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Wasserstein Weight Estimation for Stochastic Petri Nets 6

Wasserstein Weight Estimation for Stochastic Petri Nets

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6th international conference on Process mining (ICPM)

作者： Brockhoff, Tobias Uysal, Merih Seran van der Aalst, Wil M. P. Rhein Westfal TH Aachen Proc & Data Sci Grp PADS Aachen Germany

ISBN: (纸本)9798350365030

Traditional process models like Petri nets effectively describe the control flow of processes but fail to capture stochastic information such as choice likelihoods. To address this, Stochastic Labeled Petri Nets (SPNs) have recently gained attention, extending Petri nets with transition weights that allow to associate executions with probabilities. the language of an SPN thereby becomes a probability distribution over traces (i.e., sequences of activities). To assess an SPN's quality, Earth Mover's Stochastic Conformance (EMSC) emerged as a natural metric that measures the similarity of the SPN's trace distribution to the observed real-world distribution. In this paper, we propose a locally optimal approach for fine-tuning (or finding) transitions weights to maximize an SPN's EMSC. Leveraging the relationship between EMSC and the Wasserstein distance, which recently gained attention as a loss function in machine learning, we compute subgradients for EMSC to optimize transition weights via subgradient descent. Besides, we propose a straightforward solution to handle models that allow for infinitely many traces. Our optimization approach is broadly applicable for EMSC that is, for EMSC using arbitrary trace-to-trace distances-unlike existing works that either to not explicitly consider EMSC or only special variants. We demonstrate the applicability of our approach on several real-life event logs and discovery algorithms, comparing it to state-of-the-art stochastic process discovery methods and a recent full automated simulation approach.

关键词： Stochastic Process mining Stochastic Petri Nets Earth Mover's Distance Optimal Transport Wasserstein Loss

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6th international conference on communication and computational technologies, ICCCT 2024

6th International conference on communication and computatio...

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6th international conference on communication and computational technologies, ICCCT 2024

ISBN: (纸本)9789819774227

the proceedings contain 39 papers. the special focus in this conference is on communication and computational technologies. the topics include: Conceptual Framework for Risk Mitigation and Monitoring in Software Organizations Based on Artificial Immune System;a Multilevel Home Fire Detection and Alert System Using Internet of things (IoT);smart Baby Warmer with Integrated Weight Sensing;a Robust Multi-head Self-attention-Based Framework for Melanoma Detection;Domain Knowledge Based Multi-CNN Approach for Dynamic and Personalized Video Summarization;Efficient Information Retrieval: AWS Textract in Action;text Summarization Techniques for Kannada Language;Parkinson’s Detection From Gait Time Series Classification Using LSTM Tuned by Modified RSA Algorithm;human Action recognition Using Depth Motion Images and Deep learning;maximizing Portfolio Returns in Stock Market Using Deep Reinforcement Techniques;Detecting AI Generated Content: A Study of Methods and Applications;A Systemic Review of machine learning Approaches for Malicious URL Detection;digital Image Forgery Detection Based on Convolutional Neural Networks;Banana Freshness Classification: A Deep learning Approach with VGG16;greenHarvest: data-Driven Crop Yield Prediction and Eco-Friendly Fertilizer Guidance for Sustainable Agriculture;real-Time Deep learning Based Image Compression Techniques: Review;fog-Cloud Enabled Human Falls Prediction System Using a Hybrid Feature Selection Approach;a 4-Input 8-Bit Comparator with Enhanced Binary Subtraction;multivalued Dependency in Neutrosophic database System;traffic Sign recognition Framework Using Zero-Shot learning;machine learning Techniques to Categorize the Sentiment Analysis of Amazon Customer Reviews;alzheimer’s Disease Diagnosis Using machine learning and Deep learning Techniques;sentinel Eyes Violence Detection System;Detection of Alzheimer’s Disease from Brain MRI Images Using Convolutional Neural Network.

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COVID-19 Cough Classification Models Based on machine learning: A Comparative Analysis 6

COVID-19 Cough Classification Models Based on Machine Learni...

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

作者： Mohammed, Mohammed Abdulkreem AlNoor University College Mosul Iraq

ISBN: (纸本)9798350303339

A In December 2019, the SARS-CoV-2-caused coronavirus infection (COVID-19) spread to all countries, infecting thousands of people and killing several of them. COVID-19 often results in a mild sickness, while it may sometimes make some people quite unwell. this has resulted in the production of a wide range of vaccinations, some of which have even received national approval. the current state of affairs makes it obvious that a quick repair is desperately required for the Covid-19 vaccine development. Non-clinical methods like data mining and machine learning may be used to achieve this. the progress of COVID-19 worldwide vaccination will be examined in this study using machine learning classification techniques. the study's findings show which approach is more effective for a particular dataset. Weka is used to run tests on actual data using five output classification techniques (Decision Tree, K-nearest Neighbors, Random Tree, Naive Bayes and Support Vector machines). the Decision Tree was demonstrated to be superior to other algorithms in both of these areas based on a comparison of accuracy and performance duration. © 2023 IEEE.

关键词： COVID-19

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Design Methodology for Single-Channel CNN-Based FER Systems 6

Design Methodology for Single-Channel CNN-Based FER Systems

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6th international conference on Information and Computer Technologies, ICICT 2023

作者： Parra, Dorfell Camargo, Carlos Universidad Nacional Department of Electric and Electronic Engineering Bogota Colombia

ISBN: (纸本)9798350300956

Facial Expression recognition (FER) systems classify emotions by using geometrical approaches or machine learning (ML) algorithms such as Convolutional Neural Networks (CNNs). Due to their complexity, these FER systems need to be implemented on high-performance hardware, which makes them unsuitable for embedded devices. To address this challenge, we propose a methodology for the design of low-complexity, CNN-based FER systems. Our methodology includes data preprocessing, Local Binary pattern (LBP) implementation, data Augmentation (DA), and CNN design. Here, we also introduce the Model M6, a single-channel CNN that reaches an accuracy of 94% in less than 30 epochs. M6 has 306,182 parameters that correspond to 1.17 MB of memory. therefore, our methodology and M6 model are feasible for implementation onto embedded systems capable of computing floating point operations. We validated our methodology and M6 model using 66 tests with 6 CNN models and 4 training parameters (batch size, learning rate, number of epochs, optimizer). this validation was performed using the Japanese Female Facial Expression (JAFFE) dataset and TensorFlow. In each test, the relationship between parameters, layers, overfitting, and underfitting was studied. Moreover, we present a step-by-step guideline on how to design the single-channel CNN and provide open-source code for readers interested in reproducing our work. © 2023 IEEE.

关键词： Local binary pattern

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Face Value: On the Impact of Annotation (In-)Consistencies and Label Ambiguity in Facial data on Emotion recognition 26

Face Value: On the Impact of Annotation (In-)Consistencies a...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Gebele, Jens Brune, Philipp Fausser, Stefan Neu Ulm Univ Appl Sci Technol Transfer Ctr Gunzburg Neu Ulm Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Artificial Intelligence (AI)-based emotion recognition using various kinds of data has attracted vast attention in recent years. Impressive results have been achieved, but only recently the influence of the training data with its potential biases and variations in annotation quality are discussed. Still, the majority of the research literature focuses on improving machine learning techniques and model performance using single data sets. Literature on the impact of training data remains scarce. therefore, in this paper we investigate the influence of the training data on the accuracy of recognizing emotional states in facial expressions by a comparative evaluation, using multiple established facial image databases. Results reveal inconsistencies in the data annotations as well as ambiguities in the emotional states expressed. thus, they allow to critically discuss data quality of the training data, contributing to a more in-depth understanding of previous emotion recognition approaches, and improving the design of more transparent AI solutions.

关键词： Emotion recognition Facial Expression recognition Emotional Artificial Intelligence data Quality

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Exploratory data Analysis & data mining on NBA Match Prediction 6

Exploratory Data Analysis & Data Mining on NBA Match Predict...

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6th international conference on Computing and Big data, ICCBD 2023

作者： Yao, Yuheng School of Mathematical Sciences Beihang University Beijing China

ISBN: (数字)9798350317008

ISBN: (纸本)9798350317008

Basketball has been a popular domain for researchers to collect and analyse vast amount of data. With the data of players, teams, games, and seasons, researchers can gain useful insights into players and matches. Nowadays, machine learning is an important tool for data analysis and prediction and has achieved powerful effect and inspiring accuracy in existing research of NBA data. In this paper, we are motivated to research on the factors that are essential for NBA match outcome prediction and how to build the most effective data set and most powerful models to make the best prediction. Multi- variate regression analysis was used to analyse the importance of factors and how these factors influence the outcome of a match. then machine learning models, like naive bayes, random forest, Logistic regression, K nearest neighbors and LDA are applied. Finally, an experiment was conducted in search of the best data setting. It turns out that the most significant factor is the recent win rate, while the best model in accuracy is logistic model with average accuracy of 67% over all seasons and the best strategy is generating data from last season. © 2023 IEEE.

关键词： data accuracy

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6th EAI international conference on Emerging Technologies for Developing Countries, AFRICATEK 2023

6th EAI International Conference on Emerging Technologies fo...

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6th EAI international conference on Emerging Technologies for Developing Countries, AFRICATEK 2023

ISBN: (纸本)9783031639982

the proceedings contain 11 papers. the special focus in this conference is on Emerging Technologies for Developing Countries. the topics include: Comprehensive Review of Smart Parking Occupancy Prediction Models in Nairobi City: Strengths, Weaknesses, and Research Gaps;Integration of IK, Satellite Imagery data, Weather data and Time Series Models in Season Behaviour Predictions. Case of Swayimane, KZN, South Africa;preface;state-of-the-Art Review on Recent Trends in Automatic Speech recognition;predicting Malaria Outbreak Using Indigenous Knowledge and Fuzzy Cognitive Maps: A Case Study of Vhembe District in South Africa;A Systematic Review on the Use of AI-Powered Cloud Computing for Healthcare Resilience;Towards a Smart Healthcare System for Non-Communicable Diseases (NCDs) Management: A Bibliometric Analysis;a machine learning Approach to Mental Disorder Prediction: Handling the Missing data Challenge;adopting Blockchain for Enhancing data Security and Privacy in Service-Based Digital Platforms: A Case Study of a Distributed Application (Dapp) Global Mission Services.

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Proceedings - 2023 6th international conference of Women in data Science at Prince Sultan University, WiDS-PSU 2023

Proceedings - 2023 6th International Conference of Women in ...

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6th international conference of Women in data Science at Prince Sultan University, WiDS-PSU 2023

ISBN: (纸本)9781665477239

the proceedings contain 40 papers. the topics discussed include: data science approach for climate change in Saudi Arabia: trend analysis;visualization approach to forecasting retail business;text mining to analyze mammogram screening results for breast cancer patients in Saudi Arabia;implementation of an effective framework in merging cybersecurity and software engineering;alarm rationalization for cybersecurity monitoring;a comparative assessment of accomplishment of sustainable development goals in tropical region;properties and future of the skew Kalman filters;web scraping for data analytics: a beautiful soup implementation;and a machine learning approach to identifying facial masks in real time.

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Performance Comparison of Different machine learning Classifiers for Diabetes Prediction 6th

Performance Comparison of Different Machine Learning Classif...

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

作者： Ghosh, Dipayan Ganguly, Abhik Chakraborty, Rounak Singh, Pawan Kumar Li, Aimin Department of Information Technology Jadavpur University West Bengal Kolkata India Shinawatra University Pathum Thani Thailand School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

ISBN: (纸本)9789819780891

this paper presents a thorough evaluation of machine learning algorithms for assessing the risk of having diabetes with the help of the Pima Indian Diabetes dataset. In view of the global diabetes epidemic, timely and precise risk assessment is imperative. Our study involves an in-depth exploration of the data, uncovering a robust correlation between glucose levels and the likelihood of diabetes. We deploy a diverse set of nine machine learning models, encompassing logistic regression, decision trees, random forests, AdaBoost, support vector machine, K-nearest neighbors, Naive Bayes, XGBoost, and an artificial neural network approach. the results illuminate the strengths and weaknesses of each model, providing valuable insights for potential clinical applications. the logistic regression approach, in particular, showcases its ability to capture intricate patterns within the dataset, underscoring its effectiveness in diabetes risk assessment. In addition to traditional classifiers, we introduce an ensemble model that combines the strengths of five best performing classifiers. these findings not only improve the accuracy of diabetes risk assessment but also establish a benchmark for future research in medical diagnosis. Identifying the most effective model assists healthcare practitioners in early intervention and tailored treatment strategies. this research advances the field of healthcare analytics, facilitating more informed decisions in diabetes prevention and management. the integration of a robust ensemble model approach broadens the scope of potential applications, marking a significant contribution to the field. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Keyword Extraction-Based Audio and Video recognition Technology in Mobile Office Environment 6

Keyword Extraction-Based Audio and Video Recognition Technol...

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6th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Lu, Teng Cuii, Zhaowe Li, Qingbo Li, Xiaomeng Yuan, Zhou Xia, Ang State Grid Information & Telecommunication Co. Ltd. Beijing China

ISBN: (纸本)9781837242672

this paper explores the integration of keyword extraction techniques into audio and video recognition systems designed for mobile office environments. As the prevalence of mobile devices reshapes traditional workspaces, the demand for advanced technologies that can efficiently process and analyze audio and video data in real time has increased significantly. this research examines the unique challenges presented by dynamic mobile contexts, such as fluctuating background noise, varying audio quality, and the mobility of devices, which complicate traditional recognition methods. To address these issues, a novel framework is proposed that combines both machine learning and deep learning techniques to enhance the accuracy and robustness of audio and video recognition in such environments. Experimental validation of the proposed model demonstrates a high accuracy of 92% in keyword detection, confirming its effectiveness in real-world applications. the results highlight the potential of this framework to not only improve recognition performance but also to create more adaptive, context-aware systems capable of optimizing user experience in mobile office settings. this study contributes to the development of smarter, more efficient recognition systems that are better suited to the evolving demands of modern workspaces. © the Institution of Engineering & Technology 2024.

关键词： Deep learning

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