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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6946 条记录，以下是611-620 订阅

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SpectraVue - An InteractiveWeb Application Enabling Rapid data Visualization and Analysis forWearable Spectroscopy Research

SpectraVue - An InteractiveWeb Application Enabling Rapid Da...

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ACM international Joint conference on Pervasive and Ubiquitous computing / ACM international Symposium on Wearable computing (UbiComp/ISWC)

作者： Hamid, Tarek Lee, Insup Watson, Amanda Univ Penn Philadelphia PA 19104 USA Univ Virginia Charlottesville VA USA

ISBN: (纸本)9798400702006

Spectroscopic analysis of physiological phenomena has remained an important yet underutilized application in wearable technology today. Lumos has recently been introduced as an open-source wearable device capable of on-body spectroscopic research across the visible spectrum, enabling scientists and researchers to study the optical properties of various clinical biomarkers in real-time. However, a key limitation in the data output of this device is the lengthy process required to visualize and plot the spectral responses of observed mediums. In this paper, we present SpectraVue, an interactive web application that allows for visualization of Lumos spectral data. Utilizing a user-friendly interface, SpectraVue enables researchers to quickly generate three-dimensional plots from Lumos data stored in csv or text files, providing a comprehensive view of the spectral response of the medium under investigation. Additionally, SpectraVue offers features such as comparison of spectral data with a clinical biomarker, various data export options, and interactive plotting, further enhancing the user experience and researcher efficiency. The output graphs can be used to provide a standardization of spectral responses across a wide range of mediums, including characterization of these responses in clinical biomarkers such as glucose and alcohol. SpectraVue aims to facilitate these investigations by streamlining the data processing and visualization workflow, thereby accelerating clinical diagnostic research.

关键词： spectroscopy wearable technology visualization web application Plotly Dash

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data Mining-based Classification Methods for Mental Health Prediction 3

Data Mining-based Classification Methods for Mental Health P...

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3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Boina, Roja Sangeethapriya, S. Pokhariya, Hemant Singh Sharma, Ashish Rao, A.L.N. Singh, Surendra Morrisville NC United States H&s Department of English Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Gla University Department of Computer Engineering and Applications U. P. Mathura281406 India Lloyd Institute of Engineering and Technology Department of CSE Greater Noida India Quantum University Uttarakhand Roorkee India

ISBN: (纸本)9798350322842

The advancement of mental health education and prognosis is crucial since the mounting pressures of contemporary society have contributed to an increase in psychological issues. In order to assess mental health intelligence, this research work suggests a method that makes use of data mining and classification algorithms. An improved method is created to overcome the shortcomings of existing methods by integrating decision trees with Artificial Neural Network (ANN) algorithms. Using data mining-derived compound learning algorithms, the system analyses and classifies mental health IQ test data. To assess the system's effectiveness and superiority, novel simulation tests are carried out. In order to increase accuracy and efficacy, the research also presents new methods for assessing mental health intelligence. While ensuring stability, the suggested system seeks to address the drawbacks of the current approaches. © 2023 IEEE.

关键词： Decision trees

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Link load balancing scheme for elephant flow in SDN data center 21

Link load balancing scheme for elephant flow in SDN data cen...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Hao, Feifan Jing, Shan Zhao, Chuan University of Jinan School of Information Science and Engineering Jinan China Quan Cheng Laboratory Jinan China

ISBN: (纸本)9798350329223

In recent years, the rapid emergence of Internet industries such as big data and cloud computing has led to significant growth in data centers. The integration of Software Defined Network(SDN) technology with data centers empowers the controller to manage the network globally and regulate traffic forwarding. This seamless integration significantly enhances the overall operational efficiency of data centers. Nevertheless, as data center traffic surges, a substantial volume of traffic floods the links, potentially leading to load imbalances and severe link congestion under traditional traffic scheduling modes. The concept of 'mice flow and elephant flow' introduces a novel idea to enhance load balancing in data centers. In this paper, we propose a novel data center link load balancing method called EDDQN (Elephant Detection Deep Q-learning Network). EDDQN involves a dynamic, two-stage detection process for identifying elephant flow at the edge. Subsequently, the DQN algorithm is utilized to reroute the detected elephant flow, optimizing the load distribution in the data center network. With the advantages of SDN, the link utilization and throughput of data center networks can be improved. Additionally, the packet loss rate and transmission delay can be reduced, thereby improving the network performance. © 2023 IEEE.

关键词： Reinforcement learning

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Enhancing Declarative Temporal Model Mining in Relational databases: A Preliminary Study 23

Enhancing Declarative Temporal Model Mining in Relational Da...

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27th international database Engineered Applications Symposium, IDEAS 2023

作者： Appleby, Samuel Bergami, Giacomo Morgan, Graham Newcastle University Newcastle Upon Tyne United Kingdom

ISBN: (纸本)9798400707445

Propositionalisation tampers the running time of state-of-the-art algorithms in declarative temporal model mining, as they exhaustively generate the clauses instantiated with the results of frequent itemset mining algorithms. Existing algorithms also exploit non-indexed data representations, thus negatively affecting the overall running time. This paper proposes a novel temporal model mining algorithm, Bolt, twinning confidence and support metrics as heuristics for candidate pruning with data structures enabling fast temporal data scanning. Bolt outperforms both state-of-the-art and renditions of existing mining algorithms using KnoBAB as a library. © 2023 Owner/Author.

关键词： data mining

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Crawling data with Social Network Analysis to Detect Accounts Spreading Phishing on Social Media Indonesia 1

Crawling Data with Social Network Analysis to Detect Account...

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1st international conference on Technology, Engineering, and computing Applications, ICTECA 2023

作者： Ermawan, Andri Tono Mantoro, Teddy Ayu, Media Anugerah Maarif, Haris Al Qodri Nusa Putra University School of Computer Science Sukabumi Indonesia Nasional Intelligence Collage Science in Intelligence Bogor Indonesia Sampoerna University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

This paper presents a methodology for detecting accounts involved in the dissemination of phishing attacks through social media platforms. The research methods used include crawling data from social media platforms, analyzing it using machine learning algorithms, and identifying accounts that could spread phishing attacks. The results obtained show that the proposed methodology is effective in detecting accounts involved in the dissemination of phishing attacks. The conclusions produced suggest that the proposed methodology could be used as a proactive strategy for identifying and mitigating phishing attacks on social media platforms. © 2023 IEEE.

关键词： Learning algorithms

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A Study on Wordle Game data Analysis and Prediction Methods using Various algorithms and Improvement Approaches

A Study on Wordle Game data Analysis and Prediction Methods ...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Li, Yuxin Yu, Ziheng Lian, Ziqing Mathematics Lancaster University Lancaster United Kingdom

ISBN: (纸本)9798350313413

This article analyzes Wordle game stats, using diverse models to explain current trends and predict future outcomes. We start with Pearson Correlation Algorithm (PCCS) to find relationships between variables and Nonlinear Least Square (NLS) to explain report number changes, indicating a strong positive correlation over time. Next, Long-Short Term Memory (LSTM) predicts future reports. On March 1, 2023, we predict 59,495 reports with R-squared at 0.98949. K-means clustering and Apriori Association Rule (AAR) reveal patterns for words with two vowels. From this process, we conclude that the words with exactly two vowels have discerned tendency in numbers. Partial Least Square (PLS) models predict word gaming performance. For 'EERIE' on March 1, 2023, attempts break down as follows: Try1=0.276%, Try2=4.072%, Try3=19.345%. Lastly, words classify into three difficulty levels. 'EERIE' rates 2.08, as 'Normal.' This study enhances player understanding of the game and offers intriguing data for future research. © 2023 IEEE.

关键词： apriori association rule long-short term memory nonlinear least square partial least square pearson correlation algorithm

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Natural Language processing in Healthcare: Intelligent System for Extracting Insights from Clinical Text data

Natural Language Processing in Healthcare: Intelligent Syste...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Rashmi, C. Srinivas, Dava Prabu, S. Santosh Kumar, P.N. Siddiqua, Ayesha Malla Reddy Engineering College for Women It Department Hyderabad India Jyothishmathi Institute of Technology & Science Department of Computer Science and Engineeing Karimnagar India Mahendra Institute of Technology Department of Ece Namakkal India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The World Health Organization identified COVID-19 to be a new pandemic in March 2020. This fatal virus spread over the world, affecting many different countries. Massive volumes of useful data were provided by social media platforms like Twitter on the outbreak to help evaluate health-related decision-making. To predict disease occurrence and issue early warnings, thus implement that user's attitudes be evaluated through the use of efficient deep learning. The gathered tweets were pre-processed and divided into three categories like neutral, positive, and negative. In the second step, various features were extracted from the articles using a number of popular techniques to capture feature dataset, including TF-IDF, FastText, and Glove. The implemented TFIDF has combination of semantic features (Glove and FAsText) to accurately describe posts and enhance the classification process utilizing the deep learning method. The implemented TF-IDF-CNN method's performance is evaluated using Twitter dataset. Implemented method achieved better performance than the existing intrusion detection methods including NLP-BiLSTM, TF-IDF, SVM and CF and the metrics are 96% of accuracy, 0.89 of f-measure, 0.93 of precision, and 0.90 of recall. © 2023 IEEE.

关键词： COVID-19 Deep learning Feature extraction Sentiment analysis Twitter

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SocialNLP'23: 11th international Workshop on Natural Language processing for Social Media

SocialNLP'23: 11th International Workshop on Natural Languag...

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32nd World Wide Web conference (WWW)

作者： Li, Cheng-Te Ku, Lun-Wei Natl Cheng Kung Univ Tainan Taiwan Acad Sinica Taipei Taiwan

ISBN: (纸本)9781450394161

SocialNLP is an inter-disciplinary area of natural language processing (NLP) and social computing. SocialNLP has three directions: (1) addressing issues in social computing using NLP techniques;(2) solving NLP problems using information from social networks or social media;and (3) handling new problems related to both social computing and natural language processing. The 11th SocialNLP workshop is held at TheWebConf 2023. We accepted nine papers with acceptance ratio 56%. We sincerely thank to all authors, program committee members, and workshop chairs, for their great contributions and help in this edition of SocialNLP workshop.

关键词： Natural Language processing Social Media Machine Learning Deep Learning Text Mining Large Language Models ChatGPT

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Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models 37

Solving Linear Inverse Problems Provably via Posterior Sampl...

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Rout, Litu Raoof, Negin Daras, Giannis Caramanis, Constantine Dimakis, Alexandros G. Shakkottai, Sanjay Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781713899921

We present the first framework to solve linear inverse problems leveraging pre-trained latent diffusion models. Previously proposed algorithms (such as DPS and DDRM) only apply to pixel-space diffusion models. We theoretically analyze our algorithm showing provable sample recovery in a linear model setting. The algorithmic insight obtained from our analysis extends to more general settings often considered in practice. Experimentally, we outperform previously proposed posterior sampling algorithms in a wide variety of problems including random inpainting, block inpainting, denoising, deblurring, destriping, and super-resolution.

关键词： data handling

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Enhancing the Classification Accuracy using CNN, GANs, and data Augmentation Techniques

Enhancing the Classification Accuracy using CNN, GANs, and d...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Gupta, Shreya Rani, Ritu Jaiswal, Garima Sharma, Arun Cse Igdtuw Delhi India Centre of Excellence - Artificial Intelligence Igdtuw Delhi India Computer Science Engineering Bennett University Greater Noida India

ISBN: (纸本)9798350313413

This research paper's primary objective revolves around addressing the critical issue of handling imbalanced data sets, particularly in the context of image classification tasks with an uneven distribution of images. The imbalanced data set can lead to biased predictions, which can have adverse impacts in numerous domains such as medical diagnostics and agriculture. This clearly shows the urgency of addressing this problem and finding effective solutions to mitigate its impact. Hence, this study began with an imbalanced image data set, resulting in biased accuracy, and to address this problem, this research has proposed an innovative two-step approach using data augmentation and generative adversarial networks (GANs) to balance the data set. This balanced data set was then used to train the CNN classification model, achieving impressive results with an accuracy of 97.03%, a precision of 0.75, a recall of 0.83, and an F1-score of 0.79. Hence, the proposed methodology significantly improves classification accuracy and performance, demonstrating the potential of this approach to rectify imbalances in image classification tasks. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Deep CNN Generative Adversarial Networks (GANs) Image Augmentation Imbalanced dataset

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