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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791-800 订阅

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Predicting Autism Spectrum Disorder (ASD) meltdown using Fuzzy Semi-Supervised learning with NNRW

Predicting Autism Spectrum Disorder (ASD) meltdown using Fuz...

引用

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

作者： Karim, Sara Akter, Nazina Patwary, Muhammed J.A. International Islamic University Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9781665483971

Autism Spectrum Condition (ASD) is a notable psychological disorder that affects a human's ability to communicate socially. The need of early diagnosis prompted researchers' attention to the usage of various machine learning-based approaches. A number of supervised and unsupervised learning methods from the disciplines of machine learning were used to enhance the precision of the Autism prediction system. In supervised learning systems, only labeled data is utilized to build a classification model, but gathering enough classified data is time-consuming and usually involves field specialist's assistance. However, in various real-world circumstances, unlabeled samples are easily available. By integrating large volumes of labeled and unlabeled data to produce a stronger classification model, Semi-Supervised learning (SSL) solves this problem better than all other machine learning methods. The study proposes a novel fuzziness-based semi-supervised learning strategy for ASD meltdown prediction that employs mislabeled data in conjunction with training labeled data to increase the model's reliability. The prediction performance utilizing this method on the dataset demonstrated significant advancements to increasing the classifier's results in comparison to other classification methods such as Fuzzy MinMax Classifier, Naive bayes, Fuzzy data Mining, ZeroR, Random tree, and others. © 2022 IEEE.

关键词： Fuzzy systems

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Enhancing Dynamic Ankle Joint Torque Estimation through Combined data Augmentation Techniques 10

Enhancing Dynamic Ankle Joint Torque Estimation through Comb...

引用

10th IEEE RAS/EMBS international conference for Biomedical Robotics and Biomechatronics (BioRob)

作者： Zhang, Haocheng Kizyte, Asta Wang, Ruoli KTH Dept Engn Mech Royal Inst Technol KTH MoveAbil Stockholm Sweden

ISBN: (纸本)9798350386523;9798350386530

Robotic-powered exoskeletons represent a promising avenue for aiding individuals with movement disorders in their daily activities and rehabilitation efforts. However, achieving precise joint torque estimation, particularly during dynamic movements, remains a significant challenge. While machine learning and deep learning techniques have been explored for estimation, their efficacy has been limited, especially in dynamic scenarios. Our target is to improve ankle joint torque estimation during dynamic movements by employing multiple data augmentation techniques. Augmentation methods did not significantly improve cases involving the same subject or session. However, our experiments reveal substantial performance gains when combining spatial and signal augmentation methods, particularly in scenarios involving different subjects. This indicated that when facing an over-fitting problem caused by a lack of subjects, a combined data augmentation method will be a proper solution to improve the predicting performance.

关键词： Exoskeleton (Robotics)

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Research of data-driven Iterative Optimization Scheme for machine learning Models 5

Research of Data-driven Iterative Optimization Scheme for Ma...

引用

5th international conference on Computing and Big data, ICCBD 2022

作者： Yang, Jinsen Zou, Zhijian Xiu, Jiapeng Wang, Peinian Yang, Yiting Beijing University of Posts and Telecommunications Software Systems and Engineering Center Beijing China Beijing Qiyuan Capital Market Development Service Co. Ltd. Beijing China Law School Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665457163

With the development of big data technology providing massive data information for machine learning, more researchers are focusing on data methods in big data technology, by constructing better data set to improve machine learning model's performance. This research based on the data-driven and human-machine collaboration methods in big data technology, continuously build data set to optimize machine learning models. We take the growth and risk assessment model of technology companies as an example, design and implement a data-driven machine learning model pipeline. The experiments show that the pipeline can effectively and continuously optimize these machine learning models, and ensure the stability of machine learning application. © 2022 IEEE.

关键词： Big data

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Drug Discovery for Alzheimer's Disease Using machine learning 8

Drug Discovery for Alzheimer's Disease Using Machine Learnin...

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8th international conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Priyadharsini, M. Sugantha Mallika, S.S. Karuppasamy, K. Snegha, R. Sri Ramakrishna Engineering College Department of Information Technology Tamilnadu Coimbatore India Rvs College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Sri Ramakrishna Engineering College Tamilnadu Coimbatore India

ISBN: (纸本)9781665408165

Due to a variety of factors, pipelines are long and complicated for drug discovery and development. A set of tools are offered by machine learning (ML) approaches for improving discovery and decision-making for well- defined issues with large amount of good data. In this paper, analysis of digital pathology data, target validation and identification of prognostic biomarkers in clinical trials are all possibilities for machine learning. The context and methodology of the applications varied, with some generating precise forecasts and insights. The lack of interpretability and reproducibility of machine learning generated findings, which may limit their applicability, is one of the main obstacles of using machine learning. Systematic and thorough high dimensional data are still required in all fields. machine learning has the ability to speed up the drug discovery and development process and minimize failure rates by promoting data-driven decision making. © 2022 IEEE.

关键词： Decision making

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learning to Rank for Non Independent and Identically Distributed datasets 24

Learning to Rank for Non Independent and Identically Distrib...

引用

international conference on the Theory of Information Retrieval (ICTIR)

作者： Cecchetti, Jacopo Tonellotto, Nicola Perego, Raffaele Univ Pisa Pisa Italy ISTI CNR Pisa Italy

ISBN: (纸本)9798400706813

With the growing data privacy concerns, federated machine learning algorithms capable of preserving the confidentiality of sensitive information while enabling collaborative model training across decentralized data sources are attracting increasing interest. In this paper, we address the problem of collaboratively learning effective ranking models from non-independently and identically distributed (non-IID) training data owned by distinct search clients. We assume that the learning agents cannot access each other's data, and that the models learned from local datasets might be biased or underperforming due to a skewed distribution of certain document features or query topics in the learning-to-rank training data. Thus, we aim to instill in the local ranking model learned from local data the knowledge from other models to obtain a more robust ranker capable of effectively handling documents and queries underrepresented in the local collection. To achieve this, we explore different methods for merging the ranking models, thus obtaining in each client a model that excels in ranking documents from the local data distribution but also performs well on queries retrieving documents having distributions typical of a partner's node. In particular, our findings suggest that by relying on a linear combination of the local models, we can improve IR models effectiveness by up to +17.92% in NDCG@10 (moving from 0.619 to 0.730), and by up to +19.64% in MAP (moving from 0.713 to 0.853).

关键词： learning to Rank Non-IID Distributed Search

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Using learning Analytics to Improve Students' Enrollments in Higher Education 2

Using Learning Analytics to Improve Students' Enrollments in...

引用

2nd international conference on Innovative Research in Applied Science, engineering and Technology (IRASET)

作者： Sghir, Nabila Adadi, Amina El Mouden, Zakariyaa Ait Lahmer, Mohammed Moulay Ismail Univ Higher Sch Technol Informat & Commun Syst Engn Meknes Morocco

ISBN: (纸本)9781665422093

In the last years there has been a growing interest in adopting learning analytics (LA) in higher and further education systems. LA assists the institutional stakeholders to enhance the learning process, ameliorate the teaching activities, make adequate decisions and take appropriate actions based on the collection, analysis, and reporting of data generated from individual learners. The learning analytics approach aims to achieve many objectives, one of them is prediction which is the center of this research. In this paper, we conduct a comparative study between three machine learning algorithms;Decision Tree (DT), Random Forest (RF), and Support Vector machine (SVM);to predict the stream of new enrollments in the first year of higher education. As a case study, the predictive model is applied to new enrollments in the first year of the University Diploma of Technology (DUT) at the Higher School of Technology in Meknes, Morocco (ESTM). The performance of the classifiers is tested with and without the use of SMOTE data balancing on a dataset of 53554 students collected between 2016 and 2019. The obtained results show the best algorithm to predict the most accurate enrollments of students.

关键词： learning analytics Prediction machine learning Decision Tree Random Forest Support Vector machine

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Technical research on machine learning framework based on optimization algorithm 2

Technical research on machine learning framework based on op...

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2nd international conference on machine learning and Intelligent Systems engineering, MLISE 2022

作者： Li, Yansong Railway Police College Zhengzhou China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

In order to overcome the drawbacks of traditional machine learning algorithms and their frameworks, K-means algorithm and random forest classification algorithm are deeply analyzed, and improved AKM and ARF algorithms are proposed, and an AMLF machine learning application framework based on Spark platform technology is established. It can be seen from the verification results that the classification accuracy of the AKM algorithm in each data set is close to 100%, and it has strong data clustering ability. Furthermore, the AKM algorithm has a high acceleration in each data set, so the upgradeability is also relatively high. powerful. The ARF verification results show that it not only has a high classification accuracy, but also has strong upgradeability. © 2022 IEEE.

关键词： K-means clustering

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Research of the Extreme learning machine as Incremental learning

Research of the Extreme Learning Machine as Incremental Lear...

引用

2022 international conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2022

作者： Abramova, Elena S. Orlov, Alexey A. Makarov, Kirill V. Vladimir State University Information Technology Faculty Vladimir Russia

ISBN: (纸本)9781665483698

The external environment of the world is dynamically changing, which requires the ability of continuous learning and memorization from intelligent systems. Iterative learning methods neural networks have smooth convergence, so they do not allow achieving such an effect. An exception is the Extreme learning machine (ELM), which allows for incremental learning. In the ELM, the input weights are randomly generated. The weights between hidden and output layers are computed in one step, resulting in a faster learning rate compared to traditional SLFN learning algorithms. Incremental training allows the neural network to retain existing data while at the same time adapting to new data. This article presents the research and assessment of the ELM as incremental learning. In particular, research of the sample size effect for different numbers of neurons in the hidden layer on the ELM performance is presented. © 2022 IEEE.

关键词： Intelligent systems

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A machine learning-Aware data Re-partitioning Framework for Spatial datasets 38

A Machine Learning-Aware Data Re-partitioning Framework for ...

引用

38th IEEE international conference on data engineering (ICDE)

作者： Chowdhury, Kanchan Meduri, Venkata Vamsikrishna Sarwat, Mohamed Arizona State Univ Tempe AZ 85281 USA

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Spatial datasets are used extensively to train machine learning (ML) models for applications such as spatial regression, classification, clustering, and deep learning. Most of the real-world spatial datasets are often too large, and many spatial ML algorithms represent the geographical region as a grid consisting of several spatial cells. If the granularity of the grid is too fine, that results in a large number of grid cells leading to long training time and high memory consumption issues during the model training. To alleviate this problem, we propose a machine learning-aware spatial data re-partitioning framework that substantially reduces the granularity of the spatial grid. Our spatial data re-partitioning approach combines fine-grained, adjacent spatial cells from a grid into coarser cells prior to training an ML model. During this re-partitioning phase, we keep the information loss within a user-defined threshold without significantly degrading the accuracy of the ML model. According to the empirical evaluation performed on several real-world datasets, the best results achieved by our spatial re-partitioning framework show that we can reduce the data volume and training time by up to 81%, while keeping the difference in prediction or classification error below 5% as compared to a model that is trained on the original input dataset, for most of the ML applications. Our re-partitioned framework also outperforms the state-of-the-art data reduction baselines by 2% to 20% w.r.t. prediction and classification errors.

关键词： Spatial machine learning Spatial data Training Time Reduction Training data Volume Reduction

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Predicting ESG Ratings by machine learning and Analyzing Influencing Factors by XAI 24

Predicting ESG Ratings by Machine Learning and Analyzing Inf...

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7th international conference on Software engineering and Information Management, ICSIM 2024

作者： Choi, Jeong-Cheol Chen, Qiuying Lee, Sang-Joon Interdisciplinary Program of Digital Future Convergence Service Chonnam National University Korea Republic of

ISBN: (纸本)9798400709197

This study examines the role of Environmental, Social, and Governance (ESG) management in corporate strategy, particularly focusing on predicting ESG ratings with machine learning. Given the diverse ESG evaluation criteria employed by global rating agencies, there's a need for clear guidelines to facilitate effective ESG management. The research aims to develop an ESG rating prediction model utilizing a triennial compendium of Korean corporate financial data. This process involves a comparative analysis of linear models, tree-based models, and neural network-based models. Additionally, this study explains the importance of various variables by applying SHAP, one of the XAI techniques. The results indicate that XGB is the most effective, achieving an 85.1% F1 score in ESG rating predictions. By understanding how financial factors impact ESG ratings, companies can develop more effective ESG strategies, forming an essential foundation for sustainable growth. © 2024 ACM.

关键词： machine learning

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