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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1351-1360 订阅

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Comparison of machine learning and Deep learning models for Cervical Cancer Prediction 6

Comparison of Machine Learning and Deep Learning models for ...

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6th international conference on Devices, Circuits and Systems, ICDCS 2022

作者： Sundarambal, B. Karthikeyini, Chokiyan Bommi, R.M. Subramanian, Suresh Jacintha, V. Chennai Institute of Technology Dept. of Cse Chennai India Excel Engineering College Dept. of Ece Namakkal India Saveetha School of Engineering SIMATS Dept. of Ece Chennai India Sri Indu Institute of Engineering and Technology Dept. of Ece Hyderabad India Kings Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665480949

Traditional cervical cancer type categorization relies heavily on the pathologist's experience, which is inefficient and inaccurate. Colposcopy is an important part of preventing cervical cancer. Over the last 50 years, colposcopy, in combination with precancer screening and therapy, has played a critical role in lowering the incidence and death of cervical cancer. It justifies the importance of predicting cervical cancer at an early stage. For prediction, the proposed approach makes use of the UCI data repository and machine learning classifiers. The data is preprocessed, and the repository is updated with feature extraction and validations. After that, the pre-processed data is then applied to various machine learning classifiers namely Logistic Regression, Random Forest, Gradient Boosting, Support vector machine (SVM), and all its ensemble classifier. The ensemble classifier has not surpassed the results of individual classifier and thus preferred deep learning model using artificial neural network. The comparison is done in terms of accuracy and fmeasure along with recall and precision. The results revealed that the neural network model is the best with 97.67% of accuracy and 98.78% of F-score. © 2022 IEEE.

关键词： Deep learning Support vector machines Measurement Medical treatment Artificial neural networks Predictive models Feature extraction

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Player Recommendation System for Fantasy Premier League using machine learning 19

Player Recommendation System for Fantasy Premier League usin...

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19th international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Rajesh, Vimal Arjun, P. Jagtap, Kunal Ravikumar Suneera, C. M. Prakash, Jay Natl Inst Technol Calicut Calicut India

ISBN: (数字)9781665485104

ISBN: (纸本)9781665485104

Before the rise of popularity of Fantasy Sports, people were restricted to the passive consumption of sports via television and print media. With the rise of this new age industry, people are more involved with their stakes on their selected players. This aims to enable an average interested person to make informed decisions on which players to choose and invest in based on visualizations, statistical measures, and analytics. In the past, parameters like Return of Investment (ROI) were used as a metric, but that alone is insufficient to make decisions. We attempt to solve the favoritism bias (people tend to choose from their favorite teams) and generate actionable insights using Statistical Analysis and data Science. We use the data extracted from Fantasy Premier League (FPL) API and test against the English Premier League 2021-22 (Soccer).

关键词： Recommendation System Fantasy Sports machine learning Statistics Football

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CONSISTENCY ASSESSMENT OF WAVE DIRECTIONAL SPECTRUM PREDICTIONS FROM machine learning BASED SHIP-AS-A-WAVE-BUOY METHODS 41

CONSISTENCY ASSESSMENT OF WAVE DIRECTIONAL SPECTRUM PREDICTI...

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ASME 41st international conference on Ocean, Offshore and Arctic engineering (OMAE)

作者： Scholcz, T. P. Hageman, R. B. Duz, B. Mak, B. Maritime Res Inst Netherlands MARIN POB 28 NL-6700 AA Wageningen Netherlands

ISBN: (纸本)9780791885901

Real-time knowledge of the ocean wave directional spectrum is valuable for maritime safety and navigation. When end-to-end machine learning is used to learn ocean wave directional spectra from ship motions, the spectrum predictions may be inconsistent with measured ship motions from a physical point of view. In order to assess this consistency, a method is proposed that makes use of a numerical seakeeping code to recalculate ship motions from predicted wave spectra. The recalculated ship motions are subsequently compared to the measured ship motions. The method is applied using data from three different vessels, each having a different level of data completeness. For these cases it is concluded that the consistency can be assessed when the sea state is not too extreme. The method may be improved by using higher fidelity seakeeping codes.

关键词： machine learning deep learning ocean wave spectra ship motions wave buoy analogy inverse problem consistency assessment

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Student Dropout Prediction Using machine learning 9

Student Dropout Prediction Using Machine Learning

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9th international conference on Business and Industrial Research, ICBIR 2024

作者： Kuntintara, Wichukorn Warabuntaweesuk, Piya Rattapasakorn, Sountaree School of Engineering Bangkok University Bangkok Thailand

ISBN: (纸本)9798350383010

Student dropout is a critical challenge for universities worldwide. To address this concern, Bangkok University implemented additional caring, complemented by a machine learning model developed to predict freshman dropout probability. This research investigates the factors contributing to student dropout, including demographic characteristics, academic performance, and financial status. Utilizing data from the 2021st academic year, we perform a comparative analysis of multiple classifier algorithms to identify the most effective model for dropout prediction. The Extreme Gradient Boosting (XGBoost) algorithm emerges as the top performer based on its precision-recall curve (AUC-PR) score, highlighting the significance of features like RegS2 (second semester registration status) and CumCredit_S1 (median first-semester credits earned). The combined oversampling and undersampling technique are used to address this imbalance and improve the performance of the XGBoost algorithm with fine-tuned parameters. To implementation on 2022nd students, tuned classifier achieve precision up to 84.96% and recall up to 86.84%. It can assist advisers to identify at-risk students from many variables, not only GPA. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced machine learning Scenarios for Real World Applications using Weka Platform

Advanced Machine Learning Scenarios for Real World Applicati...

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2022 international conference on Electronics and Renewable Systems, ICEARS 2022

作者： Madugula, Sujatha Kiran, Siripuri Chandra Shekhar Rao, V. Venkatramulu, S. Phridviraj, M.S.B. Pratapagiri, Sreenivas Jyothishmathi Institute of Technolog and Science Department of Cse Telangana Karimnagar India Kakatiya Institute of Technology and Science Department of Cse Telangana Warangal India

ISBN: (纸本)9781665484251

The most significant predictive Analysis, machine and Deep learning in a wide range of fields, including engineering, finance, economics, and real-time imaging. To obtain greater accuracy, scientists are experimenting with a various customs frame works and including tools and technologies. According to Market Research News US, machine learning-based implementations would have a global market value of nearly 50 billion dollars by year *** and social services are currently implementing machine and Deep learning in order to reach barest minimum of mistakes reductions. There are several companies in the industry working on cutting-edge algorithms and implementation views for machine learning. © 2022 IEEE.

关键词： data Analytics

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data-Centric Operational Design Domain Characterization for machine learning-Based Aeronautical Products 1

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42nd international conference on Computer Safety, Reliability and Security (SAFECOMP)

作者： Kaakai, Fateh Adibhatla, Sridhar (Shreeder) Pai, Ganesh Escorihuela, Emmanuelle Thales 3 Ave Charles Lindbergh F-94628 Rungis France Rockdale Syst LLC Cincinnati OH 45246 USA NASA KBR Ames Res Ctr Moffett Field CA 94401 USA Airbus Operat SAS 316 Route Bayonne F-31060 Toulouse France

ISBN: (数字)9783031409233

ISBN: (纸本)9783031409226;9783031409233

We give a first rigorous characterization of Operational Design Domains (ODDs) for machine learning (ML)-based aeronautical products. Unlike in other application sectors (such as self-driving road vehicles) where ODD development is scenario-based, our approach is data-centric: we propose the dimensions along which the parameters that define an ODD can be explicitly captured, together with a categorization of the data that ML-based applications can encounter in operation, whilst identifying their system-level relevance and impact. Specifically, we discuss how those data categories are useful to determine: the requirements necessary to drive the design of ML Models (MLMs);the potsince operating outside the flightential effects on MLMs and higher levels of the system hierarchy;the learning assurance processes that may be needed, and system architectural considerations. We illustrate the underlying concepts with an example of an aircraft flight envelope.

关键词： Aeronautical products Development assurance machine learning Operational design domain System safety

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Role of machine learning in Managing Cloud Computing Security 2

Role of Machine Learning in Managing Cloud Computing Securit...

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2nd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Kumar, Santosh Nithya, S. Prethi, K.N Apinaya Singh, Suraj Lourens, Melanie Patil, Nisha Computer Science and Engineering Dr. Apj Abdul Kalam Women's Institute of Technology Darbhanga India Coimbatore Institute of Technology Department of Computer Science and Engineering India Cit Department of CSE India Mullana Electrical Engineering Department Ambala India Durban University of Technology Department of Human Resources Management South Africa School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9781665437899

Cloud computing is one of the most trending technology through which digitalized data management and storing becomes easier and more effective. However, besides the advancement of technology, data protection is another top priority concerning factor as millions of sensitive data are gets stored and transferred through the cloud computing system. As per the current days of cyber-activities, data security threat in cloud computing have increased, which posses threat in the development of the business. In that field, machine learning based advanced algorithm develop the virtual encrypted environment to protect the data from unauthorized access and hacking. © 2022 IEEE.

关键词： Cryptography

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Second-Year University Student Retirement Prediction Model Using machine learning 16

Second-Year University Student Retirement Prediction Model U...

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16th international conference on Knowledge and Smart Technology, KST 2024

作者： Saelim, Prapatsorn Chochiang, Kitsiri Vasupongayya, Sangsuree College of Digital Science Prince of Songkla University Songkhla Thailand Prince of Songkla University Division of Computational Science Songkhla Thailand Prince of Songkla University Department of Computer Engineering Songkhla Thailand

ISBN: (纸本)9798350370737

Student retirement is an important issue in all higher education institutions, affecting the income of each faculty and the university. In this research, a model for predicting student retirement is created and the factors affecting the student retirement are also analyzed. The information of the second-year Faculty of Science undergraduate students, Prince of Songkla University, from the academic year 2018-2022-totally 3,092 students-are used as the workload. The researcher performed the analysis using classification machine learning techniques namely the support vector machine algorithm to compare between the traditional data import method and data enhancement using the synthetic minority over-sampling technique (SMOTE). The kernel functions in the experiments include linear, polynomial, radial basis function kernel (RBF), and sigmoid. 10-fold cross-validation is used. The results show that 9 significant factors that affect the model predicting retirement of the student status include academic performance of 4 basic science subjects, field of study, student loans, hometown, and parent's income. The model with the best efficiency and time efficiency is the one with the SMOTE technique and the RBF kernel support vector machine algorithm. The performance accuracy is at 89.64%. © 2024 IEEE.

关键词： Students

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Privacy-preserving Model Training for Disease Prediction Using Federated learning with Differential Privacy 44

Privacy-preserving Model Training for Disease Prediction Usi...

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44th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Khanna, Amol Schaffer, Vincent Gursoy, Gamze Gerstein, Mark Johns Hopkins Univ Dept Biomed Engn Dept Appl Math & Stat Baltimore MD 21218 USA Yale Univ Dept Comp Sci New Haven CT 06520 USA Columbia Univ Dept Biomed Informat New York NY 10032 USA New York Genome Ctr New York NY 10013 USA Yale Univ Dept Stat & Data Sci Dept Comp Sci Program Computat Biol & BioinformatDept Mol Biop New Haven CT 06250 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

machine learning is playing an increasingly critical role in health science with its capability of inferring valuable information from high-dimensional data. More training data provides greater statistical power to generate better models that can help decision-making in healthcare. However, this often requires combining research and patient data across institutions and hospitals, which is not always possible due to privacy considerations. In this paper, we outline a simple federated learning algorithm implementing differential privacy to ensure privacy when training a machine learning model on data spread across different institutions. We tested our model by predicting breast cancer status from gene expression data. Our model achieves a similar level of accuracy and precision as a single-site non-private neural network model when we enforce privacy. This result suggests that our algorithm is an effective method of implementing differential privacy with federated learning, and clinical data scientists can use our general framework to produce differentially private models on federated datasets. Our framework is available at https://***/gersteinlab/idash20FL.

关键词： Training Differential privacy machine learning algorithms Biological system modeling Training data machine learning Predictive models

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Sentiment Analysis on Reviews of E-commerce Sites Using machine learning Algorithms

Sentiment Analysis on Reviews of E-commerce Sites Using Mach...

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2022 international conference on Innovations in Science, engineering and Technology, ICISET 2022

作者： Hossain, Md. Jahed Joy, Dabasish Das Das, Sowmitra Mustafa, Rashed Port City International University Department of Computer Science and Engineering Chattogram Bangladesh University of Chittagong Department of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9781665483971

Customers of e-commerce platforms exchange their thoughts with such kinds of languages. In the age of the present competitive business world, sentiment analysis is widely used in the e-commerce industry to improve efficiency and better understand to make business decisions. Earlier research on sentiment analysis was in English but there is no such significant work in Bangla language and Romanized Bangla language reviews. Therefore, we have developed a machine learning model where reviews on three different languages (Bangla, English, and Romanized Bangla) are used and applied six machine learning algorithms. We have demonstrated a comparative analysis with existing work and have discussed the detailed accuracy, precision, recall, F1 scores, and ROC area. We have prepared three datasets and labeled all the reviews data as Negative, Positive, Neutral, Slightly Negative, and Slightly Positive sentiment. To perform the analysis, the preprocessed datasets were trained using machine learning techniques, and the model performances is evaluated. For the Bangla dataset, Support Vector machine(SVM) algorithm performed best by achieving 94% accuracy and for the English and Romanized Bangla dataset, Random Forest algorithm performed best by achieving 93% and 94% accuracy respectively. © 2022 IEEE.

关键词： Sentiment analysis

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