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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 13410 条记录，以下是181-190 订阅

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Model and data Management for machine learning (M2ML): Integrating Instruments, Edge and HPC for Accelerated machine learning

Model and Data Management for Machine Learning (M2ML): Integ...

引用

2024 IEEE international conference on Big data, Bigdata 2024

作者： Zheng, Weijian Sharma, Hemant Chard, Ryan Kenesei, Peter Park, Jun-Sang Schwarz, Nicholas Miceli, Antonino Foster, Ian T. Kettimuthu, Rajkumar Argonne National Laboratory Data Science and Learning Division LemontIL United States Argonne National Laboratory X-Ray Science Division LemontIL United States

ISBN: (纸本)9798350362480

The use of data produced by scientific instruments, such as the Advanced Photon Source Upgrade (APS-U), to train and fine-tune machine learning models is becoming increasingly challenging due to high data production rates, large data volumes, and the growing complexity of machine learning models. To address these challenges, researchers have developed frameworks like fairDMS to efficiently organize vast amounts of data and models for rapid querying when model degradation is detected. However, the complexity of these frameworks and the physically distributed nature of experimental facilities complicate their *** we introduce a high-performance model and data management framework for machine learning, M2ML. In contrast to previous frameworks, M2ML abstracts the tasks into three key elements that can be easily called and accessed by users. M2ML is capable of utilizing a variety of computational resources, that are distributed across scientific facilities, to accelerate machine learning tasks. For example, it can automatically transfer data from an experimental facility (such as APS-U) to a high performance computing (HPC) facility (such as the Argonne Leadership computing Facility (ALCF)), train machine learning models at the HPC facility, and deploy the trained models on edge computing devices back at the experimental facility for inferencing. M2ML provides a unified interface for (on-the-fly) model (re)training, storage, evaluation, fine-tuning, and inferencing using heterogeneous resources that can be geographically distributed. M2ML uses Globus services such as Globus Transfer and Globus Compute (formerly FuncX). We evaluate M2ML using a high energy diffraction microscopy (HEDM) workflow that employs BraggNN to predict the diffraction peak locations. Results show that, although the BraggNN model is small, M2ML can significantly accelerate the workflow through selective assignment of tasks to different computing resources. © 2024 IEEE.

关键词： Information management

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Role of machine learning in Acute Stroke Imaging: A Technical Review 1

Role of Machine Learning in Acute Stroke Imaging: A Technica...

引用

1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Singh, Gurmeet Singh, Mahender Gupta, Akansha Singh, Deepjyot Department of Electronics and Communication Engineering New Delhi India Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350367720

The utilization of machine learning (ML) and pattern recognition algorithms, which is used for analysis of complex datasets to detect similarities, differences, or trends, which can be particularly useful in medical diagnostics, has become essential in enhancing the prognostics of various neurological diseases such as Acute Ischemic Stroke (AIS). These advanced techniques have particularly transformed the evaluation and exploitation of neuroimaging data in the assessment and management of Acute Ischemic Stroke (AIS). In addition, these techniques also enable rapid and accurate analysis of complex imaging data, aiding clinicians in timely diagnosis, personalized treatment planning, and outcome prediction for stroke patients. This paper focuses on a comprehensive examination of machine learning algorithm strategies to enhance the accuracy and effectiveness of predicting strokes. The author has explored and reviewed different ML algorithms and has also delved into stroke prevention to advance our understanding. © 2024 IEEE.

关键词： Adversarial machine learning

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data science Approaches for Churn Prediction 15

Data Science Approaches for Churn Prediction

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Agarwal, Pratiksha Santa Clara CA United States

ISBN: (纸本)9798350370249

Customer churn poses a significant challenge for subscription-based businesses, leading to revenue losses and decreased profitability. Predicting and managing churn is crucial for sustaining business growth and ensuring customer satisfaction. This paper reviews state-of-the-art machine learning techniques employed in churn prediction, ranging from traditional algorithms like logistic regression and decision trees to advanced approaches such as ensemble methods and neural networks. By exploring methodologies and practical applications, this paper provides insights for researchers and practitioners aiming to optimize churn prediction strategies. Additionally, the paper discusses the background of churn prediction, highlighting the limitations of traditional statistical models and the emergence of data science techniques. Through feature engineering, exploratory data analysis, and the utilization of various machine learning algorithms, businesses can develop effective churn prediction systems to enhance customer retention and satisfaction. ©2024 IEEE.

关键词： Prediction models

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Cricket Performance Analysis System Using machine learning Techniques 5

Cricket Performance Analysis System Using Machine Learning T...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mishra, Suraj Garg, Animesh Batra, Vansh Saxena, Devansh Om Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications 603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications India

ISBN: (纸本)9798331530013

The Cricket Performance Analysis System based on big data tools, like Hadoop and Apache Spark, gives realtime deep insights into performances from players and teams and conditions in matches based on large-scale cricket match data. It scales up speedily and finds patterns using distributed computing capabilities of Spark, which makes it possible to take decisions around performance improvements and strategies. The system applies a set of machine learning techniques including K-means clustering, Logistic Regression, and Random Forest classifiers to be used for the analysis of player performance metrics and match outcomes. Thus, through the conversion of raw data into actionable insights, teams can improve their strategies, make better improvements in performance, and thus gain an upper hand against other competing professional cricket teams. As a result, teams are best positioned to make adjustments in good time and direct finer tactics toward further enhancement of overall performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Predictive Modelling of Physical Activity Intensity Using Deep Neural Networks

Predictive Modelling of Physical Activity Intensity Using De...

引用

2024 international conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Murala, Praveena Sivaraman, K. Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

This study presents a novel approach to quantifying physical exertion using various machine learning algorithms. Such techniques include, among others, random forests, decision trees, linear regression, and gradient boosting. A large dataset has the potential to predict numerous variables, including but not limited to heart rate, calories expended, intensity, steps, and time range. This is the primary objective of the investigation. To mitigate the risk of hybridization, a comprehensive evaluation is conducted on each machine learning model before granting it autonomous operation to ascertain its capability of comprehending the complexity of exercise data. The study thoroughly examines the intricacies of contrasting multiple methodologies to improve the capacity to forecast diverse types of physical activity. This study establishes the foundation for enhanced machine learning models that predict physical activity levels. Such advancements may have far-reaching consequences for individualized treatments, health surveillance, and fitness monitoring. © 2024 IEEE.

关键词： Deep neural networks

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machine learning-Driven Traffic Flow Prediction Using Cloud Control Big data Analysis 10

Machine Learning-Driven Traffic Flow Prediction Using Cloud ...

引用

10th international conference on Virtual Reality, ICVR 2024

作者： Jalil, Khurrum Chen, Qian Zahid, Muhammad Noaman Jalil, Fazila University of Shanghai for Science and Technology Department of Traffic Engineering Shanghai200093 China Hunan University of Humanities Science and Technology School of Information Loudi417000 China University of Science and Technology Beijing School of Economics and Management Beijing100083 China

ISBN: (纸本)9798350364231

Due to the increasing number of intelligent vehicles (IVs) on existing roads, IVs present several challenges, such as congestion, CO2 emissions, idling, and travel delays. Transportation departments rely on forecasting methods using traffic flow data to address issues before implementing new plans for smooth traffic flow. To achieve this, managing the vast amounts of intelligent transportation system (ITS) data necessitates the adoption of cloud computing for data storage capabilities and machine learning methods to facilitate efficient traffic prediction analysis. In addition, real-time data analysis is crucial for evaluating the effectiveness of the on-going implementation of the ITS plans and identifying gaps for improvement. In this paper, the proposed method integrates a machine learning-based traffic flow prediction model that uses cloud computing for traffic data analytics. Then, the efficiency ratio of the proposed method is evaluated. Through experimentation, we demonstrate the effectiveness of our method, achieving a prediction accuracy of up to 90%. This result suggests that the proposed approach is highly competitive compared to existing solutions. © 2024 IEEE.

关键词： Adversarial machine learning

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CTG-Based Fetal Health Prediction: A Comparative Study of machine learning Models 15

CTG-Based Fetal Health Prediction: A Comparative Study of Ma...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kosuru, Vishnu Vardhan Ramaraju, Sree Virajitha Rajan, Akshay Santhosh, Gouri Anjali, T. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

This study explores the use of machine learning approaches for fetal health classification utilizing Cardiotocogram (CTG) data to improve prenatal treatment and maternal-fetal health. The study systematically examines five machine learning algorithms: Random Forest, Gradient Boosting, Support Vector machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, using an extensive record with 2,126 well-annotated CTG recordings. This work is peculiar since it uses a wide range of feature extraction and preprocessing techniques to ensure high model correctness and dependability. With a 95% accuracy rate, the Random Forest algorithm proven to be the most effective, surpassing the other models by far. This study emphasizes how machine learning, especially ensemble approaches, may effectively predict health problems in fetuses. The study lays the groundwork for future developments in fetal health detection using cutting-edge technologies and machine learning algorithms by emphasizing interpretability and transparency. © 2024 IEEE.

关键词： Electronic health record

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Solar Power Output Prediction with AI: Leveraging Meta Learner Ensemble Stacked Regressor

Solar Power Output Prediction with AI: Leveraging Meta Learn...

引用

2024 international conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Shreeja, G. Kannan, Shruthi Sivakumar, Soubraylu SRM Institute of Science and Technology Computing Technologies School of Computing Chennai India

ISBN: (纸本)9798331541170

As the world shifts from fossil fuels to renewable energy, solar power has become essential in meeting global energy demands. The efficiency of solar energy depends on effectively capturing the environmental and physical factors that impact electricity production by solar panels. This study aims to leverage available data on power output and environmental conditions to build advanced machine learning models for predicting solar power. Models such as KNN, DNN, LGBM, RF, and a Meta Learner will be rigorously trained and fine-tuned using various data subsets and hyperparameter optimization. By employing the stacking regressor module from Scikit-learn, a Python machine-learning library, we successfully combined four distinct models, achieving an R-squared value of 0.68 in predicting solar power. This method not only improves the accuracy of solar power predictions but also enhances our ability to capture the complex data relationships critical for forecasting renewable energy. © 2024 IEEE.

关键词： Adversarial machine learning

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Analyze the Performance of Bone Marrow Transplantation (Adults) using machine learning Algorithms

Analyze the Performance of Bone Marrow Transplantation (Adul...

引用

2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Garg, Lucy Singla, Mukesh Baba Mastnath University Asthal Department of Computer Science & Engineering Haryana Bohar India

ISBN: (纸本)9798350376470

This study investigates the performance of bone marrow transplantation (BMT) in adult patients using machine learning techniques. Bone marrow transplantation is a complex medical procedure used to treat various hematological disorders, including leukemia, lymphoma, and aplastic anemia. By analyzing a large dataset of adult patients undergoing BMT, machine learning algorithms are employed to predict patient outcomes, including survival rates, disease recurrence, and transplant-related complications. The study aims to identify key prognostic factors and risk factors associated with BMT outcomes, enabling health care providers to personalize treatment strategies and optimize patient care. Through comprehensive data analysis and predictive modeling, this research contributes to enhancing the understanding of BMT efficacy and improving patient outcomes in adult populations. © 2024 IEEE.

关键词： Lung cancer

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Vulnerability Testing on IoT Networks Using machine learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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2024 international conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Mohandas, R. Prasanna, D.S.John Deva Baiju, B.V. Nathiya, S. Rajkumar, T. Dhiliphan Sivashankar, C. Srm Institiute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur India Srm Institiute Of Science And Technology Department Of Datascience And Business Systems Chennai Kattankulathur India Vit University TamilNadu Vellore India Prince Shri Venkateshwara Padmavathi Engineering College Department Of Electronics And Communication Engineering Ponmar Chennai India Kalasalingam Academy Of Research And Education Dept. Of Computer Science And Engineering Tamilnadu Krishnankovil India Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities. © 2024 IEEE.

关键词： Internet of things

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