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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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Predicting COVID-19 Transmission in Southern California with Machine Learning Methods

Predicting COVID-19 Transmission in Southern California with...

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Han Li Ran Wei Wenyu Wang Nanpeng Yu School of Computer Science and Engineering University of California Riverside Riverside USA School of Public Policy University of California Riverside Riverside USA Department of Electrical and Computer Engineering University of California Riverside Riverside USA

ISBN: (数字)9798350352962

ISBN: (纸本)9798350352979

The COVID-19 pandemic has posed significant chal-lenges to global health, economies, and social structures since its emergence in late 2019. Southern California, a region with unique characteristics and diverse communities, has experienced difficul-ties in controlling the virus's spread. Understanding the factors that influence COVID-19 transmission in Southern California is crucial for informing public health policies and mitigating the virus's impact on the communities. In this study, we collected weekly zip code level data from March to December 2020 in Southern California and applied various machine learning algorithms, such as graph neural networks (GNN), multi-layer perceptrons (MLP), and gradient boosting trees (XGBoost), to model the spread of COVID-19. Our main contributions include the development of various COVID-19 forecasting models that incorporate socioeconomically significant variables and zip code level mobility data, coupled with variable impact assessments to provide valuable insights for informing future public health policies and strategies. Our predictive model not only attains high levels of accuracy in forecasting but also facilitates the understanding of how each variable contributes to the final outcome. These findings have the potential to enhance our understanding of COVID-19 transmission patterns and inform targeted mitigation strategies in the region.

关键词： COVID-19 Analytical models Pandemics Sociology Predictive models Data models public healthcare

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Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark

arXiv

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arXiv 2024年

作者： Pazho, Armin Danesh Yao, Shanle Noghre, Ghazal Alinezhad Ardabili, Babak Rahimi Katariya, Vinit Tabkhi, Hamed Department of Electrical and Computer Engineering University of North Carolina Charlotte United States Public Policy Program University of North Carolina Charlotte United States

Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors that deviate from normal. At its core, human-centric VAD faces substantial challenges, such as the complexity of diverse human behaviors, the rarity of anomalies, and ethical constraints. These challenges limit access to high-quality datasets and highlight the need for a dataset and framework supporting continual learning. Moving towards adaptive human-centric VAD, we introduce the HuVAD (Human-centric privacy-enhanced Video Anomaly Detection) dataset and a novel Unsupervised Continual Anomaly Learning (UCAL) framework. UCAL enables incremental learning, allowing models to adapt over time, bridging traditional training and real-world deployment. HuVAD prioritizes privacy by providing de-identified annotations and includes seven indoor/outdoor scenes, offering over 5× more pose-annotated frames than previous datasets. Our standard and continual benchmarks, utilize a comprehensive set of metrics, demonstrating that UCAL-enhanced models achieve superior performance in 82.14% of cases, setting a new state-of-the-art (SOTA). The dataset can be accessed at https://***/TeCSAR-UNCC/HuVAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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DAY-AHEAD DISPATCH RESCHEDULING FOR CONGESTION MITIGATION UNDER OPTIMAL POWER FLOW 13

DAY-AHEAD DISPATCH RESCHEDULING FOR CONGESTION MITIGATION UN...

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13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2022

作者： Kelepouris, Nikolaos S. Bouhouras, Aggelos S. Koltsaklis, Nikolaos Panapakidis, Ioannis Tsiatsios, Constantinos Christoforidis, Georgios C. Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Energy & Environmental Policy Laboratory School of Economics Business and International Studies University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering University of Thessaly Volos Greece

ISBN: (纸本)9781839538445

In this paper the problem of congestion management in the power grid under the application of the day-ahead dispatch schedule, is faced based on developing a day-ahead guideline regarding the required modifications to the power units’ output to yield a more uniform loading to the grid’s lines. These modifications result by comparing the dispatch schedule provided by an optimal power flow (OPF) analysis for the grid, under the objective function of power loss minimization, with the schedule for the day-ahead dispatch schedule. The point here is that the solution for the latter approach is based on an economical perspective while the former one on a technical one. The proposed approach is tested on the benchmarked 118 bus system which was modified to simulate the Greek transmission network. The day-ahead schedule is initially applied on the system to depict possible congestion issues via a power flow (PF) analysis. Then, an OPF analysis is applied in order to reschedule the power output of the power units, within their operational limits, aiming at power loss minimization. The results indicate that congestion mitigation in terms of lines’ loading below their thermal limit could result under proper and guided alterations for the power units output. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Electric power transmission networks

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Classifying Bicycle Infrastructure Using On-Bike Street-Level Images

Classifying Bicycle Infrastructure Using On-Bike Street-Leve...

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International Conference on Intelligent Transportation

作者： Kal Backman Ben Beck Dana Kulić Department of Electrical and Computer Systems Engineering School of Public Health and Preventive Medicine Monash University

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

While cycling offers an attractive option for sus-tainable transportation, many potential cyclists are discouraged from taking up cycling due to the lack of suitable and safe infrastructure. Efficiently mapping cycling infrastructure across entire cities is necessary to advance our understanding of how to provide connected networks of high-quality infrastructure. Therefore we propose a system capable of classifying available cycling infrastructure from on-bike smartphone camera data. The system receives an image sequence as input, temporally analyzing the sequence to account for sparsity of signage. The model outputs cycling infrastructure class labels defined by a hi-erarchical classification system. Data is collected via participant cyclists covering 7,006Km across the Greater Melbourne region that is automatically labeled via a GPS and OpenStreetMap database matching algorithm. The proposed model achieved an accuracy of 95.38%, an increase in performance of 7.55% compared to the non-temporal model. The model demonstrated robustness to extreme absence of image features where the model lost only 6.6% in accuracy after 90% of images being replaced with blank images. This work is the first to classify cycling infrastructure using only street-level imagery collected from bike-mounted mobile phone cameras, while demonstrating robustness to feature sparsity via long temporal sequence analysis.

关键词： Accuracy Databases Bicycles Feature extraction Cameras Robustness Vectors Image sequences Data mining Global Positioning System

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cpp11armadillo: An R Package to Use the Armadillo C++ Library

arXiv

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arXiv 2024年

作者： Sepúlveda, Mauricio Vargas Malamud, Jonathan Schneider Department of Political Science University of Toronto Munk School of Global Affairs and Public Policy University of Toronto Canada Department of Electrical and Computer Engineering University of Toronto Canada

This article introduces ‘cpp11armadillo’, a new R package that integrates the powerful Armadillo C++ library for linear algebra into the R programming environment. Targeted primarily at social scientists and other non-programmers, this article explains the computational benefits of moving code to C++ in terms of speed and syntax. We provide a comprehensive overview of Armadillo’s capabilities, highlighting its user-friendly syntax akin to MATLAB and its efficiency for computationally intensive tasks. The ‘cpp11armadillo’ package simplifies a part of the process of using C++ within R by offering additional ease of integration for those who require high-performance linear algebra operations in their R workflows. This work aims to bridge the gap between computational efficiency and accessibility, making advanced linear algebra operations more approachable for R users without extensive programming backgrounds. © 2024, CC BY-NC-SA.

关键词： MATLAB

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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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Performance Evaluation of HV Equipment in a Powerplant Switchyard for Life Extension, Practical Experience in Thailand 10

Performance Evaluation of HV Equipment in a Powerplant Switc...

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10th International Conference on Condition Monitoring and Diagnosis, CMD 2024

作者： Suwanasri, Thanapong Panmala, Nattapon Suwanasri, Cattareeya Thanasettagone, Thanachat Lacharochana, Sakda The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Technical Education Department of Teacher Training in Electrical Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Unipower Engineering Co. Ltd. Bangkok Thailand Global Power Synergy Public Company Limited Bangkok Thailand

ISBN: (纸本)9788986510225

in a 115 kV switchyard substation, there are several major equipment that are gradually deteriorated due to ageing and degradation. Those equipment are one of the important element in a power generating utility to supply the reliable electricity to diverse customers. Therefore, their failures would cause catastrophic consequences and lead to long outage time. Thus, the need for condition and performance assessments of those switchyard equipment for life extension purposes is obligatory to ensure system reliability, to meet customer requirements and to optimize the investment cost. To access the condition of such high voltage equipment, protective relays, building structure and equipment foundations, their technical specification as well as visual inspection and preventive maintenance results of individual equipment must be systematically analyzed to determine the possibility for life extension according to manufacturer guideline as well as international standards and recommendations for condition aging assessment representing in term of conditional aging index. While the other aspects as age, rating adequacy, technology obsolescence, satisfaction in operation, failure rate and defect severity are required to determine the need for renovation/replacement known as replacement index. Then, the proposed methodology has been employed to assess the condition and performance of each equipment such as every major high voltage equipment, control building, protective relays in a switchyard of power station with satisfactory result. © 2024 The Korean Institute of electrical Engineers (KIEE).

关键词： Obsolescence

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Colorectal Polyp Localization: From Image Restoration to Real-time Detection with Deep Learning 17

Colorectal Polyp Localization: From Image Restoration to Rea...

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17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Manshadi, Mahsa Dehghan Mousavi, Milad Golzarian, Arian Soltani, Madjid Mosavi, Amir K.N. Toosi University of Technology Department of Mechanical Engineering Tehran Iran University of Waterloo Department of Electrical and Computer Engineering Ontario Canada Obuda University John von Neumann Faculty of Informatics Budapest Hungary University of Public Service Budapest Hungary

ISBN: (纸本)9798350321104

Increasing demands for artificial intelligence tools in the medical sector show the huge interest of physicians. In recent years, one of the most effective assistant systems in this field is the real-time detection of early-stage colorectal polyps during colonoscopy. Since even experienced physicians may miss polyps during colonoscopy, the real-time assistance system is designed to prevent this and hence contributes to diminish the number of missed critical cases. One challenge in this field is the detection of false positives. These systems are prone to mistake artifacts for colorectal polyps. This review provides an overview over current quality assessment and restoration techniques to make a high-quality training dataset for training deep neural network algorithms. Furthermore, four of the latest, fastest, and most accurate methods are introduced and analyzed in the rest of the review. Our main contribution is to provide an analysis of current methods used to detect colorectal polyps. We present a list of available datasets and present a range of challenges colorectal cancer detection systems face. © 2023 IEEE.

关键词： Image reconstruction

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Classifying Bicycle Infrastructure Using On-Bike Street-Level Images

arXiv

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arXiv 2024年

作者： Backman, Kal Beck, Ben Kulić, Dana Department of Electrical and Computer Systems Engineering School of Public Health and Preventive Medicine Monash University Australia

While cycling offers an attractive option for sustainable transportation, many potential cyclists are discouraged from taking up cycling due to the lack of suitable and safe infrastructure. Efficiently mapping cycling infrastructure across entire cities is necessary to advance our understanding of how to provide connected networks of high-quality infrastructure. Therefore we propose a system capable of classifying available cycling infrastructure from on-bike smartphone camera data. The system receives an image sequence as input, temporally analyzing the sequence to account for sparsity of signage. The model outputs cycling infrastructure class labels defined by a hierarchical classification system. Data is collected via participant cyclists covering 7,006Km across the Greater Melbourne region that is automatically labeled via a GPS and OpenStreetMap database matching algorithm. The proposed model achieved an accuracy of 95.38%, an increase in performance of 7.55% compared to the non-temporal model. The model demonstrated robustness to extreme absence of image features where the model lost only 6.6% in accuracy after 90% of images being replaced with blank images. This work is the first to classify cycling infrastructure using only street-level imagery collected from bike-mounted mobile phone cameras, while demonstrating robustness to feature sparsity via long temporal sequence analysis. © 2024, CC BY.

关键词： Bicycles

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