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intelligent extraction methods of vegetation information from remote sensing images: progress, challenges, and trends 3

Intelligent extraction methods of vegetation information fro...

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3rd international conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Wu, Beibei Sun, Qun Xu, Qing Wang, Longhao Chen, Xin Information Engineering University Zhengzhou450001 China

ISBN: (纸本)9781510689077

Vegetation plays a key role in the ecological function of Earth's ecosystem. The extraction of remote sensing information on vegetation is crucial for analyzing the vegetation status, providing timely, accurate, and large-scale data on vegetation distribution and changes. This supports scientific decision-making and sustainable development. With the continuous development of remote sensing, artificial intelligence, big data, and other technologies, as well as the expansion of application fields, the remote sensing information extraction system is constantly developing and evolving. In the present study, we systematically review the vegetation extraction methods based on remote sensing images in the past decade, discussing the main challenges, problems, and future research trends. The classification and extraction of vegetation information from remote sensing data will aid to achieve higher level of automation and intelligent development. © 2025 SPIE.

关键词： Vegetation Remote sensing Feature extraction Image fusion Machine learning Image classification data modeling data fusion Evolutionary algorithms Visualization

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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 ...

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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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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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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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Literature Review on Advanced Cloud Robotics for Enhanced Industrial Automation 3

Literature Review on Advanced Cloud Robotics for Enhanced In...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Manonmani, A. Akash, S. Aswinraj, A. Manikandan, A. Saveetha Engineering College Department of Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798331509675

Cloud robotics is transforming industrial automation by combining cloud computing with robotic systems. This paper reviews recent advancements in cloud robotics and their industrial applications, highlighting the synergy between cloud infrastructure and robotic platforms. Key technologies like artificial intelligence, machine learning, and the Internet of Things are explored for their roles in enhancing scalability, computational efficiency, and real-time data processing. The paper also addresses challenges such as latency and data security, proposing solutions to overcome them. The findings suggest that cloud robotics can significantly improve productivity, cost-efficiency, and flexibility in manufacturing and other sectors, with future research directions aimed at promoting its broader adoption. © 2025 IEEE.

关键词： Cloud platforms

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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...

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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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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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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...

引用

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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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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Research on Machine Vision-Based intelligent Vehicle Obstacle Recognition and Range Measurement Algorithm 3

Research on Machine Vision-Based Intelligent Vehicle Obstacl...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Wang, Han Niu, Wenlai Liu, Yahui Tan, Ye Xiu, Chunlian Chen, Xiangyu College Of Computer Science And Engineering Chongqing University Of Technology Chongqing China School Of Vehicle Engineering Chongqing University Of Technology Chongqing China College Of Foreign Languages Chongqing University Of Technology Chongqing China

ISBN: (纸本)9798350318609

With the rapid development of autonomous driving technology, obstacle recognition and range measurement for intelligent vehicles have become critical research areas. This study aims to delve into the data collection, preprocessing, and annotation processes of the obstacle recognition model for intelligent vehicles. Additionally, it explores the utilization of various deep learning models, such as the FCN and DeepLabV2networks, to achieve accurate obstacle identification. Furthermore, we investigated distortion coefficient solving and calibration methods for binocular cameras to achieve accurate measurement of obstacle distances. This paper conducted experiments using the ACDC dataset. Through extensive experimentation and analysis, we found that DeepLabV2achieved a recognition accuracy as high as 0.839, with a detection speed of 0.539 images per second. Additionally, we provided a comparative table of recognition accuracy for each model to assist researchers and engineers in better understanding the performance of these models. The calibration method proposed in this paper for binocular cameras has an error proportion lower than 2%. The achievements of this study are expected to propel further advancements in intelligent vehicle technology, enhancing road safety and the feasibility of autonomous driving. © 2024 IEEE.

关键词： Cameras

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