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Research on Personalized Recommendation Algorithm for Housing Rental Based on data mining 4

Research on Personalized Recommendation Algorithm for Housin...

引用

4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Xu, Yingzhuo Bai, Yu School of Computer Science Xi'an Shiyou University Xi'an China

ISBN: (纸本)9798400709449

As China's economy develops and people's living standards improve, the demand for housing is increasing day by day. Many people choose to rent a house as a temporary solution to their housing needs. In view of the large amount of data in the housing rental industry and the difficulty for users to obtain information, this paper proposes a personalized recommendation algorithm for house rental based on data mining. A user interest model is extracted based on the user's historical behaviour, and a grey correlation analysis is used to calculate the correlation between all housing data and the user interest model, and the housing information with a high correlation is recommended to the user. In this paper, the personalized recommendation algorithm will be studied to provide a method for personalized recommendation service for users' rental properties. © 2023 ACM.

关键词： data mining

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Online Q&A for machine learning Courses Based on Formula recognition 4

Online Q&A for Machine Learning Courses Based on Formula Rec...

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4th international conference on Information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Chen, Xiaopan Li, Minghao Zhang, Tianxiang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan University Kaifeng Henan Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9798350394610

Mathematical formulas are ubiquitous in courses such as machine learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. this paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection. © 2023 IEEE.

关键词： Deep learning

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4th Congress on Intelligent Systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on Intelligent Systems, CIS 2023

ISBN: (纸本)9789819990429

the proceedings contain 35 papers. the special focus in this conference is on Intelligent Systems. the topics include: Track learning Agent Using Multi-objective Reinforcement learning;performance Assessment of Gaussian Filter-Based Image Fusion Algorithm;a Cognitive Comparative Analysis of Geometric Shape-Based Cryptosystem;a Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer;energy Optimization of Electronic Vehicle Using Blockchain Method;pattern recognition: An Outline of Literature Review that Taps into machine learning to Achieve Sustainable Development Goals;novel Approach for Stock Prediction Using Technical Analysis and Sentiment Analysis;visualizing and Exploring the Dynamics of Optimization via Circular Swap Mutations in Constraint-Based Problem Spaces;An Approach to Increase the Lifetime of Traditional LEACH Protocol Using CHME-LEACH and CHP-LEACH;denseMammoNet: An Approach for Breast Cancer Classification in Mammograms;aspect-Based Sentiment Classification Using Supervised Classifiers and Polarity Prediction Using Sentiment Analyzer for Mobile Phone Tweets;microbial Metabolites and Recent Advancement;design of a 3D-Printed Accessible and Affordable Robotic Arm and a User-Friendly Graphical User Interface;profit Maximization of a Wind-Integrated System by V2G Method;detection of Partially Occluded Area in Images Using Image Segmentation Technique;Application of IP Network Modeling Platforms for Cyber-Attack Research;enhancing Information Integrity: machine learning Methods for Fake News Detection;Optimum Selection of Virtual machine in Cloud Using Improved ACO;data Imputation Using Artificial Neural Network for a Reservoir System;depth Multi-modal Integration of Image and Clinical data Using Fusion of Decision Method for Enhanced Kidney Disease Prediction in Medical Cloud;an Efficient Prediction of Obstructive Sleep Apnea Using Hybrid Convolutional Neural Network.

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4th Congress on Intelligent Systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on Intelligent Systems, CIS 2023

ISBN: (纸本)9789819990399

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conference Committees

Conference Committees

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pattern recognition and machine learning (PRML), IEEE international conference on

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Incorporating Large Language Models into Named Entity recognition: Opportunities and Challenges 4

Incorporating Large Language Models into Named Entity Recogn...

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4th international conference on Computer, Big data and Artificial Intelligence, ICCBD+AI 2023

作者： Luo, Zhizhao Yang, Yuting Qi, Rui Fang, Zhi Guo, Yikai Wang, Youchen Beijing Institute of Computer Technology and Application Beijing China China Life Property & Casualty Insurance Company Limited Changsha China

ISBN: (纸本)9798350373233

Named entity recognition (NER) is a critical task in natural language processing, involving the identification and classification of entities in text. Traditional approaches to NER, such as rule-based methods and supervised machine learning, faced limitations in adapting to diverse domains and languages due to the need for large labeled datasets. Large language models (LLMs) such as GPT3, which undergo vast pretraining, demonstrate the promising ability to substantially improve NER beyond supervised baselines as evidenced by over a 100% increase in F1 score on domain-specific evaluation *** survey comprehensively examines the pivotal roles and methodologies of LLMs in NER. It provides a thorough analysis of two key techniques: in-context learning and fine-tuning. In-context learning enables quick NER task resolution without major model changes, while fine-tuning offers significant improvements, especially in domain-specific applications. the survey also covers essential resources, including NER corpora, LLM types, and methods of fine-tuning. It addresses challenges related to computational resources and model size. the survey concludes by outlining potential future research directions, emphasizing LLMs' contributions to advancing NER technologies. © 2023 IEEE.

关键词： Supervised learning

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Physics-Aware machine learning for Dynamic, data-Driven Radar Target recognition 4th

Physics-Aware Machine Learning for Dynamic, Data-Driven Rada...

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4th international conference on Dynamic data Driven Applications Systems, DDDAS 2022

作者： Gurbuz, Sevgi Zubeyde Department of Electrical and Computer Engineering The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9783031526695

Despite advances in Artificial Intelligence and machine learning (AI/ML) for automatic target recognition (ATR) using surveillance radar, there remain significant challenges to robust and accurate perception in operational environments. Physics-aware ML is an emerging field that strives to integrate physics-based models with data-driven deep learning (DL) to reap the benefits of both approaches. Physics-based models allow for the prediction of the expected radar return given any sensor position, observation angle and environmental scene. However, no model is perfect and the dynamic nature of the sensing environment ensures that there will always be some part of the signal that is unknown, which can be modeled as noise, bias or error uncertainty. Physics-aware machine learning combines the strengths of DL and physics-based modeling to optimize trade-offs between prior versus new knowledge, models vs. data, uncertainty, complexity, and computation time, for greater accuracy and robustness. this paper addresses the challenge of designing physics-aware synthetic data generation techniques for training deep models for ATR. In particular, physics-based methods for data synthesis, the limitations of current generative adversarial network (GAN)-based methods, new ways domain knowledge may be integrated for new GAN architectures and domain adaptation of signatures from different, but related sources of RF data, are presented. the use of a physics-aware loss term with a multi-branch GAN (MBGAN) resulted in a 9% improvement in classification accuracy over that attained with the use of real data alone, and a 6% improvement over that given using data generated by a Wasserstein GAN with gradient penalty. the implications for DL-based ATR in Dynamic data-Driven Application Systems (DDDAS) due to fully-adaptive transmissions are discussed. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Automatic target recognition

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Research on traffic sign recognition algorithms for autonomous vehicles in smart city traffic systems 4

Research on traffic sign recognition algorithms for autonomo...

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4th international conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Wang, Chuanfei School of Government Beijing Normal University Beijing100875 China

ISBN: (纸本)9781510682528

With the continuous advancement of smart city construction, autonomous driving technology is playing an increasingly important role in urban traffic systems. this study aims to explore the development and optimization of traffic sign recognition algorithms for autonomous vehicles in smart city traffic environments. By comprehensively analyzing current traffic sign recognition technologies, this paper proposes a traffic sign recognition system based on the YOLOv5 algorithm and utilizes the open-source COCO dataset for model training and testing. the images were preprocessed and annotated, employing CSPDarknet53 as the backbone network, which effectively extracts image features through multiple convolutional layers and residual blocks. A deep learning model was trained, capable of recognizing and classifying various traffic signs such as stop, speed limit, and turn signs. the research results indicate that the model demonstrates high average precision (AP) on the COCO dataset, effectively identifying traffic signs of different sizes and angles, even in complex backgrounds, with high robustness. Compared to traditional methods, the recognition accuracy has improved by 15%, and it has a significant advantage in real-time processing. © 2024 SPIE.

关键词： Object detection Detection and tracking algorithms Unmanned vehicles Performance modeling Feature extraction Image processing data modeling Image segmentation Mathematical optimization

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4th Congress on Intelligent Systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on Intelligent Systems, CIS 2023

ISBN: (纸本)9789819990368

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Implementation of machine learning Methods on data to Analyze Emotional Health 4th

Implementation of Machine Learning Methods on Data to Analyz...

引用

4th international conference on Recent Trends in Advanced Computing - Computer Vision and machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

作者： Jadala, Vijaya Chandra Pasupuleti, Sai Kiran Hrushikesava Raju, S. Gole, Srihari Babu Ravinder, N. Sreedhar, B. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Greenfields Vaddeswaram Guntur522502 India

ISBN: (纸本)9789811971686

the design and implementation of the intelligent model based on machine learning methodologies on datasets to analyze emotional and mental health, majorly concentrating to measure, cognize, simulate, and react to human emotions. the data captured by the apparatus is always available to the user and provides contact to interact with patients in case of Emergency. It stores data for future analysis and for pattern identification, Experts will be able to use Artificial Intelligent based machines to augment the human capacity to detect, diagnose, and treat mental health issues. machines have a significantly higher computational capacity than humans, creating assessments from large pools of patient’s data that will eventually make informed clinical decisions. this article will examine AI applications on emotional and mental health, It also highlights the implications of using emotional AI in identifying, diagnosing, and improving mental health. We focused on the conceptual and simulation model for result analysis where data stored in the cloud is retrieved as per the requirement and analyzed. Different machine learning algorithms are implemented on the dataset and results are identified through graphical analysis. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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