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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： M.D. Wilson R. Datta S. Savarimuthu D. Moller C. Ruf Geospatial Research Institute Toi Hangarau University of Canterbury New Zealand Dept. Electrical Computer and Software Engineering University of Auckland New Zealand Dept. Climate and Space Sciences and Engineering University of Michigan United States

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

We developed a random forest model to predict soil moisture at the field scale (∼1 km), combining 6.5 years of CYGNSS Global Navigation Satellite System Reflectometry (GNSS-R) data with observations from 1049 gauges which are part of the International Soil Moisture Network. Several relevant predictor variables were developed, including geophysical parameters related to soil properties, topography, land cover and climatology, and dynamic variables including precipitation from the Global Precipitation Mission and evapotranspiration derived from MODIS temperature. The model achieved good accuracy for predicted soil moisture (R 2 = 0.68; RMSE = 0.064 mm/mm), which was further improved through postprocessing using a linear regression model of the residuals for bias correction (R 2 = 0.82; RMSE = 0.048 mm/mm). Our work demonstrates the potential for field-scale prediction of soil moisture from GNSS-R data, which may be further improved through enhanced co-variates, and is applicable to recent and near-future GNSS-R missions such as Rongowai and HydroGNSS.

关键词： Precipitation Accuracy Temperature Soil moisture Soil properties Forestry Predictive models

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Evaluation of Incremental Conductance MPPT Algorithm Under Varying Conditions

Evaluation of Incremental Conductance MPPT Algorithm Under V...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Ahmad M. NEZAM Musaria K. MAHMOOD Sulaiman M. KARIM Abdirahim ADDAWE dept. of Energy Systems Engineering Ankara Yildirim Beyazit University Ankara Türkiye dept. of Medical Instr. Tech. Eng. Jannat Iraq College Ramadi Iraq dept. of Computer Technology Engineering Al-Hadba'a University College Mosul Iraq dept. of Computer Engineering Karabük Üniversitesi Karabük Turkey

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Photovoltaic-based renewable energy production is constantly developing and becoming an increasing part of mod-ern energy markets. The power efficiency of a PV system is one of the most challenging problems, especially in changing climate conditions. Tracking the Maximum Power Point (MPP) to achieve optimal power production is one of the most important requirements of any PV-based system. This article explores the functionality of the Incremental Conductance (INC) algorithm as a highly effective approach for Maximum Power Point Tracking (MPPT). Simulations are conducted to investigate the effectiveness of INC across diverse operational case studies, considering both Standard Test Conditions (STC) and Partial Shading Conditions (PSC). In this investigation, key factors including system power efficiency, settling time, and steady-state power oscillation are taken into account. Simulation reveals rapid tracking and reduced oscillation under STC, meanwhile, it occasionally demonstrates inadequacy in accurately tracking the MPP under PSC in the case of multi-panel systems.

关键词： Maximum power point trackers Solar irradiance Photovoltaic systems Performance evaluation Renewable energy sources Radiation effects Production

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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Multi-disciplinary Conference in Emerging Research Trends (IMCERT), International

作者： Muhammad Shahbaz Muneer Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Haniya Maqsood Dpertment of Computational Finance NED University of Engineering and Technology Karachi Pakistan Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed Uuniversity of Engineering and Technology Karachi Pakistan Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast.

关键词： Climate change Rain Machine learning algorithms Droughts Weather forecasting Predictive models Prediction algorithms

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Understanding the Impact of AI-driven Clinical Decision Support systems 15

Understanding the Impact of AI-driven Clinical Decision Supp...

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

作者： Mohammed, Abdul Sajid Neravetla, Anuteja Reddy Nomula, Varun Kumar Gupta, Ketan Dhanasekaran, S. University of the Cumberlands Dept of School of Computer and Information Sciences KY United States University of the Cumberlands Department of Information Technology KY United States Georgia Institue of Technology Department of Analytics Industrial & Systems Engineering United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of engineering Department of Electronics and Communication engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

Medical selection aid structures (CDSSs) are facts systems designed to assist healthcare experts in the decision-making manner. Conventional CDSSs rely upon analytical fashions and understanding-based algorithms to guide choices. However, the latest advances have visible the emergence of AI-pushed CDSSs that utilize artificial intelligence (AI) to help clinicians in their selection-making. AI-pushed CDSSs are predicted to enhance selection-making accuracy and results by way of leveraging massive datasets and greater state-of-the-art modeling strategies. But, there is nonetheless a lot to understand about the impact of AI-driven CDSSs on healthcare shipping and results. This paper evaluates the present-day literature on the impact of AI-driven CDSSs, exploring the results of this generation for healthcare experts and the healthcare gadget as an entire. It highlights areas in need of additional research and suggests possibilities for the design and implementation of AI-pushed CDSSs. © 2024 IEEE.

关键词： Decision making

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PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking

arXiv

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arXiv 2023年

作者： Liu, Xinran Liu, Xiaoqiong Yi, Ziruo Zhou, Xin Le, Thanh Zhang, Libo Huang, Yan Yang, Qing Fan, Heng Institute of Software Chinese Academy of Sciences Dept. of Computer Science & Engineering University of North Texas United States

Planar object tracking is a critical computer vision problem and has drawn increasing interest owing to its key roles in robotics, augmented reality, etc. Despite rapid progress, its further development, especially in the deep learning era, is largely hindered due to the lack of large-scale challenging benchmarks. Addressing this, we introduce PlanarTrack, a large-scale challenging planar tracking benchmark. Specifically, PlanarTrack consists of 1,000 videos with more than 490K images. All these videos are collected in complex unconstrained scenarios from the wild, which makes PlanarTrack, compared with existing benchmarks, more challenging but realistic for real-world applications. To ensure the high-quality annotation, each frame in PlanarTrack is manually labeled using four corners with multiple-round careful inspection and refinement. To our best knowledge, PlanarTrack, to date, is the largest and most challenging dataset dedicated to planar object tracking. In order to analyze the proposed PlanarTrack, we evaluate 10 planar trackers and conduct comprehensive comparisons and in-dept. analysis. Our results, not surprisingly, demonstrate that current top-performing planar trackers degenerate significantly on the challenging PlanarTrack and more efforts are needed to improve planar tracking in the future. In addition, we further derive a variant named PlanarTrackBB for generic object tracking from PlanarTrack. Our evaluation of 10 excellent generic trackers on PlanarTrackBB manifests that, surprisingly, PlanarTrackBB is even more challenging than several popular generic tracking benchmarks and more attention should be paid to handle such planar objects, though they are rigid. All benchmarks and evaluations will be released at the project webpage. Copyright © 2023, The Authors. All rights reserved.

关键词： Augmented reality

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Energy Efficient joint user association and power allocation using Parameterized Deep DQN

Energy Efficient joint user association and power allocation...

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International Conference on computer and Communication engineering, ICCCE

作者： Amna Mughees Mohammad Tahir Muhammad Aman Sheikh Angela Amphawan Kian Meng Yap Mohamed Hadi Habaebi Md. Rafiqul Islam Dept. of Computing and Information Systems Sunway University Petaling Jaya Malaysia Dept. of Computing University of Turku Turku Finland Dept. of Electronics and Computer Systems Engineering Cardiff Metropolitan University Cardiff U.K Dept. of Electrical and Computer Engineering International Islamic University Kuala Lumpur Malaysia

Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption is a crucial parameter for sustainable future wireless networks. In order to improve quality of service (QoS) and Energy Efficiency (EE), efficient resource allocation strategies are required. This paper investigates a Parameterized Double Deep Q-Network (PDDQN) based framework for joint user association and power allocation to improve EE and throughput. Apart from other conventional machine learning approaches, considering single state space of the joint optimization problem, our proposed framework considers both discrete and continuous state spaces. Our proposed PDDQN technique also solves the generalization problem that occurs due to similar states. The simulation results indicate that the proposed work significantly improves energy EE and throughput in large-scale learning problems.

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Predicting Optimal Placement of Electric Vehicle Charge Stations Using Machine Learning: A Case Study in Glasgow, UK 12

Predicting Optimal Placement of Electric Vehicle Charge Stat...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Mousaei, Arash Naderi, Yahya University of Tabriz Dept. Computer and Electrical Engineering Tabriz Iran Ricardo Aea Sustainable Energy Systems Department Glasgow United Kingdom

ISBN: (纸本)9798331525132

Given the reduced costs of establishing Electric Vehicle (EV) charging stations and the need to improve their accessibility, it is essential for EV developers and governments to identify optimized locations for new stations. This study presents a predictive modeling approach using Machine Learning to determine the optimal sites for EV charging stations in Glasgow. While various machine learning methods are available for this purpose, we focus on Linear Regression due to its simplicity and high accuracy. By analyzing a comprehensive dataset of 207 AC charging stations-including factors latitude, longitude, population density, number of kinds of plugs, total number of plugs, price per kWh, traffic conditions, distance to the nearest station, Air Quality Index (AQI), and land value - we aim to provide urban planners and policymakers with actionable insights. The study evaluates the performance of the Linear Regression model in predicting the most suitable locations for charging stations. The results indicate that the proposed model achieved impressive accuracy, with R2, Mean Absolute Error (MAE), and Mean Squared Error (MSE) values of 0.89, 0.05, and 0.08, respectively. This research contributes to the ongoing efforts in urban planning for sustainable mobility by offering a data-driven methodology for EV infrastructure development. © 2025 IEEE.

关键词： Linear regression

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AIX Implementation in Image-Based PM2.5 Estimation: Toward an AI Model for Better Understanding

AIX Implementation in Image-Based PM2.5 Estimation: Toward a...

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15th International Conference on Knowledge and Smart Technology, KST 2023

作者： Utomo, Sapdo John, A. Pratap, Ayush Jiang, Zhi-Sheng Karthikeyan, P. Hsiung, Pao-Ann National Chung Cheng University Grad. Ins. Ambient Intelligence and Smart Systems Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan Indian Institute of Technology Dept. of Metallurgical and Materials Engineering Ropar Rupnagar India

ISBN: (纸本)9781665477123

In accordance with the Sustainable Development Goals, the exponential expansion of machine learning (ML) and artificial intelligence (AI) presents an excellent chance to build more effective tools and solutions and generate positive social impact. According to the WHO report, global PM pollution causes more than 8 million deaths annually. This is the fundamental reason we are performing this research. This research proposes estimating air quality using deep learning. The proposed model can surpass the state-of-the-art model in terms of RMSE, R-squared, and accuracy, which have respective values of 30.10, 0.83, and 76.92%. In order to explain the model's output, LIME has been implemented. According to LIME's explanation, the proposed model's output is trustworthy. Because it reveals that the sky, and not other places such as buildings, was the source of the most impactful superpixels on the model's decision. We hope that with this discovery, we can contribute to the theme of 'AI for social good,' notably in the domains of the environment and human welfare. © 2023 IEEE.

关键词： Air quality

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A Bi-LSTM Based Model for Social Media Extremism Detection in Context of Bangladesh

A Bi-LSTM Based Model for Social Media Extremism Detection i...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sajib Saha Zahidul Islam Tanvirul Islam Md Monzur Morshed Tithee Chakma Mama Subhenur Latif Md Walid Bin Jashim Dept. of Computer Science & Engineering Daffodil Internation University Dhaka Bangladesh Department of Accounting & Information Systems University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In today’s modern world, digital technology has advanced social networking with revolutionary changes. People use some most popular platforms like Facebook, YouTube and some other social media platforms to engage with each other for business, information sharing and so on. Attacking someone on social media platforms through comments, in response to their speech, has become a common occurrence in Bangladesh as well as worldwide. Very little research has been done in this related field for Bengali NLP. In this study we primarily focus on classifying these comments into two categories: extremist and non-extremist. Comments were collected from two of the most popular platforms, YouTube and Facebook, used in Bangladesh to build our "BEC (Bangla Extremist Comments)" dataset. To classify the comments, a CNN + Bi-LSTM hybrid model was employed considering extremist comments detection systems using deep learning-based sentiment analysis techniques. The proposed model achieved an accuracy rate of 85% in identifying the extremist comments. This research opens opportunities for future researchers to take advantage of contributing and collaboration in this field.

关键词： Deep learning Analytical models Sentiment analysis Video on demand Social networking (online) Computational modeling Information sharing Collaboration Web sites Context modeling

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Data Augmentation Techniques for Chinese Disease Name Normalization

Data Augmentation Techniques for Chinese Disease Name Normal...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenqian Cui Xiangling Fu Shaohui Liu Mingjun Gu Xien Liu Ji Wu Irwin King Dept. Computer Science and Enginnering The Chinese University of Hong Kong Hong Kong China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Dept. Electronic Engineering Tsinghua University Beijing China Dept. Electronic Engineering College AI Tsinghua University Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Disease name normalization is an important task in the medical domain. It classifies disease names written in various formats into standardized names, serving as a fundamental component in smart healthcare systems for various disease-related functions. Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data. Consequently, we present a novel data augmentation approach that includes a series of data augmentation techniques and some supporting modules to help mitigate the problem. Through extensive experimentation, we illustrate that our proposed approach exhibits significant performance improvements across various baseline models and training objectives, particularly in scenarios with limited training data 1 .

关键词： Training Biological system modeling Training data Medical services Data augmentation Data models Natural language processing Bioinformatics Diseases

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