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

共 6977 条记录，以下是621-630 订阅

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Secured Framework for Banking Chatbots using AI, ML and NLP 7

Secured Framework for Banking Chatbots using AI, ML and NLP

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Chanda, Rajat Prabhu, Sandeep Symbiosis Institute of Digital and Telecom Management Aff. Symbiosis InternationalDeemed University Pune India

ISBN: (纸本)9798350397253

Artificial intelligence (AI) Machine Learning (ML), and Natural Language processing (NLP)are being employed in a growing number of sectors and is evolving as the technology of the future. It has been widely used to improve the quality of decision-making and problem-solving. This research study proposes a secured framework for banking chatbots using AI-ML and NLP. The framework based on qualitative in-depth experts interviews can be used for developing efficient chartbots for managing customer interactions of various data forms. AI enabled chatbots involves use of machine learning algorithms that can communicate with consumers in a conversational manner. AI enabled chatbots can speak common man's language language and utilize voice as a user interface. Chatbots are used in the public and private sectors to provide virtual help in areas such as telecommunications, media, tourism, retail, stock markets, and banking. In the banking business, chatbot can act as a communication tool between customers and banks. It has a special advantage in financial industry as it can be automated and does not necessitate human interaction;and can enhance accessibility and provide a lot of features to customers. © 2023 IEEE.

关键词： Decision making

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

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

引用

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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processing Time Performance Analysis of Scheduling algorithms for Virtual Machines Placement in Cloud computing Environment 6th

Processing Time Performance Analysis of Scheduling Algorith...

引用

6th international conference on Big data Cloud and Internet of Things, BDIoT 2022

作者： Mikram, Hind El Kafhali, Said Saadi, Youssef Hassan First University of Settat B.P. 539 Settat26000 Morocco Data Science For Sustainable Earth Laboratory Faculty of Sciences and Techniques Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (纸本)9783031283864

In recent years, a large range of applications have been migrated from traditional computing environments to cloud systems. On the other hand, organizations with existing infrastructure investments leverage the server consolidation in the cloud to serve the upcoming load from various clients around the world. Server consolidation techniques in cloud computing are applied at infrastructure levels and spilled into three main steps;load detection, selection and placement of migrated virtual machines. The placement process can be divided to initial placement and task scheduling while allowing fair load and better resource utilization. The challenge is executing several tasks with the available shared computing resources. Further, the cloud provider should apply a good task strategy depending on the customer’s need. To maintain the best performance of the cloud system, the optimal resource utilization and the shortest completion time for task execution are the essential keys. The heuristics and metaheuristics methods are applied to achieve this goal. In this work, some of the proposed scheduling algorithms will be compared through CloudSim as a simulation tool and through processing time as a performance metric. More specifically, the comparison is made by proposing various simulation scenarios, using the processing time to classify algorithms according to time-shared and space-shared. The evaluation results show that SJF (Short Job First) and FCFS (First Come First Serve) outperforms other baseline algorithms according to different simulations set up. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

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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...

引用

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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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 ...

引用

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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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...

引用

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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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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Research on Distributed Multi-Task Learning System Based on data Analysis Algorithm 1

Research on Distributed Multi-Task Learning System Based on ...

引用

1st IEEE international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Liu, Xin Wang, Yanjun Wang, Qiaoli Ma, Jun Cao, Jinping State Grid Xinjiang Electric Power Co. Ltd. Xinjiang Urumqi830000 China Information and Telecommunication Company State Grid Xinjiang Electric Power Co. Ltd. Xinjiang Urumqi830000 China

ISBN: (纸本)9798350323795

This paper presents a research study on distributed multi-task learning systems based on data analysis algorithms. The paper starts by providing an introduction to distributed multi-task learning and data analysis algorithms, along with their importance in machine learning and artificial intelligence applications. The background section provides a more detailed discussion of these concepts, highlighting their specific applications and challenges. The paper then focuses on data analysis algorithms, discussing their types, features, and usage in different applications, including distributed multi-task learning. The paper also highlights some of the popular data analysis algorithms, such as clustering, classification, regression, and dimensionality reduction, along with their mathematical formulations and applications. The section on distributed multi-task learning provides an overview of its concepts, benefits, and challenges. The paper then discusses some of the popular algorithms used in distributed multi-task learning, such as Bayesian methods, transfer learning, and multi-task feature learning, along with their mathematical formulations and applications. The paper concludes by discussing some of the recent research trends in this field, including the use of deep learning and reinforcement learning in distributed multi-task learning systems. Overall, this paper provides a comprehensive overview of the distributed multi-task learning system based on data analysis algorithms, highlighting its importance and potential in various applications. © 2023 IEEE.

关键词： Deep learning

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Advancing Optical Character Recognition for Handwritten Text: Enhancing Efficiency and Streamlining Document Management 14

Advancing Optical Character Recognition for Handwritten Text...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Benedict Vinusha, V. Indhuja, V. Varshitha Reddy, M. Nikhitha, N. Reddy, Naga Venkata Siva R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai601206 India

ISBN: (纸本)9798350335095

This research paper explores the challenge of converting handwritten text into editable digital text, in both native and English languages. We propose optical character recognition (OCR) as a solution, leveraging algorithms and machine learning models to recognize and convert text into various digital formats. The paper highlights the significance of OCR in digitizing paper documents and streamlining document management across diverse industries. Specifically, the study focuses on developing OCR software capable of identifying regional languages, providing a comprehensive assessment of its precision and effectiveness. The findings demonstrate how OCR technology can significantly improve efficiency and cost-effectiveness for businesses dealing with large volumes of paper documents. Our approach involves using image inputs, analyzing them with Keras and OpenCV, and implementing Convolutional Neural Network (CNN) techniques and Natural Language processing (NLP) libraries. © 2023 IEEE.

关键词： Cost effectiveness

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Modeling a Hybrid Stemmer for Kokborok

Modeling a Hybrid Stemmer for Kokborok

引用

2023 international conference on Intelligent Systems, Advanced computing and Communication, ISACC 2023

作者： Debbarma, Pankaj Hrangkhawl, Pusparwng Debbarma, Swapan Tripura Institute of Technology Department of Computer Science and Engineering Narsingarh India Ambedkar College Department of Information Technology Fatikroy India National Institute of Technology Department of Computer Science and Engineering Agartala India

ISBN: (纸本)9798350399387

Kokborok is a resource scarce and vulnerable language and is spoken only by around a million people in the north-east Indian state of Tripura. Lots of unstructured textual data in Kokborok is now available however efficient data mining for such a resource is not applicable. Reasons are the absence of an efficient pre-processing step that can help in solving the information retrieval or natural language processing. In this work, we present a model of hybrid stemming algorithm for the vulnerable and resource-scarce Kokborok language and assess its performance using various methods. Evaluation of the hybrid stemmer was performed on a corpus of 52,482 words that resulted in achieving an accuracy of 89.28%. Recall and Precision of 89.28% and 89.61 %, respectively, with F1-measure of 89.44 has been found for the stemmer. © 2023 IEEE.

关键词： data mining

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