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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

the ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. the framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. the proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. the system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. the proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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An automated Diagnosis of Diabetic Retinopathy Grading Using DenseNet169

An Automated Diagnosis of Diabetic Retinopathy Grading Using...

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international conference on Computer Research and Development (ICCRD)

作者： Cheng-Chung Yang Na Li Kulevome Delanyo Kwame Bensah Salah-Ud-Din Khokhar Mahrukh Mahrukh School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

this study used DenseNet121, DenseNet169, DenseNet201, EfficientNetB0, EfficientNetB7, Inception ResNetV2, Inception V3, MobileNet, MobileNetV2, MobileNetV3Large, ResNet50, ResNet101 and ResNet152, 13 different deep learning neural network models were used for automatic grading of diabetes retinopathy(DR). the change of Validation Accuracy trend was initially observed with 10, 20, 30, 40 and 50, different EPOCHs. Before grading, pre-processing such as elimination, amplification, denoising, data enhancement, normalization, etc. were carried out for the problems in the retinal image data set in the experiment. then we used the deep learning neural network model proposed in this study to embed the attention mechanism into DenseNet169 for automatic grading of DR, enable deep learning neural network models to focus on features such as exudates, thick blood vessels and microaneurysms in retinal images. In order to avoid vanishing gradients in the neural network model and ensure classification accuracy, DenseNet169 was adjusted by using a targeted swish function for processing, and classify retinal images into three levels and finally the classification accuracy of the neural network model proposed in this study reached 99.76%, macro avg precision reached 99.76%, macro avg recall reached 99.76% and macro avg f1-score reached 99.76%. It is significantly better than other models and on average. It can correctly identify and automatically grading more than 4600 images in the verification of 4,617 retinal images. the method proposed in this study has been validated to effectively improve the accuracy of DR classification and can be used for large-scale DR medical imaging screening. It can effectively assist doctors in achieving efficient diagnosis of DR grading. this work was supported by Research Program of Qilu Institute of Technology (***24NN032).

关键词： Deep learning Accuracy Attention mechanisms Neural networks Noise reduction Medical services Retina Research and development Biomedical imaging Residual neural networks

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Exploration and Practice of Online and Offline Blended Teaching Mode for Chemical Reaction engineering Based on Goal Problem Orientation

Exploration and Practice of Online and Offline Blended Teach...

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international conference on Educational and Information Technology (ICEIT)

作者： Shufenp Shan Jie Chen Haolong Li Zhuolin Tang Zhiyuan Deng Li Wang Feifei Chen Jin Sun Rujin Zhou School of Chemical Engineering Guangdong University of Petrochemical Technology Maoming China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

In view of problems existing in the teaching process of Chemical reaction engineering, i.e., low level of knowledge on reaction kinetics and reactors, weak ability to solve complex reaction engineering problems and design reactors considering safety and energy saving, the course adopts an online and offline blended teaching mode based on “goal problem orientation”, introduces petrochemical production cases, and reconstructs the teaching content with “five types of goal problems”. Based on the national digital “process simulation + virtual simulation + engineering training” platform, the intelligent practical teaching with the penetration and integration of production and education is implemented after class, and thus students' awareness of safety and energy conservation is cultivated, finally improving their engineering practice ability and comprehensive application ability. Build a flipped classroom through “Rain classroom” + “Chaoxing learning” and Internet+ big data feedback. Ideological and political education is implemented through well-designed goal problems and the “Quality extension project”. the results of questionnaire feedback showed that 80% of students believed that their “engineering consciousness” had been enhanced, and their “critical thinking ability” and ability to connect theory with practice had been improved. Moreover, their participation in classroom increases, and the excellent rate of the latest “Chemical reaction engineering” final exam paper score has increased to 25%.

关键词： Training Knowledge engineering Technological innovation Rain Education Energy conservation Production Chemical reactions Petrochemicals Inductors

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IoT-Based Analysis of Environmental and Motion data for Comfort and Energy Conservation in Optimizing HVAC Systems

IoT-Based Analysis of Environmental and Motion Data for Comf...

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25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Abdulwaheed, Badmus McGarry, Ken Eliot, Neil Baglee, David School of Computer Science Faculty of Technology University of Sunderland Sunderland United Kingdom School of Engineering Faculty of Technology University of Sunderland Sunderland United Kingdom

ISBN: (纸本)9783031859229

Growing energy consumption from campus infrastructure including lecture halls that run heating, ventilation and air conditioning (HVAC) systems motivates data-driven optimization. this research demonstrates an integrated application of Internet of things (IoT) sensors and cloud-hosted predictive data analytics to enable smart lecture room policies improving efficiency and sustainability. A Raspberry Pi Pico W IoT device was interfaced with BME680 sensor for temperature, humidity and air quality data. the device also incorporated a PIR sensor for occupancy detection and Wi-Fi connectivity to transmit multivariate time series data. the prototype was installed in a university lecture room for real-time data capture. data was directed to a cloud analytics pipeline including MySQL storage and Node-RED for pre-processing. Time series forecasting was conducted by training autoregressive integrated moving average (ARIMA), Prophet and machine learning models on historical data to predict temperature, occupancy levels, and usage patterns 24 h into the future. An interactive dashboard visualized both real-time streams and model forecasts using Grafana for analytical insights. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Predictive analytics

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An intelligent Approach to Cost-effective Management of Typhoid Fever in Children

An Intelligent Approach to Cost-effective Management of Typh...

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international conference on Signal Processing and Communication (ICSC)

作者： Kingsley T. Igulu thipendra P Singh Tosin C. Olayinka Ojei H. Onyijen Edwin Onatuyeh Wilson Nwankwo Department of Computer Science Kenule Beeson Saro-Wiwa Polytechnic Bori Bori Nigeria Bennett University Greater Noida India Department of Software Engineering Wellspring University Benin City Edo State Nigeria Department of Physics Edo State University Uzairue Edo State Nigeria Department of Accounting Delta State University of Science & Technology Ozoro Nigeria Department of Cyber Security Delta State University of Science & Technology Ozoro Nigeria

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Typhoid fever poses a significant health concern among children, due to its potential for severe complications and high treatment costs. this paper proposes an intelligent approach to modelling the prognosis and management of paediatric typhoid infections leveraging machine learning and data analytics to optimize healthcare resources and improve patient outcomes. the dataset was sourced from hospitals in Edo State, Nigeria. the dataset comprises laboratory tests of blood samples of children, from age zero (0) to five (5) years. Six (6) attributes that exhibit the highest information gain: widal test, malaria count, monocyte, platelets, HB, eosinophils, cost of treatment respectively, were selected and ranked thus: 0.6759, 0.4936, 0.3147, 0.2843, 0.2416, 0.2127, and 0.20. Five different classification algorithms (J48_Consolidated, LMT, RepTree, MultiBoost Decision Stump) were employed. and Random Forest) and their results were compared. Performance analysis on the five (5) classifiers shows that Multiboost Decision Stump exhibited the best accuracy of 94% and least MAE of 0.0604. the finding shows that the developed model when integrated into automated disease diagnostic workflows will provide an enhanced and flexible solution for quick prognosis of typhoid fever in children in the tropics with significant cost savings with respect to hospitalization. the finding presupposes that we can enhance the efficiency of typhoid fever management while ensuring the delivery of quality care to children in resource-limited settings.

关键词： Pediatrics Machine learning algorithms Costs Signal processing algorithms Predictive models Prediction algorithms Classification algorithms Performance analysis Prognostics and health management Random forests

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Using Artificial Neural Networks for automated Lithologic Mapping and Mineral Exploration in Mayantoc, Tarlac, and San Juan, Batangas

Using Artificial Neural Networks for Automated Lithologic M...

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17th Asian conference on intelligent Information and database Systems, ACIIDS 2025

作者： Soriano, April Kaye A. Naval, Prospero C. National Graduate School of Engineering University of the Philippines Diliman Quezon City Philippines Department of Computer Science University of the Philippines Diliman Quezon City Philippines

ISBN: (纸本)9789819660049

the Philippine government emphasizes responsible mineral development, advocating for sustainable exploration and conservation of mineral resources [1]. Lithologic mapping plays a vital role in identifying rock types associated with valuable minerals like nickel, gold, and copper. Traditional lithologic mapping methods, however, are labor-intensive, costly, and time-consuming, especially in remote or inaccessible areas [2]. this study evaluates a machine learning approach utilizing Sentinel-2 multispectral data to map lithologic units in Mayantoc, Tarlac, and San Juan, Batangas, regions with significant economic potential. the study employs a Multilayer Perceptron Artificial Neural Network with Backpropagation (MLP-ANN-BP) as the learning algorithm to enhance spatial and spectral classification accuracy. the results demonstrate the model’s robustness, even with low sample sizes. At 250 sample points per lithologic unit, the model achieved an accuracy of 80.72%, which improved to 89.29% with 1000 points. Notably, even at smaller sample sizes, the model showed fair performance, with certain classes. Hyperparameter tuning further optimized performance, raising accuracy to 81.95% for 250 points and enhancing classification metrics across most classes. While challenges remained in distinguishing specific lithologic units due to feature overlap, the model proved effective in delivering reliable predictions with limited data. this study underscores the potential of machine learning-based lithologic mapping as a cost-effective and scalable solution for sustainable mineral resource exploration in the Philippines, particularly in regions characterized by vast and geologically diverse landscapes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mineral exploration

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threat Detection Using MLP for IoT Network

Threat Detection Using MLP for IoT Network

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作者： Taylor, Genea Johnson, David Roy, Kaushik Department of Computer Science North Carolina Agricultural and Technical State University GreensboroNC27411 United States

ISBN: (纸本)9783031859229

While the popularity of IoT networks has grown significantly, they remain highly vulnerable to various cyber-attacks. these attacks can disrupt services, compromise sensitive data, and damage the integrity of IoT ecosystems. Machine learning (ML) techniques, particularly deep learning (DL) models, have been employed to effectively detect and mitigate such threats by identifying abnormal patterns in network traffic. In this paper, we propose utilizing a multilayer perceptron (MLP)-based machine learning model to detect cyber-attacks by using the NF-ToN-IoT dataset, which contains a diverse set of cyber-attacks. the MLP model has been trained and optimized to distinguish between normal and malicious activity. Our results demonstrate the effectiveness of this approach, with the MLP achieving a training accuracy of approximately 97% and a test accuracy of around 95–97%. this high accuracy indicates the model’s capability to generalize well across different attacks while creating a robust solution for real-time threat detection and mitigation in IoT networks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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A data-Driven Driving Under the Influence (DUI) Detection, Notification and Prevention System Using Artificial Intelligence and Internet-Of-things (IoT)

A Data-Driven Driving Under the Influence (DUI) Detection, N...

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作者： Li, Aaron Sun, Yu Francis Parker School San Diego United States Computer Science Department California State Polytechnic University Pomona Pomona United States

ISBN: (纸本)9783031859229

Drunk driving represents a significant public health crisis not only in the United States but also globally [16]. Sober Guardian is developed to mitigate this issue by leveraging advanced technologies including Machine learning, Computer Vision, and potentially chemical sensors in the future. this system employs facial recognition and machine learning algorithms to swiftly determine if a driver is impaired, with the aim of preventing them from starting the vehicle [17]. We used an IoT System to control the blocker and successfully prevent vehicle start with 100% rate. Additionally, Sober Guardian plans to incorporate hardware solutions such as sensors that can detect alcohol levels directly from the driver’s breath or skin, enhancing the accuracy and reliability of impairment detection. throughout its development, the project has faced challenges such as image clarity, limited datasets, and ensuring user-friendliness. Preliminary experiments with both image and video inputs have demonstrated the system’s ability to predict sobriety with an accuracy between 86% and 87.5% [18]. However, video analysis for impaired individuals was hindered by the scarcity of appropriate datasets. Despite these challenges, Sober Guardian, in its nascent stages, has proven to be a promising solution for detecting driver impairment and preventing drunk driving, with ongoing enhancements expected to improve its functionality and deployment readiness. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drunk Driving Facial Recognition Internet-Of-things Machine learning

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22nd international conference on Scientific Computing and Bioinformatics, CSC 2024, and 25th international conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

22nd International Conference on Scientific Computing and Bi...

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22nd international conference on Scientific Computing and Bioinformatics, CSC 2024, and 25th international conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859014

the proceedings contain 31 papers. the special focus in this conference is on Scientific Computing and Bioinformatics,. the topics include: Austere Runway Simulated Identification;using Rhai to Create and Benchmark Bevy Entities;pyO3: Building Python Extension Modules in Native Rust with Performance and Safety in Mind;Leveraging Python Interpreters for Concurrency in SeQUeNCe;benchmark Performance of a Rust-Based Python Extension;quantum Error Correction in Repetition Codes;investigating Selective Reliability with the Laminar Networking Package;an Entanglement Swapping throughput Analysis for Quantum Networks Using Linear Quantum Optics;notes on Symmetric Generalized Tent Map: Route to Chaos;scalable Service Model and Scheduler for Delay-Sensitive Services;regret-Minimization Heuristics for Identifying Monotone Boolean Functions;multi-class Classification of Satellite Orbits for database Quality Control;real-Time Motion Planning for Autonomous Vehicles in Dynamic Environments;A Novel Deep learning Method for Solving PDE’s Applied to a Shallow Water Problem;exploring the Impact of Social Media on Mental Health and Well-Being: A Multi-dimensional Analysis;the Strategic Optimization of a Tabletop Role-Playing Game;Problem-Solving Using Logic and Reasoning, Mathematics, Algorithms, Python, and Generative AI: Part Two;acquisition and Analysis of Mobile data Using Digital Forensics Tools and Techniques;structural Health Monitoring for Risk Assessment and Reliability of a Structure After Extreme Loads;scientific Constructing Adequate Statistical Decision Rules Under Parametric Uncertainty of Applied Mathematical Models via the Smart Use of Pivotal Quantities and Ancillary Statistics;a Smart Air Quality Analysis and Pollutant Diffusion Detection and Prediction System Based on Tree Canopy Shape Research Using Machine learning and Artificial Intelligence;a High Order Scheme for Modelling Viscous Incompressible Fluid Flow in a Channel with a Step;Feasibility Study of Neutron M

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A System-driven Automatic Ground Truth Generation Method for DL Inner-City Driving Corridor Detectors 25

A System-driven Automatic Ground Truth Generation Method for...

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IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Ruthardt, Jona Michalke, thomas Robert Bosch GmbH Robert Bosch Pl 1 D-70839 Gerlingen Germany

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

data-driven perception approaches are wellestablished in automated driving systems. In many fields even super-human performance is reached. Unlike prediction and planning approaches, mainly supervised learning algorithms are used for the perception domain. therefore, a major remaining challenge is the efficient generation of ground truth data. As perception modules are positioned close to the sensor, they typically run on raw sensor data of high bandwidth. Due to that, the generation of ground truth labels typically causes a significant manual effort, which leads to high costs for the labelling itself and the necessary quality control. In this contribution, we propose an automatic labeling approach for semantic segmentation of the drivable ego corridor that reduces the manual effort by a factor of 150 and more. the proposed holistic approach could be used in an automated data loop, allowing a continuous improvement of the depending perception modules.

关键词： Machine learning Deep learning automated Ground Truth data Loop

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