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检索条件"任意字段=2023 IEEE Visualization in Data Science, VDS 2023"

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A Precise Weather Image Classification Model using Machine Learning Methods

A Precise Weather Image Classification Model using Machine L...

引用

Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC), ieee

作者： Megha Dabas Aryan Sannidhya Pal Computer Science and Engineering Guru Nanak Institutions Telangana India Computer Science and Engineering Chandigarh University Mohali India

ISBN: (数字)9798350384659

ISBN: (纸本)9798350384666

In order to comprehend and predict meteorological patterns and make well-informed decisions in a variety of industries, including disaster management, transportation, and agriculture, it is essential to classify the weather. This study investigates the use of Orange, a potent data mining and visualization tool, for weather classification by leveraging advances in machine learning. The goal of the study is to develop a reliable and precise weather classification model utilizing actual meteorological data. To assure the quality and relevance of the input data, the data preprocessing step begins with data cleansing, addressing missing values, and feature selection. Numerous meteorological factors, including temperature, humidity, wind speed, cloud cover, and precipitation, are included in the features that were chosen. To find the best model for weather categorization, a comparison analysis of various machine learning algorithms, including K-Nearest Neighbors Algorithm (KNN), Support Vector Machines (SVM), and Random Forests, is carried out. Based on parameters like accuracy, precision, recall, and F1-score, the performance of each method is evaluated. The Orange platform is used to put the chosen machine learning algorithm into practice and create a simple and straightforward weather classification model. By supporting model interpretation and insight development, Orange’s interactive data visualization tools help users gain a greater knowledge of the underlying patterns and correlations in the meteorological data. The outcomes show that the proposed weather categorization model in Orange succeeds in forecasting a variety of weather conditions with astounding precision. There is a lot of room for practical applications given that the model can distinguish between distinct weather kinds like sunny, overcast, rainy, and others with accuracy.

关键词： Support vector machines Machine learning algorithms Temperature Wind speed data visualization Transportation data models

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Semi-automatic Analysis of cells in honeybee comb images

Semi-automatic Analysis of cells in honeybee comb images

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ieee Students' Conference on Electrical, Electronics and Computer science (SCEECS)

作者： Neha Rathore Praveen Kumar Tyagi Dheeraj Agrawal Department of electronics and communication engineering Maulana Azad National Institute of Techonology Bhopal India

Our environment depends on honeybees. In the face of global honey bee decline, colony strength assessment can help apiary management and provide valuable research data. Counting honey, brood, pollen, larvae, and bee cells manually and classifying them based on visual judgement and estimation is time-consuming, error-prone, and requires a qualified inspector. Digital image processing and AI developed automated and semi-automatic solutions to make this arduous job easier. Prior to classification of cells, it is necessary to detect all the cells correctly. The objective of this study is to provide an efficient method to identify all the uncapped cells in honey comb images. The paper is organized with the discussion of problem's background and evaluation first. Further, we give a thorough review of the existing work and explore a possible application. After that, we discussed the methodology adopted in literature. Overall, our investigation is thorough and extensive.

关键词： Computer science visualization Image segmentation Image edge detection Digital images Estimation Transforms

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GeoBeeDashboard: An Intelligent Geospatial Platform for Enhancing Waste Management in Tangerang City

GeoBeeDashboard: An Intelligent Geospatial Platform for Enha...

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ieee International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Richie Muljana Syauqi Akmal Deffansyah Hari Andrew Alexander Agung Santoso Gunawan Edy Irwansyah Fabian Surya Pramudya Muhamad Keenan Ario Muhammad Edo Syahputra Said Achmad Computer Science Department Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

In this study, we created an Intelligent Geospatial Platform called GeoBeeDashboard. The GeoBeeDashboard combines advanced geospatial technologies and artificial intelligence (AI), allowing real-time data visualization and optimized decision-making across multiple sectors. The system architecture consists of a ***-based front end, a FastAPI-driven back end, and PostGIS for geospatial data management. One of the GeoBeeDashboard’s primary use cases is addressing waste management issues in Tangerang City, Indonesia. As rapid urbanization increases waste generation, effective management becomes critical. The dashboard identifies critical trends and inefficiencies in Tangerang’s districts by K-Means clustering waste management data from 2020 to 2023. Our findings identify distinct clusters, highlighting areas with effective waste management and those that require targeted interventions. The benefits that local government can get from GeoBeeDashboard by utilising insights based on clustering analysis of data per each district in Tangerang to optimise waste collection, allocate resources efficiently, and improve the policy-making process for each district in Tangerang. The GeoBeeDashboard improves local government capabilities by providing a scalable model for other cities facing similar urban challenges. In conclusion, GeoBeeDashboard provides local governments with a powerful tool for addressing waste management inefficiencies and fostering sustainable urban development.

关键词： Waste management Satellites Local government Urban areas data visualization Systems architecture Production Real-time systems Geospatial analysis Artificial intelligence

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Driving Next-Generation Workflows from the data Plane

Driving Next-Generation Workflows from the Data Plane

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ieee International Conference on e-science and Grid Computing

作者： Frédéric Suter Rafael Ferreira Da Silva Ana Gainaru Scott Klasky Oak Ridge National Laboratory Oak Ridge TN USA

We observe the emergence of a new generation of scientific workflows that process data produced at a sustained rate by scientific instruments and large scale numerical simulations. This data is consumed by multiple analysis, visualization, or Machine Learning components not only to enable inference and justify the scientific program, but also to monitor and steer the evolution of these experiments. In such workflows, moving intermediate data efficiently is key to performance, more than efficiently scheduling computational tasks. However, most traditional workflow management systems focus on optimizing task scheduling and then deal with data management, assuming a “move little, compute for long” model, which makes them unfit to the efficient management of this new generation of workflows. Therefore, we advocate for a new way to manage scientific workflows. We propose to consider an efficiently and independently managed data plane that can store and stream data. Workflows compute components, in the application plane can then interact with the data plane, abstracted from complexities of data management. Then, the role of a workflow management system would become that of a control plane that allows users to connect services together to execute the workflow and manages connections between the application and data planes. In this position paper, we characterize several next-generation workflow motifs and describe how their interaction with the data plane is a challenge to traditional workflow management systems. Then, we express a set of requirements that a workflow management system should meet to efficiently manage next-generation workflows at different scales. Based on these requirements, we expose our vision of driving next-generation workflows from the data plane and list remaining open challenges.

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An Exploratory data Analysis and visualizations of Underprivileged Communities Diabetes dataset for Public Good

An Exploratory Data Analysis and Visualizations of Underpriv...

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ieee WIC ACM International Conference on Web Intelligence (WI)

作者： Majdi Owda Amani Yousef Owda Maria Fasli Faculty of Data Science UNESCO Chair in Data Science for Sustainable Development Arab American University Ramallah West Bank Palestine Deprtment of Natural Engineering and Technology Sciences Faculty of Graduate Studies Arab American University Ramallah West Bank Palestine University of Essex School of Computer Science and Electronic Engineering Institute for Analytics and Data Science Colchester UK

Exploratory data analysis and visualization are essential tools for discovering patterns, highlighting anomalies, and discovering insights from datasets. In addition, visualizations tend to provide information in a way that is more accessible for the audience to understand and comprehend. Underprivileged communities will need an easier way to comprehend issues, challenges, and threats such as avoiding diseases. The use of exploratory data analysis and visualizations of diabetes datasets can be used as a means to make this topic easier to understand for underprivileged communities. A methodology has been developed to explore a dataset collection of diabetes patients. The dataset was obtained from the Palestinian community medical center (first-hand dataset), then the dataset was prepared and cleaned, and all rows with missing values or wrong values were dropped and removed, then an exploratory data analysis with a large number of data visualizations was produced, and last, a sample of the produced visualizations have been used to conduct a social acceptance experiment to educate an underprivileged population in the Palestinian community about the risks of diabetes. The result of the experiment was excellent and very promising in which participants understood the contents in a much easier way and provided very positive feedback on utilizing visualizations of diabetes datasets for public awareness and good.

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Detection of Breast Cancer Using Machine Learning Approach

Detection of Breast Cancer Using Machine Learning Approach

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ieee UP Section Conference on Electrical Computer and Electronics (UPCON)

作者： Nagesh Sharma Sandeep Singh Kang Computer Science Engineering Chandigarh University Gharuan Mohali Punjab

Breast cancer affects a substantial portion of the global female population and ranks as the second-leading cause of female mortality. However, the potential for successful treatment increases significantly with early detection and effective intervention. Early identification not only enhances prognosis but also elevates survival rates by facilitating timely therapeutic measures. Additionally, accurate categorization of benign tumors might save patients from receiving needless therapies. This research focuses on leveraging the Wisconsin breast cancer dataset for data visualization and performance evaluations across multiple ML algorithms, specifically decision trees, SVM, random forests, and logistic regression. The primary objective is to rigorously assess the precision, recall, accuracy, and F-1 score of each algorithm, gauging their effectiveness and efficiency in accurate data classification.

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Benchmark of Deep Learning Visual and Far-Infrared Videos Toward Weather-tolerant Pedestrian Traffic Monitoring

Benchmark of Deep Learning Visual and Far-Infrared Videos To...

引用

Smart Mobility (SM), ieee International Conference on

作者： Takumi Fukuda Ismail Arai Arata Endo Masatoshi Kakiuchi Kazutoshi Fujikawa Graduate School of Science and Technology Nara Institute of Science and Technology Nara Japan Information Initiative Center Nara Institute of Science and Technology Nara Japan

Sidewalks should be developed and integrated into city planning for economic growth and livability. And it is necessary to obtain data on pedestrians. One type of pedestrian data, pedestrian counting, commonly uses visible and far-infrared cameras to detect and track people. However, it is not clear in which situations the number of pedestrians can be efficiently estimated from each video. In this study, we use visible and far-infrared videos captured on sidewalks to estimate the number of pedestrians. And we investigate the effects of various times and weather on the estimation accuracy. Experimental results showed that the head count accuracy decreased when using visible video at night and in the rain. When using far-infrared video, the accuracy of head count decreased in the range of 23°C-30°C of ambient temperature. This study revealed that in order to achieve a highly accurate head count throughout the day, it is necessary to change the video used depending on the time and ambient temperature or to use both types of video.

关键词： visualization Temperature distribution Temperature dependence Rain Error analysis Gears Urban planning

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A Simplified Approach to DEM Based on Important Points and the Triangular Irregular Network Angles

A Simplified Approach to DEM Based on Important Points and t...

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Electronic Technology, Communication and Information (ICETCI), ieee International Conference on

作者： Shun Zhang Kun Liu Jinguang Yao School of Information Science and Engineering University of Jinan Jinan China School of Information Science and Engineering University of Jinan China High-Resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan China

A digital elevation model (DEM) is a digital representation of terrain. With the extension of the range and increase in resolution, there are a number of problems with the high-density elevation data contained in the digital elevation model. For example, data redundancy and huge amount of data, etc., which can cause inconvenience in subsequent storage, processing and applications. It is therefore necessary to simplify the DEM to varying degrees, depending on the needs. In this paper, a DEM simplification method based on important points and the triangular irregular network (TIN) angle was proposed. Firstly, the DEM was simplified by extracting important points from regular grid model. Subsequently, the TIN is created by the important points. A secondary simplification of the TIN was performed based on the angle between adjacent triangles to obtain the final simplified result. The experimental results demonstrated that the algorithm can effectively reduce the data redundancy of the DEM while ensuring the accuracy of the terrain. At the same time, the original features of the terrain were well maintained and good visualization was retained.

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Towards Trustworthy Multi-Label Sewer Defect Classification via Evidential Deep Learning

Towards Trustworthy Multi-Label Sewer Defect Classification ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chenyang Zhao Chuanfei Hu Hang Shao Zhe Wang Yongxiong Wang The College of Software Engineering Southeast University Nanjing China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

An automatic vision-based sewer inspection plays a key role of sewage system in a modern city. Recent advances focus on utilizing deep learning model to realize the sewer inspection system, benefiting from the capability of data-driven feature representation. However, the inherent uncertainty of sewer defects is ignored, resulting in the missed detection of serious unknown sewer defect categories. In this paper, we propose a trustworthy multi-label sewer defect classification (TMSDC) method, which can quantify the uncertainty of sewer defect prediction via evidential deep learning. Meanwhile, a novel expert base rate assignment (EBRA) is proposed to introduce the expert knowledge for describing reliable evidences in practical situations. Experimental results demonstrate the effectiveness of TMSDC and the superior capability of uncertainty estimation is achieved on the latest public benchmark.

关键词： Deep learning visualization Uncertainty Urban areas Estimation Inspection Benchmark testing

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An Explainable AI based Clinical Assistance Model for Identifying Patients with the Onset of Sepsis

An Explainable AI based Clinical Assistance Model for Identi...

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ieee International Conference on Information Reuse and Integration (IRI)

作者： Snehashis Chakraborty Komal Kumar Balakrishna Pailla Reddy Tanushree Meena Sudipta Roy Artificial Intelligence & Data Science Jio Institute Navi Mumbai India Artificial Intelligence Centre of Excellence (AICoE) Reliance Jio Hyderabad India

The high mortality rate of sepsis, especially in Intensive Care Unit (ICU) makes it third-highest mortality disease globally. The treatment of sepsis is also time consuming and depends on multi-parametric tests, hence early identification of patients with sepsis becomes crucial. The recent rise in the development of Artificial Intelligence (AI) based models, especially in early prediction of sepsis, have improved the patient outcome. However, drawbacks like low sensitivity, use of excess features that leads to overfitting, and lack of interpretability limit their ability to be used in a clinical setting. So, in this research we have developed a smart, explainable and a highly accurate AI based model (called XAutoNet) that provides quick and early prediction of sepsis with a minimal number of features as input. An application based novel convolutional neural network (CNN) based autoencoder is also implemented that improves the performance of XAutoNet by dimensional reduction. Finally, to unbox the “Black Box” nature of these models, Gradient based Class Activation Map (GradCAM) and SHapley Additive exPlanations (SHAP) are implemented to provide interpretability of autoencoder and XAutoNet in the form of visualization graphs to assist clinicians in diagnosis and treatment.

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