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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

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African Vulture Optimization with Deep learning Based COVID-19 Fake News Detection Model 6

African Vulture Optimization with Deep Learning Based COVID-...

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6th Iraqi international conference on Engineering Technology and its Applications, IICETA 2023

作者： Ibrahim, Karrar Abed, Z. Alkhafaji, Mohammed Ayad Alawadi, Ahmed Hussien Sheel, Shaid Al-Zahraa University for Women Karbala Iraq Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq The Islamic University Najaf Iraq Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350303339

Nowadays, Text mining technology is broadly utilized for a wide range of business government, and research needs. Fake news recognition intends to assist users to divulge various fake news. Fake news on several vaccinations, medicines, and foods concerning the COVID19 pandemic has augmented intensely. Such fake news reports make people trust false and harmful stories and claims, and they even affect the vaccination opinions of people. Immediate detection of COVID19 fake news may assist to decrease the spread of confusion fear, and health risks among residents. Text mining related data analytics techniques are employed to detect COVID19 based fake news on social networking sites. this article introduces an African Vulture Optimization with Deep learning based COVID-19 Fake News Detection (AVODL-FND) method. the goal of the AVODL-FND model lies in the identification of fake news relevant to COVID19 circulated in social media. In the presented AVODL-FND method, the initial stage of data preprocessing is carried out in three distinct levels to transform the input data into a useful format. the presented AVODL-FND model applies multi-head attention-based bidirectional long short-term memory (MHA-BLSTM) approach for the detection of fake news. For enhancing the detection performance of the MHA-BLSTM model, the AVO algorithm is utilized for the hyperparameter tuning process. the experimental outcomes of the AVODL-FND model on the fake news dataset show the significant performance of the AVODL-FND model over other DL methods. © 2023 IEEE.

关键词： COVID-19

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Human Resources Analysis Based on machine learning 6

Human Resources Analysis Based on Machine Learning

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6th international conference on Information Technologies and Electrical Engineering, ICITEE 2023

作者： Guo, Ning School of Public Administration Guangxi University Nanning China

ISBN: (纸本)9798400708299

this study is grounded in a human resource dataset, examining the correlation between employee performance and various factors, encompassing age, and educational background. the dataset comprises 13 dimensions, and meticulous data cleaning and processing were applied to rectify missing and incongruous data issues. Subsequent to the preparatory steps, data mining techniques were employed to scrutinize the influence of age and education on employee value. the paper then details the methodology for establishing and optimizing the model, utilizing the Particle Swarm Optimization (PSO) algorithm and BP00 neural network. Validation of the model's efficacy follows, with results indicating its robust capability to predict employee value accurately. these findings offer valuable insights for enhancing human resources management practices. the paper concludes by summarizing research outcomes and underscored the pivotal role of a judicious performance assessment system in human resources management. Future research directions are also delineated. © 2023 ACM.

关键词： Decision support systems

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

6th International Conference on Information and Communicatio...

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

ISBN: (纸本)9789811935701

the proceedings contain 149 papers. the special focus in this conference is on Information and Communication Technology for Intelligent Systems. the topics include: COVID-19 Triggers a Paradigm Shift in Technology for Insurance Industry;internet of things in Saudi Arabia Universities: State of the Art, Future Opportunities, and Open Challenges;study of Security in Cryptocurrency;study and Analysis of Network Steganography Methods;preface;Review of Different ML Techniques Used for Heart Disease Prediction;survey on Driver Fatigue Detection Using Sensors, Big data Analytics and machine learning Techniques;on Characteristics of κ–Q–Fuzzy Translation and κ–Q–Multiplication in T-Ideal in T-Algebras;blockchain-driven Smart Healthcare System: Challenges, Technologies and Future Research;a Future to the Blockchain Technology and Its Concepts;Impacts of DDoS Attacks in Software-Defined Networks;ioT-Based Agriculture Trend Prediction Based on Weather data;EEG in Optic Nerves Disorder Based on FSVM Using Kernel Membership Function;DDoS Detection in ONOS SDN Controller Using Snort;classification of Keratoconus Using Corneal Topography pattern with Transfer learning Approach;intelligent Heuristic Keyword-Based Search Methodologies Applied to Cryptographic Cloud Environment;image Enhancement by Various Segmentation Techniques Using machine learning;Surakhsha Kavach: ML-Based Cross-platform Application for COVID-19 Vulnerability Detection;Malware Family Categorization Using Genetic Algorithm-CNN-Based Image Classification Technique;master data Management Maturity Evaluation: A Case Study in Educational Institute;malware Family Classification Using Music Information Retrieval Techniques;text-Based Prediction of Heart Disease Doctor Chatbot Using machine learning;intelligent Proctoring System;project-Based learning: A Contemporary Approach to Blend theory and Practical Knowledge of database Management Course;student’s Employability Concern for Colleges and Universities.

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Small Sample MKFCNN-LSTM Transfer learning Fault Diagnosis Method 6th

Small Sample MKFCNN-LSTM Transfer Learning Fault Diagnosis M...

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conference of the-Efficiency-and-Performance-Engineering-Network (TEPEN) / 6th international conference on Maintenance Engineering (IncoME)

作者： Guo, Yonglun Wu, Guoxin Liu, Xiuli Beijing Informat Sci & Technol Univ Minist Educ Key Lab Modern Measurement & Control Technol Beijing 100192 Peoples R China

ISBN: (纸本)9783030990756;9783030990749

Aiming at the problem that there are all kinds of noise interference in the planetary gearbox of wind turbine in the general experimental scene, the vibration data obtained is less and the fault characteristics are not obvious. A MKFCNN-LSTM migration learning algorithm based on multi-kernel fusion convolution neural network (MKFCNN) and long and short time memory neural network (LSTM) is proposed to realize the fault diagnosis of wind turbine planetary gearbox. Firstly, the MKFCNN is constructed to extract the multi-scale spatial features of the sample signal, and then it is connected in series with LSTM to extract the corresponding time information of the sample signal. In view of the associated fault feature information between the rolling bearing data set and the planetary gearbox data set, the rolling bearing vibration signal of the Western Reserve University is input into the MKFCNN-LSTM as the source domain sample data, and iterative training is used to update the network weight and offset value. the pre-trained MKFCNN-LSTM is obtained, and then fine-tuned by inputting the vibration data of the planetary gearbox with small samples in the target domain, the weight and offset values are transferred from the source domain to the target domain, and finally the accuracy of fault recognition based on the number of small samples in the target domain is improved. the experimental results show that the proposed method can apply the original fault diagnosis knowledge to the vibration data set of the planetary gearbox in the laboratory. Compared with stack autoencoders (SAE), support vector machine (SVM) algorithm, the accuracy of fault identification and classification is improved to a certain extent.

关键词： MKFCNN LSTM Transfer learning Rolling bearing Planetary gearbox Fault diagnosis

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

6th International Conference on Information and Communicatio...

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

ISBN: (纸本)9789811935749

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Topic Modelling and Interpretable Cost Estimation for Medical Insurance Fraud Detection 1

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16th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Kemp, James Barker, Christopher Good, Norm Bain, Michael Univ New South Wales Sydney NSW Australia Australian Govt Dept Hlth & Aged Care Sydney NSW Australia Commonwealth Sci & Ind Res Org Canberra Qld Australia

ISBN: (数字)9783031670886

ISBN: (纸本)9783031670879;9783031670886

Medical insurance incurs significant costs and can be susceptible to fraud or waste. machine learning approaches to automating fraud detection are becoming commonplace. Real-world pipelines including decision support systems for compliance activities on medical insurance claims may include requirements such human-interpretability and estimates of recoverable costs, to assist with prioritisation and investigation. We previously developed a framework for learning claim contexts and provider roles, incorporating domain knowledge through the insurance item ontology, and ranking providers for audit based on cost differences between similar providers. We extend this by comparing an interpretable pattern-identification cost estimator to the original scoring method and evaluating on a large real-world claims dataset. Compared to our previous cost estimator performance was similar, but with the advantage of immediately interpretable results. Results show incorporating context discovery and domain knowledge into fraud detection algorithms assists identification of comparable providers and generation of interpretable results for subject-matter experts in the decision-support process.

关键词： Unsupervised machine learning data mining Orthopedic procedures National health insurance Fraud

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Web Development and Evaluation of Instructional Objectives’ Quality Using Natural Language Processing 6th

Web Development and Evaluation of Instructional Objectives’...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Mohanta, Jyotiprava Jain, Mahim Jain, Mahim M. Mandal, Shyamal Kumar Das Advance Technology Development Centre Indian Institute of Technology Kharagpur India Aerospace Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789819780891

Outcome-based education is currently recommended in every field of education by the accreditation bodies. Outcome-based education is the process of developing the curriculum in a well-defined framework. the stated outcome or the instructional objective should be properly defined for successful implementation. Here, a methodology is proposed where the curriculum designer can check whether the instructional objective is properly designed. the methodology consists of website development and a machine learning model (ML) using natural language processing. the input to the model is an instructional objective statement, and the result will be in three categories: poor, good, and perfect. the website consists of a Flask-written backend and a Next. js-developed frontend. the backend was integrated with the ML model. For the model, the Bidirectional Encoder Representations from Transformers model is used. the designed model was applied to 50 instructional objective datasets for training the model and 40 datasets for testing the prediction. the BERT model was found to be effective. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Tool Support for learning Architectural Guidance Models from Architectural Design Decision Models 28

Tool Support for Learning Architectural Guidance Models from...

引用

28th European conference on pattern Languages of Programs, EuroPLoP 2023

作者： Amiri, Amirali Ntentos, Evangelos Zdun, Uwe Geiger, Sebastian University of Vienna Faculty of Computer Science Software Architecture Group Vienna Austria University of Vienna Doctoral School Computer Science Vienna Austria Siemens Vienna Austria

ISBN: (纸本)9798400700408

this paper presents an approach to architectural knowledge management that does not assume existing architectural design decisions or pattern applications are documented as architectural knowledge, but benefits from more existing data. We drew inspiration from manual qualitative research methods for mining patterns and architectural knowledge and created a guideline model of which the ADD models are instances. We evaluated our approach on 11 cases from the gray literature. We found that it can provide suitable recommendations after modeling only a single case and reaches theoretical saturation and recommendations with low to very low errors after only 6-8 cases. Our approach shows that creating a reusable architectural design space is possible based only on limited case data. Our approach not only provides a novel approach to architectural knowledge management but can also be used as a tool for pattern mining. © 2023 Copyright held by the owner/author(s).

关键词： Knowledge management

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An Approach Towards learning K-Means-Friendly Deep Latent Representation 27th

An Approach Towards Learning K-Means-Friendly Deep Latent Re...

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27th international conference on pattern recognition, ICPR 2024

作者： Roy, Debapriya Institute of Engineering and Management Kolkata India

ISBN: (纸本)9783031781650

Clustering is a long-standing problem area in data mining. the centroid-based classical approaches to clustering mainly face difficulty in the case of high dimensional inputs such as images. With the advent of deep neural networks, a common approach to this problem is to map the data to some latent space of comparatively lower dimensions and then do the clustering in that space. Network architectures adopted for this are generally autoencoders that reconstruct a given input in the output. To keep the input in some compact form, the encoder in AE’s learns to extract useful features that get decoded at the reconstruction end. A well-known centroid-based clustering algorithm is K-means. In the context of deep feature learning, recent works have empirically shown the importance of learning the representations and the cluster centroids together. However, in this aspect of joint learning, recently a continuous variant of K-means has been proposed;where the softmax function is used in place of argmax to learn the clustering and network parameters jointly using stochastic gradient descent (SGD). However, unlike K-means, where the input space stays constant, here the learning of the centroid is done in parallel to the learning of the latent space for every batch of data. Such batch updates disagree with the concept of classical K-means, where the clustering space remains constant as it is the input space itself. To this end, we propose to alternatively learn a clustering-friendly data representation and K-means based cluster centers. Experiments on some benchmark datasets have shown improvements of our approach over the previous approaches. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： K-means clustering

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Restaurant Queuing Time Prediction Using Random Forest Regression 12

Restaurant Queuing Time Prediction Using Random Forest Regre...

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12th international conference on pattern recognition Systems (ICPRS)

作者： Xue, Yijia Zhang, Xiang DukeKunshan Univ Suzhou 215316 Peoples R China Southeast Univ Nanjing 210096 Peoples R China

ISBN: (数字)9781665466943

ISBN: (纸本)9781665466943

Unknown queuing time often brings negative consumer experience in restaurant service. Prediction of queuing time has been a critical task to benefit both consumers and restaurants. However, current research mainly focuses on the queuing theory, which requires collecting prior knowledge and survey. this paper proposes a queuing time prediction model based on Random Forest Regression with features selection method to ease the limitations of previous research. Random Forest Algorithm have achieved enormous success in various fields. Experiments on a real-life restaurant queuing database validate the practicality of the model. the proposed method outperforms the baseline model on the experimental data. We conclude the intrinsic information of the queuing model with the feature selection method, which will help improve other application models in queuing scenarios.

关键词： Queuing time prediction feature selection random forest regression data mining machine learning

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