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knowledge engineering in the Social Era 23

Knowledge Engineering in the Social Era

引用

23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Kajan, Ejub Maamar, Zakaria State University of Novi Pazar Novi Pazar Serbia Zayed University Dubai United Arab Emirates

ISBN: (纸本)9781665494571

We have never been so connected like nowadays. We have become more and more social by forming spontaneous relations and initiating interactions to share information with each other (even with strangers). In this keynote speech, we highlight the evolution of knowledge engineering, point out social systems' characteristics, and then, discuss how these characteris-tics could impact knowledge engineering. Indeed, we expect that social systems' characteristics will make knowledge engineering adjust to tackle the social era's challenges. Existing knowledge engineering models, practices, techniques, and languages need to be revisited and social systems' complexity needs to be mastered with new and advanced knowledge engineering models, practices, techniques, and languages for a better appreciation of knowledge in the social era. © 2021 IEEE.

关键词： knowledge engineering Technology management Social networking (online) Organizations Complexity theory Software engineering

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HUMAN-ARTIFICIAL INTELLIGENCE TEAMING FOR SCIENTIFIC INFORMATION EXTRACTION FROM data-DRIVEN ADDITIVE manufacturing RESEARCH USING LARGE LANGUAGE MODELS

HUMAN-ARTIFICIAL INTELLIGENCE TEAMING FOR SCIENTIFIC INFORMA...

引用

ASME 2024 international Design engineering Technical conferences and Computers and Information in engineering conference, IDETC-CIE 2024

作者： Safdar, Mutahar Xie, Jiarui Mircea, Andrei Zhao, Yaoyao Fiona McGill University MontrealQC Canada University of Montreal Mila MontrealQC Canada

ISBN: (纸本)9780791888346

data-driven research in Additive manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and Artificial Intelligence (AI) contexts that haven’t been mined and formalized in an integrated way. It requires substantial effort and time to extract scientific information from these works. AM domain experts have contributed over two dozen review papers to summarize these works. However, information specific to AM and AI contexts still requires manual effort to extract. The recent success of foundation models such as BERT (Bidirectional Encoder Representations for Transformers) or GPT (Generative Pretrained Transformers) on textual data has opened the possibility of expediting scientific information extraction. We propose a framework that enables collaboration between AM and AI experts to continuously extract scientific information from data-driven AM literature. A demonstration tool is implemented based on the proposed framework and a case study is conducted to extract information relevant to the datasets, modeling, sensing, and AM system categories. We show the ability of LLMs (Large Language Models) to expedite the extraction of relevant information from data-driven AM literature. In the future, the framework can be used to extract information from the broader design and manufacturing literature in the engineering discipline. © 2024 by ASME.

关键词： Information retrieval

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Enhancing Credit Card Fraud Detection with Light Gradient-Boosting Machine: An Advanced Machine Learning Approach

Enhancing Credit Card Fraud Detection with Light Gradient-Bo...

引用

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

作者： Sruthi, Srigiri Emadaboina, Siddharth Jyotsna, C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350359688

In the era of digital financial transactions, credit card fraudemerges as a significant threat, demanding advanced detection methodologies. This paper presents an efficient and effective solution using Light Gradient Boosting Machine, a cutting edge machine learning technique. Our approach meticulously preprocesses a dataset of anonymized credit card transactions, emphasizing normalization, feature selection, and strategic data splitting to address class imbalance. The Light Gradient Boosting Machine model, optimized through Randomized Search Cross-Validation, fine-tunes key parameters to enhance predictive accuracy while mitigating overfitting. The model's performance, evaluated on various metrics such as accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve exhibits high precision, ensuring minimal false positives, and substantial recall for effective fraud detection. With an impressive Area Under the Curve score, our model demonstrates superior discriminative capabilities. The findings of this study underscore the potential of Light Gradient Boosting Machine in credit card fraud detection, offering a scalable, reliable tool for integration into financial security systems. © 2024 IEEE.

关键词： Feature Selection

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GuavaCareNet: Empowering Guava Farms with Federated Learning for Real-Time Disease Diagnosis 2

GuavaCareNet: Empowering Guava Farms with Federated Learning...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Kirubha, D. Krishnan, R. Santhana Raj, J. Relin Francis Soundiraraj, N. Murali, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Rajarajeswari College of Engineering Department of Computer Science and Engineering Bangalore India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331505745

This research presents a Federated Learning-based approach for the detection of guava leaf diseases, aimed at enhancing agricultural practices while ensuring data privacy. Guava farms across various regions provide images of guava leaves, both healthy and diseased, to train a deep learning model for disease classification. The EfficientNet-B3 model, optimized with Neural Architecture Search (NAS) and Transfer Learning, is used for accurate disease detection, including conditions like Anthracnose and Powdery Mildew. To ensure privacy, Federated Learning (FL) is employed, where the model is trained locally on edge devices such as smartphones and Jetson Nano devices situated at guava farms. These devices process the data locally and send model updates, rather than raw data, to a central server. This method enables the aggregation of knowledge from multiple farms without sharing sensitive information, thereby protecting farmers' data privacy. The use of Federated Learning allows the model to adapt to specific conditions at each farm, such as different diseases, leaf appearances, and lighting variations, improving its robustness and accuracy. Additionally, AWS IoT integration allows real-time monitoring and alerts, further empowering farmers with timely disease detection. The decentralized nature of this approach not only enhances disease detection in guava crops but also fosters data privacy, ensuring farmers' information remains secure. © 2025 IEEE.

关键词： Sensitive data

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data Mesh for Managing Complex Big data Landscapes and Enhancing Decision Making in Organizations 15

Data Mesh for Managing Complex Big Data Landscapes and Enhan...

引用

15th international conference on knowledge Management and Information systems, KMIS 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Azeroual, Otmane Nacheva, Radka Berlin10117 Germany Department of Informatics University of Economics Varna Varna9002 Bulgaria

ISBN: (纸本)9789897586712

In the age of digitization, data is of the utmost importance. Organizations can gain competitive advantage by being ahead of the curve in organizing data, deriving insights from it, and turning those insights into action. In practice, however, many organizations fail to meet this challenge. Far too many decisions are made without data, decision makers don't trust their own data. The data warehouse, later the data lake and more recently the data lakehouse have been propagated as solutions to these problems in recent decades. In some cases, this actually succeeds, in other cases challenges remain. The recently prominent data mesh approach changes the perspective on data and in this respect provides valuable impulses for data architectures in general. data mesh is a new architectural concept for data management in organizations. Therefore, in this paper, we introduce this new data concept and provide a clear overview of the design of a data mesh architecture. We will then show how it can be technically implemented and what potential there is for using data mesh in organizations. Our methodology is a type of investigation that provides a helpful and practical guide to understanding the principles and patterns of data mesh and their implementation in organizations. Our research result has shown that the data mesh approach is therefore a very good tool for organizations where data sharing and reuse is crucial. In addition to facilitating scalability, data mesh can enable better data integration and data management, improving data quality while fostering a culture of data-driven decision-making. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Decision making

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Proceedings of the 5th international conference on Artificial Intelligence and Computer engineering, ICAICE 2024

Proceedings of the 5th International Conference on Artificia...

引用

5th international conference on Artificial Intelligence and Computer engineering, ICAICE 2024

ISBN: (纸本)9798400718007

The proceedings contain 134 papers. The topics discussed include: an intelligent aids to navigation system for waterway transportation;advanced framework for equilibrium management in intricate supply chains using collective reinforcement learning techniques;a password application scheme in industrial information systems;construction of ocean observation data transmission system based on flattened model;knowledge-enhanced large language model-based assistance training system for subway maintenance personnel;design and implementation of the experimental platform based on cloud computing;research on scheduling operation platform monitoring technology based on classified layered observability indicators;and research on prediction and optimization of network sorting centers based on deep learning model.

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Harmonizing Human-Centric Collaborative Hybrid Intelligence: A Deep Dive into the Transition from Industry 4.0 to Industry 5.0—A Case Study Experiment 4th

Harmonizing Human-Centric Collaborative Hybrid Intelligence:...

引用

4th international conference on Electronic engineering and Renewable Energy systems, ICEERE 2024

作者： Kihel, Ali El Embarki, Soufiane Laboratory of Industrial Engineering and Seismic Engineering National School of Applied Sciences ENSA University Mohammed First Oujda Oujda Morocco

ISBN: (纸本)9789819799749

This study explores the evolution of the manufacturing industry, focusing on the transition to Industry 5.0 and its integration of advanced technologies like artificial intelligence, big data, and the Internet of Things (IoT). Industry 5.0 represents a radical shift towards adaptive, autonomous systems, building upon the advancements of its predecessors, particularly Industry 4.0. The analysis compares and contrasts Industry 4.0 and Industry 5.0, highlighting significant data-driven insights, including notable increases in AI applications and corresponding improvements in production efficiency and cost reduction during Industry 4.0. The study also investigates the rapid deployment of transformative technologies like 5G connectivity and quantum computing within Industry 5.0, projecting substantial growth in human–machine cooperation and decreases in error rates by 2033. Ultimately, the abstract outlines how Industry 5.0 will revolutionize smart manufacturing into intelligent, flexible, and responsive production systems, addressing both its potential benefits and challenges. This paper presents a detailed case study exploring the practical applications of IoT-driven innovations in Industry 5.0. Through analysis of IoT sensor data, specific operational conditions including factory temperature, warehouse humidity, and manufacturing line pressure are revealed. A data-driven strategy led to notable reductions in energy use and CO2 levels, facilitated by the diverse skills of case study participants. Key indicators of Industry 5.0, such as production efficiency, energy usage, quality control, and inventory turnover, demonstrate significant improvements, highlighting the importance of metrics evaluation and data-driven methodologies in showcasing the transformative potential of IoT-driven innovations within Industry 5.0. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality control

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Eye-Tracking supported design of digital assistance systems for smart factories 34

Eye-Tracking supported design of digital assistance systems ...

引用

34th CIRP Design conference, CIRP 2024

作者： Gelec, Erdem Lindenlaub, Stefan Fraunhofer Instute ForIndustrial Engineering IAO Stuttgart70569 Germany University of Stuttgart Institute for Technology Management IAT Stuttgart70569 Germany

Cognitive assistance systems enable people to receive valuable support in their industrial work tasks and can offer the highest flexibility and individualization possibilities. The increasing need for knowledge and its growing importance lead to a great demand for user-centric cognitive assistance systems. In this sense, eye-tracking is a potential technology to provide better insights in Use behavior in the task fulfillment process. The analysis of the data contributes to understand where the person is directing their attention, how long they are looking at certain areas and whether potentially unnecessary distractions exist. In this way, data-based adjustments can be specifically identified to be able to increase reliability and efficiency in the process. EyeTracking is rarely used in the development of assistance systems for the manufacturing industry. The expected benefit and the effort remain unclear. We show what potential it can offer in the development of cognitive assistance systems and present recommendations for its integration in the design process. The results are based on the empirical findings of a manual industrial application of complex quality measurement of battery module housings for electric vehicles. © 2024 Elsevier B.V.. All rights reserved.

关键词： Smart manufacturing

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Antibiotic Resistance Gene Identification from Metagenomic data Using Ensemble of Finetuned Large Language Models 16

Antibiotic Resistance Gene Identification from Metagenomic D...

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16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Syama, K. Jothi, J. Angel Arul Department of Computer Science Birla Institute of Technology Science Pilani Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9789897587160

Antibiotic resistance is a potential challenge to global health. It limits the effect of antibiotics on humans. Antibiotic resistant genes (ARG) are primarily associated with acquired resistance, where bacteria gain resistance through horizontal gene transfer or mutation. Hence, the identification of ARGs is essential for the treatment of infections and understanding the resistance mechanism. Though there are several methods for ARG identification, the majority of them are based on sequence alignment and hence fail to provide accurate results when the ARGs diverge from those in the reference ARG databases. Additionally, a significant fraction of proteins still need to be accounted for in public repositories. This work introduces a multi-task ensemble model called ARG-LLM of multiple large language models (LLMs) for ARG identification and antibiotic category prediction. We finetuned three pre-trained protein language LLMs, ProtBert, ProtAlbert, and Evolutionary Scale Modelling (ESM), with the ARG prediction data. The predictions of the finetuned models are combined using a majority vote ensembling approach to identify the ARG sequences. Then, another ProtBert model is fine-tuned for the antibiotic category prediction task. Experiments are conducted to establish the superiority of the proposed ARG-LLM using the PLM-ARGDB dataset. Results demonstrate that ARG-LLM outperforms other state-of-the-art methods with the best Recall of 96.2%, F1-score of 94.4%, and MCC of 90%. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Gene transfer

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Interactive Visualization of Temporal Brain Connectivity data based-on Frequent Feature Mining 35

Interactive Visualization of Temporal Brain Connectivity Dat...

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35th international conference on Software engineering and knowledge engineering, SEKE 2023

作者： Zhang, Guangwei Jing, Ming Liu, Yunjing Zhang, Li Dong, Anming Yu, Jiguo School of Computer Science and Technology Qilu University of Technology Jinan China Big Data Institute Qilu University of Technology Jinan China Shandong Fundamental Research Center for Computer Science Jinan China

Medical data visualization is instrumental in assisting disease diagnosis and exploring brain function and structure. In this paper, we constructed a brain connectivity network using changes in BOLD signals at different time intervals and identified frequent characteristics to help doctors quickly pinpoint areas of interest. To study the changes in connectivity between brain regions, we visualize frequent sequences and compare them, highlighting important temporal features of patient brain areas. This makes the study and analysis of fMRI data more convenient and assists doctors in investigating abnormalities in the connections between brain functional areas. © 2023 knowledge systems Institute Graduate School. All rights reserved.

关键词： Brain

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