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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 34017 条记录，以下是1341-1350 订阅

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Evaluation of Normalization Algorithms for Breast Mammogram Mass Segmentation 2

Evaluation of Normalization Algorithms for Breast Mammogram ...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Urvioza Gohel, Bakul Kumar, Pankaj Gujarat Gandhinagar382007 India University of Petroleum and Energy Studies Dehradun Bidholi248007 India

Medical images acquired at different institutions have different data distributions due to varying scanners, imaging protocols, or patient cohorts. Thus, normalizing samples before using them to train the Deep learning (DL) models is an essential task. Scholars have used various normalization methods to normalize mammograms. However, which normalization method works best on mammograms and if it can help training generalize the DL model has not been analyzed in the literature. This paper presents a comparative analysis of commonly used normalization methods, such as scaling, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Z-score normalization on mammograms for breast mass segmentation tasks. We trained the Attention Unet model on INbreast and the subset of the Cohort of Screen-Aged Women dataset (CSAW-S). We performed cross-dataset analysis on trained models to investigate how normalization methods helped train robust models to handle diverse datasets. Our results suggest that the normalizing method of input data affects the segmentation model's performance. We achieved the best scores with the Z-score normalization closely followed by CLAHE. However, further investigation suggests that the normalization method's performance may depend on the variability of mammograms in the dataset. Results of cross-dataset analysis suggest the importance of choosing the normalization method for consistent DL model performance. To our knowledge, this study represents the first systematic analysis of the performance of prevalent normalization techniques on a diverse dataset of mammogram images. The insights gained from our experiments can guide the selection of normalization methods for improved mass segmentation accuracy. © 2024 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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An Efficient Decision Support System for Stock Market Prediction using Technical Indicators as Features by machine learning Approaches 4

An Efficient Decision Support System for Stock Market Predic...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Naga Pranava Shashank, V. Ramanan, Vimal S Altrin Lloyd Hudson, D. Sheik Abdullah, A. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350335750

In this Research, we have built machine learning Models to predict the next day price for the Nifty Index, Reliance and TCS Stocks. We will use R Project Library, quantmod, TTR for data acquisition and technical analysis. We have used the R Project machine learning Library caret along with the daily data for the last five years (June 2017 - June 2022) for the NIFTY Index and the Reliance and TCS Stocks. Our data will consist of Open, High, Low, Last (Close) and Volume. Our objective is to predict the price change for the next day using 6 technical indicators as features which include RSI, Williams%R, etc. For building machine learning Models, we have used the R Library caret. The primary methods which are used are KNN, Classification Regression Trees, Naive Bayes, SVM with Radial Basis Function and DNN. For k-nearest neighbors, we are able to achieve an accuracy of 72.78%, for DNN 72.533%, for CART 72.36%, for NB 70.08%, and for SVM 72.69% for the prediction accuracy of the next day. © 2023 IEEE.

关键词： data acquisition

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Analysis on Role of Quantum Computing in machine learning 4

Analysis on Role of Quantum Computing in Machine Learning

引用

4th international conference on Cognitive Computing and Information Processing, CCIP 2022

作者： Saju, Binju Gopal, Madhwaraj Kango Nithya, B. Asha, V. Kumar, Vikash New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9781665456487

Quantum machine learning is a process by which quantum computers are used to learn from data. It is still in its begining stages of development, but has the potential to be much more efficient than classical machine learning algorithms. The main advantages of machine learning is that it can exploit the massive parallelism of quantum computers. This means that quantum machine learning algorithms can potentially learn from data much faster than classical algorithms. Another advantage is that quantum machine learning algorithms can deal with data that is too large or too complex for classical algorithms. For example, a quantum algorithm could be used to learn from a dataset that is too large to fit into a classical computer's memory. There are still many challenges to overcome before quantum machine learning can be used in practice, but the potential benefits are huge. If successful, Quantum machine learning could revolutionize the field of machine learning and have a profound impact on many other areas of science and technology. © 2022 IEEE.

关键词： Large dataset

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learning WITH NON-UNIFORM LABEL NOISE: A CLUSTER-DEPENDENT WEAKLY SUPERVISED APPROACH 49

LEARNING WITH NON-UNIFORM LABEL NOISE: A CLUSTER-DEPENDENT W...

引用

49th IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhang, Mengtian Jiang, Bo Ling, Yuye Wang, Xinbing Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

learning with noisy labels is a challenging task in machine learning. Furthermore in reality, label noise can be highly non-uniform in feature space, e.g. with higher error rate for more difficult samples. Some recent works consider instance-dependent label noise but they require additional information such as some cleanly labeled data and confidence scores, which are usually unavailable or costly to obtain. In this paper, we consider learning with non-uniform label noise that requires no such additional information. Inspired by stratified sampling, we propose a cluster-dependent sample selection algorithm followed by a contrastive training mechanism based on the cluster-dependent label noise. Despite its simplicity, the proposed method can distinguish clean data from the corrupt ones more precisely and achieve state-of-the-art performance on most image classification benchmarks, especially when the number of training samples is small and the noise rate is high. The code is released at https://***/MattZ-99/ClusterCL.

关键词： Non-uniform label noise cluster dependent sample selection contrastive training mechanism

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Simulation and implementation of deep learning based expression recognition

Simulation and implementation of deep learning based express...

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2024 international conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Wang, Shiyi Chen, Jiayu School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Facial expressions are a challenging area in image recognition, and ensuring the universality of the model in the presence of a large sample size is a difficult point in such research. This article conducts repeatability experiments on two extreme scenarios: high-quality small samples and low-quality large samples, to explore solutions. Through transfer learning, data augmentation, and substantive evaluation of confusion matrices, the article demonstrates the accurate classification of seven facial expressions and the design of a 4-class model with high universality, which is presented in the form of an APP. © 2025 SPIE.

关键词： Deep learning

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Proceedings - 2022 international conference on Intelligent Computing and machine learning, 2ICML 2022

Proceedings - 2022 International Conference on Intelligent C...

引用

2022 international conference on Intelligent Computing and machine learning, 2ICML 2022

ISBN: (纸本)9798350346619

The proceedings contain 9 papers. The topics discussed include: lpdata: a data placement for high-throughput and low-latency;deep transfer learning optimization techniques for medical image classification: a review;research on fast retrieval algorithm for sports training target images;NLP K-means algorithm incorporated into a proactive chatbot to assist failing students;adaptive segmentation of basketball game video based on Markov random fields;exploration and research of waste management system for high-rise buildings based on computer technology;research on the transformer fault diagnosis method based on LSTM artificial neural network and DGA;specialized risk evaluation of agricultural products on live-streaming e-commerce platforms using interval-valued intuitionistic fuzzy group decision-making;and recognition algorithm of basketball dynamic movement behavior based on multimedia network technology.

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Quantum Clustering for Cybersecurity 5

Quantum Clustering for Cybersecurity

引用

2024 international conference on Quantum Computing and engineering

作者： El Maouaki, Walid Innan, Nouhaila Marchisio, Alberto Said, Taoufik Bennai, Mohamed Shafique, Muhammad Hassan II Univ Casablanca Fac Sci Ben Msick Quantum Phys & Magnetism Team LPMC Casablanca Morocco New York Univ Abu Dhabi NYUAD Div Engn eBRAIN Lab Abu Dhabi U Arab Emirates NYUAD NYUAD Res Inst Ctr Quantum & Topol Syst CQTS Abu Dhabi U Arab Emirates

ISBN: (纸本)9798331541378

In this study, we develop a novel quantum machine learning (QML) framework to analyze cybersecurity vulnerabilities using data from the 2022 CISA Known Exploited Vulnerabilities catalog, which includes detailed information on vulnerability types, severity levels, common vulnerability scoring system (CVSS) scores, and product specifics. Our framework preprocesses this data into a quantum-compatible format, enabling clustering analysis through our advanced quantrun techniques, QCSWAPK-means and QkerneIK-means. These quantum algorithms demonstrate superior performance compared to state-of-the-art classical clustering techniques like k-means and spectral clustering, achieving Silhouette scores of 0.491, Davies-Bouldin indices below 0.745, and Calinski-Harahasz scores exceeding 884, indicating more distinct and well-separated clusters. Our framework categorizes vulnerabilities into distinct groups, reflecting varying levels of risk severity: Cluster 0, primarily consisting of critical Microsoft-related vulnerabilities;Cluster 1, featuring medium severity vulnerabilities from various enterprise software vendors and network solutions;Cluster 2, with high severity vulnerabilities from Adobe, Cisco, and Google;and Cluster 3, encompassing vulnerabilities from Microsoft and Oracle with high to medium severity. These findings highlight the potential of QML to enhance the precision of vulnerability assessments and prioritization, advancing cybersecurity practices by enabling more strategic and proactive defense mechanisms.

关键词： Quantum machine learning Quantum Clustering Cybersecurity

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A machine learning-based Automatic Model to Predicting Performance of Students

A Machine Learning-based Automatic Model to Predicting Perfo...

引用

2022 IEEE international conference on Current Development in engineering and Technology, CCET 2022

作者： Garg, Atul Garg, Nidhi Bansal Ghosh, Pinaki Bansal, Ankit Lilhore, Umesh Kumar Simaiya, Sarita Chitkara University Institute of Engineering and Technology Chitkara University Karnataka India School of Advanced Computing Sage University Bhopal India Chandigarh University Department of Computer Science and Engineering Karnataka Mohali India

ISBN: (纸本)9781665454155

machine learning offers a variety of techniques for use in diverse fields. Massive quantities of student data are produced in educational institutions, and machine-learning techniques are a precious tool for identifying patterns in individuals' behavior. Academicians can learn valuable data concerning the students' expertise and how it relates to their educational assignments by critically examining and evaluating these data. This data serves as the input for cutting-edge techniques and algorithms that can forecast students' academic achievement. This research presents a machine learning-based automatic model for predicting the performance of students. The popular ML methods are Random Forest, K-Neighbors, Extra Trees, Ada-Boost, Decision Tree, Logistic Regression, Gaussian NB, and Bernoulli NB. This research utilizes an online student performance dataset collected from Kaggle. The findings demonstrate that Random forest and decision trees outperform other techniques based on high precision as well as the capacity to interpret the guidelines and structure that were developed for predicting students' educational success. © 2022 IEEE.

关键词： Students

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Spatio-temporal characteristics of highway toll stations based on K-means++

Spatio-temporal characteristics of highway toll stations bas...

引用

2022 international conference on Internet of Things and machine learning, IoTML 2022

作者： Zhan, Jiahao Yang, Shengwen Wang, Xueyin Li, Jun Chen, Fuze Southwest Forestry University College of Mechanics and Transportation Kunming650000 China Key Laboratory of Environmental Protection Safety of Motor Vehicles in Highland Mountainous Areas of Yunnan Province Universities Kunming650000 China Yunnan Provincial Department of Transport Road Network Monitoring and Emergency Command Center Kunming650000 China

ISBN: (纸本)9781510664913

The spatial and temporal characteristics of regional traffic flows and their socio-economic significance are investigated by using machine learning and ArcGIS technology with multiple attributes of highway toll station data. The study found that: (1) the overall characteristics of the traffic flow at 10~11, 14~16 and 17~19 hours show a "three-peak" structure, while the spatial distribution of high, medium and low traffic types has obvious clustering characteristics. (2) The specific features of the K-means++ based highway toll station classification are "point-line surface" structure in space, with Kunming West Toll Station, Dujiaying Toll Station, Kunming North Toll Station and Liangmiansi Toll Station as unique Node, Kunchu line and other toll stations along the axis, the rest of the toll stations constitute the surface;time, each type of toll station traffic flow also shows the "three peaks" structure, but there are "peak" and "sub-peak The "peak" and "sub-peak" are divided. (3) Based on ArcGIS technology, the dynamic visualization spatial expression of traffic flow "three peaks" separated by time series reflects the distinctive "day-night" pattern of human travel activities across regions. © 2023 SPIE.

关键词： machine learning

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Application of machine learning in Enterprise Financial Risk Assessment: A Study About China's A-Share Listed Manufacturing Companies 5th

Application of Machine Learning in Enterprise Financial Risk...

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5th international conference on Neural Computing for Advanced Applications (NCAA)

作者： Yul, Kexin Yul, Zengyi Ma, Shuomin Xu, Pan Zhejiang Univ Technol Hangzhou 310023 Peoples R China SpringAI LLC Bellevue WA 98007 USA

ISBN: (纸本)9789819770038;9789819770045

Based on the financial data of domestic A-share listed firms from 2000 to 2022, this paper aims to explore the effectiveness of machine learning in identifying the financial risk of Chinese A-share listed manufacturing firms. To this end, a variety of machine learning models, including logistic regression, decision tree, random forest, XGBoost, SVM, and LSTM, are used to assess the financial risk of enterprises, and the key attributes of enterprise financial risk are extracted through the interpretability exploration and importance coefficient measurement. From this paper, the following conclusions are drawn: (i) XGBoost model performs the best on all attributes, showing its strong ability in dealing with complex financial datasets, and LSTM, which adds time-series factors, performs poorly, which is speculated that it may be related to the incompatibility of the characteristics of the financial data, the reliance on the time-series features, and the need for financial fine features. (ii) Audit opinion, Net Profit and ROA are key influencing factors.

关键词： Financial risk machine learning XGBoost LSTM

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