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Model of collaborative organizational learning using soft systems and storytelling 11

Model of collaborative organizational learning using soft sy...

引用

11th international conference on Information Technology and Quantitative Management, ITQM 2024

作者： Córdova, Felisa Montt, Cecilia Gutierrez, Margareth Faculty of Engineering Architecture and Design University San Sebastián Bellavista 7 Recoleta Santiago8420524 Chile Faculty of Engineering University Santiago de Chile Ecuador 3769 Estación Central Santiago Chile

Collaborative learning in the organization promotes interaction among participants to achieve common learning objectives and shared goals, based on peer interdependence, personal and group responsibility, skills and teamwork, through direct communication and purposeful reflection of members. In this context, this paper proposes a model of collaborative learning using the narrative provided by the storytelling as a mechanism for externalizing tacit knowledge in complex organizations. Storytelling is considered as an important tool, since stories have the virtue of connecting participants emotionally, providing them with a shared meaning in the concepts to be analysed, and stimulating reflection, creativity, memorization and retention of data in the resolution of complex problems. Relevant factors of collaborative learning and storytelling were developed and also the role of the actors and readers participating in the story and the context. A methodology of soft systems was implemented, as a problem-solving approach that aims to understand and address the complexity of human systems, in this case collaborative learning groups in the organization. Factors influencing the learning process were identified. the soft systems approach helps participants understand the multifaceted nature of educational challenges and consider a variety of perspectives and factors that influence the learning process. A collaborative learning method using storytelling and soft system methodology was used that brings the organizational professional closer to the reality of strategic business management, sectioning three case studies: a company from the productive sector, another from the services sector and a start-up company. © 2024 the Authors.

关键词： Collaborative learning

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Integrative Deep learning Forecasting of Air Quality Index in India: A Fusion of Bidirectional LSTM and Sensor data 11

Integrative Deep Learning Forecasting of Air Quality Index i...

引用

11th international conference on Information Technology: IoT and Smart City, ICIT 2023

作者： Mudhanai Sanjeevirayar, Bala Murugan Mudhana, Gopinath Rajagopal, Manoj Kumar Arumugam, Paventhan Vijayan, K. Varadarajan, Vijayakumar School of Electronics Engineering Vellore Institute of Technology Chennai India School of Advanced Sciences Vellore Institute of Technology Chennai India ERNET Ministry of Electronics and IT Govt. of India Chennai India Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798400709043

AQI as a vital metric for evaluating pollution levels, which directly impacts the health and well-being of the population. We have devised a hybrid deep learning (DL) framework that combines the Bi-directional LSTM with a sensor fusion approach. Our integrated model combines the strengths of sensor fusion and Bi-LSTM, enhancing both spatial and temporal dependencies in the data. Additional training of the data from independent sensors prior to the AQI calculation and training of the proposed method provided much better AQI prediction capability due to the added information on the spatial variation of the data. Empirical validation with real-world data from the Chennai city in India, demonstrates superior accuracy, achieving a Root Mean Square Error (RMSE) of 21.7 for AQI prediction. © 2023 ACM.

关键词： Weather forecasting

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Prediction of Breast Cancer Using Machine learning and Deep learning Models 11th

Prediction of Breast Cancer Using Machine Learning and Deep ...

引用

11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： VijayaLaxmi, I. Shaila, S.G. Monish, L. Kumar, Rahul Ruchith, B.M. Sagar, M.A. Sridhar, Sai Lakshmi Dayananda Sagar University Bangaluru India

ISBN: (纸本)9789819717231

Breast cancer is the most common problem in women caused due to the outgrowth of cells in breast. Due to the absence of specific data and lack of technology, doctors aren’t able to develop effective treatment plans that can increase patient lifeline. To increase the accuracy of breast cancer diagnosis, it is necessary to develop techniques that minimize errors. In this paper, the proposed approach compared the performance of random forest and decision tree, bagging algorithms, RESNET50, and VGG16 models in predicting breast cancer outcomes using different datasets. All experiments were performed in a simulation environment using the Collab platform. To compare benign and malignant breast cancers, the study also employed sequential prior selection based on feature selection and transfer learning VGG16 mode feature extraction analysis. the results showed that the random forest and ensemble classification models have the accuracy of 99.10 and 99.73% accuracy comparing the two widely used breast cancer associating datasets. the main goal of this project is to predict breast cancer accurately. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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A Gramian Angular Field Transform-Based Higher-Dimension data-Driven Method for Post-Fault Short-Term Voltage Stability Assessment 11

A Gramian Angular Field Transform-Based Higher-Dimension Dat...

引用

11th international conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Tang, Yuchi Ren, Chao Zhang, Rui Ren, Shuo School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore School of Electrical Engineering and Telecommunication University of New South Wales Australia Curtin University Singapore Singapore

ISBN: (纸本)9798350399660

As the complexity of power systems increases, together with the increasing integration of phasor measurement units, an intelligent assessment for short-term voltage stability (STVS) becomes necessary and feasible. Especially for post-fault STVS assessment, the task requires a fast and accurate conclusion. Given this, an intelligent post-fault STVS assessment method is proposed in this research. Based on Gramian Angular Field transform, two-dimensional convolutional neural network, and adaptive confidence interval, the proposed method with an offline-online data-driven framework shows better performance when the amount of input data is limited. the related simulations are conducted on the New England 10-machine 39-bus system. © 2022 IEEE.

关键词： intelligent systems

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REthINKING SYMBOLIC REGRESSION: MORPHOLOGY AND ADAPTABILITY FOR EVOLUTIONARY ALGORIthMS 11

RETHINKING SYMBOLIC REGRESSION: MORPHOLOGY AND ADAPTABILITY ...

引用

11th international conference on learning Representations, ICLR 2023

作者： Fong, Kei Sen Wongso, Shelvia Motani, Mehul Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore

Symbolic Regression (SR) is the well-studied problem of finding closed-form analytical expressions that describe the relationship between variables in a measurement *** this paper, we rethink SR from two perspectives: morphology and ***: Current SR algorithms typically use several man-made heuristics to influence the morphology (or structure) of the expressions in the search *** man-made heuristics may introduce unintentional bias and data leakage, especially with the relatively few equation-recovery benchmark problems available for evaluating SR *** address this, we formulate a novel minimalistic approach, based on constructing a depth-aware mathematical language model trained on terminal walks of expression trees, as a replacement to these ***: Current SR algorithms tend to select expressions based on only a single fitness function (e.g., MSE on the training set).We promote the use of an adaptability framework in evolutionary SR which uses fitness functions that alternate across *** leads to robust expressions that perform well on the training set and are close to the true functional *** demonstrate this by alternating fitness functions that quantify faithfulness to values (via MSE) and empirical derivatives (via a novel theoretically justified fitness metric coined MSEDI).Proof-of-concept: We combine these ideas into a minimalistic evolutionary SR algorithm that outperforms a suite of benchmark and state of-the-art SR algorithms in problems with unknown constants added, which we claim are more reflective of SR performance for real-world *** claim is then strengthened by reproducing the superior performance on real-world regression datasets from *** researchers interested in equation-recovery problems, we also propose a set of conventions that can be used to promote fairness in comparison across SR methods and to reduce unintentional bias. © 2023 11th Internation

关键词： Morphology

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A User Experience Study on University of Isfahan's LMS and Exploring Features of AI-Enhanced E-learning Platforms

A User Experience Study on University of Isfahan's LMS and E...

引用

international conference on E-learning and E-Teaching (ICELET)

作者： Milad Ghaffari Ali Akbari Morteza Mohammadi Ostani Dep. of Mechatronics Engineering Islamic Azad University Majlesi Branch Isfahan Iran Dep. of Knowledge & Information Science University of Isfahan Isfahan Iran

the main objective of this paper was to evaluate the user experience (UX) of students in higher education by conducting usability testing in the e-learning context, specifically utilizing the University of Isfahan (UI) learning Management System (LMS). the survey method was used in the current study and to collect data we utilized random sampling to evaluate the usability aspects defined in ISO 9241-11 standards based on a modified system usability scale (SUS) involving 377 end-users. the data were analyzed using Microsoft Excel software and findings showed that SUS score for the University of Isfahan's LMS is 60.05 which falls in the “OK usability” range and is acceptable. Also, we explored the features of AI-enhanced LMSs using a ranking question to comprehensively understand students' perspectives suggesting that Chatbots for support and intelligent content recommendations are among the most expected features for prominent educational outcomes. From the NPS assessment, -43 implies areas for improvement.

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Applying Blockchain Technology in learning Management Systems: A Novel Approach to Secure and Transparent Education Management 11th

Applying Blockchain Technology in Learning Management Syste...

引用

11th international conference on Future data and Security engineering, FDSE 2024

作者： Khiem, H.G. Nam, T.B. Khanh, D.L. Phat, P.T. Quan, V.H. Luong, Huong Hoang FPT University Can Tho city Viet Nam

ISBN: (纸本)9789819604364

In the rapidly evolving educational landscape, technology plays an essential role in reshaping teaching methodologies and managing educational resources. While learning Management Systems (LMS) have become crucial infrastructures for delivering, tracking, and managing educational content, they often suffer from significant issues such as data breaches, lack of transparency, and centralized control over sensitive educational records. this paper presents a novel blockchain-based LMS designed to address these challenges through the integration of blockchain technology and smart contracts. By decentralizing data management and enhancing security measures, the proposed system ensures the immutability, transparency, and privacy of educational records. Our LMS goes beyond traditional score management by offering decentralized course management, assignment submission, and credential verification, giving educators and students more control over their data while fostering a secure and trustless learning environment. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decentralized finance

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GraphAush: Combining Adversarial learning and Graph Embedding for a Novel Shilling Attack Model Towards Robust Recommender Systems 11

GraphAush: Combining Adversarial Learning and Graph Embeddin...

引用

11th IEEE/ACM international conference on Big data Computing, Applications and Technologies, BDCAT 2024

作者： Barnett, Clayton Kashef, Rasha Toronto Metropolitan University Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798350367300

Recommender systems (RS) are integral to modern e-commerce and content platforms. Yet, their reliance on user-item interaction data makes them vulnerable to shilling attacks, where fake data is injected to manipulate recommendations. Traditional shilling attack strategies utilize basic statistical properties of user-item data to create deceptive profiles. Still, recent advancements have shifted towards model-based attacks leveraging machine learning to enhance effectiveness and evade detection. this paper introduces GraphAush, a novel neural shilling attack model that employs Generative Adversarial Networks (GANs) with a novel generator shilling loss tailored to manipulate the user-item interaction graph. By incorporating a novel generator shilling loss function that leverages Node2Vec embeddings, GraphAush optimizes fake profile generation to maximize the effectiveness of attacks while minimizing detectability. this method overcomes the limitations of previous models, which either require intricate knowledge of the target RS or use indirect graph-based approaches. the efficacy of GraphAush was validated through experiments with multiple benchmark datasets, revealing its strong performance against traditional heuristic and other GAN-based attack methods. this innovative approach highlights a significant advancement in adversarial techniques for RS and sets a new benchmark for evaluating shilling attack strategies. © 2024 IEEE.

关键词： Generative adversarial networks

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Deep learning-Based Recommender Systems—A Systematic Review and Future Perspective 11th

Deep Learning-Based Recommender Systems—A Systematic Review...

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11th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2023

作者： Krishnamoorthi, S. Shyam, Gopal K. Department of Computer Science and Engineering School of Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819967056

With the ever-increasing volume of online information, recommender systems (RS) have been an effective technique to overcome such information overload. Given its widespread implementation in a variety of web applications, the value of RS cannot be emphasized enough. RS has the potential to improve numerous issues identified with over-choice. Deep learning's influence is also widespread, with new evidence of its efficacy in information retrieval and RS research. In RS, the field of deep learning is emerging. this article presents a thorough evaluation of recent research findings on traditional RS and deep learning-based recommender systems (DLRS). this review covers articles published between 2018 and 2022 in four major research databases: Science Direct, IEEE Explore, Springer, and Wiley. First, we provide a complete overview, comparison, and summary of traditional RS. We then systematically analyze DLRS challenges and related solution approaches. Finally, under the discussion of open issues at DLRS, we highlight possible research directions in this area. In this review, we report quantitative data from previous studies to highlight comparisons based on metrics such as F1-score accuracy, recall, accuracy, and error functions. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Code Type Identification and Parameter Estimation of Satellite Jamming Channel Based on Machine learning 11th

Code Type Identification and Parameter Estimation of Satelli...

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11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： He, Yifeng Cao, Jiadong Wei, Yinyu Zhang, Hao Xi’an Institute of Space Radio Technology Xi’an710100 China

ISBN: (纸本)9789819721153

the interference signal in satellite communication may seriously affect the efficiency and stability of data transmission. In order to solve the interference problem, it is necessary to encode the satellite interference channel and estimate its parameters. Traditional coding and parameter estimation methods have weak adaptability and robustness, and cannot effectively deal with complex interference situations. As an effective tool, machine learning has been widely used in the field of signal processing, which can realize adaptive identification and parameter estimation of interference channel coding types and improve data transmission efficiency and stability. In this paper, machine learning is used to adaptively identify the coding type of satellite jamming channel, and the accuracy of identification is improved by parameter estimation method. the comparative experimental results show that the application of machine learning algorithm in satellite jamming channel coding type identification and parameter estimation has achieved remarkable results in improving data transmission efficiency and stability. Compared with the traditional fixed coding method, machine learning can improve the transmission efficiency by about 4.76%. the results highlight the importance of machine learning in coding type identification and parameter estimation of satellite jamming channels, and provide new ideas and directions for the development of satellite communication technology. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Satellites

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