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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3325 条记录，以下是21-30 订阅

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Automated Breast Cancer Detection: A Review 4th

Automated Breast Cancer Detection: A Review

引用

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

作者： AlKhater, Rozah Al-maadeed, Somaya Qatar Univ Doha Qatar

ISBN: (纸本)9783031821554;9783031821561

Over the past decade, medical imaging research has grown significantly in image processing and computer vision, particularly in detecting, classifying, and segmenting breast cancer. Advancements in pattern recognition and Deep learning (DL) methodologies have significantly impacted this field. In light of the swift advancements in deep learning technology and the escalating seriousness of breast cancer, it is imperative to synthesize previous achievements and pinpoint forthcoming obstacles that require attention. thus, this paper extensively reviews the classification of histopathological images for breast cancer detection using machine learning and deep learning techniques. the article emphasizes the publicly accessible datasets of histopathological images for classifying breast cancer. this article examines and outlines recent research on histopathology imaging for breast cancer screening and explores potential future developments. Upon reviewing the literature, it was discovered that only a limited number of studies utilize image processing and machine learning approaches. Most research in the past five years has utilized deep learning techniques.

关键词： Deep learning AI machine learning MRI Neural networks

来源：评论

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Metagenomic Face: Applying machine learning to Facial Microbiome data for pattern recognition and Disease Classification 4

Metagenomic Face: Applying Machine Learning to Facial Microb...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Guru Prasad, M.S. Devadas, Raghavendra M Hiremani, Vani Jahan, Afsar Praveen Gujjar, J. Dahiya, Aryan Department of Computer Science and Engg Dehradun India Manipal Institute of Technology Department of Information Technology Bengaluru India Manipal Academy of Higher Education Manipal India University Pune India Department of Computer Applications Dehradun India Faculty of Management Studies Bengaluru India

ISBN: (纸本)9798350349900

this innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. this framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. the investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. these findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. the Support Vector machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. these additional insights contribute to a comprehensive understanding of the Metagenomic Face framework's efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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A Symptom-based Disease Diagnosis: A Pre-Classification ML Approach 4

A Symptom-based Disease Diagnosis: A Pre-Classification ML A...

引用

4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Rohith, Suram Kalaiselvi, K. Reddy, K. Jawanth Reddy, V. J. Venkata Rami SRM Inst Sci & Technol Dept Networking & Commun Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Networking & Commun Fac Engn & Technol Chennai Tamil Nadu India

ISBN: (纸本)9798350386356;9798350386349

In emergency hospitals, the hospital conveyance framework is seen as an important component of the clinical, financial, and executive well-being areas. this study incorporates Artificial Intelligence (AI) techniques to assess and investigate cases of various ailmentsthat occur in low-income families. the proposed system utilizes unstructured and newly presented hospital data by applying the machine learning Certificate Tree computation. mining anticipates hospitalization this study proposes a data-driven, scalable alternative to conventional diagnostic techniques by addressing the issues like unbalanced data and guaranteeing model interpretability.

关键词： hospital recommendation machine learning decision tree algorithm KNN

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AI-Based Identification of Wear and Corrosion on Steel Surfaces 24

AI-Based Identification of Wear and Corrosion on Steel Surfa...

引用

4th international conference on Internet of things and machine learning

作者： Zhu, Yuze Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798400710353

this study utilizes deep learning technology to automatically identify wear and corrosion on steel surfaces. A total of 1,770 high-resolution images of steel surfaces were obtained, and after preprocessing and data augmentation, 1239 images were selected as the training set and 531 images as the test set. Five models, including ResNet, DenseNet121, InceptionV3, MobileNet, and VGG, were compared, with results indicating that ResNet performed best in terms of accuracy and stability. the study also explored the impact of optimizers, learning rates, and attention mechanisms on model performance as well as found that the introduction of attention mechanisms and transfer learning significantly improved recognition capabilities. the findings provide a reliable technical foundation for steel damage detection, with broad industrial application potential. Future work will focus on further optimizing the model and reducing reliance on labeled data to enhance detection efficiency and accuracy.

关键词： Deep learning Steel Surface Inspection Attention Mechanisms Transfer learning

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Survey on Advanced Techniques for Cardiac Arrest Prediction Using machine learning 4

Survey on Advanced Techniques for Cardiac Arrest Prediction ...

引用

4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Periasamy, J.K. Raagavi, K. Tarun Ashwanth, P. Sri Sai Ram Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Cardiac arrest has been a leading cause of mortality worldwide, with limited opportunities for intervention. this project introduces a novel machine-learning approach to predict and process cardiac arrest risk in high-risk patients. Our system integrates 1. Advanced signal processing techniques for ECG analysis 2. Deep learning algorithms for pattern recognition 3. Real-time monitoring and alert system. By analyzing ECG data, medical history, and vital signs, our system provides personalized risk scores, enabling early intervention and improving patient outcomes. Our approach has the potential to revolutionize cardiac arrest prediction, saving lives and reducing healthcare costs. © 2024 IEEE.

关键词： Electrocardiography

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Student Performance Prediction in College Using Artificial Bee Colony Algorithm and machine learning 4

Student Performance Prediction in College Using Artificial B...

引用

4th IEEE international conference on Electronic Communications, Internet of things and Big data, ICEIB 2024

作者： He, Tao College of Intelligent Manufacturing Chongqing Three Gorges Vocational College Chongqing China

ISBN: (纸本)9798350360721

Student performance prediction in college is improved by adopting an artificial bee colony (ABC) algorithm paired with machine learning techniques. the fundamental problem was the prediction accuracy of individualized learning, early intervention, and academic planning. To solve the challenge, an innovative method was developed by incorporating the ABC algorithm to improve predictive accuracy and interpretability. data was preprocessed for cleansing the input, using the feature selection approach, ABC. A machine learning model was trained for prediction. the experimental results showed the effectiveness of the suggested method in determining the students' performances. the model helps teachers make the right decisions to promote the students' success. © 2024 IEEE.

关键词： data mining

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4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

4th Annual International Conference on Data Science, Machine...

引用

4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

ISBN: (纸本)9781032426853

the proceedings contain 50 papers. the special focus in this conference is on data Science, machine learning and Blockchain Technology. the topics include: An unsupervised approach to creating a restaurant recommendation system;Classification of alzheimer’s disease using D-DEMNET framework;comparison of machine learning and deep learning methods for detection of liver abnormality;soil micronutrient detection using machine learning;a review of tracking concept drift detection in machine learning;wearable electrogastrogram perspective for healthcare applications;computer vision based home automation;Early autism detection using ML on behavioural pattern;implementation of application prototypes for human-to-computer interactions;recommendation system for anime using machine learning algorithms;predicting bitcoin price fluctuation by Twitter sentiment analysis;microarchitecture design and verification of co-processor for floating point operation;blockchain based higher education ecosystem;document verification using blockchain;blockchain-based traceability system for readymade food products;a cloud based interactive framework for emergency medical data sharing;the analysis and interpretation of higher education teachers based on student and teachers feedback;Implementing AI on microcontrollers in fog and edge architectures;abnormality detection in chest radiograph using deep learning models;a comprehensive review on hate speech recognition utilizing natural language processing and machine learning;prevalence of migraine among collegiate students in greater Noida;Indoor navigation using BLE beacons;strategic health planner and exercise suggester;perspective of deep learning strategies for analysis of 1D biomedical signals;revolution in agriculture sector using blockchain technology;customer churn prediction using ensemble learning with neural networks;Securing crime case summary and E-FIR using blockchain concept.

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Classification of Diabetic data using Novel Optimized Deep Ensemble learning 4

Classification of Diabetic Data using Novel Optimized Deep E...

引用

4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Mohapatra, Saumendra Kumar Khilar, Rashmita Mohanty, Mihir Narayan Sri Sri University Faculty of Emerging Technology India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Department of Ece India

ISBN: (纸本)9798350349856

machine learning application in medical data analysis is one of the most popular approaches in the diagnosis of various diseases. Authors in this work have classified the diabetic data collected from the UCI machine learning repository by using a modified ensemble machine learning approach. In the proposed method, a stacked ensemble model with a different meta-classifier is considered for classification. In the first stage, the performance is compared with the Support vector machine and gradient boosting meta-model. Again the learning rate for the meta model has been changed to get better accuracy. From the result, it is observed that the modified model is providing better results compared to others. © 2024 IEEE.

关键词： data mining

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Building a data-mining-Based Teaching Performance Assessment System for College Teachers 24

Building a Data-Mining-Based Teaching Performance Assessment...

引用

4th international conference on Internet of things and machine learning

作者： Sun, Hanqiao Shandong Management Univ Dept Econ & Trade Jinan Peoples R China

ISBN: (纸本)9798400710353

Given the prevalence of big data, data mining has emerged as a key tool for raising educational management standards. this research examines the application of data mining's CART decision tree algorithm to college instructors' performance management. the elements influencing teaching quality, such as teaching methods, teaching preparation, and teaching attitude, are taken into consideration as decision attributes to build a decision tree based on statistical data. Examples show how the CART decision tree method is specifically constructed for evaluating university teachers' teaching performance, and it also generates a classification model for teaching performance based on the CART tree. It is found that teachers with sufficient teaching preparation often show good teaching attitude and good teaching performance. For the staff with good teaching quality but low teaching readiness score, if they have good teaching methods or good teaching attitude, they will have higher teaching performance;If the teaching method is not skilled or the attitude is not good, the performance is ordinary. It is evident that various aspects of teaching, including preparation, methods, attitude, performance, and quality, influence and are restricted by one another. Lastly, some focused and sensible recommendations for performance evaluation are made.

关键词： data mining CART decision tree Performance management

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Predictive Flood Management Using Environmental and Infrastructural data: A machine learning Approach 4

Predictive Flood Management Using Environmental and Infrastr...

引用

4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Ezhilarasan, D. Bhavani, N.P.G. Saveetha School of Engineering Chennai India

ISBN: (纸本)9798350352931

Flood management has more recently become highly essential in countering the destructive impact of climatic changes, especially in vulnerable regions. this research focuses on developing a predictive framework for flood management through environmental and infrastructural data by applying machine learning methods. the proposed system will be based on multiple data sources, including meteorological data, river water levels, terrain features, land use patterns, and urban infrastructure, to enhance the accuracy and timeliness of flood warning predictions. With this paper, the author expects to develop a model that precisely predicts flood events, so that actionable insights are offered to support proactive disaster management strategies. High-dimensional data processing and pattern recognition are conducted using advanced machine learning algorithms, such as neural networks and ensemble models, that optimize the accuracy of the predictions. Real-time data, along with historical flood records, is used for training and validation. Reliability of the model is tested for various flood-prone areas through the metrics of evaluation applied: prediction accuracy, response time, and false alarm rates ensuring robustness and feasibility in operations. the expected outcomes will depict precisely how data-driven approaches might be incorporated into flood management systems that can contribute effectively to the development of resilient infrastructure and community preparation. this research underscores a scalable approach well-suited to different kinds of geographic contexts, especially the potential of machine learning toward environmental resilience. © 2024 IEEE.

关键词： Adversarial machine learning

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