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BiblioDAP'21: The 1st workshop on Bibliographic data Analysis and Processing 21

BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysi...

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27th ACM SIGKDD international Conference on Knowledge Discovery and data mining (KDD)

作者： Boukhers, Zeyd Mayr, Philipp Peroni, Silvio Univ Koblenz Landau Inst Web Sci & Technol Koblenz Germany GESIS Leibniz Inst Social Sci Cologne Germany Univ Bologna Dept Class Philol & Italian Studies Res Ctr Open Scholarly Metadata Bologna Italy

ISBN: (纸本)9781450383325

Automatic processing of bibliographic data becomes very important in digital libraries, data science and machine learning due to its importance in keeping pace with the significant increase of published papers every year from one side and to the inherent challenges from the other side. This processing has several aspects including but not limited to I) Automatic extraction of references from PDF documents, II) Building an accurate citation graph, III) Author name disambiguation, etc. Bibliographic data is heterogeneous by nature and occurs in both structured (e.g. citation graph) and unstructured (e.g. publications) formats. Therefore, it requires data science and machine learning techniques to be processed and analysed. Here we introduce BiblioDAP'21: The 1st workshop on Bibliographic data Analysis and Processing.

关键词： Bibliographic data Digital libraries machine learning data Science

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machine learning for Dynamically Predicting the Onset of Renal Replacement Therapy in Chronic Kidney Disease Patients Using Claims data 1

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1st international workshop on Applications of Medical Artificial Intelligence (AMAI)

作者： Lopez-Martinez, Daniel Chen, Christina Chen, Ming-Jun Google Res Palo Alto CA 94304 USA

ISBN: (数字)9783031177217

ISBN: (纸本)9783031177217;9783031177200

Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT) including dialysis or renal transplantation. Early identification of patients who will require RRT (as much as 1 year in advance) improves patient outcomes, for example by allowing higher-quality vascular access for dialysis. Therefore, early recognition of the need for RRT by care teams is key to successfully managing the disease. Unfortunately, there is currently no commonly used predictive tool for RRT initiation. In this work, we present a machine learning model that dynamically identifies CKD patients at risk of requiring RRT up to one year in advance using only claims data. To evaluate the model, we studied approximately 3 million Medicare beneficiaries for which we made over 8 million predictions. We showed that the model can identify at risk patients with over 90% sensitivity and specificity. Although additional work is required before this approach is ready for clinical use, this study provides a basis for a screening tool to identify patients at risk within a time window that enables early proactive interventions intended to improve RRT outcomes.

关键词： Dialysis Kidney transplant Chronic kidney disease Claims data Dynamic modeling

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12th international workshop on Biological Knowledge Discovery from data, BIOKDD 2021, 5th international workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international workshop on Time Ordered data, ProTime2021 and 1st international workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international Conference on database and Expert Systems Applications, DEXA 2021

12th International Workshop on Biological Knowledge Discover...

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12th international workshop on Biological Knowledge Discovery from data, BIOKDD 2021, 5th international workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international workshop on Time Ordered data, ProTime2021 and 1st international workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international Conference on database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030871000

The proceedings contain 23 papers. The special focus in this conference is on database and Expert Systems Applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired data-Sampling Prototype for Time-Series Analysis;membership-Mappings for data Representation learning: Measure Theoretic Conceptualization;membership-Mappings for data Representation learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep learning Processes;approximate Fault Tolerance for Edge stream Processing;deep learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series pattern Discovery by Deep learning and Graph mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical Systems;integrating Gene Ontology Based Grouping and Ranking into the machine learning Algorithm for Gene Expression data Analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative study of Deep learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard Analysis Using FMEA: A DAM Case study;Privacy Preserving machine learning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in Simulation Graphs;Walk Extraction strategies for Node Embeddings with RDF2Vec in Knowledge Graphs;bridging Semantic Web and machine learning: First Results of a Systematic Mapping study.

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Role of machine learning in Acute stroke Imaging: A Technical Review

Role of Machine Learning in Acute Stroke Imaging: A Technica...

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Advanced Computing and Emerging Technologies (ACET), international Conference on

作者： Gurmeet Singh Mahender Singh Akansha Gupta Deepjyot Singh Department of Electronics and Communication Engineering Guru Tegh Bahadur Institute of Technology (GGSIPU) New Delhi India Department of Computer Science and Engineering Guru Tegh Bahadur Institute of Technology (GGSIPU) New Delhi India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

The utilization of machine learning (ML) and pattern recognition algorithms, which is used for analysis of complex datasets to detect similarities, differences, or trends, which can be particularly useful in medical diagnostics, has become essential in enhancing the prognostics of various neurological diseases such as Acute Ischemic stroke (AIS). These advanced techniques have particularly transformed the evaluation and exploitation of neuroimaging data in the assessment and management of Acute Ischemic stroke (AIS). In addition, these techniques also enable rapid and accurate analysis of complex imaging data, aiding clinicians in timely diagnosis, personalized treatment planning, and outcome prediction for stroke patients. This paper focuses on a comprehensive examination of machine learning algorithm strategies to enhance the accuracy and effectiveness of predicting strokes. The author has explored and reviewed different ML algorithms and has also delved into stroke prevention to advance our understanding.

关键词： Neuroimaging machine learning algorithms Accuracy Prevention and mitigation Imaging machine learning stroke (medical condition) Prediction algorithms Planning pattern recognition

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$k$-Subspace Method

$k$-Subspace Method

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Consumer Technology (ICCT-Pacific), international Conference on

作者： Yasuhito Fujii Yoriaki Murata Hitoshi Sakano Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Graduate School of Natural Science and Technology Shimane University Matsue Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

We propose the k-subspace method, a clustering method based on the subspace method, and the k-subspace classification method, a pattern recognition method that utilizes multiple subspaces for each class. Conventional clustering algorithms, such as the k-means method, operate based on centroids. However, in image recognition, subspaces provide a more essential representation. Existing techniques, such as CLAFIC, define a single subspace per class. However, real-world data often exhibit significant intra-class variations, such as diverse character styles within the same class. We hypothesize that partitioning subspaces into subsets, which account for intra-class variations, will enhance classification accuracy. In this study, we examine the feasibility of partitioning a sub-space into $k$ subclasses. We apply the proposed method to image recognition and experimentally demonstrate its effectiveness.

关键词： Handwriting recognition Image recognition machine learning algorithms Clustering algorithms Training data machine learning Solids Classification algorithms Partitioning algorithms Convergence

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Finding Case Law: Leveraging machine learning Research to Enhance Public Access to UK Judgments 3

Finding Case Law: Leveraging Machine Learning Research to En...

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3rd international workshop on Artificial Intelligence Technologies for Legal Documents and the 1st international workshop on Knowledge Graph Summarization, AI4LEGAL-KGSUM 2022

作者： Conroy, Amy Nemsic, Editha Hoadley, Daniel Hafnaoui, Imane Mishcon de Reya LLP LondonWC2B 6AH United Kingdom University of Bristol BristolBS8 1TH United Kingdom University College London LondonWC1E 6BT United Kingdom Queen Mary University of London LondonE1 4NS United Kingdom

Once ranked last in Europe for public access to judgment data, the United Kingdom has taken large strides in recent years to improve the accessibility of judgments. This paper discusses how the new platform from The National Archives, Find Case Law, was developed for the publication of UK judgments;in particular how we created the engine responsible for the enrichment of judgment text. We argue that the new system is necessary to address existing issues with the accessibility of judgment data, and if the platform were to leverage the abundance of research conducted in areas such as legal text classification, summarisation, and entity recognition, the UK could quickly become a world leader for public accessibility of judgments. We develop a proof of concept system, MyJudgments, that demonstrates a potential direction for development. Whilst it is early days, the launch of Find Case Law provides a unique opportunity to remind ourselves of the opportunities machine learning presents for broadening the accessibility of judgments to new users and expanding their utility for novel use-cases. To do this, we review existing research performed on UK judgment data and suggest how the various strands could practically be integrated into a case law publication system. © 2022 Copyright for this paper by its authors.

关键词： Classification (of information)

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Leveraging AI & MI for Predictive Analytics in stress Level of Private Insurance Employees : Comprehensive Review 1

Leveraging AI & MI for Predictive Analytics in Stress Level ...

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1st international Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Vij, Renu Nazir, Hena Chandigarh University Usb Department of AIT-Management Mohali India

ISBN: (纸本)9798350330915

This comprehensive study delves into the revolutionary possibilities of AI and ML in predictive analytics for gauging private insurance industry employees' stress levels. Recognizing the root causes and anticipating stress patterns becomes critical as the industry faces increasing workplace stress. The use of AI and ML allows us to explore several aspects of stress, including stress in the workplace and personal factors that affect stress levels. starting with an overview of the present state of affairs in the private insurance sector, we underscore the critical nature of resolving this matter immediately. stress event forecasting, pattern recognition in historical data, and early indication identification are areas where AI and ML are finding practical use. In order to reduce the negative effects of stress, AI-driven algorithms can quickly determine which employees are at high risk and then intervene proactively. Additionally, this article discusses possible biases in AI systems, data privacy, and ethical concerns. The substantial contribution is found in the exhaustive analysis of the revolutionary role of AI and ML in changing the insurance industry, namely in determining which employees pose a high risk and in developing specific interventions to improve their health. Industry experts, lawmakers, and researchers will find this research paper useful since it sheds light on the complex nature of stress and provides concrete suggestions for creating more positive workplaces. © 2023 IEEE.

关键词： AI-driven Models Artificial Intelligence Bias data Privacy Employee Satisfaction Employee Well-being Ethical Considerations machine learning Predictive Analytics Private Insurance Productivity stress Levels Work Environments Workplace stress

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Predictive modeling toward identification of sex from lip prints - machine learning in cheiloscopy 4

Predictive modeling toward identification of sex from lip pr...

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Joint of the 4th international Conference on Applied Informatics workshops, ICAIW 2021, 2nd international workshop on Applied Artificial Intelligence, WAAI 2021, 3rd international workshop on Applied Informatics for Economy, Society, and Development, AIESD 2021, 4th international workshop on data Engineering and Analytics, WDEA 2021, 1st international workshop on Knowledge Management and Information Technologies, WKMIT 2021 and 4th international workshop on Smart and Sustainable Cities, WSSC 2021

作者： Sabelli, Agustín F. Chatterjee, Parag Pollo-Cattaneo, Ma Florencia Universidad Tecnológica Nacional Facultad Regional Buenos Aires Buenos Aires Argentina

Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip traces hold multifarious features and could be analyzed in different ways to identify the links with personal identifying features. machine learning holds strong application in this domain, especially for pattern recognition and further interpretation. This work is focused on a brief survey of existing machine learning approaches in Cheiloscopy. Also, a comparative study of predictive models has been presented based on an original dataset of lip prints where supervised models have been used to predict the biological sex of the persons using their lip traces. machine learning on one hand automatizes the identification process and poses a significant potential in analyzing huge number of lip traces with considerable accuracy. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR workshop Proceedings (***)

关键词： Image processing

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Depression Detection and recognition Research Based on Audio Analysis and Artificial Intelligence Algorithms

Depression Detection and Recognition Research Based on Audio...

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Future Intelligent Technologies for Young Researchers (FITYR), IEEE international workshop on

作者： Zhuozheng Wang Yunlong Wang Xixi Zhao Bingxu Chen Haonan Chen Gang Wang Lei Feng Faculty of Information Technology Beijing University of Technology Beijing China National Clinical Research Center for Mental Disorders & National Center for Mental Disorders Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China National Clinical Research Center for Mental Disorders & National Center for Mental Disorders Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Beijing China

ISBN: (数字)9798331539757

ISBN: (纸本)9798331539764

In response to the prevalent symptoms of depression, the complexity of diagnostic processes, and individuals’ resistance to psychological testing in contemporary society, this study proposes an automated depression detection method based on audio analysis. This method aims to enhance the accuracy of depression state recognition by comparing the efficiency of different models, including machine learning and deep learning. Initially, the study collected audio sample data from 50 individuals, including both healthy subjects and patients with depression. During the audio data processing phase, features related to depression recognition, such as Mel-frequency cepstral coefficients (MFCC) and fundamental frequency, were extracted from these samples. Subsequently, a series of recognition comparison experiments were conducted based on the extracted features, involving various algorithmic models such as Support Vector machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN). Experimental results demonstrated that the Convolutional Neural Network exhibited higher accuracy in recognizing depression states from audio data, with an average recognition accuracy rate of 95.82%. This finding indicates that deep learning models, especially Convolutional Neural Networks, have significant advantages in addressing such issues, providing robust technical support for the future automatic detection of depression.

关键词： Support vector machines Deep learning Resistance Radio frequency Accuracy Depression Feature extraction Convolutional neural networks Random forests Testing

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Governmental Anti-Covid Measures Effectiveness Detection 27

Governmental Anti-Covid Measures Effectiveness Detection

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27th international Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Žid, Pavel Haindl, Michal Havlícek, Vojtech Institute of Information Theory and Automation Czech Academy of Sciences Pattern Recognition Department Pod Vodárenskou veží 4 Prague182 08 Czech Republic

We present a retrospective analysis of Czech anti-covid governmental measures' effectiveness for an unusually long three years of observation. Numerous Czech government restrictive measures illustrate this analysis applied to three years of COVID-19 data from the first three COVID-19 cases detected on 1st March 2020 till March 2023. It illustrates the course from the dramatic combat of unknown illness to resignation to country-wide measures and placing COVID-19 into a category of common nuisances. Our analysis uses the derived adaptive recursive Bayesian stochastic multidimensional Covid model-based prediction of nine essential publicly available COVID-19 data series. The COVID-19 model enables us to differentiate between effective measures and solely nuisance or antagonistic provisions and their correct or wrong timing. Our COVID model allows us to predict vital covid statistics such as the number of hospitalized, deaths, or symptomatic individuals, which can serve for daily control of anti-covid measures and the necessary precautions and formulate recommendations to control future pandemics. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Anti-pandemic measures COVID-19 machine learning method Prediction Recursive forecasting model

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