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Human Activity Classification Using Supervised machine learning Algorithms 1

19th Control Instrumentation System conference, CISCON 2022

作者： Akhila Rao, Vidya S. Jayalakshmi, N.S. Pai, Smitha N. Kolekar, Suchetha V. Department of Instrumentation and Control Engineering Manipal Academy of Higher Education Manipal India Department of Electrical and Electronics Engineering Manipal Academy of Higher Education Manipal India Department of Information and Communication Technology Manipal Academy of Higher Education Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9789819995547

ISBN: (纸本)9789819995530

In this paper, using smartphones the human static and dynamic activities are measured via the inbuilt inertial measurement sensors. The dataset obtained from the experiments carried out on thirty individuals contains all the data regarding the angle change with different activities like walking, laying, sitting, etc. The machine learning techniques are applied to this dataset right from the onset of data cleaning to eliminate outliers and missing values and exploratory data analysis (EDA) to visualize the data and find relation between the variables. For eliminating the redundant data and improve execution time, a feature engineering technique called principal component analysis (PCA) is employed on the dataset. Finally, machine learning algorithms such as logistic regression, support vector machine and linear discriminant analysis are applied to the processed dataset. The accuracy is compared with all the algorithms with logistic regression outperforming the other two mentioned classifiers. These classifier models are found to be useful in applications where it is vital to monitor sensor data such as elderly monitoring, patient rehabilitation, fall detection and so on. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Unfair AI: It Isn't Just Biased data 22

Unfair AI: It Isn't Just Biased Data

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22nd IEEE international conference on data Mining (ICDM)

作者： Haider, Chowdhury Mohammad Rakin Clifton, Chris Zhou, Yan Purdue Univ W Lafayette IN USA Univ Texas Dallas Dallas TX USA

ISBN: (纸本)9781665450997

Conventional wisdom holds that discrimination in machine learning is a result of historical discrimination: biased training data leads to biased models. We show that the reality is more nuanced;machine learning can be expected to induce types of bias not found in the training data. In particular, if different groups have different optimal models, and the optimal model for one group has higher accuracy, the optimal accuracy joint model will induce disparate impact even when the training data does not display disparate impact. We argue that due to systemic bias, this is a likely situation, and simply ensuring training data appears unbiased is insufficient to ensure fair machine learning.

关键词： machine learning Fairness Systemic Bias

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FC-MIDTR-WCCA: A machine learning Framework for PM2.5 Prediction

IAENG International Journal of Computer Science

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IAENG international Journal of Computer Science 2024年第5期51卷 544-552页

作者： Tu, Tianyi Su, Ye Ren, Sheng School of Computer and Electrical Engineering Hunan University of Arts and Science Chang De415000 China School of Computer and Electrical Engineering Hunan University of Arts and Science Chang De China School of Computer and Electrical Engineering Hunan University of Arts and Science Chang De China

The rapid acceleration of urbanization and industrialization has led to a significant increase in PM2.5 pollution, making it a critical global concern. The accurate prediction of PM2.5 concentrations is of utmost importance for the effective implementation of protective measures and environmental management. This study presents a machine learning framework for PM2.5 prediction called FC-MIDTR-WCCA. The framework is composed of three main components. The first component involves conducting an analysis of air quality PM2.5 data to identify features highly correlated with PM2.5 and to examine seasonal patterns. This approach facilitates feature crossing (FC) by combining different relevant features. The second component utilizes a feature selection algorithm known as the mutual information decision tree regressor (MIDTR) to effectively account for correlations and contributions among features. This algorithm identifies the optimal feature dataset. The third component involves the adoption of a weighted arithmetic mean fusion algorithm that combines canonical correlation analysis (WCCA) for PM2.5 prediction. This algorithm considers the correlations between prediction models and addresses collinearity issues to achieve stable model weight vectors. We experimentally assessed the performance of four ensemble tree models and the stacking algorithm. The results demonstrated that the FC-MIDTR-WCCA model outperformed all the other methods evaluated in terms of R2 and MAE. © (2024), (international Association of Engineers). All Rights Reserved.

关键词： Decision trees

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Mitigating data Scarcity in Supervised machine learning Through Reinforcement learning Guided data Generation 40

Mitigating Data Scarcity in Supervised Machine Learning Thro...

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40th IEEE international conference on data engineering, ICDE 2024

作者： Chai, Chengliang Jin, Kasisen Tang, Nan Fan, Ju Qiao, Lianpeng Wang, Yuping Luo, Yuyu Yuan, Ye Wang, Guoren Beijing Institute of Technology China Hong Kong Renmin University China

ISBN: (纸本)9798350317152

One primary problem for supervised ML is data scarcity, which refers to the inadequacy of well-labeled training data. Recently, deep generative models have shown the capability of generating data objects that closely resemble real data for datasets in different modalities, including images, natural language, and tabular data. Naturally, a promising approach for tackling data scarcity involves training a generative model to produce a collection of data objects, and then employing machine-labeling solutions (e.g., weak supervision or semi-supervised learning) to incorporate these generated data objects for supervised ML. However, it is important to note that because the provided training data may exhibit a different data distribution compared to the validation (or unseen testing) data, the generative model learned from these seen training data cannot guarantee the generation of high-quality data relative to this ML task. To address this challenge, we introduce an iterative approach that gradually calibrates the generative model by interacting with an environment that tells whether generated tuples are good or bad, by using a validation dataset that is not exposed to the generative model. In each iteration, we first use a pre-trained generative model to create unlabeled data objects, label them, and integrate this freshly generated data into the learning process. Afterwards, the model will be tested in the environment to assess the quality of the generated data. The iterative framework can be naturally controlled using reinforcement learning (RL), where an agent generates and labels tuples, an environment tests the generated tuples and sends reward back to the agent to progressively enhance the generative model for a specific supervised ML task. Experimental results over 8 datasets and multiple baselines demonstrate that our RL guided data synthesis, together with off-the-shelf semi-automatic labeling solutions, can significantly improve the performance of supervised M

关键词： Reinforcement learning

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machine learning-BASED CUTTING CONSTANT ESTIMATION FOR MECHANISTIC FORCE MODELS OF END MILLING OPERATION 17

MACHINE LEARNING-BASED CUTTING CONSTANT ESTIMATION FOR MECHA...

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ASME 17th international Manufacturing Science and engineering conference (MSEC) held jointly with the 50th North American Research conference (NAMRC)

作者： Vaishnav, Shubham Desai, K. A. Indian Inst Technol Jodhpur Dept Mech Engn Jodhpur Rajasthan India

ISBN: (纸本)9780791885819

A predictive cutting force model is essential for power requirement estimation, cutting tool design, surface error estimation and stability analysis during the end milling operation. Mechanistic model estimates cutting forces by correlating analytically computed chip geometry with lumped coefficients combining tool-work material properties through empirical relationships. Establishing reliable relationships through the statistical curve-fitting is demanding due to the need for several experiments, anomaly or noise in the experimental data, and process disturbances that deteriorate the goodness of fit. machine learning models can effectively deal with such inherent uncertainties and serve as an alternative to the statistical curve-fitting. The present work proposes to improve the empirical relationship between instantaneous uncut chip thickness and cutting coefficients by employing a deep learning algorithm, namely Adaptive Moment Estimation (ADAM). The ADAM algorithm is augmented with decoupled weight decay and warm restart features for the improved performance. The decoupled weight decay assigns dynamic sensitivity values to the data points for outlier removal resulting in better model generalization, while warm restart allows better guesses through adaptive learning rates. The proposed approach has been implemented as a computational tool to determine improved coefficients values and empirical relationships. The cutting forces predicted using coefficient values determined using statistical curve fitting and ADAM-based machine learning are compared with experimentally measured data over an extensive range of cutting conditions. It is concluded that the augmentation of the ADAM approach enables the Mechanistic force model to effectively capture end milling process physics by estimating better coefficients values resulting in enhanced prediction abilities.

关键词： End Milling Cutting force Mechanistic Model Cutting force coefficients machine learning

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Application of Deep Wavelet Kernel Extreme learning machine in Fault Diagnosis of Tamping Vehicle

Application of Deep Wavelet Kernel Extreme Learning Machine ...

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2022 international conference on Applied Mathematics and Digital Simulation, AMDS 2022

作者： Wang, Hairui Li, Dongwen Zhu, Guifu Yang, Xiuqi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Information Technology Construction Management Center Kunming University of Science and Technology Kunming China School of Materials Science and Engineering Kunming University of Science and Technology Kunming China

Since it is easy to overfit due to the long training time of the fault diagnosis model for machinery. Introducing the idea of autoencoder (AE) into the wavelet extreme learning machine (WELM) and then stacking to form WELM-AE can convert the underlying fault features to more abstract and advanced ones. And then the adaptive boosting kernel extreme learning machine (Adaboost-KELM) is used as the top-level classifier for fault recognition. The experimental results verify the feasibility of the proposed algorithm in the fault diagnosis of tamping machine with the characteristics of the fast training speed of the extreme learning machine, and a higher accuracy rate than back propagation (BP), support vector machine (SVM), stacked autoencoder (SAE), and convolutional neural networks (CNN). © Published under licence by IOP Publishing Ltd.

关键词： Failure analysis

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data fusion and machine learning for ship fuel efficiency modeling:Part Ⅰ-Voyage report data and meteorological data

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Communications in Transportation Research 2022年第1期2卷 244-272页

作者： Xiaohe Li Yuquan Du Yanyu Chen Son Nguyen Wei Zhang Alessandro Schonborn Zhuo Sun Centre for Maritime and Logistics Management Australian Maritime CollegeUniversity of TasmaniaLauncestonTAS 7250Australia College of Power and Energy Engineering Harbin Engineering UniversityHarbin150001China Institute for Marine and Antarctic Studies College of Sciences and EngineeringUniversity of TasmaniaTaroonaTAS 7053Australia Maritime Energy Management World Maritime UniversityFiskehamnsgatan 120124 MalmoSweden College of Transportation Engineering Dalian Maritime UniversityDalian116026China

The international Maritime Organization has been promoting energy-efficient operational measures to reduce ships'bunker fuel consumption and the accompanying emissions,including speed optimization,trim optimization,weather routing,and the virtual arrival *** theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed,displacement/draft,trim,weather conditions,and sea *** report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye *** overcome this issue,this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion *** widelyadopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping *** best datasets found reveal the benefits of fusing voyage report data and meteorological data,as well as the practically acceptable quality of voyage report *** randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG)present the best fit and generalization *** R^(2) values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set,and 0.74 to 0.90 for the test *** fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/*** models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.

关键词： Ship fuel efficiency Fuel consumption rate Voyage report data fusion machine learning

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Volume-Wise Decoding of Human Brain's Task fMRI via Deep learning 4

Volume-Wise Decoding of Human Brain's Task fMRI via Deep Lea...

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4th international conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Wu, Yueyang Wang, Yanming Qiu, Bensheng Wang, Xiaoxiao University of Science and Technology of China The Medical Imaging Center Department of Electronic Engineering and Information Science Hefei China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China

ISBN: (纸本)9798350394375

In recent years, significant progress has been made in decoding human cognitive tasks from functional magnetic resonance imaging (fMRI) images using deep learning methods. However, existing approaches predominantly rely on the assumption of temporal stationarity of neural activity, decoding static brain networks from task-based fMRI data at the block-wise. The study of dynamic brain networks poses challenges in terms of limited accuracy and complexity of research methods. This limitation hampers the development of neuroscience in achieving a broader and finer-grained understanding of human brain function across a wider range of temporal scales. To address this issue, we propose an volume-wise decoder for task fMRI, adopts end-to-end deep learning method. Compared with traditional machine learning methods, the decoding accuracy and efficiency are significantly improved. This method not only offers a more accurate and efficient decoding approach for a finer-grained comprehension of cognitive processes, but also offers new perspectives and insights into human cognition. © 2024 IEEE.

关键词： Decoding

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Hierarchical Federated learning Algorithm Based on Efficient Aggregation Mechanism 3

Hierarchical Federated Learning Algorithm Based on Efficient...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Li, Fangjing Zhang, Wenbo Zhu, Hongbo School of Information Science and Engineering Shenyang Ligong University Shenyang110159 China

ISBN: (纸本)9781510664814

The existing federated learning algorithm is affected by the heterogeneity of multiple clients and the frequent information interactions between clients and servers, resulting in low training efficiency. Therefore, the edge servers are introduced to the client and the center server in this paper, in order to construct a hierarchical federated learning framework that can effectively reduce the frequency and cost of communication between the client and the center server, which also improves the communication efficiency. At the same time, an efficient time-based hierarchical aggregation mechanism is designed for the hierarchical federated learning framework. The edge server receives local models within a specified period of time and achieves dynamic weighted aggregation according to the obsolescence of the local models obtained in each round, so as to improve the overall training efficiency and ensure the accuracy of the models. In experiments, it is shown that compared with the existing federated learning framework, the hierarchical federated learning framework based on efficient aggregation mechanism proposed in this paper can significantly improve the training efficiency on both MNIST and CIFAR-10 datasets. © 2023 SPIE.

关键词： learning algorithms

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The GitHub Recent Bugs dataset for Evaluating LLM-based Debugging Applications 17

The GitHub Recent Bugs Dataset for Evaluating LLM-based Debu...

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17th IEEE international conference on Software Testing, Verification, and Validation (ICST)

作者： Lee, Jae Yong Kang, Sungmin Yoon, Juyeon Yoo, Shin Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350308198;9798350308181

While Large Language Models (LLMs) have demonstrated strong natural language and code processing capabilities, concern has been raised as to whether existing bug benchmarks are included in their training data. We examine the training data of the open-source LLM StarCoder, and find it likely that data from the widely used Defects4J benchmark was included, raising the possibility of its inclusion in the training data of the GPT model as well. This makes it difficult to tell how well LLM-based results on Defects4J would generalize, as for any results it would be unclear whether a technique's performance is due to LLM generalization or memorization. To remedy this issue and facilitate continued research on LLM-based SE, we present the GitHub Recent Bugs (GHRB) framework, which continuously gathers real-world Java bugs for use in evaluation of LLM-based techniques. To date, we have gathered 89 bugs reported after the GPT-3.5 training data cutoff point of September 2021.

关键词： Benchmark Debugging machine learning

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