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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 37467 条记录，以下是421-430 订阅

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Restaurant Recommendation System using Machine Learning Algorithms

Restaurant Recommendation System using Machine Learning Algo...

引用

2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Goyal, Shefali Singh, Nongmeikapam Thoiba Bajetha, Iesh Singh, Sukhpreet Singh, Himanshu Kumar, Prashant Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350359688

The necessity for recommendation systems spans across various domains, not least in the realm of dining and exploring new locales. Restaurants, in particular, benefit from such systems to both attract customers and offer them a chance to indulge in preferred and renowned dishes. Discovering these dishes, especially in unfamiliar areas, poses a significant challenge. This paper introduces a restaurant recommendation system that leverages food and service rating distributions to calculate matrix density. Additionally, we implement a popularity-based model to suggest restaurants to patrons. A ranking mechanism is utilized, based on scoring, to recommend top-rated restaurants and their most popular dishes. We use singular value decomposition and collaborative filtering to improve the model. The model's performance is assessed using the RMSE metric. This study is conducted using data from Kaggle and includes the development of a web-based application created with Python's Flask framework. © 2024 IEEE.

关键词： Adversarial machine learning

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Effective knowledge graph embeddings based on CNN-LSTM for drug-drug interactions prediction 24

Effective knowledge graph embeddings based on CNN-LSTM for d...

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2nd international conference on Frontiers of Intelligent manufacturing and Automation, CFIMA 2024

作者： Peng, Guodong Ji, Xiangmin School of Automation and Electrical Engineering Inner Mongolia University of Science and Technology Baotou China

ISBN: (纸本)9798400710681

Adverse drug reactions induced by drug-drug interactions are important causes of morbidity and mortality. To address the issues that the existing models ignore the multidirectional mutual semantic transmission and local-global feature interaction in drug pairs, we propose an effective knowledge graph embeddings based on CNN-LSTM for drug-drug interactions prediction (KGECL). This framework consists of four components: data preprocessing module, interaction feature extraction module, attention module and prediction modules. The data preprocessing module conducts low-dimensional embeddings of drug data. The interaction feature extraction module explores multidirectional semantics and local-global characteristics, and simulate the complex relationships and interactions across multiple adverse reactions effectively. The attention module enhances the ability of network to recognize the critical information. Finally, the prediction module uses a fully-connected layer for prediction. Comparative experiments on two available datasets demonstrates that KGECL achieves higher accuracy and helps to the improve drug safety. © 2024 Copyright held by the owner/author(s).

关键词： knowledge graph

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Research on industrial park production system based on big data 3

Research on industrial park production system based on big d...

引用

3rd international conference on Advanced manufacturing Technology and manufacturing systems, ICAMTMS 2024

作者： Liu, Caiyan Liu, Yajun Yin, Cuijun Sun, Jingwen Guangdong University of Science and Technology Dongguan City523083 China

ISBN: (纸本)9781510681804

The AUTOM standard Internet of Things architecture is extended to a new architecture of multi-decision and multi-stage seamless information transfer. The purpose is to solve the problems of asynchronous production information, low execution efficiency, and high operation cost in industrial parks. In this paper, based on the analysis of industrial parks' production and operation process combined with advanced Internet of things technology, the production linkage intelligent management system under the environment of industrial parks is developed. This kind of collaborative lean production operation driven by the real-time demand of distributed production lines puts higher real-time management requirements on the effectiveness of resources, the lower cost of distribution routes, and the accuracy of handover data. The research shows that the linkage control among multiple enterprises in the industrial park can effectively reduce production costs. The later the dynamism occurs, and the larger the order demand magnitude, the more complex the linkage control, the better the overall economic benefit. © 2024 SPIE.

关键词： Costs

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Trust-Object Identification: Approach Towards Trustworthy Objects Identification in S-IoT

Trust-Object Identification: Approach Towards Trustworthy Ob...

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2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Rahul Araddy, Dakshayani Venkatesh University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

The primary goal of Social-Internet-of-Things (SIoT) is to enhance the capabilities of IoT by introducing a social layer, where objects can engage in meaningful relationships, share experiences, and collectively contribute to a more interconnected and intelligent environment. Within the realm of S-IoT, objects possess the capability to form social connections and engage in interactions with other objects. A critical concern demanding attention is the establishment of these relationships in a dependable and trustworthy manner. To address this, a trust framework named Object Identification, based on artificial neural networks, has been conceptualized. Its purpose is to discern the intricate nonlinear connections between input and output for the classification of reliable objects. Object identification is designed to incorporate a range of important trust metrics as input, such as direct trust that combines past and present interactions, the fairness and goodness of an object, the level of relationship, and trustworthy recommendations. This is achieved through the utilization of knowledge graph embedding. Ultimately, comprehensive experiments have been conducted to assess Object Identification's performance on the dataset. The results show that Object Identification achieves an F1 score of 80%, with lower MAE at 56% and MSE scores at 34%. The results of data analysis depend solely on techniques that extract essential information from the underlying structure of S-IoT with huge uncertainty data embedded in it. © 2024 IEEE.

关键词： Graph embeddings

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35th international conference on Advanced Information systems engineering , CAiSE 2023

35th International Conference on Advanced Information System...

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35th international conference on Advanced Information systems engineering , CAiSE 2023

ISBN: (纸本)9783031346736

The proceedings contain 18 papers. The special focus in this conference is on Advanced Information systems engineering. The topics include: Integrating IoT-Driven Events into Business Processes;adaptive Task-Oriented Chatbots Using Feature-Based knowledge Bases;Cicero: An AI-Based Writing Assistant for Legal Users;AIDA: A Tool for Resiliency in Smart manufacturing;conceptual Formalization of Massive Storage for Advancing Decision-Making with data Analytics;towards Leveraging Tests to Identify Impacts of Metamodel and Code Co-evolution;Advancing IS Strategy for Natural History Museums with Method engineering in the Digital Age;The KYKLOS Tool for Modeling Changing Capabilities;Towards a UML Profile for Designing Smart IoT data-Centric Applications;an End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information systems;towards Designing Energy-Aware Cybersecurity Policies;a data-Driven Methodology for Guiding the Selection of Preprocessing Techniques in a Machine Learning Pipeline;parsing Causal Models – An Instance Segmentation Approach;visualising Personal data Flows: Insights from a Case Study of ***;rsourcer: Scaling Feedback on Research Drafts;predictive Recommining: Learning Relations Between Event Log Characteristics and Machine Learning Approaches for Supporting Predictive Process Monitoring.

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Cryptography Role in Industry 4.0 Through Privacy of data and Communications using Hybrid Encryption Algorithms 17

Cryptography Role in Industry 4.0 Through Privacy of Data an...

引用

17th international conference on Developments in esystems engineering, DeSE 2024

作者： Abdulredha, Mohammed Najm Hadi, Musadaq A. Barakat, Mustafa M. Hamdullah, Ali Hussein Department of Cybersecurity Engineering AlShaab University -Iraq Baghdad Iraq Baghdad Iraq

ISBN: (纸本)9798350368697

Cryptography plays a crucial role in Industry 4.0, safeguarding the security and privacy of data and communications within highly interconnected and automated industrial systems. As vast amounts of data are generated and transmitted across devices, systems, and cloud platforms, cryptographic methods are essential for ensuring data confidentiality and restricting access to authorized entities. This paper introduces two novel hybrid encryption algorithms: modified HIGHT-Simon and modified HIGHT-Trivium, which integrate the lightweight HIGHT cipher with Simon and Trivium ciphers, respectively. Both algorithms are evaluated across three configurations with different rounds 8, 10, and 12 to assess their encryption and decryption performance. The results reveal that the modified HIGHT-Simon algorithm consistently achieves faster encryption and decryption times, making it ideal for real-time applications in Industry 4.0, such as industrial IoT and smart manufacturing systems, where both speed and security are critical. In contrast, the modified HIGHT-Trivium algorithm, while providing a more balanced encryption-decryption process, exhibits slower performance, making it better suited for applications that prioritize security over speed. Overall, the modified HIGHT-Simon algorithm emerges as the more efficient solution for ensuring both privacy and rapid data communication in the fast-paced, data-intensive environments of Industry 4.0. © 2024 IEEE.

关键词： Smart manufacturing

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Human Activity Recognition with Smartphones using Machine Learning Algorithm

Human Activity Recognition with Smartphones using Machine Le...

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2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Ghamya, K. Prema, K. Reddy, D. Abhinay Varaprasad, K. Sai, K. Navyasri Tejesh, S. School of Computing Tirupati India Sree Vidyanikethan Engineering College Department of Cse Tirupati India

ISBN: (纸本)9798350359688

Human Activity Recognition with Smartphones (HAR) leverages the ubiquitous nature of smartphones equipped with sensor technology to identify and classify daily human activities from the generated data. This paper explores the field of HAR, focusing on the use of smartphone sensors like magnetometers, gyroscopes, and accelerometers to capture movement patterns. This provides a comprehensive exploration of HAR, delving into methodologies such as feature extraction data acquisition, and the application of various machine learning algorithms for activity classification. The discussion encompasses challenges and limitations intrinsic to smartphone-based HAR, addressing issues like data variability and device heterogeneity. Promising research directions are highlighted, with an emphasis on context awareness, multi sensor fusion, and deep learning approaches, aiming to enhance the robustness and accuracy of activity recognition systems. The abstract concludes by emphasizing the potential applications of HAR in healthcare, where it could revolutionize patient monitoring, fitness monitoring, optimizing routines, and personalized user experiences, showeasing its role in shaping the future of health-related technologies. This model contributes valuable insights into the dynamic field of HAR, considering the synergy between smartphones, sensor technologies, and machine learning algorithms to propel innovative applications and personalized user experiences. © 2024 IEEE.

关键词： Gyroscopes

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Evolution of Technical Projects with Social Commitment in Degrees of Industrial engineering 26th

Evolution of Technical Projects with Social Commitment in De...

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26th international conference on Interactive Collaborative Learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Pernia-Espinoza, Alpha Sanz-Garcia, Andres Javier Martinez-de-Pison-Ascacibar, F. Navaridas-Nalda, Fermin Blanco-Fernandez, Julio Univ La Rioja Dept Mech Engn Logrono 26004 Spain Univ Salamanca Dept Mech Engn Bejar 37700 Spain Univ La Rioja Dept Educ Sci Logrono 26004 Spain

ISBN: (纸本)9783031526664;9783031526671

A service-learning project, the so-called OSOR (Open-source and SOcial Responsibility) have been implemented from 2018 to 2022 in the "manufacturing Technology" (MT) subject. Its aim is to manufacture products to assist people in a dependency situation. We investigate how the OSOR evolution has affected students' learning outcomes, the subject's significance, the level of difficulty, and other factors. We identified four main variants, which depended on internal and external factors. We discovered that the various variants had no effect on MT's final score. The COVID-19 pandemic in the academic year 2019-2020 reduced hands-on activities and, presumably, the acquisition of essential technical knowledge. Regardless of the implemented format, a sizable portion of students thought theMT subject was fascinating and crucial to their future jobs. The introduction of an assigned patient and the ill-defined nature of cases could be the reason for an increase in the perceived subject difficulty. Additionally, this may help to explain why students' general feedback focused less on the OSOR and more on their overall 'satisfaction with the subject'. Having complete creative freedom and control over the deliverables during variant 1 gave a higher level of satisfaction than working on scenarios with loose requirements imposed by actual patients during variants 2 to 4. The most notable products were successfully produced despite the challenges in variants 3 and 4 being more complex. The final poll for the upcoming edition of OSOR will include particular questions to evaluate the development of social responsibility competency.

关键词： manufacturing Technology service-learning social responsibility

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T2TNet: Tree-to-Transformer Neural Network for High-precision Prediction of Ship Machinery Noise 24

T2TNet: Tree-to-Transformer Neural Network for High-precisio...

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2nd international conference on Frontiers of Intelligent manufacturing and Automation, CFIMA 2024

作者： Liu, Chang Zhao, Guo Wang, Lei Xie, Xiaotian Chen, Hailong Xie, Yuxin Gao, Fan Tang, Yang Lang, Jicai Yantai Research Institute Harbin Engineering University Yantai China National Key Laboratory of Automatic Target Recognition Beijing Huahang Radio Measurement Institute Beijing China College of Shipbuilding Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798400710681

Traditional ship machinery noise prediction methods face many challenges, including cumbersome data processing procedures, numerous complex coupling factors, and poor generalization capabilities. In response to these issues, this paper proposes an innovative ship machinery noise prediction algorithm, named as Tree-to-Transformer Network (T2TNet). We first utilize an ensemble learning algorithm to learn the low-dimensional feature association knowledge, and then employs the Transformer network to deeply explore the high-dimensional global knowledge relationships, thereby enhancing the predictive capability for ship machinery noise prediction. Experimental results show that the algorithm achieves a prediction accuracy rate as high as 99.9% within a 3dB error range, with an overall average accuracy rate of 99.6%, demonstrating excellent predictive performance and noise forecasting ability. This achievement not only meets the basic requirements of the ship noise prediction field but also provides strong technical support for ship design and maintenance. © 2024 Copyright held by the owner/author(s).

关键词： Predictive maintenance

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Research on Modeling Aerospace Cable Process knowledge Based on E3

Research on Modeling Aerospace Cable Process Knowledge Based...

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2023 international conference on Design Science, ICDS 2023

作者： Ni, Yongjun He, Xiaobin Hu, Ruiqi Que, Chunlan Liu, Shuangbao Huang, Wanru Information Management Department Shanghai Aerospace Equipment Manufacturing Co. Ltd Shanghai China Electronic Assembly Manufacturing Factory Shanghai Aerospace Equipment Manufacturing Co. Ltd Shanghai China Quality and Technical Management Department Shanghai Aerospace Equipment Manufacturing Co. Ltd Shanghai China

ISBN: (纸本)9798350343786

Preliminary research was conducted on the implementation approach and specific practices of aerospace cable process knowledge modeling according to the E3 model. The cable process E3 model application, process knowledge modeling methods and specific implementation cases were analyzed. Establishing a cable process knowledge modeling method based on the matching rules of E3 model data and process knowledge data, accomplishing the intelligent design of aerospace cable process, improving the design quality and efficiency of aerospace cable process models. © 2023 IEEE.

关键词： Cables

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