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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Cordova, Jonathan Jiang, Xunfei Jia, Xudong Li, Bingbing California State University Department of Computer Science NorthridgeCA91330 United States College of Engineering and Computer Science California State University NorthridgeCA91330 United States California State University Department of Manufacturing Systems Engineering and Management NorthridgeCA91330 United States

ISBN: (纸本)9798350374889

Vehicle detection and classification are important for intelligent transportation planning, directly impacting traffic flow and management efficiency. There have been a lot of research on vehicle detection and classification for autonomous driving, but research on roadside traffic flow has not been fully investigated. This research explores various methods for vehicle detection and classification that could be applied to 3D roadside traffic flow analysis. Using data collected from a Velodyne 32C Lidar sensor 3D camera and a stereo-based depth 2D camera, this project implemented a Complex-YOLO projection-based model, by converting 3D point cloud data into a 2D bird-eye-view projection and applying the YOLO model for vehicle detection and classification. Our experimental results demonstrated that using transfer learning as a training technique, along with normalization of the rotation angle, enhanced the model performance for vehicle detection and classification. Without normalization of the rotation angle, the highest precision achieved was 89.71 %, with a recall of 85.15 %. With normalization of the rotation angle, the best model achieved a precision of 92.20 % with a recall of 84.90 %, demonstrating the effectiveness of both transfer learning and normalization in handling and aligning the dataset for more effective learning. © 2024 IEEE.

关键词： Vehicle detection

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Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction 19

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Re...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Hyeonmin Kim, Dongchan Beom, Donggyu Byun, Gyurin Vo, Van-Vi Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9798331507817

This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural Networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. The model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. The attack type of each timestamp were visualized using distinct colors to the grid images. These images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. This graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. This research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences. © 2025 IEEE.

关键词： Convolutional Long Short-Term Memory Cyberattack Detection Deep Learning Graph Neural Networks Network Traffic Prediction

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Early Prediction and Diagnosis Cardiovascular Disease Using Deep Learning Models

Early Prediction and Diagnosis Cardiovascular Disease Using ...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Rani, Rudra Yamini Prasad, Mudarakola Lakshmi Reddy, Induru Sunnihith Tayubi, Iftikhar Aslam Sultan, Ghazala Reddy, Pundru Chandra Shaker VNR Vignana Jyothi Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Dundigal India Computer Science and Business Systems RMK Engineering College Tamilnadu Tiruvallur India King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia Aligarh Muslim University Department of Computer Science Uttar Pradesh Aligarh India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350372502

Cardiovascular disease (CVD) risk assessment and prognosis in otherwise healthy people is an important part of disease management. Early detection and diagnosis of CVD, supported by the extensive health data on the condition already stored in hospital systems, has the ability to greatly improve disease outcomes. Clinical practice for the management of Cardiovascular Diseases (CVDs) stands to benefit greatly from the use of machine learning approaches. Patients and the healthcare system can save money thanks to these methods, which can be used to create evidence-based clinical guidelines and management algorithms. This will cut down on expensive and time-consuming clinical and laboratory examinations. This study suggests creating new deep learning algorithms that can automatically identify important variables and detect early-stage heart disease;these algorithms should be resilient, effective, efficient, and unique in order to improve early anticipation and interference for CVDs. An F1-score of around 89% is produced by the suggested Catboost model. Hence, it outperformed numerous other state-of-the-art methods by maximizing classification efficiency with better correctness and precision. © 2024 IEEE.

关键词： Hospitals

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On tuning of ADRC with competing design indices:a quantitative study

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Control Theory and Technology 2023年第1期21卷 16-33页

作者： Yutao Zhao Yi Huang Zhiqiang Gao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Department of Electrical Engineering and Computer Science Center for Advanced Control TechnologiesCleveland State UniversityClevelandOH 44115USA

Controller optimization has mostly been done by minimizing a certain single cost *** practice,however,engineers must contend with multiple and conflicting considerations,denoted as design indices(DIs)in this *** to account for such complexity and nuances is detrimental to the applications of any advanced control *** paper addresses this challenge heads on,in the context of active disturbance rejection controller(ADRC)and with four competing DIs:stability margins,tracking,disturbance rejection,and noise *** this end,the lower bound for the bandwidth of the extended state observer is first established for guaranteed closed-loop ***,one by one,the mathematical formula is meticulously derived,connecting each DI to the set of controller *** our best knowledge,this has not been done in the context of *** formulas allow engineers to see quantitatively how the change of each tuning parameter would impact all of the DIs,thus making the guesswork *** example is given to show how such analytical methods can help engineers quickly determine controller parameters in a practical scenario.

关键词： Uncertain systems Active disturbance rejection control(ADRC) Design indices(DIs) Extended state observer(ESO)

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A Real-Time Traffic Monitoring System Based on YOLOv8 for Vehicle Detection and Classification

A Real-Time Traffic Monitoring System Based on YOLOv8 for Ve...

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2024 IEEE International Conference on Green Energy and Smart systems, GESS 2024

作者： Castillo, Jimwell Rosculete, Robin Babayans, Raymond Gharapeti Merzoian, Anthony Groves, Kaylee Calva, Gisela Dollente, Alma Jia, Xudong Li, Bingbing Jiang, Xunfei California State University Department of Computer Science NorthridgeCA91330 United States College of Engineering and Computer Science California State University NorthridgeCA91330 United States California State University Department of Manufacturing Systems Engineering and Management NorthridgeCA91330 United States

ISBN: (纸本)9798350379617

Urban traffic congestion presents significant challenges that require efficient and intelligent management solutions. Traditional traffic monitoring methods lack precision and adapt-ability, especially under varying conditions. Our project aims to develop a real-time traffic monitoring system using 2D camera technology and machine learning algorithms to address these gaps. The system includes a camera array for data collection, a data processing unit, and a user interface for real-time monitoring. Our solution employs YOLOv8 for accurate vehicle detection and classification and is demonstrated through rigorous testing. The system offers a robust framework for enhancing urban traffic management, reducing congestion, and improving road safety. Experimental results highlight the system's effectiveness in data aggregation, storage, and real-time processing. © 2024 IEEE.

关键词： Traffic congestion

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Classification of Stroke Using Machine Learning Techniques: Review Study

Classification of Stroke Using Machine Learning Techniques: ...

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2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Sawan, Aktham Awad, Mohammed Qasrawi, Radwan Al Quds University Department of Computer Science Jerusalem Palestine Arab American University Department of Computer Systems Engineering Ramallah Palestine Istinye University Department of Computer Engineering Istanbul34010 Turkey Al Quds University Jerusalem Palestine

ISBN: (纸本)9798350347074

Presently, stroke is the leading cause of adult injury worldwide. The World Health Organization estimates that each year 15 million people around the world suffer a stroke. Five million of them die, and another five million are disabled for life. There is a chance to dramatically enhance the classification of strokes in the early stages. In this article, we reviewed all portable devices that produced electroencephalogram(EEG) data and all machine learning (ML) methods and deep-learning methods used to identify stroke using EEG data, and we noted that the amount of work on ML and deep learning in analyzing EEG data have increased rapidly in recent years. Such analysis has achieved greater precision compared to that conventional methods. We also discussed in this study the opportunities and key challenges for improving the accuracy of future work. © 2023 IEEE.

关键词： Electroencephalography

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ACO-Based Hyperparameter Tuning of a DL Model for Lung Cancer Prediction 5

ACO-Based Hyperparameter Tuning of a DL Model for Lung Cance...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Dilip, Kumar Ghantasala, G. S. Pradeep Rao, D. Nageswara Rathee, Manisha Bathla, Priyanka Alliance University Department of Computer Science and Engineering Bengaluru India Jawaharlal Nehru University School of Computer & Systems Sciences New Delhi India Chandigarh University Mohali Punjab Gharuan India

ISBN: (纸本)9798350383522

Machine learning models could provide human intelligence to perform complex tasks effectively. Among them, deep learning models have successfully been applied in several domains. However, that efficacy can be achieved only through effective design and training of deep learning models, which is a complex task to achieve and requires a lot of expertise in the domain. Finding the optimal hyperparameter values is crucial for high-performance machine learning models, especially tree-based models and neural network-based models. Estimation of the parameters such as the weight of neural networks can be done using data sets and these are internal model parameters, however the hyperparameters such as number of layers, size of layers, learning rates etc. are external to the model. The values of hyperparameters can either be determined intuitively by domain experts or by manually and randomly trying various possible configurations until we arrive at some acceptable values. Both have severe limitations for machine learning models with a lot of hyperparameters. Several attempts have been made in the direction of automatic hyperparameter optimization to save human efforts in designing machine learning models. Nature-inspired computing has gained a lot of popularity in this direction in recent years. In this paper, we have applied ant colony optimization, a nature-inspired approach for hyperparameter optimization for deep learning models for the prediction of lung cancer. © 2024 IEEE.

关键词： Ant colony optimization combinatorial optimization Deep learning model Hyperparameter optimization Metaheuristic

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Phase Re-Service in Reinforcement Learning Traffic Signal Control 27

Phase Re-Service in Reinforcement Learning Traffic Signal Co...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Zhiyao Gunter, George Quinones-Grueiro, Marcos Zhang, Yuhang Barbour, William Biswas, Gautam Work, Daniel Institute for Software Integrated Systems Vanderbilt University Department of Civil and Environmental Engineering United States Institute for Software Integrated Systems Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University Department of Computer Science United States

ISBN: (纸本)9798331505929

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation. © 2024 IEEE.

关键词： Markov processes

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Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction systems in High-Speed Trains

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Chinese Journal of Electronics 2023年第5期32卷 982-991页

作者： CHENG Chao WANG Weijun MENG Xiangxi SHAO Haidong CHEN Hongtian Department of Computer Science and Technology Changchun University of Technology Department of Mathematics and Statistics Changchun University of Technology Institute of System Research China Industrial Control Systems Cyber Emergency Response Team Department of Mechanical and Vehicle Engineering Hunan University Department of Chemical and Materials Engineering University of Alberta

Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter(UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space *** network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation(MAISD) is designed for detecting *** the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.

关键词： Degradation Network topology Fault detection Rail transportation State-space methods Safety Kalman filters

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Early Time Series Anomaly Prediction with Multi-Objective Optimization

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第1期9卷 972-987页

作者： Chao, Ting-En Huang, Yu Dai, Hao Yen, Gary G. Tseng, Vincent S. National Yang Ming Chiao Tung University Institute of Information Management Hsinchu300093 Taiwan Oklahoma State University Department of Electrical and Computer Engineering StillwaterOK74078 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300093 Taiwan National Chung Hsing University Department of Management Information Systems Taichung402 Taiwan

Anomaly prediction, aiming to predict abnormal events before occurrence, plays a key role in significantly reducing costs and minimizing potential threats to mechanical devices. Monitoring machines using fixed-length time windows faces challenges in accommodating the varying characteristics of anomaly events. The lengthy and imbalanced sequence data associated with anomaly events further complicates the resolution of these challenges. Additionally, the inherent trade-off between accurate prediction and timely alarm is a crucial concern, posing difficulties in decision-making. This study puts forward a novel framework for early anomaly prediction, named early time series Anomaly Prediction with Neighbor Over-sampling and Multi-objective Optimization (APNOMO), pronounced as 'abnormal'. The framework employs three key techniques: 1) sliding windows that divide long input sequences into segments for prediction at proper intervals, 2) over-sampling and proposed neighbor over-sampling that handle imbalanced data in a novel way, and 3) multi-objective optimization that searches optimal thresholds to balance accurately prediction and timely alarm the abnormal. Experiments on a real-world dataset demonstrate APNOMO's superior performance over some state-of-the-art designs, with higher recall, F1 score, and more suitable earliness. It can predict anomalies 0.78-3.56 hours in advance, showcasing excellent early anomaly prediction capabilities for enabling predictive maintenance. © 2017 IEEE.

关键词： Predictive maintenance

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