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2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring System using Mobile Application

2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring Sy...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Vanna-Iampikul, Pruek Boonnateephisit, Visutr Limsoontharakul, Somphop Moonrinta, Jednipat Dailey, Matthew Ekpanyapong, Mongkol Burapha University Department of Electrical Engineering Chonburi Thailand School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Prince of Songkla University Dept. Computational Science Songkhla Thailand Computer Science Field of Study School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Information & Communication Tech. School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand School of Engineering and Technology Asian Institute of Technology Dept. of Industrial Systems Engineering Pathumthani Thailand

ISBN: (纸本)9798331543952

LiDAR technology has rapidly advanced, offering highly accurate solutions for tree geometry estimation, which is particularly valuable in agriculture and forestry. Its ability to capture precise 2D and 3D data has revolutionized tree detection and measurement. In Eucalyptus plantations, continuous monitoring is critical to maximizing growth, enhancing paper production, and mitigating environmental damage. However, existing solutions face challenges in real-time monitoring due to factors such as parasitic interference, low-light conditions, and uneven terrain. In this work, we present a novel, end-to-end framework that enhances Eucalyptus tree measurement and monitoring. Our framework significantly improves data collection efficiency by allowing measurements at walking speed, reducing measurement distance by 50% compared to traditional manual approaches. This system integrates 2D-LiDAR with additional environmental sensors to extract key tree parameters, including circumference, position, and tree count. The collected data is stored in real-time and uploaded to a centralized server for further analysis. Experimental evaluations across 160 trees, under five distinct farm conditions, demonstrate that our system achieves 89% accuracy in tree diameter estimation and 98.5% accuracy in tree counting, significantly outperforming traditional methods. These results establish our framework as a highly effective solution for improving the accuracy and efficiency of tree monitoring in challenging environments. © 2025 IEEE.

关键词： Deforestation

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Predict of the Hidden Drug Interactions Based on Similarity Utilizing the Knowledge Graph

Predict of the Hidden Drug Interactions Based on Similarity ...

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International Conference on computer and Applications (ICCA)

作者： Mariam Gamal Elfiky Samy Ghoniemy Ahmed Sharaf Eldin dept. of Information Technology Management for Business University of Hertfordshire hosted by GAF Cairo Egypt Faculty of Computer Science and Engineering Galala University Cairo Egypt Faculty of Information Technology and Computer Science Sinai University Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Drug-drug interactions (DDIs) can significantly impact treatment outcomes by causing adverse effects or reducing therapeutic efficacy. Whereas many DDIs are already known, there are numerous hidden interactions. Therefore, this study tries to predict unknown interactions based on drug structure and target information from the Drug Bank dataset. This paper trains the random forest model integrated with a knowledge graph on a dataset containing 922 unique drugs and 731,196 drug interactions. To illustrate the complex interactions between these drugs, we created a knowledge graph with Neo4j. This graph integrates information gathered from the dataset and extracts relevant data using Cypher queries. The inference engine was created according to the model's predictions on unknown DDIs. The random forest model, with an F1-score of 0.82, achieved a recall of 0.99 and a precision of 0.70. Also, 34,580 potential DDIs were extracted from the sample dataset of drugs. By embedding the knowledge network, our inference engine significantly enhanced predicting the hidden interactions and was found strong in the balance of precision and recall. Our research is applicable to the enhancement of drug safety and effectiveness via mitigating potential DDIs by identifying them.

关键词： Drugs Knowledge engineering Ciphers Computational modeling Knowledge graphs Predictive models Safety Data mining Random forests Engines

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Analysis of Public Sentiment on Dhaka Metro Rail with Transformer Based Architectures

Analysis of Public Sentiment on Dhaka Metro Rail with Transf...

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International Conference on computer and Information technology (ICCIT)

作者： Samiha Maisha Jeba Tanjim Taharat Aurpa Farshid Hossain Siyam Rajib Khan Faniyam Maria Mansia Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Dept. of Educational Technology Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh

Dhaka Metro Rail is a mesmerizing initiative of the Bangladesh Government that has brought comfort to people. This project got a huge appreciation from people for its continuous service. Mass people use the train for rambling, going to the workplace, etc. Again, for some unwanted circumstances, Dhaka Metro Rail gleaned negative criticism from various *** this research, we analyzed public sentiment on Dhaka Metro Rail from popular social media using recent NLP architectures like BERT, XLNet and Electra, where BERT outperformed than other with an accuracy of 91.11% in correctly identifying the sentiments of the public *** sentiment of a comment is identified into predefined categories, such as positive, negative, or neutral.

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SpeckleDiff-2D: Complex-Valued Speckle Reduction Diffusion Network

SpeckleDiff-2D: Complex-Valued Speckle Reduction Diffusion N...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Danwei Lu Chao Liu Xunhao Lin Junjun Yin Jian Yang Dept. of Electronic Engineering Tsinghua University Beijing China School of Computer and Communication University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

SAR despeckling is a crucial preprocessing step for various SAR applications. This work introduces SpeckleDiff2D, a complex-valued speckle reduction diffusion network. Our approach emphasizes the design of a forward noise-accumulation process that mimics the generation mechanism of speckle noise. The proposed despeckling algorithm is trained exclusively on synthesized speckled images generated by optical dataset and is validated on both simulated and real SAR images. Experimental results demonstrate the method’s efficiency and resilience to domain shifts, positioning it as a promising solution to the persistent challenge of data insufficiency in SAR despeckling design.

关键词： Noise Speckle Diffusion models Optical imaging Radar polarimetry Adaptive optics Optical sensors

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Estimating Effective Material Thermal Properties for Use in CFAST

Estimating Effective Material Thermal Properties for Use in ...

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2021 Symposium on Obtaining Data for Fire Growth Models

作者： Janssens, Marc L. Huczek, Jason P. Schluneker, Alexandra M. Icove, David J. Geosciences and Engineering Dept. Southwest Research Institute 6220 Culebra Rd. San AntonioTX78238 United States Fire Technology Dept. Southwest Research Institute 6220 Culebra Rd. San AntonioTX78238 United States Dept. of Electrical Engineering and Computer Science University of Tennessee 1520 Middle Dr. KnoxvilleTN37996 United States

ISBN: (纸本)9780803177314

A material thermal property database was developed for use in Consolidated Model of Fire and Smoke Transport (CFAST) simulations performed in support of fire investigations. The database includes thermal properties for interior surface materials;ignition, metal, and plastic target materials;and damage target materials. CFAST assumes that thermal properties of interior surface and target materials remain constant throughout the course of the fire. In reality, these properties vary with temperature. Assuming ambient temperature properties remain constant may result in significant errors in the heat conduction calculations. This paper presents the results of Fire Dynamics Simulator (FDS) compartment fire simulations to evaluate the effect on room temperature predictions of using constant versus temperature-dependent thermal properties for the gypsum board on walls and ceiling. The simulations show that assuming ambient temperature properties will overestimate the room temperature during the peak burning and decay periods of the fire. Fidelity of the predictions can be improved by increasing the specific heat in the constant property simulations to account for the gypsum dehydration energetics indirectly. The paper describes methodologies developed to estimate effective constant thermal properties for ignition and metal target materials and provides examples to illustrate their application. The methodology for ignition target materials relies on FDS heat conduction simulations of a series of ignition experiments conducted in the Cone Calorimeter. An evolutionary method is used to obtain values for thermal conductivity, specific heat, and ignition temperature that optimize agreement between calculated and measured ignition times. The approach to estimate effective thermal properties of metal target materials is similar. First, FDS and temperature-dependent property values of the metal are used to determine the time for the target surface to reach the melting point for a

关键词： Fires

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Adaptive Neighborhood Sampling and KAN-Based Edge Features for Enhanced Fraud Detection

Adaptive Neighborhood Sampling and KAN-Based Edge Features f...

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IEEE International Conference on Big Data

作者： Subbareddy Batreddy Giridhar Pamisetty Manish Kothuri Priya Verma Ch Sobhan Babu Dept. of Electrical Engineering Indian Institute of Technology Hyderabad India Dept. of Computer Science and Engineering Indian Institute of Technology Hyderabad India Centre for Management Studies Administrative Staff College of India Hyderabad India

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Fraud detection models have shown immense improvement in performance with the incorporation of graph neural networks (GNNs), as they are naturally capable of modeling relational data and capturing complex relations between users. However, real-world data has a significant class imbalance with very few fraudulent labels, resulting in a bias toward predicting the majority class as the decision boundaries are skewed towards it. To alleviate this class imbalance, we propose adaptive neighborhood sampling, an effective and optimal neighborhood sampling approach that uses sampling probability comprising of class frequency and global importance of the node through the page rank to reduce the dilution of the fraud nodes. We also propose a novel Kolmogorov-Arnold Network (KAN) based edge feature extraction approach, which treats incoming and outgoing edges separately to effectively capture the directional patterns that are crucial in distinguishing the patterns of both labels. In addition to this, we employed topology-aware margin loss to further improve the class imbalanced node classification. Incorporating these proposed approaches in the GNN-based fraud detection model helped outperform the state-of-the-art fraud models on open-source and complex real-world datasets.

关键词： Training Measurement Adaptation models Image edge detection Scalability Feature extraction Data models Graph neural networks Fraud Optimization

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FinCaT: Fingerprint cancellable template protection remediation schemes, challenges, and future directions 4

FinCaT: Fingerprint cancellable template protection remediat...

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4th International Conference on Computational Intelligence and Communication Technologies, CCICT 2021

作者： Sehar, Eain Ul Selwal, Arvind Sharma, Deepika Central University of Jammu Dept. of Computer Science and Information Technology Jammu and Kashmir India

ISBN: (纸本)9781665423922

Recent years have revealed a rapid expansion in the use of various biometric technologies for authentication management systems. Although biometric systems have innumerable merits over conventional token-based and knowledge-based identification systems like ID cards or passwords, but they are still susceptible to diverse security threats. Among all, the attack on the biometric template database is the most crucial and adverse attack. If the user templates are jeopardized at any level of biometric system, then they cannot be further used for security mechanism. Henceforth, it is requisite to provide privacy and security to biometric template data by modifying these through revocable and non-invertible transformations to acquire cancellable biometric templates. The cancellable biometrics offers promising remedies for biometric template protection. Our work aims to expound a detailed review of state-of-the-art transformation-based fingerprint template protection schemes. Additionally, the paper discusses various publicly available benchmark datasets. We also deliberate the research challenges emerging from this study, which requires to be addressed in this rapidly evolving field of research. The selection of a suitable function for transformation process is still a major open challenging task, so that the results obtained after applying the function does not result in cross-matching of the templates in the database. © 2021 IEEE.

关键词： Authentication

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MSTLA: Multi-Stage Transfer Learning Approach for Breast Carcinoma Diagnosis

MSTLA: Multi-Stage Transfer Learning Approach for Breast Car...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Gunjan Chugh Shailender Kumar Nanhay Singh Dept. of Computer Science & Engg. Delhi Technological University New Delhi India Dept. of Computer Science & Engg. Netaji Subhas University of Technology (East Campus) New Delhi India

In the recent era, Breast Carcinoma has been observed as a fatal disease in women. In India and other developing countries, survival rates are very low because detection occurs very late. computer-aided diagnosis(CAD) has emerged as a tool for helping doctors and experts with early detection and diagnosis. Medical Image Processing relies upon processing medical images to segment and categorize several diseases. Machine Learning(ML) and Deep Learning(DL) have shown tremendous success in recent years in various real-life applications, including speech recognition, face recognition, autonomous vehicles, etc. Transfer Learning is a technique where features learned from one domain are transferred to another. This approach is generally followed when the data required for training a model is unavailable in abundance. In this work, we have designed a model for the early diagnosis and categorization of breast malignancy using the Multi-Stage Transfer Learning Approach(MSTLA). DenseNet169 and ResNetl52 are utilized for three stage transfer learning strategy. The results show that both DenseNet169 and ResNetl52 performed remarkably with an accuracy of 100% and 99% in the third stage of transfer learning.

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Advancing Environmental Sustainability: Renewable Energy Transition, Carbon Footprint Reduction, and Emission Control Innovations

Advancing Environmental Sustainability: Renewable Energy Tra...

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Learning and technology Conference (L&T)

作者： Aziza I. Hussein Roba W. Alghamdi Amal Abdullah Al-Amoudi Omar Hisham Altahawi Narjisse Kabbaja Mohamed Mourad Mabrook Mostafa Wageh Lotfy Electrical & Computer Eng. Dept. Effat University Jeddah Saudi Arabia Space Communication Dept. Faculty of Navigation Science & Space Technology Beni-Suef University Beni-Suef Egypt Dept. of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Combating climate change and promoting environmental sustainability necessitate a dedicated emphasis on renewable energy sources and lowering carbon footprints. Shifting from fossil fuels to renewable resources, such as solar, wind, biomass, and geothermal energy, is imperative. Equally important is enhancing energy efficiency across industries, transportation, buildings, and household appliances to reduce overall energy consumption and greenhouse gas emissions. Technological advancements, such as improved energy storage and smart grid systems, are essential for boosting the efficiency and dependability of renewable energy sources. Strategies to lower carbon footprints include transitioning to clean transportation, optimizing land use, reducing waste, recycling, promoting sustainable agriculture, and implementing carbon offset initiatives. Public education, corporate responsibility, and personal environmental awareness play vital roles in supporting carbon reduction. By combining sustainable energy practices with carbon reduction measures, we can collectively work towards curbing climate change and fostering a sustainable future. Additionally, utilized techniques to address the problem of high air pollution during vehicle cold starts. We modeled catalytic converter dynamics in MATLAB Simulink, using simplifying assumptions. One significant assumption involved treating the catalytic converter as a Continuous Stirred-Tank Reactor (CSTR), which allowed us to model its temperature uniformly, without accounting for temperature changes along its length. This approach is aimed at saving lives and reducing costs by effectively minimizing air pollution.

关键词： Renewable energy sources Technological innovation Transportation Carbon dioxide Air pollution Mathematical models MATLAB Greenhouse gases Carbon footprint Exhaust systems

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Machine Learning Model to Reduce the Various Defects on Die Casting Process 3

Machine Learning Model to Reduce the Various Defects on Die ...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Jagadeesan, Sowmya Janardhan, M. Singh, Brijesh Sasank, V.V.S. Kapila, Dhiraj Vels University Dept of Bca and It INurture Education Solutions Chennai India Spring Fields Engineering College Department of Mechanical Engineering Hyderabad India Cmr Institute of Technology Department of Mba Bangalore India Koneru Lakshmaiah Education Foundation Dept. of Computer Science and Engineering Vaddeswaram India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350302288

To avoid flaws such as porosity gaps, low-pressure die casting (LPDC) is often utilised for high-performance wheel castings aluminium alloy cars. Casting process parameters have a significant impact on LPDC component quality. There is a requirement to fine-tune the process variables to boost the component's quality against challenging flaws like gas and shrinkage porosity. Examine Defect rates needed for measured process variables. This article culled Information using cloud-based tools typical of the Industry 4.0 paradigm. Develop Supervised machine learning classification models are anticipate defectives in an actual foundry Aluminium LPDC process using this data. Since defects in this process were low and happened against many relevant process measurement factors, determining the underlying cause is challenging. XGboost technique relates the process-related conditions with defectives at the time of the production stage. Used a single LPDC machine and die mould to collect data over three shifts and six days. Using a total of 36 entities or features of the process from this, consider 13 variables. All these features are considered from the 1077 wheel, which is small skewed and 62 samples from the large crooked. From this, existing data need to identify the defective rate with 87% of accuracy for non-defective parts and 74% of accuracy for faulty parts. As a result of this work, pre-series production of innovative products might have fewer problems. © 2023 IEEE.

关键词： Wheels

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