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RICE-HDM: a remote sensing framework for monitoring rice high-temperature heat damage in the middle and lower reaches of the Yangtze River 24

RICE-HDM: a remote sensing framework for monitoring rice hig...

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3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Liu, Li Yuan, Cian Chen, Yan Chen, Zixuan Ren, Dong Xiong, Biao China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hy Yichang Hubei Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang Hubei Peoples R China Zhejiang Acad Surveying & Mapping Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798400709777

In the middle and lower reaches of the Yangtze River Basin in China, a large amount of rice enters the heading and flowering stage during the hot summer following the plum rain season. The prolonged high temperature easily triggers widespread rice high-temperature heat damage, resulting in severe economic losses. This paper proposes a based remote sensing paddy rice high-temperature heat damage monitoring (RICE-HDM) framework, to monitor the high-temperature heat damage in Hubei, Hunan, Jiangxi, Jiangsu, and Anhui province, from 2003 to 2019. Firstly, based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products and other data, we use Random Forest model to estimate the daily average temperatures in the study area. Secondly, a multi-platform data fusion and spatial interpolation module for processing daily average temperature images is proposed to construct a fully covered high-precision remote sensing dataset of daily average temperature. Finally, we utilize the produced daily average temperature dataset and rice information dataset obtained from our previous studies to estimate the proportion of rice heat damage areas from 2003 to 2019 of study area and to conduct disaster extent assessments, producing disaster distribution maps. The experimental results show that the proposed framework achieves quantitative and accurate monitoring of rice high-temperature heat damage, which is helpful for guiding real-time monitoring of the disaster and the assessment of the disaster.

关键词： High-temperature heat damage data fusion Paddy rice Remote sensing Random Forest

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A Review of FPGA-based Graph Neural Network Accelerator Architectures 24

A Review of FPGA-based Graph Neural Network Accelerator Arch...

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7th international conference on Signal Processing and machine learning, SPML 2024

作者： Zhang, Xiaojun Xia, Yongwang Kang, Ziyang Du, Xiangbao Gao, Jian Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798400717192

Graph Neural Networks (GNNs) can effectively process graph data and have been widely applied in fields such as drug discovery, social networks, and recommendation systems. With the continuous increase in datasets and the emergence of more advanced GNNs models, improving the inference speed of GNNs has become an urgent need. Field programmable gate array (FPGA) has the characteristics of high parallelism and programmability, which can achieve higher performance and lower power consumption. Using FPGA to design GNNs accelerators offers more advantages over CPU and GPU. However, designing FPGA-based GNNs accelerators still faces many challenges, such as the inability to fully utilize allocated computing resources and the difficulty in achieving a universal GNNs architecture. In this paper, we first summary the challenges that FPGA-based GNNs accelerators need to address. Secondly, we introduce some classic models of GNNs, as well as several frequently-used graph datasets for GNNs accelerators. Then, we explain the architectures of GNNs accelerators and analyse their performance. Finally, we discuss the challenges and development directions of FPGA-based GNNs accelerators in the future. © 2024 ACM.

关键词： Graph neural networks

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Virtual learning machine for Tiny Devices 20th

Virtual Learning Machine for Tiny Devices

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20th Principle and Practice of data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Kitagawa, Nozomi Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Recent developments in artificial intelligence (AI) have increased the demand for high-performance computational devices. However, edge devices are highly restricted in terms of computational power and memory capacity. To address this, a learning engine called swap kernel regression (SKR) [Masaharu et al. 2019] was developed that enables both inference and learning using an edge device. The key feature of SKR is ability to extend beyond the real physical capacity by collaborating with a second storage device. SKR thereby enables the virtual construction of a large-scale learning machine on a tiny device. We developed an improved version of SKR that maximizes performance by solving the thrashing problem and further reduces the computational cost. Using this improved version, we aim to achieve few-shot class-incremental learning (FSCIL), which continuously adapts to new tasks with limited samples, envisioned for on-site implementation. The proposed method uses a kernel machine that operates in a restricted environment in collaboration with a secondary storage system. The kernel parameters, which are not essential for calculating the output values for upcoming inputs, are stored in secondary storage to create a space in the main memory. The essential kernel parameters stored in secondary storage are loaded into main memory when required. Using this strategy, the system can realize recognition or regression tasks without compromising its generalization capability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Automatic classification of migraine and tension-type headaches using machine learning methods 30

Automatic classification of migraine and tension-type headac...

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30th National and 8th international Iranian conference on Biomedical Engineering (ICBME)

作者： Esfahan, Soroor Shafieizadegan Haratian, Arezoo Haratian, Azadeh Shayegh, Farzaneh Kiani, Soroor Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran Islamic Azad Univ Sci & Res Branch Dept Psychol Tehran Iran Islamic Azad Univ Arak Branch Dept Psychol Arak Iran

ISBN: (纸本)9798350359732;9798350359749

Migraines and tension-type headaches (TTH) are the more prevalent types of primary headaches. Usually, the diagnosis of these types of headaches is imprecise. Meanwhile, emotional and cognitive dysfunctions are associated with these disorders and have potential diagnostic power. This paper aims to classify patients with TTH and migraines using machine learning methods. It uses psychological questionnaires and demographic data as features for the classification. The Boruta algorithm is applied to select the most relevant features, and various machine learning models are compared to find the best one. In this study, 79 patients with migraine headaches and 81 with TTH referring to the two neurology clinics in Isfahan City, Iran, from 2019 to 2020 completed the cognitive emotion regulation questionnaire and perseverative thinking questionnaire. Results show that random forest (RF) and naive Bayes achieve the highest accuracy, sensitivity, and specificity in distinguishing TTH and migraine. In fact, RF and naive Bayes outperformed other classifiers in training and testing sets with a slight difference. The TTH and migraine were classified with 98% accuracy, 100% sensitivity, and 96% specificity in the testing set. Applying machine learning algorithms to distinguish TTH and migraine using these disorders' psychological roots helps significantly in the more accurate diagnosis and treatment of these two disorders.

关键词： machine learning migraine tension-type headache psychological distress feature selection

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DDoS Attack Detection Method Based on Improved Bagging 4

DDoS Attack Detection Method Based on Improved Bagging

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4th international conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Zhi, Haiyou Li, Jingxian Yu, Mengyue Wang, Jin Hu, Ziyan Li, Yihan Wuhan Donghu University School of Computer Science Wuhan China

ISBN: (纸本)9798331528973

In view of the complexity and concealment of distributed denial of service attacks, a DDoS attack detection method based on improved Bagging ensemble learning is proposed. In order to effectively deal with the changing attack modes and improve the stability and accuracy of the detection model, this method enhances the recognition ability of DDoS attacks by introducing a weighted voting mechanism and combining multiple machine learning algorithms. Experiments on the CIC-DDoS2019 dataset show that after preprocessing steps such as feature selection, data normalization and feature correlation analysis, the improved Bagging model using RF, DT and KNN as base learners has an accuracy of 99.98% on the test set, which is significantly better than the traditional Bagging method and other detection models. This study shows that the improved Bagging method not only improves the accuracy and robustness of DDoS attack detection, but also provides new ideas for solving challenges such as high-dimensional data processing and dynamic network traffic analysis. © 2024 IEEE.

关键词： Denial-of-service attack

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COVID-19 and its early Diagnosis: A Systematic Literature Review of SOTA machine learning Approaches 28th

COVID-19 and its early Diagnosis: A Systematic Literature Re...

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28th international conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Kärger, Sophia Klaiber, Marco Gerschner, Felix Fernandes, Marc Rössle, Manfred Aalen University of Applied Sciences Beethovenstraße 1 Aalen73430 Germany

The coronavirus disease 2019 (COVID-19) has had and continues to have a major impact on public health worldwide. Therefore, early detection of COVID-19 is of great importance to control the spread of the pandemic. In the course, several machine learning (ML) and Deep learning (DL) approaches using various imaging modalities such as X-ray, CT, or ultrasound images have been introduced to enable faster detection and better decision making. In this context, this paper provides an overview of the state of the art (SOTA) and the development of ML and DL in the detection of COVID-19. Based on a Systematic Literature Review (SLR), a comprehensive and systematic tabular was created with the key aspects of the identified articles, such as the image modality, the techniques used for recognition, the dataset, the number of images, and the performance metrics of the developed approach. In addition, the advantages and disadvantages of the individual approaches are discussed and the need for further research is identified, which is also intended to help fight against potential future diseases. © 2024 The Authors.

关键词： COVID-19

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Task Scheduler for Heterogeneous data Centres Based on Deep Reinforcement learning 14th

Task Scheduler for Heterogeneous Data Centres Based on Dee...

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14th international conference on Parallel Processing and applied Mathematics, PPAM 2022

作者： Fomperosa, Jaime Ibañez, Mario Stafford, Esteban Bosque, Jose Luis Dpto. de Ingeniería Informática y Electrónica Universidad de Cantabria Santander Spain

ISBN: (纸本)9783031304415

This article advocates for the leveraging of machine learning to develop a workload manager that will improve the efficiency of modern data centres. The proposals stem from an existing tool that allows training deep reinforcement agents for this purpose. However, it incorporates several major improvements. It confers the ability to model heterogeneous data centres and then it proposes a novel learning agent that can not only choose the most adequate job for scheduling, but also determines the best compute resources for its execution. The evaluation experiments compare the performance of this learning agent against well known heuristic algorithms, revealing that the former is capable of improving the scheduling. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Comparative Study of machine learning Models for Predicting COVID-19

Comparative Study of Machine Learning Models for Predicting ...

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2024 international conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Okikiola, Folasade Mercy Mustapha, Abiodun Muyideen Oladiboye, Olasunkanmi Esther Lawal, Olaitan Lateef Yaba College of Technology Department of Computer Technology Yaba Lagos Nigeria Anchor University Department of Mathematical Sciences Lagos Nigeria

ISBN: (纸本)9798350358155

Amid the ongoing global COVID-19 pandemic's significant impact on societies since 2019, accurate predictions of its future spread have gained urgency. This study aims to evaluate four models - two-time series (ARIMA and Prophet) and two machine learning (Random Forest and Light Gradient Boosting machine) - for predicting COVID-19 case trends in West African countries. Utilizing COVID-19 case data from Johns Hopkins University, the study employs these models in repeated experiments to forecast future trends, evaluating their performance using mean absolute error (MAE). The ARIMA model consistently outperforms the others, displaying notably low MAE values across all countries. The Prophet model follows with the second-best performance, particularly effective for certain nations, while the remaining models demonstrate weaker predictive abilities. The research underscores ARIMA's exceptional accuracy in forecasting COVID-19 transmission, suggesting its potential to bolster preparedness and response strategies in West African countries for controlling the virus's spread, despite limitations inherent in model predictions. © 2024 IEEE.

关键词： Prediction models

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Monitoring Hyderabad's Carbon Emission using machine learning for Sustainability Assessment of Smart Cities

Monitoring Hyderabad's Carbon Emission using Machine Learnin...

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2024 international conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Gill, Kanwarpartap Singh Reddy, Gowrugari Ritesh Sai Kumar, Mukesh Rawat, Ruchira Hsiung, Pao-Ann Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity University Maharastra Mumbai India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun248002 India National Chung Cheng Univeristy Taiwan

ISBN: (纸本)9798350375237

This research is about the assessment of Hyderabad City to reach SDG 11 goal, SDG(Sustainable development goal)11-Goal 11 is about making cities and human settlements inclusive, safe, resilient and sustainable. Today, more than half the world's population live in cities. By 2050, an estimated 7 out of 10 people will likely live in urban areas. As Hyderabad a upcoming mega city of India possible be the IT and Financial Capital of India and also the host to Countries defence and missile research centre DRDO, was already a big city but for the last two years this city name was spoken all over the world for being the best place to start up and invest because of having a good ecosystem with good logical support and was told will be a mega city and leading world IT by the year 2030 and will be growing more till 2050, so to make the city more sustainable development this paper will assess the past carbon emissions and also predict the future trends and also be predicting some Sustainable Development aspects like future transport pollutions and future road and transportation upgrade to be made in order to make the city resilient and make it go forward in an Sustainable manner. The research utilizes the Hyderabad city data from the year 1993 to 2019 and came up with the Emission Factor and Formulae to give the Carbon Estimates and made the Carbon estimates data set from the year 2002 to 2019 for the Transport sector and also predicted the vehicles growth and the carbon estimates till the year 2030. © 2024 IEEE.

关键词： machine learning

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Two -Level Graph Representation learning with Community -as -a -Node Graphs 23

Two -Level Graph Representation Learning with Community -as ...

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23rd IEEE international conference on data Mining (IEEE ICDM)

作者： Park, Jeong-Ha Lee, Kisung Kwon, Hyuk-Yoon Seoul Natl Univ Sci & Technol Grad Sch Data Sci Seoul South Korea Louisiana State Univ Div Comp Sci & Engn Baton Rouge LA USA

ISBN: (纸本)9798350307887

In this paper, we propose a novel graph representation learning (GRL) model that aims to improve both representation accuracy and learning efficiency. We design a Two-Level GRL architecture based on the graph partitioning: 1) local GRL on nodes within each partitioned subgraph and 2) global GRL on subgraphs. By partitioning the graph through community detection, we enable elaborate node learning in the same community. Based on Two-Level GRL, we introduce an abstracted graph, Community -as-a -Node Graph (CaaN), to effectively maintain the high-level structure with a significantly reduced graph. By applying the CaaN graph to local and global GRL, we propose Two -Level GRL with Community-as-a -Node (CaaN 2L) that effectively maintains the global structure of the entire graph while accurately representing the nodes in each community. A salient point of the proposed model is that it can be applied to any existing GRL model by adopting it as the base model for local and global GRL. Through extensive experiments employing seven popular GRL models, we show that our model outperforms them in both accuracy and efficiency.

关键词： graph representation learning community detection representation accuracy learning efficiency

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