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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20256 条记录，以下是711-720 订阅

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Machine learning-Based Daily Rainfall data Gap-Filling Method: A Case Study of DKI Jakarta Area 3

Machine Learning-Based Daily Rainfall Data Gap-Filling Metho...

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3rd international conference on Electronic and Electrical engineering and intelligent System, ICE3IS 2023

作者： Asshidiqi, Muhammad Fajar Saputro, Adhi Harmoko University of Indonesia Department of Physics Depok Indonesia

ISBN: (纸本)9798350327762

Rain is a hydrometeorological event that cannot be separated from human life. Rainfall is an important part of the process of human life ranging from agriculture, marine, to the environment and natural disasters. Some rainfall measurement equipment was invented to help measure rainfall intensity. However, problems occur in the density of the rainfall data. Many factors cause rainfall data vacuum. This study aims to overcome the problem of rainfall data void by conducting gap-filling based on machine learning (ML). A total of seven rain gauge locations in DKI Jakarta area were used to take daily rainfall data as samples. Some ML used for gap-filling in this study such as extreme gradient boosting (XGB), k-nearest neighbors (KNN), random forest (RF), stochastic gradient descent (SGD), and multi-layer perceptron (MLP). The results of the study obtained were that KNN modeling dominated for the best performance value in training data, but in SGD testing data dominated for the best performance with the average value of RMSE, MAE, and R2 was 5.95, 2.40, and 0.87, respectively. © 2023 IEEE.

关键词： Machine learning

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2023 3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

2023 3rd International Conference on Advance Computing and I...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

ISBN: (纸本)9798350399264

The proceedings contain 575 papers. The topics discussed include: an analysis of medical images using deep learning;development and construction of an automated sanitizing robot incorporating ultrasonic sensor and sanitizer level detection sensor;mutual user authentication using inherent techniques for cloud and fog computing;design and optimization of downlink massive MIMO system based on OTFS modulation enabling modified 3D-SOMP channel estimation;comparative analysis of performance indicators to improve the quality in Indian educational institutions using Grd ensemble methods;improved algorithm for solar power systems for efficient tracking system;cryptocurrency trading using machine learning;development of smart grid infrastructure in cybersecurity;development of a sleep monitoring system using cloud computing and big data techniques;and securing international law against cyber-attacks through blockchain integration.

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TinyLidarNet: 2D LiDAR-based End-to-End Deep learning Model for F1TENTH Autonomous Racing

TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model ...

引用

2024 international conference on intelligent Robots and Systems

作者： Zarrar, Mohammed Misbah Weng, Qitao Yerjan, Bakhbyergyen Soyyigit, Ahmet Yun, Heechul Univ Kansas Lawrence KS 66045 USA

ISBN: (纸本)9798350377712;9798350377705

Prior research has demonstrated the effectiveness of end-to-end deep learning for robotic navigation, where the control signals are directly derived from raw sensory data. However, the majority of existing end-to-end navigation solutions are predominantly camera-based. In this paper, we introduce TinyLidarNet, a lightweight 2D LiDAR-based end-to-end deep learning model for autonomous racing. An F1TENTH vehicle using TinyLidarNet won 3rd place in the 12th F1TENTH Autonomous Grand Prix competition, demonstrating its competitive performance. We systematically analyze its performance on untrained tracks and computing requirements for real-time processing. We find that TinyLidarNet's 1D Convolutional Neural Network (CNN) based architecture significantly outperforms widely used Multi-Layer Perceptron (MLP) based architecture. In addition, we show that it can be processed in real-time on low-end micro-controller units (MCUs).

关键词： Convolutional neural networks

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Quantum Dots in Modern Medicine: Advancing Imaging, Diagnostics, and Drug Delivery 3

Quantum Dots in Modern Medicine: Advancing Imaging, Diagnost...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Srinivas, Sriraksha Parinitha, D.K. Padmanabha, Pujitha Paul, Sudip Sankpal, Priya R. Bnm Institute of Technology Dept. of Electronics and Communication Eng. Bengaluru India North-Eastern Hill University Department of Biomedical Engineering Shillong India

ISBN: (纸本)9798331515911

Quantum Dots (QDs) are transforming drug delivery with advanced modalities like targeted therapies, controlled release systems, and theranostics. They enable precise drug transport, reduce side effects, and allow real-time monitoring, particularly in cancer therapy. Innovations such as dendrimers and carbon nanotubes enhance their ability to deliver multiple or water-insoluble drugs, showcasing their potential in modern medicine. The adoption of Quantum Mechanics (QM) in pharmaceutical research accelerates drug discovery by enabling precise simulations of drug-receptor interactions and optimizing molecular designs. Techniques like Density Functional Theory (DFT) and machine learning integration enhance efficiency, reduce costs, and improve the safety of therapeutic candidates, driving innovation in drug development. © 2025 IEEE.

关键词： Drug interactions

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Ensemble-Based Lightweight Machine learning Optimization for IoT Network Intrusion Detection 3

Ensemble-Based Lightweight Machine Learning Optimization for...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Samantaray, Milan Barik, Ram Chandra Kumar Biswal, Anil C.V. Raman Global University Department of Computer Science and Engineering Odisha Bhubaneswar India Udayanath Autonomous College of Science and Technology Department of Computer Science Odisha Cuttack India

ISBN: (纸本)9798331523893

The rapid development of the Internet of Things (IoT) presents various security concerns, necessitating the implementation of robust and effective Intrusion Detection Systems (IDS). With the growth of IoT networks, cyber risks have increased, putting data integrity and stability at risk. As consequently, the growth of IoT networks has coincided with an increase in cyber threats, threatening the security and integrity of data. Various IDSs have been suggested to identify malicious activity based on previously established attack patterns;this should help to protect data from abuse and odd attempts. Existing intrusion detection systems need upgrading due to the fast rise in these types of threats. This study proposes an Ensemble-Based Lightweight Machine learning (ELML) specifically optimized for IoT network IDS, leveraging the UNR-IDD dataset. The UNR-IDD dataset is a recent and extensive benchmark that includes several contemporary network attack scenarios, facilitating effective training and assessment of detection algorithms. The proposed approach utilizes feature engineering and lightweight machine learning models including K-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), AdaBoost (AB), and Decision Tree (DT). In addition, the Ensemble-Based Lightweight Machine learning (ELML) IDS model is being optimized for performance using a hyperparameter optimization technique that is based on the Crow Search Algorithm (CSA). The results show significant accuracy improvements over existing algorithms in multi-class classification contexts. © 2025 IEEE.

关键词： Network intrusion

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Modeling and Performance Analysis of PEM Fuel Cells Using Machine learning Techniques 3

Modeling and Performance Analysis of PEM Fuel Cells Using Ma...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Gaddam, Shamitha Reddy, P. Srihith Joshi, Shruti V. Bnm Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Bnm Institute of Technology Department of Electrical Engineering Bengaluru India

ISBN: (纸本)9798331515911

Proton Exchange Membrane Fuel Cells (PEMFCs) play a vital role in the transition to clean energy technologies, requiring accurate and efficient models for their control, optimization, and real-time monitoring. This research focuses on creating machine learning models as Support Vector Regression (SVR), Gradient Boosting (GBR), XGBoost, and Random Forest (RFR) and MLPRegressor to predict PEMFC key performance attributes. Using a dataset from Hamidi et al [1]., which focuses on Nafion 112 membranes and MEA activation tests. Input features include current density, voltage, power density, gas pressure, relative humidity, and impedance components (real and imaginary). The primary output is cell voltage, a key performance indicator. Among the models, Random Forest Regressor achieved the best results with an accuracy of 94.715, R2 score of 0.8049, MSE of 0.001036, and RMSE of 0.03219. Feature importance analysis revealed relative humidity, current density, and voltage as the most influential factors. This research illustrates the use of machine learning techniques in modeling the functioning of attributes of PEMFCs, providing insights for optimizing fuel cell performance and accelerating the inculcation of clean energy technologies. © 2025 IEEE.

关键词： Support vector regression

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Research on Maintenance and Fault Prediction Model of intelligent Building Driven by Internet of Things 3

Research on Maintenance and Fault Prediction Model of Intell...

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3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Li, Xin Gao, Lingyu Liu, Yuding Harbin Huade University Heilongjiang Harbin150025 China

ISBN: (纸本)9798331519032

With the rapid development of Internet of Things (IoT) technology, its application in the field of intelligent buildings is increasingly extensive, which significantly improves the energy efficiency management and fault prediction capabilities of intelligent buildings. The purpose of this paper is to discuss the construction and optimization method of maintenance and fault prediction model of intelligent building driven by IoT. Through in-depth analysis of sensor data in intelligent buildings, statistical characteristics, time series characteristics, frequency characteristics and correlation characteristics are extracted, and the running state and potential faults of building systems are fully captured. On this basis, this paper proposes a fault prediction model based on ensemble learning, which combines random forest and AdaBoost algorithm to effectively improve the prediction accuracy and model stability. The experimental results show that the model performs well in fault prediction tasks in typical intelligent building scenes, with high accuracy, recall and F1 score, and its performance is stable under different data sets. In addition, by introducing L1 and L2 regularization, the model complexity is effectively controlled and the risk of over-fitting is reduced. The research in this paper not only provides a new solution for the maintenance and fault prediction of intelligent buildings, but also provides a useful reference for the future development of intelligent building management systems. © 2024 IEEE.

关键词： Prediction models

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Research on Dynamic Detection of Vulnerabilities in Smart Contracts Based on Machine learning 3

Research on Dynamic Detection of Vulnerabilities in Smart Co...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Boxin, Yang Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350380989

The proliferation of smart contracts has led to a surge in hacking attacks, resulting in substantial financial losses and undermining the healthy growth of the blockchain ecosystem. To mitigate these challenges, this paper introduces a dynamic vulnerability detection approach for smart contracts leveraging machine learning techniques. The proposed method involves the extraction of opcode sequence features through a combination of the N-gram model and a weight penalty mechanism. The core objective is to identify vulnerabilities within deployed smart contracts by analyzing the opcode sequences during their dynamic execution. This approach falls under the category of dynamic detection, aiming to ensure the integrity and security of blockchain-based systems. © 2024 IEEE.

关键词： blockchain N-gram opcode sequence smart contracts vulnerability detection

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A Comprehensive Analysis of High-Capacity Reversible data Hiding Techniques in Medical Images 3

A Comprehensive Analysis of High-Capacity Reversible Data Hi...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Malarvizhi, N. Prabu, U. Department of CSE Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

High-capacity Reversible data Hiding in Medical Images is essential for preserving patient privacy and the authenticity of diagnostic images. The study thoroughly evaluates the latest developments in rdH techniques designed for medical imaging, focusing on their capacity for total data recovery and considerable embedding capacity. The most often used metrics for evaluating and comparing approaches created in the last two decades include RMSE, PSNR, SSIM, and EC, which stand for Root Mean Square Error, Peak Signal-to-Noise Ratio, Structural Similarity Index Measure, and Embedding Capacity respectively. This study offers a comprehensive comparative analysis utilizing a consistent set of standard medical test images, presenting the experimental results of each technique. A comprehensive analysis clarifies performance developments, revealing both the advantages and disadvantages of the approaches employed. This study provides information about the trade-offs between data hiding capacity and preserving of image quality, aiming to inform future advancements in secure, high-capacity medical image data concealing. © 2025 IEEE.

关键词： Image quality

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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