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machine learning Advances in Technology Applications: Cultural Heritage Tourism Trends in Experience Design

international JOURNAL OF ADVANCED COMPUTER science AND APPLICATIONS 2025年第4期16卷 186-196页

作者： Deng, Meihua Hunan Mass Media Vocat & Tech Coll Sch Int Commun Changsha 410100 Hunan Peoples R China

This study investigates the evolving trends in cultural heritage tourism experience design and examines how machine learning technologies are being applied to enhance visitor engagement and heritage preservation. Using bibliometric data from the Web of science (WoS) and visualization tools such as VOSviewer, the research identifies key themes, author collaborations, and keyword clusters from 2016 to 2025. The analysis reveals a shift in focus from traditional conservation and display methods to user-centered experiences supported by advanced technologies. machine learning techniques-such as deep learning, natural language processing, and multimodal data fusion-are increasingly used to personalize tours, analyze tourist behavior, restore damaged artifacts, and improve decision-making in resource management. Tools like CNNs and BERT models enable smart guiding systems and interactive Q&A features, while sentiment analysis enhances feedback mechanisms. The study also highlights several ongoing challenges, including data privacy issues, algorithmic bias, and unequal access to technological infrastructure, especially in developing regions. Ethical considerations and the need for human-centered design principles are emphasized to ensure that technological innovation aligns with cultural values and sustainability goals. In conclusion, this research provides a comprehensive overview of academic progress in cultural heritage tourism and illustrates the growing importance of AI and machine learning in creating immersive, efficient, and culturally respectful tourism experiences. The findings offer practical insights for scholars, heritage site managers, and policymakers seeking to leverage digital tools for both preservation and enhanced visitor satisfaction.

关键词： Heritage tourism tourism experience machine learning VOSviewer bibliometric data

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A defect recognition model for cross-section profile of hot-rolled strip based on deep learning

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Journal of Iron and Steel Research international 2023年第12期30卷 2436-2447页

作者： Tian-lun Li Wen-quan Sun An-rui He Jian Shao Chao Liu Ai-bin Zhang Yi Qiang Xiang-hong Ma National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing University of Science and Technology BeijingBeijing100083China Academy of Machinery Science and Technology Beijing100044China School of Engineering and Applied Science Aston UniversityBirminghamB47ETUK

The cross-section profile is a key signal for evaluating hot-rolled strip quality,and ignoring its defects can easily lead to a final *** characteristics of complex curve,significant irregular fluctuation and imperfect sample data make it a challenge of recognizing cross-section defects,and current industrial judgment methods rely excessively on human decision making.A novel stacked denoising autoencoders(SDAE)model optimized with support vector machine(SVM)theory was proposed for the recognition of cross-section ***,interpolation filtering and principal component analysis were employed to linearly reduce the data dimensionality of the profile ***,the deep learning algorithm SDAE was used layer by layer for greedy unsupervised feature learning,and its final layer of back-propagation neural network was replaced by SVM for supervised learning of the final features,and the final model SDAE_SVM was obtained by further optimizing the entire network parameters via error ***,the curve mirroring and combination stitching methods were used as data augmentation for the training set,which dealt with the problem of sample imbalance in the original data set,and the accuracy of cross-section defect prediction was further *** approach was applied in a 1780-mm hot rolling line of a steel mill to achieve the automatic diagnosis and classification of defects in cross-section profile of hot-rolled strip,which helps to reduce flatness quality concerns in downstream processes.

关键词： Hot-rolled strip cross section:Curve recognition Deep learning-Stacked denoising autoencoder Support vector machine Imperfect data

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Leveraging Local data Sampling Strategies to Improve Federated learning (Extended Abstract) 11

Leveraging Local Data Sampling Strategies to Improve Federat...

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IEEE 11th international conference on data science and Advanced Analytics (DSAA)

作者： Duesing, Christoph Cimiano, Philipp Paassen, Benjamin Bielefeld Univ CITEC Bielefeld Germany

ISBN: (纸本)9798350364941;9798350364958

Federated learning (FL) facilitates shared training of machine learning models while maintaining data privacy. Unfortunately, it suffers from data imbalance among participating clients, causing the performance of the shared model to drop. To diminish the negative effects of unfavorable data-specific properties, both algorithm- and data-based approaches seek to make FL more resilient against them. In this regard, data-based approaches prove to be more versatile and require less domain knowledge to be applied efficiently. Hence, they seem particularly suitable for widespread application in various FL environments. Although data-based approaches such as local data sampling have been applied to FL in the past, previous research did not provide a systematic analysis of the potential and limitations of individual data sampling strategies to improve FL. To this end, we (1) identify relevant local data sampling strategies for FL, (2) identify data-specific properties that negatively affect FL performance, and (3) provide a benchmark of local data sampling strategies regarding their effect on model performance, convergence, and training time in synthetic, real-world, and large-scale FL environments. Moreover, we propose and rigorously test a novel method for data sampling in FL that locally optimizes the choice of sampling strategy prior to FL participation. Our results show that FL can benefit from applying local data sampling in terms of performance and convergence rate, especially when data imbalance is high or the number of clients and samples is low. Furthermore, our proposed sampling strategy offers the best trade-off between model performance and training time.

关键词： Federated learning data Sampling data Imbalance

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machine learning Based Supply Chain Risk Prediction and Management 24

Machine Learning Based Supply Chain Risk Prediction and Mana...

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5th international conference on Computer science and Management Technology, ICCSMT 2024

作者： Li, Changxia Chen, Haolin School of Mechanical and Energy Engineering Tongji University Shanghai China School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798400709999

Traditional supply chain risk management methods show limitations when facing unexpected events and complex supply chain networks, however, machine learning techniques that can handle unstructured and multi-dimensional data and extract valuable risk information from complex data sets through efficient algorithms have provided new ideas for supply chain management in recent years. This paper proposes a continuous multi-perspective conceptual framework that aims to systematically explore how machine learning can be effectively applied in supply chain risk prediction and management. Meanwhile, this paper will also investigate the potential contribution of utilizing innovative technologies such as blockchain in enhancing supply chain security and competitiveness, with a view to providing more comprehensive and profound insights into modern supply chain management. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Comparative Study of machine learning and Deep learning Techniques for Cancer Disease Detection 5th

Comparative Study of Machine Learning and Deep Learning Tech...

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5th international conference on data science, machine learning and Applications

作者： Ala, Rajitha Nelson, Leema Jagdish, Muktha Venu, Vasantha Sandhya Chitkara Univ Inst Engn & Technol Rajpura Punjab India Vardhaman Coll Engn Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Cancer is one of the most dreadful illnesses that plague mankind. The illness has a high mortality rate. There are numerous kinds of this illness. It is challenging to identify these diseases in their early stages. Recent studies have shown the significance of machine learning and Deep learning techniques in disease diagnosis. The most promising methods are presented in this study employing several machine learning and deep learning algorithms and their comparative study to determine the specific type of cancer sickness that a patient has. Additionally, it offers the most effective models for each disease type currently in use analyzed using Accuracy and AUC ROC metrics.

关键词： Cancer Disease detection machine learning Deep learning Artificial Intelligence

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Event Detection and Classification Using machine learning applied to PMU data for the Western US Power System

Event Detection and Classification Using Machine Learning Ap...

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international conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)

作者： Yin, Tianzhixi Wulff, Shaun S. Pierre, John W. Amidan, Brett G. Pacific Northwest Natl Lab AI & Data Analyt Richland WA 99352 USA Univ Wyoming Dept Math & Stat Laramie WY USA Univ Wyoming Dept Elect & Comp Engn Laramie WY USA Brigham Young Univ Idaho Dept Math Rexburg ID USA

ISBN: (纸本)9798350312874;9798350312881

Smart grid technology enhances our comprehension and reliability of the power grid, leveraging Phasor Measurement Unit (PMU) data-time-synchronized, high-frequency measurements gathered across the US power grid. This paper employs machine learning techniques to effectively analyze the vast PMU data in Wide Area Monitoring Systems (WAMS) for power grid event detection and classification. Analyzing several months of real-world PMU data, the paper focuses on machine learning for fast, precise event detection and classification, corroborated by utility event logs. Practical challenges like feature extraction, dimensionality reduction, and model selection are addressed. A novel feature yielding improved results is discovered, and a supplementary algorithm for detecting small power grid faults is developed. The final algorithm is validated using a month-long real PMU data set, demonstrating its capability in accurately identifying power grid events in near real-time.

关键词： PMU WAMS machine learning event detection

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Demo: Enhancing Wildlife Acoustic data Annotation Efficiency through Transfer and Active learning 33

Demo: Enhancing Wildlife Acoustic Data Annotation Efficiency...

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33rd international Joint conference on Artificial Intelligence (IJCAI)

作者： Kath, Hannes Serafini, Patricia P. Campos, Ivan B. Gouvea, Thiago S. Sonntag, Daniel German Res Ctr Artificial Intelligence DFKI Interact Machine Leraning Oldenburg Germany Carl von Ossietzky Univ Oldenburg Appl Artificial Intelligence Oldenburg Germany Chico Mendes Inst Biodivers Conservat ICMBio Natl Ctr Wild Bird Conservat & Res CEMAVE Brasilia DF Brazil Univ Fed Santa Catarina UFSC Florianopolis SC Brazil Univ Fed Minas Gerais UFMG Dept Biol Geral Belo Horizonte MG Brazil

ISBN: (纸本)9781956792041

Passive Acoustic Monitoring (PAM) has become a key technology in wildlife monitoring, generating large amounts of acoustic data. However, the effective application of machine learning methods for sound event detection in PAM datasets is highly dependent on the accessibility of annotated data, a process that can be labour intensive. As a team of domain experts and machine learning researchers, in this paper we present a no-code annotation tool designed for PAM datasets that incorporates transfer learning and active learning strategies to address the data annotation challenge inherent in PAM. Transfer learning is applied to use pre-trained models to compute meaningful embeddings from the PAM audio files. Active learning iteratively identifies the most informative samples and then presents them to the user for annotation. This iterative approach improves the performance of the model compared to random sample selection. In a preliminary evaluation of the tool, a domain expert annotated part of a real PAM data set. Compared to conventional tools, the workflow of the proposed tool showed a speed improvement of 2-4 times. Further enhancements, such as the incorporation of sound examples, have the potential to further improve efficiency.

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Development, Implementation, and Evaluation of a machine learning-Based Multi-Factor Adaptive E-learning System.

IAENG International Journal of Computer Science

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IAENG international Journal of Computer science 2024年第9期51卷 1250-1271页

作者： Ezzaim, Aymane Dahbi, Aziz Haidine, Abdelfatteh Aqqal, Abdelhak Artificial intelligence at Laboratory of Information Technologies National School of Applied Sciences Chouaib Doukkali University El Jadida24002 Morocco Laboratory of Information Technologies National School of Applied Sciences Chouaib Doukkali University El Jadida24002 Morocco

Adaptive learning aims to tailor the learning experience, including content, navigation, presentation, and strategies, based on learners' cognitive and affective factors. However, many existing adaptive learning systems often fail to meet the diverse needs and preferences of learners, typically relying on unique factors such as learning style. To address this, a robust framework is proposed, incorporating an artificial intelligence (AI) driven adaptive learning model capable of considering multiple factors, including past performance, hobbies, and learning style. The approach leverages the k-means clustering algorithm to group learners with similar leisure interests and incorporates Support Vector Regression to predict student performance, utilizing demographic data and past performance as metrics. Furthermore, the implementation of the Personalized Study Guide plugin, based on k-means clustering, enables the system to create personalized learning paths tailored to individual learning styles. By adopting this approach, the objective is to enhance learner engagement and performance through the development of a robust adaptive learning system. © (2024), (international Association of Engineers). All Rights Reserved.

关键词： Adversarial machine learning

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Detecting Credit Card Theft with Various machine learning Methods 5th

Detecting Credit Card Theft with Various Machine Learning Me...

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5th international conference on data science, machine learning and Applications

作者： Sravani, G. Regulwar, Ganesh B. Sairam, G. Nikitha, M. Sowmya, Ch. Kethireddy, Bhaskerreddy Vardhaman Coll Engn Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Financial organisations and customers share a common fear about credit card fraud. Credit card fraud generally happens when the card was stolen for any of the unauthorized purposes or even when the fraudster uses the credit card information for his use. As a solution to this problem, machine learning algorithms have been developed to quickly identify and assist with fraudulent transactions. We want to use machine literacy styles to produce a model for detecting credit card fraud in this design. The design must be completed by obtaining sale data from the credit card firm, pre-processing the data to eliminate missing values and unnecessary data, and choosing crucial attributes to train the machine literacy model. The model will be trained with a range of machine learning methods, including logistic regression, SVM. Among the performance metrics that will be used to judge the model's effectiveness are perfection, recall, and best position. By altering the model's characteristics and parameters, performance will be improved as well.

关键词： Credit card fraud fraudulent transactions machine learning algorithms Precision

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Identification of cold rolling chatter statesbased on multi-source data fusion and Dempster-Shafer theory

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international JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第7-8期135卷 3633-3647页

作者： Wang, Xiaoyong Gao, Zhiying Xin, Yanli Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China

Cold rolling chatter is one of the bottlenecks to improve the production quality and efficiency of high-strength thin strip, so it is very important to predict and identify the chatter states. The accumulation of industrial data from the rolling process and the development of machine learning technology have opened up a path to solve this problem. However, due to low density and uneven distribution of actual process data, knowledge learning and states identification of cold rolling chatter phenomena are confined. Therefore, based on the combination of actual production data and simulation data, a novel identification method is proposed and applied to identify the cold rolling chatter states. Firstly, the actual vibration signals are collected and the simulation data generated from chatter model are used to supplement data in chatter states. The sample space is constructed based on the semi-supervised transfer component analysis (SSTCA) to realize the fusion of actual production data and simulation data. Then, different cold rolling states are identified by particle swarm optimization-support vector machine (PSO-SVM) and back propagation neural network (BPNN), respectively. Finally, the identification results of PSO-SVM and BPNN are combined based on the Dempster-Shafer (D-S) theory. It can be drawn that SSTCA can effectively solve the problems of low density and uneven distribution of industrial data by fusion of multi-source data, and D-S theory can realize the connection of different machine learning methods. Furthermore, the presented method can more accurately identify different chatter states in the rolling process.

关键词： Chatter identification data fusion machine learning Semi-supervised transfer component analysis Dempster-Shafer theory

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