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The Impact of AI on Knowledge Management 3rd

The Impact of AI on Knowledge Management

引用

3rd international conference on Language Processing and Knowledge Management-LPKM

作者： El Arem, Hassene Ben Jarboui, Anis Univ Sfax Dept Management Sfax Tunisia Univ Sfax Dept Finance Sfax Tunisia

ISBN: (纸本)9783031850660;9783031850677

This article theoretically studies the theoretical influence of artificial intelligence (AI) on knowledge management within companies. By combining essential concepts of knowledge management with advances in artificial intelligence, this study examines how new technologies are changing knowledge management methods and impacting human resource dynamics. Internal and external knowledge, as well as new challenges and opportunities posed by AI, are comprehensively examined to provide an in-depth view of the implications of AI for strategic knowledge management.

关键词： Artificial intelligence knowledge management internal knowledge external knowledge human resources organizational transformation

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Research on Collaborative Optimization of Big Data Analysis and Artificial intelligence Based on Deep Learning 24

Research on Collaborative Optimization of Big Data Analysis ...

引用

3rd international conference on Frontiers of Artificial intelligence and machine Learning (FAIML)

作者： Zhang, Xu Li, Suruo Chen, Huafeng Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Big Data Res Ctr Jingmen 448000 Peoples R China

ISBN: (纸本)9798400709777

Drawing upon deep learning techniques, this study investigates big data analysis methods across various scenarios to enhance data value. The proposed intelligent collection device is capable of harvesting both unstructured data, such as videos and images, and structured data, including geographical coordinates, operational metrics, and electronic device identifiers. Building on this, the paper introduces an architecture for a big data analysis system underpinned by deep learning, elaborating on its critical technologies. Furthermore, it underscores how community and end-to-end power transactions bolster the adaptability and maximize the value of power generation entities. Although Multi-Agent Deep Reinforcement Learning offers a novel approach to managing energy among multiple prosumers, challenges such as environmental volatility, the safeguarding of prosumer privacy, and computational demands persist. This research aims to explore a multi-agent reinforcement learning algorithm that employs parameter sharing and deep deterministic policy gradients to enhance learning efficiency and mitigate training complexities through shared strategies and experiences among agents. Additionally, by leveraging a reputable third party for disseminating comprehensive community market data to prosumers, this approach not only promises to secure prosumer privacy effectively but also to minimize environmental uncertainty and augment the algorithm's scalability.

关键词： Deep learning Big data analysis Artificial intelligence collaborative optimization

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Study on Fast and Slow Train Operation pattern Scheme Based on Double Island Four-Track Passing Station Operating Scenario 3

Study on Fast and Slow Train Operation Pattern Scheme Based ...

引用

3rd international conference on Data Analytics, Computing and Artificial intelligence, ICDACAI 2024

作者： Yawei, Wang Hui, Sun Risheng, Sun Hozon Intelligent Mobility Technology Co. Hangzhou311305 China Xi'an Metro Group Co. Ltd. Shaanxi Xi'an710000 China

ISBN: (纸本)9798331519032

The study of train operation schedules for subway trains has significant implications for the subway train line's throughput and related transport efficiency. Taking the FAO project as an example, it uses a double-island four-track passing platform. By compiling train operation schedules and analyzing the operating modes of fast and slow trains at passing stations with different configurations, as well as discussing the interface display modes of fast and slow trains in different operation scenarios when signal software and related external interfaces are involved, this paper aims to provide a reference for the design of subway train operation schedules. © 2024 IEEE.

关键词： Urban transportation

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Research on Employee Innovative Behavior Feedback System Based on Web-based ID3 Algorithm 3

Research on Employee Innovative Behavior Feedback System Bas...

引用

3rd international conference on Data Analytics, Computing and Artificial intelligence, ICDACAI 2024

作者： Luo, Hongchi School of Management Shanghai University Shanghai200444 China

ISBN: (纸本)9798331519032

In the Internet era, the rapid development of technology has had a profound impact on the development of enterprises. With the rapid changes in the global economy, enterprises are faced with the serious challenges brought about by the uncertainty of market structure and demand. In this case, the innovative behaviour of employees has become a key factor to promote the development of enterprises, which is an important part of their innovative capacity. The study used the questionnaire star platform and offline distribution of questionnaires for enterprise employees with different gender, age, nature of the enterprise, job level and other attributes, and successfully distributed and returned 206 valid questionnaires, using SPSS 27, AMOS 25 and other analytical software to analyse the data for reliability analysis, validity analysis and correlation test, and using machine learning decision tree algorithm -ID3 algorithm to construct the decision tree model. This study can effectively help enterprises to recruit employees, through the comprehensive analysis of the attributes and innovative behaviour of employees, to achieve the accurate identification of employee potential, and then screen out high-quality talents with innovative ability for enterprises. © 2024 IEEE.

关键词： Adversarial machine learning

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A Robust Development of Human Posture recognition Model by using Artificial intelligence based Learning Scheme 3

A Robust Development of Human Posture Recognition Model by u...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sajiv, G. Meenakshisundaram, N. Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331508456

Fall risk prediction is a critical aspect of healthcare, aiming to identify individuals at risk and prevent injuries through early intervention. This study introduces a robust human posture recognition framework utilizing a novel deep learning architecture, PoseGaitNet, for fall risk prediction. Using the MMU Fall Risk Prediction (MMU- FRiP) dataset, the proposed model leverages markerless motion capture and Human Posture Estimation (HPE) to analyze gait features. Advanced preprocessing techniques, including temporal scaling and Gaussian noise addition, enhance data variability, while a custom feature selection mechanism refines the input for optimal model performance. Comparative evaluations with nine baseline models reveal the superior accuracy of the proposed model (98.50%), outperforming prominent architectures such as SE-ResNet (97.30%) and Xception (96.10%). In addition to achieving state-of-the-art accuracy, PoseGaitNet demonstrates reduced computation time, making it suitable for real-time applications. This work provides a significant contribution to fall risk assessment, combining high predictive accuracy with computational efficiency. © 2025 IEEE.

关键词： Deep learning

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Application of Network Image Enhancement Integrating Artificial intelligence in Chronic Disease Management 3

Application of Network Image Enhancement Integrating Artific...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Song, Jiajia Sun, Yihan Wang, Yujie Qiqihar Medical University Heilongjiang Qiqihar China

ISBN: (纸本)9798350318609

Chronic diseases are a global health problem that and more scholars have conducted research on image enhancement behavior recognition technology, mainly including image feature extraction, image modeling, feature selection, etc. The feature extraction technology of images is one of the important techniques for image denoising. The grayscale and grayscale values of each pixel in the image are represented through the gray domain. In current clinical treatments, pharmacists face many complex problems when performing image fusion. For example, when a pharmacist enhances a pharmacological imaging, the image becomes disorganized, making it difficult to accurately obtain the clarity and contrast of the image, and it is difficult for pharmacists to use imaging to determine whether a specific drug is effective for a patient's disease. Due to the above problems, image recognition algorithms came into being. In this paper, image based methods have been proposed. This article first provides a detailed introduction to the basic framework of artificial intelligence, then applies artificial intelligence to network image enhancement behavior recognition in clinical treatment, tests the overall efficiency and accuracy of data, and finally uses a survey questionnaire for medical staff to demonstrate that artificial intelligence is very helpful in improving the application research of image enhancement behavior recognition in clinical treatment of chronic disease management. Therefore, Making good use of artificial intelligence for network image enhancement behavior recognition is a highly worthy research topic in clinical treatment of chronic disease management. © 2024 IEEE.

关键词： Image enhancement

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Myocardial Infarction Diagnosis: pattern Analysis of ECG Report Images Using machine Learning Techniques 3

Myocardial Infarction Diagnosis: Pattern Analysis of ECG Rep...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Raghukumar, B.S. Naveen, B. Adichunchnagiri University Electronics And Communication Engineering Bgsit BG Nagara Mandya India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

The ECG machine data is utilized to diagnose cardiac conditions, specifically focusing on identifying myocardial infarction rates by analyzing pattern variations within ECG report images. Variations in the output of electrodes 2 and 3 are noted as indicative of a heart attack. The authors employ various image processing techniques like thresholding, contrast enhancement and machine learning methods like SVM, GBC, k-neighbors to process these patterns, aiming to enhance accuracy. After extracting four features, the most effective classifiers are employed, with the Gradient Boosting Classifier (GBC) with set of features exhibiting the highest accuracy at 76.60%. This paper emphasizes effective preprocessing methods crucial for obtaining structured and refined data, facilitating better feature selection and extraction from ECG graph images. It underscores the extraction of distinctive features to aid in myocardial infarction rate prediction. The paper evaluates several machines learning classifiers, highlighting their efficiency in simplifying and expediting the diagnosis process. Furthermore, the research suggests that incorporating additional features could potentially improve accuracy. © 2024 IEEE.

关键词： Electrocardiograms

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Artificial intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced Natural Language Processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 IEEE.

关键词： Natural language processing systems

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Smartphone Continuous Authentication via Deep Behavioral Features and Isolation Forest 24

Smartphone Continuous Authentication via Deep Behavioral Fea...

引用

3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Ma, Hangyuan Li, Zhongrui Mou, Shiyu School of Computer Science and Artificial Intelligence Southwest Minzu University Sichuan Chengdu China Engineering Faculty Biomedical Engineering Department University of Malaya Petaling Jaya Malaysia

ISBN: (纸本)9798400710834

Smartphone authentication typically relies on user input of passwords or biometric features such as facial recognition, fingerprint scanning, or iris recognition. However, these methods only authenticate the user at specific points in time, meaning that once the password is compromised or the device is unlocked, unauthorized individuals can access all information on the device or perform unauthorized actions. To resolve this issue, continuous authentication has emerged, continuously verifying the user’s identity by covertly monitoring the ongoing interactions between the user and the device. This paper proposes a continuous authentication method that utilizes smartphone sensor data to extract users’ deep behavioral features through Siamese Convolutional Neural Networks (Siamese CNN). These features are then input into the Isolation Forest (iForest) algorithm for classification. Experimental results demonstrate that the proposed method can authenticate the user within one second, achieving an average accuracy of 93.79% and an average Equal Error Rate (EER) of 8.37%, significantly outperforming other comparative methods used in this study. © 2024 Copyright held by the owner/author(s).

关键词： Smartphones

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On the Different Concepts and Taxonomies of eXplainable Artificial intelligence 3rd

On the Different Concepts and Taxonomies of eXplainable Arti...

引用

3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Kochkach, Arwa Kacem, Saoussen Belhadj Elkosantini, Sabeur Lee, Seongkwan M. Suh, Wonho Higher Institute of Management of Tunis University of Tunis Tunis Tunisia Faculty of Economics and Management of Nabeul University of Carthage Tunis Tunisia Colleage of Engineering United Arab Emirates University Al Ain United Arab Emirates Hanyang University ERICA Campus Seoul Korea Republic of

ISBN: (纸本)9783031463372

Presently, Artificial intelligence (AI) has seen a significant shift in focus towards the design and development of interpretable or explainable intelligent systems. This shift was boosted by the fact that AI and especially the machine Learning (ML) field models are, currently, more complex to understand due to the large amount of the treated data. However, the interchangeable misuse of XAI concepts mainly "interpretability" and "explainability" was a hindrance to the establishment of common grounds for them. Hence, given the importance of this domain, we present an overview on XAI, in this paper, in which we focus on clarifying its misused concepts. We also present the interpretability levels, some taxonomies of the literature on XAI techniques as well as some recent XAI applications. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Taxonomies

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