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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13417 条记录，以下是41-50 订阅

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Harmonia: Accurate Federated learning with All-Inclusive dataset 17

Harmonia: Accurate Federated Learning with All-Inclusive Dat...

引用

IEEE 17th international conference on Cloud computing (IEEE CLOUD)

作者： Choi, Wonmi Ahn, Juyoung Yoo, Yeonho Yoo, Chuck Yang, Gyeongsik Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350368543;9798350368536

Federated learning (FL) is an appealing model training technique that utilizes heterogeneous datasets and user devices, ensuring user data privacy. Existing FL research proposed device selection schemes to balance the computing speeds of devices. However, we observe that these schemes compromise prediction accuracy by similar to 57.7%. To solve this problem, we present Harmonia that enhances prediction accuracy, while also balancing the diverse computing speeds of devices. Our evaluation shows that Harmonia improves prediction accuracy by similar to 1.7x over existing schemes.

关键词： Federated learning data Privacy Distributed machine learning Collaborative learning Client Selection

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Using machine learning to Predict Student Success in Undergraduate Engineering Programs 3

Using Machine Learning to Predict Student Success in Undergr...

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3rd international conference on computing and machine Intelligence (ICMI)

作者： DeJong, Brian P. Karadogan, Ernur Cent Michigan Univ Sch Engn & Technol Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

Undergraduate engineering programs are typically considered some of the most challenging as their curricula require students to have an aptitude for math, science, and engineering. The resources (time, effort, funds) required to finish an engineering degree is substantial. Therefore, it is imperative that the engineering students are supported with well-informed academic guidance as early in their education as possible so that these resources can be used most effectively. Analytical and data-driven methods such as machine learning techniques can be used to inform this guidance process by predicting student success based on features such as individual traits and academic performance. In that direction, we investigated the effectiveness of using machine learning in predicting engineering student success based on academic performance in core math, physics, and engineering courses in three undergraduate engineering programs. The data categories selected for training and testing of the machine learning models in this study are common to most engineering programs nationwide and can be customized in a straightforward manner for other engineering disciplines. The methodology and results outlined in this preliminary study shows promise for predicting degree and cumulative GPA in our three engineering programs.

关键词： machine learning undergraduate engineering education student success prediction

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machine learning-Based Imputation Techniques Analysis and Study

Machine Learning-Based Imputation Techniques Analysis and St...

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2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Pindiyan, Agnal Varghese Pramila, R.M. CHRIST University Department of Data Science Bangalore India

ISBN: (纸本)9798350378092

Missing values are a significant problem in data analysis and machine learning applications. This study looks at the efficacy of machine learning (ML) - based imputation strategies for dealing with missing data. K-nearest Neighbours (KNN), Random Forest, Support Vector machines (SVM), and Median/Mean Imputation were among the techniques explored. To address the issue of missing data, the study employs k-nearest neighbors, Random Forests, and SVM algorithms. The dataset's imbalance is considered, and the mean F1 score is employed as an evaluation criterion, using cross-validation to ensure consistent results. The study aims to identify the most effective imputation strategy within ML models, offering crucial insights about their adaptability across various scenarios. The study aims to determine the best plan for data preprocessing in machine learning by comparing approaches. Finally, the findings help to improve our knowledge and application of imputation techniques in real-world data analysis and machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Maximizing Grid Intelligence: Harnessing Substation Load data via machine learning

Maximizing Grid Intelligence: Harnessing Substation Load Dat...

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2024 international conference on Communications Control and computing Technologies for Smart Grids

作者： Azizi, Elnaz Hua, Weiqi Wallom, David McCulloch, Malcolm Univ Oxford Dept Engn Sci Oxford England Univ Birmingham Electron & Elect Engn Dept Birmingham W Midlands England

ISBN: (纸本)9798350318562;9798350318555

Distribution grids are evolving due to rising electricity demand and renewable energy integration, requiring efficient operation and effective planning. To achieve this, one essential step is translating the available load data into actionable insights. machine learning (ML) approaches have emerged as promising solutions, leveraging increasing availability of data and computational capabilities. While research papers exist on applications of ML in power grids, a review in low-voltage substation-level is missing, an aspect that will be explored in this paper. The significance of emphasis at this level is twofold: ensuring privacy protection while gaining insights into consumption behavior, and eliminating the need for installing new meters or adjusting communication infrastructure. The paper covers three main ML algorithms, supervised, unsupervised, and reinforcement learning, their applications, while providing a critical discussion of their strengths and limitations. Furthermore, the paper provides recommendations for future studies.

关键词： Distribution network reinforcement learning supervised methods unsupervised methods

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A Method For Calculating Target data Coverage Based On 3D Model 24

A Method For Calculating Target Data Coverage Based On 3D Mo...

引用

16th international conference on machine learning and computing (ICMLC)

作者： Li, Xiangzhen Huang, Ping Zhou, Kan Zhang, Peng Zhang, Qiujie Wang, Xiaohang Beijing Inst Technol Natl Key Lab Scattering & Radiat Beijing Peoples R China Beijing Inst Technol Beijing Inst Control & Elect Technol Beijing Peoples R China Natl Key Lab Scattering & Radiat Beijing Peoples R China

ISBN: (纸本)9798400709234

This paper introduces a 3D model-based approach to calculate the coverage of target data, which aims to provide an intuitive and efficient way to analyze and evaluate the performance of remote sensing data acquisition of targets. We classify existing data by load and target, build a 3D model of the target, and project the data onto a hemisphere to visually show the coverage of the data. This method combines data science, GIS technology and the application of the *** plug-in to provide a powerful tool for research and decision-making in the field of remote sensing.

关键词： 3D model data coverage remote sensing

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Optimization of Agriculture Using data science and machine learning 7

Optimization of Agriculture Using Data Science and Machine L...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Josephin Shermila, P. Rahaman, Syed Atheequr Nithosh Balaji, R. Manoj, Sai R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350372816

The research aims to optimize agriculture using data science techniques. Agriculture is a critical sector for sustaining life on earth, and optimizing it can enhance food security and increase the profitability of farmers. data science techniques such as machine learning, data analysis, and modeling to analyze agricultural data and derive insights are the key areas in this work. The project will focus on enhancing crop productivity, reducing wastage, and improving resource utilization. The findings of the project can help policymakers, farmers, and stakeholders to make informed decisions and take appropriate actions. The project aims to support the sustainable agriculture practices and promote the efficient use of resources for the benefit of both farmers and consumers. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Parallel computing for Enhanced Stock Movement Forecasting Using machine learning 7

Leveraging Parallel Computing for Enhanced Stock Movement Fo...

引用

7th international Women in data science conference at Prince Sultan University (WiDS-PSU)

作者： Aleissa, Shahd Alakkas, Maryam Albugeaey, Zainab Alshelaly, Hneen Alotaibi, Shahad Alzubaidi, Thuraya Imam Abdulruhman bin Faisal Univ Coll Comp Sci & Informat Technol Dammam Saudi Arabia

ISBN: (纸本)9798350395839;9798350395846

The advancements in computational techniques regarding parallel computing and machine learning are revolutionizing stock market prediction, This study explores the effectiveness of parallel computing architectures in predicting stock market movements. Existing literature reveals a huge shift towards employing machine learning models, especially in handling larger financial datasets, yet there remains a gap in understanding the full potential of parallel computing in this domain. Our research aims to bridge this gap by developing a comparative analysis between two Random Forest models: one utilizing parallel processing and the other based on sequential computation. Employing a comprehensive dataset that includes financial data from 2018 with 225 indicators of the US stock market, the data has been pre-processed to ensure its suitability for analysis. The methodology involves constructing and training both models, with the parallel model utilizing the multi-core capability of an Apple M1 chip and evaluating them based on accuracy and training time. The findings reveal that while both models achieve an impressive 100% accuracy, the parallel processing model significantly reduces training time, demonstrating the efficiency of parallel computing in rapid data processing. This research not only highlights the potential of parallel computing in enhancing the speed and accuracy of financial market predictions but also contributes to the broader field of financial analytics by suggesting new avenues for future research, including the application of deep learning models and the integration of a wider range of financial indicators.

关键词： stock price prediction parallel computing data processing Random Forest computational efficiency Kaggle

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Bits and Beats: computing Rhythmic Information as Bitwise Operations Optimized for machine learning 9th

Bits and Beats: Computing Rhythmic Information as Bitwise Op...

引用

9th international conference on Mathematics and Computation in Music (MCM)

作者： Gualda, Fernando Univ Fed Rio Grande do Sul BR-90020180 Porto Alegre RS Brazil

ISBN: (纸本)9783031606373;9783031606380

Effective machine learning algorithms require meticulous data preparation. In contemporary microprocessors, ensuring predictable and efficient memory access is crucial to mitigate cache misses. Therefore, compact data structures with high information density are crucial for optimal computation. This paper discusses the interplay of musical meter and microtiming, as long-term expectation of pattern repetition and short-term deviations. A review of musicological, cognitive and computational models is presented. The literature regards meter perception of rhythmic patterns as a manifestation of human attentive behavior, which is also culturally dependent. Additionally, the paper discusses dissimilarity measures of rhythmic patters. The author introduces two algorithms featuring normalized binary representations of rhythmic patterns of inter-onset intervals, activation functions for computing linear, and logarithmic time differences. Bitwise operations are implemented for the bit-sequences of rhythmic patterns. The author offers an implementation of mutual information calculation as bitwise operations.

关键词： Rhythm machine learning Vector Embedding Activation Functions Binary Neural Networks Music Information Retrieval

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An Extensive Analysis of Neuromorphic computing

An Extensive Analysis of Neuromorphic Computing

引用

2024 international conference on Advances in computing Research on science Engineering and Technology, ACROSET 2024

作者： Vajpayee, Amit Kaur, Palak Preet Sharma, Ankit Varshney, Santosh Parul Institute of Engineering and Technology Department of Computer Science and Engineering Vadodara India Apex Institute of Technology Chandigarh University Department of Bigdata Analytics Mohali India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Neuromorphic computing is a new paradigm that emerges from the structure and function of the human brain and aims to revolutionize computing. The technology is designed to simulate the high speed, low power consumption and large contraction of the brain. Neuromorphic systems provide great potential for intelligence and big data by using special algorithms and special designs to simulate neural networks. Despite encouraging progress from technology companies such as Intel and IBM, the development of neural computing chips still faces major challenges. This approach brings significant benefits to artificial intelligence, machine learning and big data. This technology is still in its infancy but has great potential to revolutionize intelligence. By mimicking the brain's performance and learning potential, neuromorphic computing could pave the way for smarter, faster, and more flexible systems that work well on water, performing tasks such as complex data analysis and on-the-fly decision making. © 2024 IEEE.

关键词： Adversarial machine learning

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An Optimised Version of Differential Evolution Heuristic for Feature Selection 9th

An Optimised Version of Differential Evolution Heuristic for...

引用

9th international conference on Metaheuristics and Nature Inspired computing (META)

作者： Anani, Thibault Delbot, Francois Pradat-Peyre, Jean-Francois Univ Paris Nanterre Nanterre France Sorbonne Univ LIP6 Paris France

It is common practice to apply machine learning to real-life data, for example from clinical trials. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

ISBN: (纸本)9783031692567;9783031692574

It is common practice to apply machine learning to real-life data, for example from clinical trials. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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