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Harnessing Transformative Potential of UAV-Based AI-Powered data Analysis for Emission Monitoring 6

Harnessing Transformative Potential of UAV-Based AI-Powered ...

引用

6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Namdev, Mayank Dholaria, Jay Rathi, Meet Manipal University Dept. of Computer Science Engineering Jaipur India

ISBN: (纸本)9798331516796

With the growing pressure to reduce environmental pollution, the combination of Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence (AI) offers a viable path forward for modernizing emission monitoring. This research investigates the revolutionary potential of UAV-based AI-powered data analysis for emissions monitoring, with a particular emphasis on the comparison of XGBoost and CatBoost algorithms. Real-time monitoring, identification, and analysis of pollutants can now be done with remarkable accuracy and efficiency thanks to UAVs outfitted with modern sensors and AI algorithms. This novel methodology has significant advantages over typical monitoring approaches, including increased spatial coverage, lower costs, and higher data quality. Furthermore, AI integration provides predictive analytics and anomaly identification, allowing for proactive environmental risk mitigation interventions. Despite its enormous promise, challenges such as regulatory frameworks, privacy concerns, and technological restrictions must be solved before widespread usage. Overall, the combination of UAVs and AI, as well as the comparison of XGBoost and CatBoost, hold enormous promise for revolutionizing emission monitoring by providing a scalable and versatile solution to protect environmental quality and human health. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Smart Plant Watering System for Advanced Irrigation 2

Smart Plant Watering System for Advanced Irrigation

引用

2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Pujitha, Pasupuleti Sai Bhoomika, H.R. Saideep, Konduri Rahul Reddy, T. Banshal, Sumit Kumar Karmakar, Mousumi Shekhar, R. Dept. of Computer Science & Engineering Alliance University Bangalore India

ISBN: (纸本)9798350383867

The Smart Plant Watering System is an Internet of Things (IoT) solution for automated plant irrigation. It combines an ESP8266 Wi-Fi module for real-time monitoring with soil moisture and DHT11 sensors for data collection. It makes use of tools such as Blynk and Thing Speak to facilitate data visualization and remote control. An online crop recommendation systems using ML approaches and written in Python makes recommendations for the best crops to plant depending on the analysis of sensor data. With its ability to maximize irrigation, reduce water use, and improve crop management, this system represents a major breakthrough in precision agriculture for increased productivity and environmental sustainability. © 2024 IEEE.

关键词： Soil moisture

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A Review on Cosmetic Product Recommendation Using Deep Learning 4

A Review on Cosmetic Product Recommendation Using Deep Learn...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Chouhan, Ruchika Barde, Snehlata Sage University Dept. of Computer Science Engineering Indore India

ISBN: (纸本)9798331515720

Cosmetic products play an important role in every one's life. Cosmetic products also called as makeup are substance that is applied on the body especially on face. The modern lifestyle can be demanding, leaving little time for self-care. However, it's crucial to prioritize self-care, including skincare, despite hectic schedules. Indeed, relying solely on best-seller products or in-store recommendations may not always be effective in determining compatibility between a skincare product and an individual's skin condition. This is because everyone's skin tone and type of the skin is unique, with varying sensitivities, concerns, and needs. Many people buy their goods online today, using various e-commerce sites. So, to choose the best skincare product during online purchasing is difficult, since it depends entirely on skin type and usage. The main aim of this paper is to survey and explores the existing recommendation systems designed to assist users in selecting cosmetics personalized to their unique skin type, by using advanced algorithms and machine learning. This study has reviewed various methods like face recognition, cosmetic product recognition, tag recommendation, facial skin image classification using convolution neural network, a new examples-rules guided deep neural network approach. Through a comprehensive review of literature and existing research, this paper examines the methodologies, algorithms, and techniques employed in cosmetic recommendation systems. Key factors such as user preferences, skin type, skin concerns, and product attributes are analysed to understand how recommendation systems use this information to generate personalized recommendations. © 2024 IEEE.

关键词： Cosmetics

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AIoT for Smart Homes: Real-Time Non-Intrusive Load Monitoring 15

AIoT for Smart Homes: Real-Time Non-Intrusive Load Monitorin...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Krishna, H. Jai Venkat Meena, S.M. Kle Technological University Dept. Computer Science Hubballi India

ISBN: (纸本)9798350370249

Facing rising energy demand, we need smarter solutions for conservation. Non-Intrusive Load Monitoring (NILM) offers a cost-effective way to track energy usage, but current methods struggle with real-world implementation. This presentation introduces a novel AI-powered approach to NILM using Deep Neural Networks (DNN). We convert power signatures from appliances into image representations, allowing our DNN to accurately classify which appliances are active. This breakthrough addresses key challenges, including the need for manual training and limited data availability. Our research paves the way for practical NILM solutions, enabling homeowners to gain real-time insights into their energy consumption and make informed decisions for conservation. © 2024 IEEE.

关键词： Deep Neural Network GAN GenAI Non-Intrusive load monitoring

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New Passive Damped-LCL-Filter Based Three-Phase Four-Leg Shunt Active Power Filter

New Passive Damped-LCL-Filter Based Three-Phase Four-Leg Shu...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Wang, Houqing Zhao, Yue Cao, Hui Peyman, Darvish Han, Dongfang Ma, Zhuxuan Zhang, Jinlai Wang, Zhenqi University of Arkansas Dept. of Electrical Engineering and Computer Science FayettevilleAR United States Xinjiang University Dept. of Computer Science and Technology Urumqi China Changsha University of Science and Technology Dept. of Mechanical and Electrical Engineering Changsha China

ISBN: (纸本)9798350376067

To suppress the resonance in an LCL filter, the passive damping method is often favored over the active damping due to its simplicity and robustness. However, the passive damping suffers from decreasing LCL filter's effectiveness in suppressing high-frequency harmonics and introducing power losses. In this paper, a new passive damping method is proposed for the three-phase four-leg shunt active power filter (SAPF) system, by adding resistors between the neutral line and the connection point of the three-phase lines. The selection of the damping resistor is guided by criteria that aim to minimize power losses while ensuring good stability characteristics. The SAPF system with the proposed passive damping demonstrates excellent attenuation of switching frequency harmonics and significant mitigation of interphase switching interference, enhancing harmonic compensation for nonlinear loads. The mathematical model based on single-phase equivalent circuit and control strategy of three-phase four-leg SAPF are analyzed respectively. Simulations and experiments with imbalanced loads are constructed to validate the theoretical analysis. © 2024 IEEE.

关键词： Equivalent circuits

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Integrating Deep Learning Techniques for Enhanced Cyclone Prediction and its Impact on Air Quality Index Utilizing Satellite Imagery

Integrating Deep Learning Techniques for Enhanced Cyclone Pr...

引用

2024 IEEE Students Conference on Engineering and Systems, SCES 2024

作者： Vijayakumar, Sushruth Jamadhiar, Sonakshi Raj, Srujan M Mr, Vishnudeep Saritha, K. PES University Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350374711

Tropical cyclones (TCs greatly impact ecosystems and weather patterns, often leading to catastrophic outcomes this research explores dynamic relationship between the Air Quality Index (AQI) and Tropical Cyclones (TCs), examining changes in air quality during different stages of TCs. Focusing on Cyclone Biparjoy's impact on India's coastline in June 2023, we uncover its influence on air quality. The study shows good predictive performance by utilizing meteorological data, satellite observations, and machine learning algorithms such as CNN and ConvLSTM. Results show promising outcomes, with CNN achieving an RMSE of about 12.03 and an MAE of approximately 8.523 for TC intensity prediction, and ConvLSTM producing an RMSE of approximately 0.22 and a loss of around 0.04 for TC track prediction. Additionally, regression techniques using meteorological data from Tuetempo for AQI prediction were employed. The ExtraTrees Regression model exhibited strong predictive performance with an RMSE of approximately 19.627, while the Xgboost model had a slightly higher RMSE of around 21.72 our dataset included meteorological data from Tuetempo website for AQI forecasts and datasets like Hursat and INSAT 3D satellite photos for TC-related data, this work offers important insights into the dynamics of air quality during TC events. © 2024 IEEE.

关键词： Tropical cyclone

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A Rubric-Centric Approach for Automated Test Correction Utilizing RAG and Fine Tuning 4

A Rubric-Centric Approach for Automated Test Correction Util...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Harshavardhan, G. Singh, Kulvinder Chandigarh University Dept. of Computer Science & Engineering Punjab India

ISBN: (纸本)9798350387490

Assessments are a popular method to quantify a person's knowledge, skill, or cognitive ability. However, to grade such assessments, particularly subjective questions requires a significant amount of time and effort for the examiners. There is also a degree of variance due to inconsistency from different examiners. Thus institutions often resort to taking objective-type questions, which may not completely capture a person's understanding. Unfortunately, many other proposed solutions, tend to be difficult to implement and scale. To address these issues, this paper proposes an innovative method to automate the grading of subjective assessments by leveraging recent advances in artificial intelligence. This approach aims to replicate the grading style of human experts, offering significant benefits for educational institutions, faculty, and students by enhancing the efficiency and quality of grading. Widespread adoption can help increase the quality of education overall, resulting in more qualified graduates. This method involves using a rubric to fine- tune pre-trained language models, providing them with clear guidelines to ensure consistent and accurate grading. Inspired by how rubrics guide human examiners to minimize variation, this approach will use rubrics to instruct the model on the most relevant grading criteria. Utilizing such clearly defined and precise grading criteria, users can obtain reliable results, comparable to human experts. © 2024 IEEE.

关键词： Natural language processing systems

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Advances in Remote Sensing Technologies for Ecological Monitoring and Applications in Carbon Stock Estimation 3

Advances in Remote Sensing Technologies for Ecological Monit...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sadhasivam, Shankaripriya Gayathri, G. Poongothai, P. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This review explores recent advancements that are made in remote sensing, enabling detailed analysis of our ecosystem through high resolution and multispectral imaging. The recent advancements in remote sensing includes developments useful for generation of synthetic images using GAN such as StyleGAN3 and for estimation of carbon stocks in forest using OBIA. Ecotones like mangroves and saltmarsh are crucial for carbon sequestration but mapping them accurately was challenging due to spectral similarity and seasonal variations. Focusing on coastal protection by this review examines the mapping methodologies such as random forest classification, OBIA and FID for habitat distinction, canopy area assessment and analysis. Through these technologies data accuracy can be enhanced for better monitoring. This review concludes with a discussion of AI-powered image synthesis and classification. © 2025 IEEE.

关键词： Carbon sequestration

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Minimizing the Age of Missed and False Alarms in Remote Estimation of Markov Sources 24

Minimizing the Age of Missed and False Alarms in Remote Esti...

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2024 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, MobiHoc 2024

作者： Luo, Jiping Pappas, Nikolaos Dept. of Computer and Information Science Linköping University Sweden

ISBN: (纸本)9798400705212

We consider the remote estimation of a discrete-state Markov source with normal and alarm states. data significance is revealed via two semantic attributes: 1) Erroneously announcing a normal state at the destination when the source is actually in an alarm state (i.e., missed alarm error) incurs a significantly higher cost than falsely announcing an alarm state when the source is in a normal state (i.e., false alarm error). 2) Successive reception of an estimation error may cause significant lasting impact, e.g., maintenance cost and wrong operations. Motivated by this, we assign different costs to different estimation errors and introduce two new age metrics, namely the Age of Missed Alarm (AoMA) and the Age of False Alarm (AoFA), to account for the lasting impact incurred by different estimation errors. We aim to achieve an optimal trade-off between the cost of estimation error, lasting impact, and communication utilization. The problem is formulated as an infinite-state Markov decision process (MDP). We show that the optimal policy exhibits a switching structure, i.e., triggering transmissions only when the AoMA or AoFA exceeds a threshold. Numerical results underscore that our approach significantly reduces the amount of less important information transmitted in the networks. © 2024 Copyright held by the owner/author(s).

关键词： Markov processes

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Optimizing Kidney Stone Detection with Vision Transformers and CNNs: A Transfer Learning Strategy

Optimizing Kidney Stone Detection with Vision Transformers a...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Suhaas Rao, S. Chopra, Tushar Kumar Yeshwanth, S.P. Bhanushree, K.J. Suneetha, K.R. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Kidney stones are a common and impactful urological condition, with timely and accurate diagnosis being critical to prevent complications such as renal damage and avoid invasive treatments. This study explores the use of advanced deep learning models, including Vision Transformers (ViT), Swin Transformer and Convolutional Neural Networks (CNNs) - specifically XResNet-50, VGG-16, MobileNetV2 and AlexNet - to enhance kidney stone detection from CT scan images. Leveraging a robust dataset of annotated CT scans in JPG format, the approach employs transfer learning which leverages features learnt from the ImageNet dataset to optimize model performance, focusing on classifying cases as either "Kidney Stone Present"or "Kidney Stone Absent". To further improve diagnostic reliability, the Grad-CAM (Gradient-weighted Class Activation Mapping) identifies and localizes kidney stones within CT images, enhancing understanding of the model's decision-making and supporting interpretability. Addressing challenges related to dataset imbalance, model interpretability, and generalizability across healthcare settings. By refining these models for kidney stone detection, the main objective of this work is to build a reliable diagnostic tool that aids in clinical judgement and treatment planning, ultimately enhancing patient outcomes and encouraging broader clinical adoption. © 2025 IEEE.

关键词： Convolutional neural networks

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