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

共 2812 条记录，以下是41-50 订阅

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Design and Implementation of a machine learning Model for Soccer Match Prediction Based on Player Statistics 17th

Design and Implementation of a Machine Learning Model for So...

引用

17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international conference on Artificial Intelligence, MICAI 2024

作者： Barrientos, Antonio Muñoz Bautista, Humberto Muñoz Díaz, Alejandro Padilla Rodríguez, Francisco Javier Álvarez Universidad Autónoma de Aguascalientes AGS Aguascalientes20100 Mexico

ISBN: (纸本)9783031838781

Football generates vast amounts of data daily on players, teams, and leagues, used for performance analysis. However, advanced analysis technologies are often inaccessible to lower-budget teams. this study developed a predictive tool to estimate the number of goals a team might score in a match, focusing on the UEFA Champions League with data from 2005 to 2022, totaling 4237 records. the model development included three stages: database creation, data preprocessing and analysis, and model construction. data from the Fbref and FIFA Index, including player data from the EA FC video game, were collected using web scraping to build an expandable database. Additional variables, such as team origin league and momentum (average goals in the last five matches), were added to improve model accuracy. the model, a deep fully connected neural network evolved from a multi-layer perceptron, achieved 57% accuracy. the findings suggest that effective analysis tools can be created using data mining and machine learning, supporting lower-budget teams by providing more information and reducing competitive disparities. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Literature review and process development for inbound transport demand forecasts 6

Literature review and process development for inbound transp...

引用

6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Jacobsen, Jens Jakob Mardanova, Marina Moroff, Nikolas Ulrich Fraunhofer Institute for Material Flow and Logistics Joseph-von-Fraunhofer-Str. 2-4 Dortmund44227 Germany

Order dates and delivery times in the inbound-logistic are subject to extern influences. the resulting fluctuations are imposing a challenge for the planning of the necessary transport and handling capacities. In order to plan in an environment of uncertain demands, the method of demand forecasting provides an approach to improve the planning accuracy. Due to the high volatility of the underlying demand time series machine learning (ML) based methods are identified to enable time series predictions of transport demands. For the identification of suitable ML methods, a representative and structured literature review is conducted. Following the analyses of the review an approach for the implementation of ML models in a data mining use case for transport demand predictions is presented. To ensure the actual implementation of the identified Long-short-term-memory (LSTM) model a process model based on the Cross-Industry-Standard-Process for data mining (CRISP-DM) is formulated to support the implementation with specific steps required for this use case. With this approach, a consistent concept is created which enables the selection and implementation of a time series forecast for transport demands and thus the optimization of transport demand planning. © 2025 the Authors. Published by Elsevier B.V.

关键词： Inbound Logistics LSTM machine learning Process Model Transport Forecast

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A Comprehensive Review of machine learning and Deep learning Techniques for Rice Leaf Disease Classification 6th

A Comprehensive Review of Machine Learning and Deep Learning...

引用

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

作者： Tabassum, Tasnim Rafiq, Shifat Ara Zulfiker, Md. Sabab Ritu, Nadia Afrin Biswas, Al Amin Department of CSE Daffodil International University Dhaka Bangladesh Department of CSE Sheikh Hasina University Netrokona Bangladesh Department of Computer Science and Engineering Bangabandhu Sheikh Mujibur Rahman University Kishoreganj Bangladesh Department of CSE Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9789819780891

Rice is one of the most significant primary crops and provides a substantial portion of the world’s food for billions of people worldwide. However, several leaf-related diseases pose significant risks to the rice crop’s yield and quality. the traditional approaches to disease identification and classification are frequently arduous and require specialized expertise. the domain of machine learning (ML), deep learning (DL), and the convergence of both of these techniques has emerged as a potential approach that can address this issue and enable immediate and accurate diagnosis of rice leaf disease. Additionally, this study offers a thorough analysis and contrast different approaches, focusing on addressing the initial research questions and identifying potential future possibilities. this study functions as a comprehensive resource for research groups engaged in the development of ML and DL applications specifically targeted at the categorization of rice leaf diseases. In addition to aiding various studies in creating feasible applications based on ML and DL techniques, this review effort will instruct the researchers on how these methods might facilitate the identification of rice leaf disease at an early stage. the insights derived from the study offer useful direction for the advancement of research related to rice leaf diseases. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Comparative Assessment of the Contemporary Models for Automatic Short Answer Grading 6th

A Comparative Assessment of the Contemporary Models for Auto...

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

作者： Haldar, Subhajit Sengupta, Souvik Das, Asit Kumar Department of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur India Aliah University Kolkata India

ISBN: (纸本)9789819780891

Automated Short Answer Grading (ASAG) is a popular task in education technology that automates the assessment of student-written short answers by leveraging computational techniques. Although ASAG has been investigated for many years, recent advancement in machine learning and deep learning has ignited new research in this field. In the past, researchers have come up with different approaches that can be broadly classified into two categories—handcrafted feature-based traditional approaches and deep-learning-based approaches. In recent times, transformer-based word embedding has revolutionized all the fields of NLP applications, including ASAG. this chapter aims to compare the performance of various methodologies employed in ASAG with particular attention to comparing feature extraction techniques, machine learning and deep learning models employed, the dataset used, and the assessment measures applied. the review of the literature reveals that combining transformer-based text embedding with handcrafted features is more effective than using conventional handcrafted features and deep learning features in both regression and classification tasks for ASAG. this finding is validated through an empirical study performed to evaluate and compare the efficiency of different handcrafted and semantic features on the ASAG task with a common dataset and two baseline models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Insights on Improving Reasoning and Increasing Capabilities in Large Language Models 6th

Insights on Improving Reasoning and Increasing Capabilities ...

引用

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

作者： Krisshna, S. Varun Sendhil, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819780891

Large Language models (LLMs) based on the Transformer architecture are designed to understand and generate human-like text by learning patterns and relationships from vast amounts of textual data. these models have been reported to have "sparks" of artificial general intelligence owing to their very attractive properties such as high generalization and performance in unseen data. these models have demonstrated strong capabilities in generalization and performance on novel data, leading some to speculate they may contain early sparks of artificial general intelligence. However, true AGI remains an unachieved goal requiring breakthroughs beyond current techniques. LLMs have poor mathematical reasoning capabilities, inherit biases present in training data, and can hallucinate and deliver false information. In this chapter, we discuss several methods that improve the current state-of-the-art models through the use of external tools such as web browsing, better prompting techniques, scaling, using Reinforcement learning (RL) and tree-based search algorithms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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CC-HIT: Creating Counterfactuals from High-Impact Transitions

CC-HIT: Creating Counterfactuals from High-Impact Transitio...

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international Workshops which were held in conjunction with the 6th international conference on Process mining, ICPM 2024

作者： Xian, Zhicong Zellner, Ludwig Tavares, Gabriel Marques Seidl, thomas Database Systems and Data Mining LMU Munich Munich Germany Munich Center for Machine Learning Munich Germany

ISBN: (纸本)9783031822247

Smooth process execution relies on high-quality insights extracted from event data. For instance, trace durations heavily affect performance and increase resource consumption. While many predictive systems aim to identify these inefficiencies, they often focus on individual process instances, missing the global perspective. It is essential to detect where delays occur globally and pinpoint specific activity transitions causing them. To address this, we propose CC-HIT (Creating Counterfactuals from High-Impact Transitions), which identifies temporal activity dependencies across the process. CC-HIT uses a modified game theoretic approach and counterfactual information to generate reference event logs to estimate the consequences of activity transitions. It highlights key activity transitions impacting process performance, offering actionable insights for optimization. Validation on the BPIC 2020 dataset demonstrates its effectiveness over baseline methods. © the Author(s) 2025.

关键词： Counterfactuals Impact Ranking Local Explanation Shapley Value

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Using Hash and Deep learning for Content-Based Image Retrieval 6th

Using Hash and Deep Learning for Content-Based Image Retriev...

引用

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

作者： Hung, Bui thanh thuy, Tran Xuan Tam, Le Duy Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Given the exponential growth of global data, research attention has increasingly focused on content-based image retrieval. While deep learning has optimized computer vision tasks, existing image search tools continue to face challenges in processing speed. If these issues are resolved, image search has the potential to fully replace traditional search methods. the drawback of prior methods lies in lengthy feature vectors, resulting in time-consuming searches and inconvenience. To mitigate these issues, we employ feature extraction through convolutional neural networks and subsequently apply binary hashing. Experimental results on the fashion-product-images dataset show that using a convolutional neural network combined with binary hashing in image search achieves a mean average precision (MAP 20) of 0.99 and significantly improves the image retrieval time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Text Classification Experiments on Contextual Graphs Built by N-Gram Series 4th

Text Classification Experiments on Contextual Graphs Built b...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Sen, Tarik Uveys Yakit, Mehmet Can Gumus, Mehmet Semih Abar, Orhan Bakal, Gokhan Abdullah Gul Univ TR-38080 Kayseri Turkiye Osmaniye Korkut Ata Univ TR-80000 Osmaniye Turkiye

ISBN: (纸本)9783031821493;9783031821509

Traditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. the main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. this research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.

关键词： text-graph transformation graph convolution network deep learning text mining

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Augmentation of Human Activity data: Convert, Generate, Transform 27th

Augmentation of Human Activity Data: Convert, Generate, Tra...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Nair, Nilah Ravi Matei, Arthur Krön, Dennis Moya Rueda, Fernando Reining, Christopher Fink, Gernot A. Chair of Material Handling and Warehousing TU Dortmund University Dortmund Germany Department of Computer Science TU Dortmund University Dortmund Germany MotionMiners GmBH Dortmund Germany

ISBN: (纸本)9783031781919

data-driven neural network models trained on human motion data facilitate human activity recognition and identity verification applications. However, large annotated and processed human motion datasets are scarce, leading to the overfitting of models to training data. thus, it is important to investigate data augmentation techniques to generate additional data to facilitate model generalisation. In addition, the choice of augmentation techniques severely impacts the performance of self-supervised learning. thus, this work experiments and evaluates various augmentation techniques on seven sensor-based human activity datasets. three supervised neural network models and one self-supervised learning model were experimented with. We note that due to the high variability in the performance of algorithmic augmentation techniques on time-series human activity datasets, generative data is highly influential in this domain. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Distributed Self-fault Outlier Detection in Wireless Sensor Network Based on machine learning 6th

Distributed Self-fault Outlier Detection in Wireless Sensor ...

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

作者： Padhi, Ruchika Mudulli, Debendra Gouda, Bhabani Sankar C. V. Raman Global University Odisha Bhubaneswar India School of Computer Science NIST Institute of Science and Technology Odisha Brahmapur India

ISBN: (纸本)9789819780891

Due to a wide range of essential applications, resource limitations, and intermittent irregular responses from cluster sensor nodes, different cluster sensor node faults and failures of distributed wireless sensor networks have occurred in recent years. Due to limited calculation capabilities, limited storage space, resource constraints, and a poor wireless communication environment, relatively few studies have been done on distributed fault diagnosis and fault tolerance in wireless sensor networks, making these networks vulnerable to impending security issues and abnormal detection. this chapter presents a thorough investigation into data quality, with a focus on the features of sensor node information with respect to time and space similarity, as well as recent advances in the study of outlier analysis. In this study, we employ classification to identify outliers by predicting the values of faulty sensor nodes using information gathered from their fault-free neighbors. the proposed method, DSFOD, uses the KNN algorithm to diagnose the proximity of a distance and achieves promising results of greater than 0.99. To accomplish the goal of data outlier detection in WSN, the data are analyzed and detected using the DSFOD method. the simulation’s findings demonstrate the network’s efficiency and superior performance at that specific time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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