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A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models

A Structured Reasoning Framework for Unbalanced Data Classif...

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Junliang Du Shiyu Dou Bohuan Yang Jiacheng Hu Tai An Shanghai Jiao Tong University Shanghai China Yale University New Haven USA University of California San Diego San Diego USA Tulane University New Orleans USA University of Rochester Rochester China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

This paper studies a Markov network model for unbalanced data, aiming to solve the problems of classification bias and insufficient minority class recognition ability of traditional machine learning models in environments with uneven class distribution. By constructing joint probability distribution and conditional dependency, the model can achieve global modeling and reasoning optimization of sample categories. The study introduced marginal probability estimation and weighted loss optimization strategies, combined with regularization constraints and structured reasoning methods, effectively improving the generalization ability and robustness of the model. In the experimental stage, a real credit card fraud detection dataset was selected and compared with models such as logistic regression, support vector machine, random forest and XGBoost. The experimental results show that the Markov network performs well in indicators such as weighted accuracy, F1 score, and AUC-ROC, significantly outperforming traditional classification models, demonstrating its strong decision-making ability and applicability in unbalanced data scenarios. Future research can focus on efficient model training, structural optimization, and deep learning integration in large-scale unbalanced data environments and promote its wide application in practical applications such as financial risk control, medical diagnosis, and intelligent monitoring.

关键词： Deep learning Support vector machines Adaptation models Decision making distributed databases Probabilistic logic Data models Cognition Optimization Markov random fields

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Advancing Urban Traffic control with IoT and Deep Learning: A YOLOv8 and LSTM-based Adaptive Signal System

Advancing Urban Traffic Control with IoT and Deep Learning: ...

引用

international conference on ent Computing and control Systems (ICICCS)

作者： K. Priya K. Priyadharshini R. Santhana Krishnan J. Relin Francis Raj Imran Javeed Settu A. Srinivasan Department of Computer Science and Engineering Ramco Institute of Technology India Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Urban traffic congestion remains a significant challenge due to increasing vehicle density, inefficient signal control, and unpredictable traffic patterns. Traditional fixed-time signal systems fail to adapt dynamically to varying congestion levels, leading to increased delays, fuel consumption, and environmental pollution. To address these limitations, this research proposes an IoT and deep learning-based adaptive traffic signal system integrating YOLOv8 for real-time vehicle detection and LSTM for congestion prediction. Traffic data is collected using IoT sensors, including cameras, ultrasonic sensors, RFID modules, GPS trackers, and air quality sensors. YOLOv8 processes real-time camera feeds to detect vehicles, classify them based on type, and estimate traffic density. Concurrently, an LSTM-based predictive model analyzes historical traffic patterns and external factors such as peak hours and weather conditions to forecast congestion trends. The system dynamically adjusts traffic signal durations based on congestion probability scores, optimizing road utilization and minimizing vehicle waiting times. Extensive training and evaluation using real-world traffic datasets demonstrate significant improvements in congestion reduction compared to conventional traffic control methods. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) validate the reliability of the predictive model. Additionally, air quality monitoring highlights the environmental benefits of reduced emissions. This intelligent traffic management framework enhances urban mobility, improves emergency vehicle response times, and contributes to sustainable smart city development.

关键词： Adaptive systems Smart cities Roads Traffic control Predictive models Air quality Real-time systems Fuels Time factors Traffic congestion

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Improving Traffic Sign Recognition with the YOLOv9 Algorithm

Improving Traffic Sign Recognition with the YOLOv9 Algorithm

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Ketan Anand Shyam Shankar Dwivedi Department of Computer Science and Engineering Rameshwaram Institute of Technology & Management AKTU Lucknow India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Modern intelligent transportation systems and autonomous driving technologies are quite helpful in light of the population's increasing growth, and they work particularly well when used in conjunction with traffic sign monitoring. By giving drivers accurate and exact traffic signals, such as speed limits, warnings, and directional assistance, this makes transportation much safer. Autonomous vehicles must correctly understand these indicators in order to make wise driving judgments. Accurately identifying and classifying a wide variety of traffic signs in a range of environmental circumstances is a difficulty. A cutting-edge deep learning model created specifically for object recognition tasks is called YOLOv9 (You Only Look Once version 9). In addition to offering respectable speed and accuracy, YOLOv9 expands on the advantages of its predecessors by implementing cutting-edge strategies such as PGI and GELAN. Because of these improvements, YOLOv9 is especially ideal for real-time applications like recognition of traffic signs, where more accuracy and quick and accurate object identification are essential. With an increased accuracy the results demonstrate the model's stability and dependability and show that it is appropriate for real-world uses in autonomous driving and traffic control. Subsequent investigations will concentrate on improving resilience in various situations, utilizing semi-supervised learning, creating more resource-efficient models, broadening the range of datasets, and combining traffic sign recognition with additional autonomous driving tasks. The development of dependable and effective traffic sign detection systems is furthered by this work.

关键词： YOLO Training Accuracy Computational modeling computer architecture Real-time systems Data models Object recognition Artificial intelligence Autonomous vehicles

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intelligent Energy Management System for Microgrids with Energy Storage Integration

Intelligent Energy Management System for Microgrids with Ene...

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Automation and Computation (AUTOCOM), international conference on

作者： Simranjeet Nanda Maddu Naveen Kumar Paramjit Baxi Monoo John Manaswini R Dhananjay Kumar Yadav Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Mangement-PG Management JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The items are a software solution, it is designed for micro grids with energy storage integration to monitor, control, and best utilize available grid resources. Also called a mini-grid, the micro grid is a distributed energy system consisting of local small-scale generation and storage facilities that could operate independently from the main grid in an emergency. Micro grids are frequently paired with solar panels and wind turbines, making them more sustainable. The capacity of energy storage systems in micro grids has been playing an important role, as renewable energy in devices with temporal availability generates the need for their more efficient use. Based on real-time data, the items apply energy transmission and distribution management algorithms to control where the power is used in a micro grid. The items continuously monitor the demand and supply of energy to investigate the ideal combination of renewable generation and stored energy for peak shaving. In doing so, the micro grid can run optimally and minimize energy waste without running up bills. The items can also predict how and when individuals use energy, thus allowing better scheduling of the available resources. This enables the items to conduct predictive maintenance on those micro grid components to long and function as efficiently as possible.

关键词： Renewable energy sources Real-time systems Software Wind turbines Reliability Solar panels Energy management systems monitoring Energy storage Predictive maintenance

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IoT-Driven Smart Irrigation for Paddy Cultivation using BCO-ResNet: A Deep Learning Approach

IoT-Driven Smart Irrigation for Paddy Cultivation using BCO-...

引用

Electronics and Renewable Systems (ICEARS), international conference on

作者： R. Anbarasu V. Parthipan Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Thandalam Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Raising crop yield and simultaneously addressing the worldwide water shortage issue depends on good water management in paddy farming. Considered conventional irrigation methods often waste water, cause poor monitoring, and produce insufficient crops. Deep learning and the Internet of Things (IoT) taken together offer a transforming solution that greatly improves real-time monitoring and decision-making in agriculture. This work attempts to introduce a special Bee Colony Optimisation (BCO)-ResNet model designed for paddy fields, so addressing intelligent irrigation systems. Soil moisture, temperature, humidity, and water level data is gathered using Internet of Things sensors. Proposed then, the BCO-ResNet model addresses this information. Although BCO is in charge of simplifying the feature selection process, the ResNet architecture guarantees accurate classification and irrigation demand prediction. One can considerably cut water waste by means of dynamic control of water distribution in the system. Ten paddy fields gathered over a six-month farming period made up a dataset used to test the model's efficiency. In irrigation accuracy of 95.6%, water use efficiency improvement of 38%, and crop yield increase of 22%, the proposed system exceeded traditional methods. Moreover, 18% of the time required for real-time decision making was shortened, which fits usage in environments with limited resources. These findings indicate that the BCO-ResNet model maximises irrigation for paddy fields, so supporting the expansion of economically sustainable agriculture. Applying this system to many various crops and environmental conditions has the potential to change the way water management techniques are applied in agricultural environments.

关键词： Support vector machines Deep learning Temperature sensors Irrigation Accuracy Decision making Real-time systems Internet of Things Water resources Farming

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Next-Gen Home Automation: Sensor-Based Connectivity and Appliance control 1st

Next-Gen Home Automation: Sensor-Based Connectivity and Appl...

引用

1st international Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Nithiyaraj, E. Emerson Srinivass, S. Mukilan, K. Muthukumar, M. Nanmaran, R. Srimathi, S. Department of Artificial Intelligence and Data Science Mepco Schlenk Engineering College Sivakasi India Department of ECE Ramco Institute of Technology Rajapalayam India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Biotechnology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9783031823824

Internet of Things (IoT) integrated smart home automation systems use sensor networks and communication modules to extend home energy efficiency, security, and convenience. IoT transforms houses into intelligent environment that are remotely monitored and controlled from anywhere using communication technologies. In this paper, a NodeMCU microcontroller and a variety of sensors, such as a PIR for motion detection, a gas sensor for monitoring hazardous gases, a flame sensor for fire detection, a DHT11 for temperature and humidity sensing, as well as smart locks, lights and fans for environmental control, are used to develop and implement a smart home system. With a user-friendly smartphone interface, the suggested system enables residents to monitor and operate their home appliances remotely in real- time, improving security, comfort, and energy efficiency. This article intends to further the development of smart home technology by providing a thorough overview of system design, sensor integration, and functionality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart homes

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Generalized hyperbolic-function method with computerized symbolic computation to the Nizhnik-Novikov-Veselov equation

Generalized hyperbolic-function method with computerized sym...

引用

2012 international conference on computer distributed control and intelligent environmental monitoring, CDCIEM 2012

作者： Liu, Jianguo Li, Fei College of Computer Jiangxi University of Traditional Chinese Medicine Nanchang China

In this paper, by the use of the generalized hyperbolic-function method, we obtain some new exact analytic solutions of the Nizhnik-Novikov-Veselov equation, including shocks waves and bells waves solutions. © 20... 详细信息

ISBN: (纸本)9780769546391

关键词： Hyperbolic functions

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distributed architecture for QoS control in wireless multimedia sensor networks (WMSNs)

Distributed architecture for QoS control in wireless multime...

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2012 international conference on computer distributed control and intelligent environmental monitoring, CDCIEM 2012

作者： Zhou, Ling Yang, Wen-Yin Department of Information Science and Engineering Central South University Changsha China Department of Computer Science Foshan University Foshan China

ISBN: (纸本)9780769546391

Due to complex surveillance in environment, more and more wireless multimedia sensor networks (WMSNs) is becoming an important hot problem in network research area. Especially, the QoS-controlled architecture is a key and significant problem. Based on the distributed policy and three-dimensional hierarchy protocol model, a novel QoS architecture for distributed control is introduced and analyzed in WMSNs, which includes two components: the QoS control module in protocol and the QoS control module in support technology/platform. At the same time, the key technologies and several parameters which are related to the QoS control in WMSNs are discussed. It is a suitable architecture for distributed QoS control and can be used to design and analyze the WMSNs system. © 2012 Crown Copyright.

关键词： Network architecture

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distributed control of power generation system

Distributed control of power generation system

引用

2012 international conference on computer distributed control and intelligent environmental monitoring, CDCIEM 2012

作者： Wang, Jian Chang, Dongying Mechanics and Acoustics Division National Institute of Metrology Beijing China China Patent Development Corporation Beijing China

ISBN: (纸本)9780769546391

The structure of a distributed power generation system based on renewable energy is presented. The energy complementary control principle is introduced for this system. A mathematical model of the system is built according to the energy complementary control principle. A vector control method is designed in the system in order to improve the performance of the system. The simulations and experiments on a wind simulator and flywheel based system have verified that proposed energy complementary control can satisfactorily regulate the power of the storage unit to store and release energy and thus to maintain a steady output from the distributed power generation system. © 2012 IEEE.

关键词： distributed power generation

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The height measurement of virtual object based on level set method

The height measurement of virtual object based on level set ...

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2012 international conference on computer distributed control and intelligent environmental monitoring, CDCIEM 2012

作者： Xu, Yan Zheng, Yi Wang, Zhong-Qiu Wang, Hong-Ru Shandong Provincial Key Laboratory of Ocean Environment Monitoring Technology Shandong Academy of Sciences Institute of Oceanographic Instrumentation Qingdao 266001 China

ISBN: (纸本)9780769546391

This paper involves a method for height measurement of the virtual objects in a gray-scale image, it is based on the algorithm of level set method through level set function gray gradient changes. This method is adequate on height measurement on calculation and could be used in rich areas such as wave height measurement. © 2012 IEEE.

关键词： Level measurement

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