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检索条件"任意字段=2024 Conference on Visualization in Data Science-VDS-Annual"

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Deep Multi-time visualization and Analytics for E-commerce Platform 4th

Deep Multi-time Visualization and Analytics for E-commerce P...

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4th International conference on Advanced Network Technologies and Intelligent Computing-ANTIC-annual

作者： Bhatnagar, Jay R. Iyer, Ravi Prakash Rawat, Krishna Tripathi, Ankit Bhatnagar, Akhil Goyal, Dhruv GEU Univ Dehra Dun 248002 Uttarakhand India Krea Univ Sricity 517646 Andhra Pradesh India IT FUZONe Syst Noida 110096 Uttar Pradesh India

ISBN: (纸本)9783031837821;9783031837838

The Online Rating visualization (ORV) is a widely used tool along with text reviews that offer business intelligence about product performance to prospective e-buyers. The rapid growth in e-business environment has led to an increasing trend to host products and related analytics for prospective consumers in a manner that is informative, credible, and fair. Surprisingly, there is not much prior research done in making the ORV productive and informative. In this paper, we formulate a deep-time resolved view of buyer sentiment analysis called as DBRV (Deep-Bin Rating-Vote visualization) based on equi-length time slices of ratings-votes. We also propose a novel performance measure called Popularity index (P) which combines ratings-votes meaningfully in time differential manner to depict discriminant power of the DBRV. Simulations performed using real data of ratings-votes for iPhone 12 and Earbuds with 4139 and 8497 reviews respectively, shows that DBRV offers more informative insights about consumer experience than average ratings-votes. The proposed DBRV-1 depicts time-binning of ratings-votes distribution thereby illustrating its information resolved into timebins. Likewise, DBRV-2 powered by P conveys higher intelligence about perceived product performance than existing ORV. Finally, we also formulate a more effective and novel user-interactive dashboard, shown for iPhone 12 and Earbuds, that offers a promising frontend analytics for prospective customers.

关键词： E-commerce Ratings Consumer Analytics Deep-Time Binning Popularity index

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iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summaries 24

iScore: Visual Analytics for Interpreting How Language Model...

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29th ACM annual International conference on Intelligent User Interfaces (IUI)

作者： Coscia, Adam Holmes, Langdon Morris, Wesley Choi, Joon Suh Crossley, Scott Endert, Alex Georgia Inst Technol Atlanta GA 30332 USA Vanderbilt Univ Nashville TN USA

ISBN: (纸本)9798400705083

The recent explosion in popularity of large language models (LLMs) has inspired learning engineers to incorporate them into adaptive educational tools that automatically score summary writing. Understanding and evaluating LLMs is vital before deploying them in critical learning environments, yet their unprecedented size and expanding number of parameters inhibits transparency and impedes trust when they underperform. Through a collaborative user-centered design process with several learning engineers building and deploying summary scoring LLMs, we characterized fundamental design challenges and goals around interpreting their models, including aggregating large text inputs, tracking score provenance, and scaling LLM interpretability methods. To address their concerns, we developed iScore, an interactive visual analytics tool for learning engineers to upload, score, and compare multiple summaries simultaneously. Tightly integrated views allow users to iteratively revise the language in summaries, track changes in the resulting LLM scores, and visualize model weights at multiple levels of abstraction. To validate our approach, we deployed iScore with three learning engineers over the course of a month. We present a case study where interacting with iScore led a learning engineer to improve their LLM's score accuracy by three percentage points. Finally, we conducted qualitative interviews with the learning engineers that revealed how iScore enabled them to understand, evaluate, and build trust in their LLMs during deployment.

关键词： data visualization visual analytics large language models explainable AI educational technology

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Exploratory data Analysis and data visualization on Accidental Drug Related Deaths 2

Exploratory Data Analysis and Data Visualization on Accident...

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2nd International conference on Computing and data Analytics, ICCDA 2024

作者： Ogwo-Ude, Ezinne Hasan, Raza Ahmad, Shakeel Mahmood, Salman Solent University Department of Science and Engineering Southampton United Kingdom Nazeer Hussain University Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798331542573

The drug overdose epidemic in the United States is rapidly getting worse with substantial associated public health effects. Covering 10,654 cases over a decade (2012-2022), this study analyses an extensive dataset of accidental drug-related deaths in Connecticut. We process and analyze this data using Python and Tableau, we then use LSTM to predict how many people will show up in the designated intervals. The ages of individuals were stated as a mean value 43.52 (SD =12.60) years with a range between 13 and 87 years, bimodally distributed around the mid-30s to mid-50's. Overall, 74.14% were male and 85.48 % white in race/ethnicity. Significant increases were seen in accidental drug-related deaths. The most implicated substances were any opioids, fentanyl (alone or in combination), cocaine alone, heroin and ethanol. Crucially, some 89.05% of the cases had co-abuse with multiple drugs by one person who showed evidence that poly-substance use is commonplace in this community. Most deaths involved fentanyl (309, with a mode at 36 years (229 out of 309 deaths) and r (0.51) associated with 'any opioid', the primary cause of death). New Haven, Hartford and Fairfield counties stood out as hotspots for overdoses in geographic analysis. The LSTM model achieved a Root Mean Square Error (RMSE) of 7.25 and a Mean Absolute Error (MAE) of 5.62, predicting a sustained annual increase in deaths over the next three years. This federal and state partnership provides a model for using existing surveillance resources to inform targeted overdose intervention strategies, with an emphasis on the rise of fentanyl positivity among decedents. © 2024 IEEE.

关键词： Lanthanum alloys

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Deep Explainable Ensemble for Medical Image Analysis

Deep Explainable Ensemble for Medical Image Analysis

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2024 IEEE Region 10 conference, TENCON 2024

作者： Syed, Hassaan Haider Aneja, Nagender Malik, Owais Ahmed Minhaj, Mohammed Abdul Rahman School of Digital Science Universiti Brunei Darussalam Bandar Seri Begawan Brunei Purdue University Department of Computer Science West LafayetteIN United States Hospital Ripas Department of Radiology Kianggeh Brunei

ISBN: (纸本)9798350350821

Traditionally, medical images have been analyzed manually. However, this manual analysis can be subjective, depending on the judgment of radiologists and pathologists, which can result in inconsistency and inefficiency. Computer vision and deep learning models have revolutionized the study and diagnosis of medical images such as X-rays, CT scans, and MRIs. Despite their effectiveness, these models often act as black boxes and are difficult to interpret. In this study, we propose an ensemble of two popular deep learning models, ResNet101 and EfficientNetB7, which not only provide higher classification accuracy for CT scans but also offer superior interpretability. Our ensemble model outperforms the original models in COVID-19 CT scan classification by more than 2%. We compared the performance of three eXplainable AI (XAI) visualization models including GradCAM, Guided-GradCAM, and LIME, against the ground truth data labeled by radiologists. The proposed ensemble method provides better interpretability for the CT scan images compared to the individual models. © 2024 IEEE.

关键词： Medical imaging

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GHG Web Portal Dashboard: A Scalable and Flexible Digital Platform for Efficient Environmental Monitoring 4th

GHG Web Portal Dashboard: A Scalable and Flexible Digital Pl...

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4th International conference on Advanced Research in Technologies Information Innovation and Sustainability-ARTIIS-annual

作者： Bastos, Goncalo Martinho, Ricardo Ferrao, Silvia Martinho, Rita Matias, Hugo Polytech Univ Leiria ESTG Leiria Portugal Polytech Univ Leiria INESCC DL ESTG Leiria Portugal CoLAB NET4CO2 Network Sustainable CO2 Econ Porto Portugal

ISBN: (纸本)9783031832062;9783031832079

The importance of efficient digital platforms for monitoring greenhouse gas (GHG) indicators cannot be overstated in contemporary environmental management and sustainability efforts. Such platforms are crucial for accurate, real-time data collection and integration, facilitating regulatory compliance, and enabling strategic decision-making. However, developing a usable digital platform for GHG monitoring is challenging due to the complexity of integrating diverse data sources, ensuring data accuracy and reliability, and providing user-friendly interfaces for varied stakeholders. Despite the availability of multiple official data sources on GHG emissions, no specific digital platform exists to comprehensively aggregate and manage this data. Existing systems often lack the flexibility to incorporate new indicators and adapt to evolving regulatory requirements. This paper presents a digital platform capable of seamlessly integrating data from various sources and offering robust usability, to enhance the effectiveness of GHG emission monitoring. The platform can be valuable to stakeholders, including regulatory bodies, industry leaders, environmental organizations, and the general public to address the complexities of environmental data and advancing global sustainability efforts.

关键词： data visualization Greenhouse gas (GHG) emissions Interactive Dashboard Usability

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Bibliometric Analysis and visualization of Machine Learning-Based Credit Card Fraud Detection

Bibliometric Analysis and Visualization of Machine Learning-...

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2024 International conference on Information Technology Research and Innovation, ICITRI 2024

作者： Suhartono Zaman, Syahiduz Chamidy, Totok UIN Maulana Malik Ibrahim Malang Department of information engineering Malang Indonesia

ISBN: (纸本)9798350376210

Machine learning is often used in credit card fraud detection. Its ability to analyze large amounts of transaction data and identify patterns of fraudulent activity. The aim of this study is to provide insights into the research status, mapping process and annual topics of machine learning research on credit card fraud detection, and to provide relevant references for future research. Research phases with three approaches. The first approach is descriptive statistical analysis for data collection using Scopus web and Herzing's Publish or Perish. The second approach uses quantitative methods for citation analysis with a bibliometric approach using Vos Viewer application. The research findings are related to the analysis of credit card fraud research divided into three clusters, the first cluster is the research stream using machine learning in data mining, the second cluster is the research stream using machine learning to detect credit card fraud, the third cluster is the research stream using machine learning to classify credit card fraud. The second cluster can resolve the complexity of credit card transaction data and increase the accuracy of the fraud detection system, for the third cluster, we can build a more effective classification in resolving imbalanced data and limited transaction records. A growing research trend is in the third cluster, research related to the performance of credit card fraud classification based on unbalanced data and limited fraud data. Further research is recommended to examine the evolution of the literature on the use of machine learning to detect credit card fraud and to conduct comparative studies between the Scopus, Web of science and scienceDirect databases to expand the literature. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT Based Sensor Network for Air Quality Monitoring in the Oil and Gas Industry

IoT Based Sensor Network for Air Quality Monitoring in the O...

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2024 SPE Nigeria annual International conference and Exhibition, NAIC 2024

作者： Ngonadi, I.V. Ajiroghene, S. Orji, O.M. Oyewo, M.O. Oshasha, R.I. Department of Computer Science and Information Technology Petroleum Training Institute Delta State Effurun Nigeria SKYIT Effurun Delta State Nigeria Department of Petroleum Engineering and Geosciences Petroleum Training Institute Delta State Effurun Nigeria

ISBN: (纸本)9781959025474

Emissions from the petroleum industry occur in every chain of the oil producing process from extraction to the consumption phase. During the extraction phase, various pollutants like methane, carbon dioxide, carbon monoxide and methanol are released. These pollutants cause air pollution which can be harmful to humans, animals and plants. This paper develops an IoT based sensor network for air quality monitoring in the oil and gas industry which monitors the quality of air around the oil and gas facilities with a view to ensuring that the quality of air is within tolerable limit at all times. This was achieved by deploying the MQ2 and MQ8 sensors together with ESP32 processor. The MQ2 and MQ8 sensors monitor gases like LPG, carbon monoxide, smoke and hydrogen and send the data to the ESP32 processor. The processor massages the raw data and sends the result to the cloud via the Message Queuing Telemetry Transport (MQTT) protocol to the losant dashboard for data analytics and visualization. A C++ program which was written in the Arduino text editor enables communication between the processor and the losant platform. When the sensors get an abnormal reading, they send messages to the control center and automatically shuts down the drilling pump until the situation is brought under control. The result of this research effort is an air quality monitoring system which deploys sensors to monitor the quality of air around the oil and gas facilities and ensures that the gases emitted around the oil and gas facility are controlled at all times. Copyright © 2024, Society of Petroleum Engineers.

关键词： Gas industry

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visualization Study on Suitability Evaluation of Land Spatial Agricultural Production in the Yangtze River Delta Based on Remote Sensing Big data 9

Visualization Study on Suitability Evaluation of Land Spatia...

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9th International conference on Big data Analytics, ICBDA 2024

作者： Zhai, Duanqiang Zhuo, Jian Mao, Yanyun College of Architecture and Urban Planning Tongji University Shanghai China Shanghai Tongji Urban Planning & Design Institute Co. Ltd. Shanghai China Urban Mobility Institute Tongji University Shanghai China

ISBN: (纸本)9798350352962

The evaluation of agricultural production space based on the carrying capacity of resources and the environment is a necessary basis for the scientific delimitation of agricultural space and permanent basic farmland. This holds significant implications for ensuring both national food security and ecological stability. The evaluation index system of agricultural production space based on the carrying capacity of resources and environment is developed and applied to quantitatively evaluate the suitability of the agricultural industry, using the Yangtze River Delta region in China as a case study. The research demonstrates that the evaluation of resource and environment carrying capacity directed by agricultural production function encompasses seven key indicators: light and heat conditions, annual average precipitation, meteorological disaster risk, slope distribution, water resource capacity, soil texture, and water resource abundance. The proportions of agricultural production suitability in the Yangtze River Delta are as follows: 46.43 % in suitable areas, 34.33% in generally suitable areas, and 10.11 % in unsuitable areas. The overall resource conditions for agricultural production space in the region are favorable, with a primary concentration in the middle of the Yangtze River Delta in a belt shape. The Taihu Lake Basin and the surrounding areas of Hangzhou Bay exhibit the highest concentration, and it is distributed in points in the cities in the northeast of the Yangtze River Delta. © 2024 IEEE.

关键词： Rivers

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Predictive Modeling of Global Warming Trends Using Machine Learning

Predictive Modeling of Global Warming Trends Using Machine L...

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2024 IEEE Silchar Subsection annual conference, SILCON 2024

作者： Manikandan, P. Teja, Gollapalli Sri Venkata Naga Mani Srikar, Jonnala Srivasteswar, S. Vidya, Iruvuri Sri Dharani, Srungarapu Sri Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Virudhunagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India

ISBN: (纸本)9798331540821

In this work, we used multiple machine learning regression models to forecast changes in sea level and temperature by doing a thorough examination of historical environmental data. The collection contains millennium-spanning measures of temperature, mean sea level, methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O). Preprocessing of the data, exploratory data analysis (EDA), training of the model, assessment, and prediction visualization are all included in our methodology. Multiple regression models such as Linear Regression, Random Forest, Gradient Boosting, Neural Networks, Support Vector Machine (SVM), XGBoost, and Lasso Regression are used, and their performances are compared. Metrics like Mean Squared Error (MSE) and R2 score is used to evaluate the performance of the model. In addition to illustrating the advantages and disadvantages of each strategy, our results show how well different machine learning models forecast temperature and sea level based on atmospheric data. Researchers and data scientists can apply a variety of machine learning techniques to real-world datasets and conduct extensive data analysis with the help of the Streamlit application, which is a powerful and easily accessible tool. In environmental research and predictive analytics, this study emphasizes the value of sophisticated data analysis techniques and machine learning models. Our findings show that while which of the models can predict temperature very well, the Random Forest and Gradient Boosting models perform better at predicting changes in sea level. This study highlights the advantages and disadvantages of several regression models for modeling environmental data while also offering insights into their relative efficiency. © 2024 IEEE.

关键词： Multiple linear regression

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Machine Learning (ML) based Electricity Price Prediction with visualization 3

Machine Learning (ML) based Electricity Price Prediction wit...

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3rd International conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Abisha, D. Kumar, Prasanna D. Christy, Ida J. Evanjalin, Navedha R. Rajesh, P. Haris, Mohamed K. Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9798331532420

Electricity price prediction is crucial for businesses reliant on heavy machinery or computing services, as it aids in cost management and energy optimization. Accurate predictions allow these businesses to forecast annual electricity expenses, enhancing cost efficiency. This project aims to forecast electricity prices using machine learning techniques, specifically the Random Forest regression algorithm. The project utilizes historical data on electricity consumption and other influencing factors, including date and time, holiday indicators, day of the week, week of the year, forecasted and actual wind production, national load, temperature, wind speed, and CO2 intensity. The methodology involves converting string values to numerical values, handling missing data, and normalizing the dataset, identifying key features that significantly impact electricity prices, Splitting the data into training and testing sets, and training the model using the Random Forest regression algorithm and assessing the model's performance with appropriate metrics to ensure accuracy and reliability. The results demonstrate that the Random Forest model effectively predicts electricity prices with high accuracy. Visualizing these predictions in Power BI provides intuitive insights into the factors influencing electricity prices. These findings enable businesses to better anticipate electricity expenses, optimize energy usage, and improve financial planning. The comprehensive approach outlined in this project offers a valuable tool for understanding and forecasting electricity prices, thereby enhancing resource management and cost efficiency. © 2024 IEEE.

关键词： data Preprocessing Electricity Pricing Energy Cost Optimization Machine Learning Random Forest Regression

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