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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21-30 订阅

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mining Haulage Road Maintenance Prediction Dashboard Utilising machine learning Techniques 6

Mining Haulage Road Maintenance Prediction Dashboard Utilisi...

引用

6th international conference on Advancements in Computing, ICAC 2024

作者： Purdon, Brad Piyathilaka, Lasitha Sul, Jung-Hoon Preethichandra, Daluwathu Central Queensland University School of Engineering and Technology Rockhampton Australia

ISBN: (纸本)9798331517878

this paper details the development of a predictive maintenance dashboard designed for haulage roads in surface mining operations using machine learning algorithms. We assessed various machine learning models, including Logistic Regression, Random Forest, and K-Nearest Neighbors (KNN), to determine their effectiveness in predicting the timing and location of necessary road maintenance. the research identifies the Logistic Regression model as the most effective, which was trained on data including vibration measurements, equipment counts, and speed metrics to evaluate road surface conditions. these inputs are processed through a logistic function to estimate the probability of required repairs. the predictions generated by the model are stored in an SQL database and visualized through Power BI, which offers a user-friendly interface. this interface enables operators, including those with limited experience, to interpret the data effectively and make informed maintenance decisions, thus improving operational efficiency and safety. © 2024 IEEE.

关键词： Predictive maintenance

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data Representation with Robust Hypergraph Coding 6

Data Representation with Robust Hypergraph Coding

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6th international conference on data-Driven Optimization of Complex Systems (DOCS)

作者： Yan, Xueming Lei, Mingqi Jin, Yaochu Zhu, Xiuyun Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou 510006 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Peoples R China Southern Univ Sci & Technol Affiliated Hosp 2 Sch Med Shenzhen 518000 Peoples R China

ISBN: (纸本)9798350377859;9798350377842

this paper introduces a novel matrix factorization framework, data Representation with Robust Hypergraph Coding (MFRHL), designed to enhance unsupervised learning techniques in data mining and machine learning. Traditional matrix factorization methods often fail to capture complex semantic information in datasets. We leverage hypergraph-based Laplacian Eigenmaps to construct a discriminative basis that captures advanced semantic relationships, followed by sparse coding to optimize this basis. the MFRHL framework significantly improves performance in data clustering tasks by effectively handling high-dimensional data and preserving intricate data relationships. Comprehensive experimental results demonstrate that this method consistently outperforms other cutting-edge matrix factorization methods in data clustering.

关键词： Matrix factorization Hypergraph learning Dimensionality reduction data clustering

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Utilizing Deep learning and machine learning Methods to Forecast Market Performance 6

Utilizing Deep Learning and Machine Learning Methods to Fore...

引用

6th international conference on Intelligent Computing in data Sciences, ICDS 2024

作者： El Mahjouby, Mohamed El Fahssi, Khalid Bennani, Mohamed Taj Lamrini, Mohamed El Far, Mohamed Faculty of Science Dhar El Mahraz Department of Computer Science Fez Morocco

ISBN: (纸本)9798350351200

Forecasting the stock market has become one of the most challenging problems for the AI research community. Stock market investment strategies are complex and depend on analyzing amounts of data. Recently, machine learning techniques have increasingly evaluated and enhanced market predictions compared to traditional methods. this interest is driven by the time-dependent nature of stock prices, making their predictions essential in data mining and a subject of significant interest and effort over the past decades. this challenge remains difficult due to the intrinsic characteristics of time series data, such as its high dimensionality, large volume, and constant updates. Researchers have explored machine learning and deep learning methods to improve the effectiveness of traditional approaches. In this document, we aim to forecast stock market performance at the end of the trading day by applying various machine learning algorithms to two datasets, 'CoinMarketCap' and 'CryptoCurrency,' and analyzing the prediction outcomes of these models. © 2024 IEEE.

关键词： Adversarial machine learning

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Transformation Network Model for Ear recognition 1

引用

6th international conference on machine learning for Networking (MLN)

作者： Booysens, Aimee Viriri, Serestina Univ KwaZulu Natal Sch Math Stat & Comp Sci Durban South Africa

ISBN: (数字)9783031599330

ISBN: (纸本)9783031599323;9783031599330

Biometrics is the recognition of a human using biometric characteristics for identification, which may be physiological or behavioural. the physiological biometric features are the face, ear, iris, fingerprint and handprint;behavioural biometrics are signatures, voice, gait pattern and keystrokes. Numerous systems have been developed to distinguish biometric traits used in multiple applications, such as forensic investigations and security systems. With the current worldwide pandemic, facial identification has failed due to users wearing masks;however, the human ear has proven more suitable as it is visible. this paper presents the main contribution to presenting the results of a CNN developed using Transfer learning to pre-train the CNN before applying a Transformer Network. the performance achieved in this research shows the efficiency of the Transformer Network on ear recognition. the experiments showed that Transformer Network achieved the best accuracy of 92.60% and 92.56% with epochs of 50 and 90.

关键词： Ear Biometrics Ear recognition Transformer Network machine learning

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Causality-Aware pattern mining Scheme for Group Activity recognition 48

Causality-Aware Pattern Mining Scheme for Group Activity Rec...

引用

48th Annual IEEE international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Kim, Hyunju Son, Heesuk Lee, Dongman Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9798350376975;9798350376968

Human activity recognition is a key challenge in pervasive computing and its solutions have been presented based on various disciplines. Specifically, for human activity recognition in smart spaces without privacy and accessibility issues, data streams generated by deployed ambient sensors are leveraged. In this paper, we focus on group activities by which a group of users perform a collaborative task without user identification and propose an efficient group activity recognition scheme that extracts causality patterns from ambient sensor event sequences, to support as good recognition accuracy as the state-of-the-art models with missing or false data tolerance. To filter out irrelevant noise events from a given data stream, a set of rules is leveraged to highlight causally related events. then, a pattern-tree algorithm extracts frequent causal patterns by means of a growing tree structure. Based on the extracted patterns, a weighted sum-based pattern-matching algorithm computes the likelihood of stored group activities to the given test event sequence using matched event pattern counts for group activity recognition. We evaluate the proposed scheme using the data collected from real-world testbed and open datasets where users perform their tasks on a daily basis. Experiment results show that the proposed scheme performs higher recognition accuracy and is tolerant to missing or false data with a smaller amount of runtime overhead than the existing schemes.

关键词： Human Activity recognition Causality-Aware learning Shallow Neural Network Ubiquitous Computing Sequential pattern mining Internet of things

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machine-learning-based predictive classifier for sand body types and reservoir properties using geological data 6

Machine-learning-based predictive classifier for sand body t...

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6th international conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Shao, Jinxiang Meng, Yujuan Zhao, Lingyan Liu, Jinzi Daqing Oilfield Limited Company The Seventh Oil Production Plant Daqing China Northeast Petroleum University School of Mathematics and Statistics Daqing China

ISBN: (纸本)9798331516550

the classification and identification of reservoir sand body types has important research significance in oil-gas oilfield development plans. the traditional recognition method mainly relies on logging curves and manual recognition, which has the disadvantages of relying on expert experience and longtime consumption. this article is driven by geological data and compares various machine learning algorithms to select the intelligent algorithm suitable for solving this problem, achieving automatic recognition of reservoir sand body types and providing reference for reservoir engineering. the results show that the accuracy of the neural network algorithm reaches 97.4%, which can be used as an effective intelligent algorithm for reservoir sand body type classification and recognition. © 2024 IEEE.

关键词： Gas oils

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Gesture Detection Using an Infrared Camera 24

Gesture Detection Using an Infrared Camera

引用

6th international conference on Image Processing and machine Vision (IPMV)

作者： Jakir, J. A. Ansari Nigel, N. B. Barker Jason, J. Q. QUan Majid, M. A. Ahmadi Univ Windsor Dept Elect & Comp Engn Windsor ON Canada

ISBN: (纸本)9798400708473

Gestures and body language exert a significant impact on communication and interactions among individuals. this project's objective entailed the development of a gesture recognition system that analyzes the way people interact with their surroundings. the careful selection of the five recognized gestures ensures that the system remains focused on key limb placements that may dictate how one is perceived by the surrounding public. As the infrared (IR) camera captures real-time data, the system's ability to operate in low-light conditions further adds to its practicality and versatility in various environments. the convolutional neural network (CNN) plays a central role in the system's accuracy and efficiency. Its intensive training on a diverse dataset of images allows it to discern the distinct visual patterns associated with each gesture. As a result, the algorithm's Mean Average Precision (mAP) of 71.84% attests to its proficiency in accurately recognizing and classifying gestures.

关键词： Gesture recognition pattern recognition Convolutional Neural Network Feature Extraction and Classification

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Explainable machine-learning for identifying the genetic biomarker MGMT in brain tumors using magnetic resonance imaging radiomics 14

Explainable Machine-Learning for identifying the genetic bio...

引用

14th international conference on pattern recognition Systems (ICPRS)

作者： Ponce, Sebastian Chabert, Steren Mayeta, Leondry Franco, Pamela Plaza-Vega, Francisco Querales, Marvin Salas, Rodrigo Univ Valparaiso Hlth Sci & Engn Valparaiso Chile Millennium Inst Intelligent Healthcare Engn iHEAL Ctr Interdisciplinary Biomed & Engn Res Hlth Valparaiso Chile Univ Valparaiso Sch Biomed Engn Valparaiso Chile Ctr Interdisciplinary Biomed & Engn Res Hlth Valparaiso Chile Univ Santiago Chile Dept Matemat & Ciencia Comp Santiago Chile Univ Valparaiso Sch Med Technol Valparaiso Chile

ISBN: (纸本)9798350375657

Brain tumors often feature the genetic biomarker O6-Methylguanine-DNA-Methyltransferase (MGMT) associated with a favorable response to chemotherapy and an improved prognosis. Currently, detecting MGMT presence relies on invasive brain biopsy procedures. thus, this study aims to develop a data mining-based radiomics methodology for non-invasive identifying and evaluating brain tumor genetic biomarkers using radiomics in magnetic resonance images (MRIs). MRIs with segmentation masks were used to extract variables and employ feature selection techniques. Several machine learning models were trained, where Logistic Regression, employing LASSO selection, emerged as the best-performing model, achieving 61% accuracy. Additionally, an explainability module utilizing Shap values identified three significant variables: a T1CE sequence variable related to texture, a FLAIR sequence variable of first-order statistics, and a T1 sequence variable of first-order statistics. this radiomic methodology, with its performance and explainable nature, could offer diagnostic support to clinicians in tumor management.

关键词： O6-Methylguanine-DNA-Methyltransferase (MGMT) methylation genetic biomarkers machine learning radiomics explainability magnetic resonance imaging brain tumors

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PAIS 2024 - Proceedings: 6th international conference on pattern Analysis and Intelligent Systems

PAIS 2024 - Proceedings: 6th International Conference on Pat...

引用

6th international conference on pattern Analysis and Intelligent Systems, PAIS 2024

ISBN: (纸本)9798350350265

the proceedings contain 85 papers. the topics discussed include: ontology population using CNN model: application to COVID-19 diagnosis;improving QoE in IoV: a review of solutions and challenges for MPEG-DASH;deep learning-based cloud services recommendation;applications of machine learning in corrosion detection;exploring multimedia streaming protocols in VANET: a systematic review;benchmarking deep learning optimizers for the classification of event-related potentials based brain-computer interface EEG data;SE-Half-UNet: accurate and low-cost retinal vessel segmentation from fundus images;an extensive examination of varied approaches in e-learning and MOOC research: a thorough overview;and transfer learning-based approach for 3D reconstruction from a single 2D image.

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Research on Customer Electricity Consumption Behavior Analysis Based on Big data 6

Research on Customer Electricity Consumption Behavior Analys...

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6th international conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Li, Jialin Yuan, Xiaodan Zhang, Ying State Grid Jinzhou Electric Power Supply Company State Grid Liaoning Electric Power Supply Co. Ltd. Jinzhou China

ISBN: (纸本)9798331541750

A new big data algorithm has been proposed to address the problem of slow and inefficient electricity consumption behavior of existing technology analysis customers. this scheme adopts a hybrid big data algorithm to achieve multiple analyses of marketing big data customers' electricity consumption behavior. the improved K-means clustering algorithm is integrated into the Apriori algorithm model, and the Extreme learning machine (ELM) principle is used to quickly classify marketing big data customers' electricity consumption behavior, improving classification ability. through data association mining, the potential relationship between user electricity consumption behavior and influencing factors is accurately discovered, which improves the ability of electricity analysis. the experiment showed that the accuracy of the research plan was as high as 93.4%. © 2024 IEEE.

关键词： Adversarial machine learning

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