technology BeijingBeijing100083China School of Automation Science and Electrical Engineering Beihang UniversityBeijing100191China School of Information Engineering Wuhan University of TechnologyWuhan430070China Department of Energy and Public Works Queensland4702Australia

In many applications such as multiuser radar communications and astrophysical imaging processing,the encountered noise is usually described by the finite sum ofα-stable(1≤α<2)*** this paper,a new parameter estimator is developed,in the presence of this new heavy-tailed *** the closed-formPDF of theα-stable variable does not exist exceptα=1 andα=2,we take the sum of the Cauchy(α=1)and Gaussian(α=2)noise as an example,namely,additive Cauchy-Gaussian(ACG)*** probability density function(PDF)of the mixed random variable,can be calculated by the convolution of the Cauchy’s PDF and Gaussian’s *** of the complicated integral in the PDF expression of the ACG noise,traditional estimators,e.g.,maximum likelihood,are analytically not *** obtain the optimal estimates,a new robust frequency estimator is devised by employing the Metropolis-Hastings(M-H)***,to guarantee the fast convergence of the M-H chain,a new proposal covariance criterion is also devised,where the batch of previous samples are utilized to iteratively update the proposal covariance in each sampling *** simulations are carried out to indicate the superiority of the developed scheme,when compared with several conventional estimators and the Cramér-Rao lower bound.

关键词： Frequency estimation additive cauchy-gaussian noise voigt profile metropolis-hastings algorithm cramér-rao lower bound

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Object Detection for Vehicles with Yolo 22

Object Detection for Vehicles with Yolo

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics, SAMI 2024

作者： Maleki, Pouria Ramazani, Abbas Khotanlou, Hassan Ojaghi, Sina Mousavi, Milad Kalinin, Alexey Mosavi, Amir Bu-Ali Sina University Department of Electrical Engineering Hamedan Iran Bu-Ali Sina University Department of Computer Engineering Hamedan Iran University of Tehran School of Computer and Electrical Engineering Tehran Iran K. N. Toosi University of Technology Tehran Iran Abylkas Saginov Karaganda Technical University Kazakhstan Obuda University John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

ISBN: (纸本)9798350317206

The rapid urban population growth has intensified the challenges associated with urban and suburban traffic, necessitating effective traffic control and management. The efficient movement of emergency vehicles, particularly ambulances and fire trucks, has emerged as a critical concern. This article presents the Vehicle Dataset, a comprehensive benchmark for object detection, encompassing seven vehicle classes, including cars, motorcycles, buses, trucks, vans, ambulances, and fire trucks. The dataset, includes 29,759 meticulously labeled images obtained from freely available online sources, enables the identification of traffic patterns through deep neural networks. Notably, the dataset emphasizes the facilitation of emergency vehicle movement. The Vehicle Dataset in this study is divided into three subsets, with 25,369 images assigned for training, 2,896 for validation, and 1,494 for testing. Through the utilization of the dataset, object detection algorithms based on YOLO versions 5, 6, and 7 have been trained. Remarkably, YOLO version 7 has yielded outstanding results, achieving a final precision of 85% and a mAP of 85% at an IoU threshold of 0.5. Moreover, at IoU thresholds ranging from 0.5 to 0.9, a mAP of 64% has been attained. The Vehicle Dataset represents significant resource for researchers and practitioners in the transportation and traffic management field. Its inclusion of emergency vehicles such as ambulances and fire trucks contribute to its unique value. This article presents a detailed exploration of the dataset, underscoring its significance in advancing object detection methodologies. © 2024 IEEE.

关键词： Object detection

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