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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33935 条记录，以下是1041-1050 订阅

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Research on construction of learner portrait based on learning analytics technology

Research on construction of learner portrait based on learni...

引用

2022 international conference on Artificial Intelligence and Industrial Design, AIID 2022

作者： Zhou, Yan Fan, Haodong School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510663893

With the development of big data, artificial intelligence, and wearable technology, the data generated by learners in the learning process can be fully recorded and stored. Using these data to study the characteristics of different types of learners and provide them with precise and personalized learning guidance is an urgent problem to be solved. This paper proposes a learner portrait construction method based on learning analysis technology. Firstly, the case frame relationship with "learning" as the central verb is established by using case grammar. Secondly, constructing a "learning-learner" feature layer with 6 sub-layers through qualitative analysis, and organizing the primary data of learners into 6 kinds of integrated data with specific meanings. Then, two methods of classifying learner types, cluster analysis and standard establishment, are proposed. Finally, giving an example of learner portrait construction. © 2023 SPIE.

关键词： data mining

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A Comprehensive Analysis of machine learning Algorithms in Diagnosis of Chronic Kidney Disease 13

A Comprehensive Analysis of Machine Learning Algorithms in D...

引用

13th international conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Savitha, S. Rajiv Kannan, A. Logeswaran, K. Kongu Engineering College Computer Science and Engineering Perundurai India K.S.R. College of Engineering Computer Science and Engineering Tiruchengode India Artificial Intelligence Kongu Engineering College Perundurai India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Chronic Kidney Disease (CKD) is a major health condition that causes millions of deaths and billions of dollars in worldwide economic loss. The classical way of diagnosis and treatment of CKDs is through routine blood tests, urine analysis and ultrasonography of kidneys. Though all of these methods are very effective in diagnosis of CKDs, they lack predictive power. Artificial Intelligence and machine learning models are more feasible options to drill in the clinical data and predict the occurrence of CKD from the vital body parameters. This article reviews the major works in the diagnosis of CKDs using data Mining (DM) and machine learning (ML) techniques. The significant challenges and limitations in the data driven predictive techniques are also discussed. Finally, the future research directions that would improve the predictive power of the models are also highlighted. © 2022 IEEE.

关键词： data mining

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An Improvised Location Aided Routing Protocol for Vehicular Ad-hoc Network using Reinforced learning

An Improvised Location Aided Routing Protocol for Vehicular ...

引用

2022 international conference on machine learning, Big data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Kumar, Arvind Dixit, Prashant Tyagi, S.S. Manav Rachna International Institute of Research & Studies Computer Science & Engineering Faridabad India IIMT College of Engineering Greater Noida India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Vehicular Ad-hoc Networks (VANETs) are dynamic networks formed among the vehicles travelling on road. They are prone to change in network topologies very quickly due to limited range of randomly moving vehicles. A vehicle is in range at a time and out of range the other moment. This limitation paved the way for development of routing protocols for them which are different from the routing protocols for mobile ad hoc networks. Since most of the vehicles quite often travel within a city, the routing protocol VANETs can also be made intelligent with the use of deep reinforcement learning (RL). With the help of deep reinforcement learning, the routing protocol will keep on learning with their experience and take suitable routing decision in the future. © 2022 IEEE.

关键词： Reinforcement learning

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Comparative Analysis of machine learning Classifiers on Forecasting Dengue Fever Infection 1

Comparative Analysis of Machine Learning Classifiers on Fore...

引用

1st international conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

作者： Kaur, Supreet Sharma, Sandeep Department of Computer Engineering & Technology GNDU ASR

ISBN: (纸本)9781643683607

Each year Dengue infection causes havoc almost across the entire globe raising the death toll and thus becoming a global burden. With the absence of Global approved vaccine and its scope not limiting itself to tropical regions anymore, it has become a rapidly growing epidemic. Researchers have comprehensively explored mechanisms to predict and diagnose infectious diseases and machine learning has revolutionized the medical field by reducing the computing time in analyzing complex data and finding hidden patterns for accurate predictions. In this study, a comparative machine learning-based analysis is achieved using python based classification to predict dengue fever infection in a person. This paper helps interested researchers choose the most efficient classification technique among the selected machine learning classifiers by not only focusing on accuracy achieved but on overall classification metrics analysis to develop a better Dengue predictive model. © 2023 The authors and IOS Press.

关键词： machine learning

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A Novel Approach for Federated learning with Non-IID data 9

A Novel Approach for Federated Learning with Non-IID Data

引用

9th international conference on Soft Computing and machine Intelligence (ISCMI)

作者： Nguyen, Hiep Warrier, Harikrishna Gupta, Yogesh HCL Technol Hanoi Vietnam HCL Technol Hyderabad Telangana India

ISBN: (纸本)9798350320886

Federated learning (FL) is an emerging technique used to collaboratively train a global machine learning model while keeping the data localized on the user devices. The main obstacle to FL's practical implementation is the Non-Independent and Identical (Non-IID) data distribution across users, which slows convergence and degrades performance. To tackle this fundamental issue, we propose a method (called ComFed) that enhances the whole training process on both the client and server sides. The key idea of ComFed is to simultaneously utilize clientvariance reduction techniques to facilitate server aggregation and global adaptive update techniques to accelerate learning. Our experiments show that ComFed can improve state-of-theart algorithms dedicated to Non-IID data.

关键词： Federated learning machine learning Distributed learning Deep learning Decentralized machine learning

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data AUGMENTATION OF engineering DRAWINGS FOR data-DRIVEN COMPONENT SEGMENTATION

DATA AUGMENTATION OF ENGINEERING DRAWINGS FOR DATA-DRIVEN CO...

引用

ASME international Design engineering Technical conferences / 42nd Annual Computers and Information in engineering conference (IDETC-CIE) / 48th Design Automation conference (DAC)

作者： Zhang, Wentai Chen, Quan Koz, Can Xie, Liuyue Regmi, Amit Yamakawa, Soji Furuhata, Tomotake Shimada, Kenji Kara, Levent Burak Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9780791886229

We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Our method is based on the randomization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizability of the machine learning models to unseen drawings.

关键词： engineering drawings Image segmentation Computers machinery Dimensions Manufacturing Design machine learning

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An interpretable machine learning-based analysis of vehicle yielding during pedestrian-vehicle interactions at unsignalized intersections 26

An interpretable machine learning-based analysis of vehicle ...

引用

IEEE 26th international conference on Intelligent Transportation Systems (ITSC)

作者： Wang, Ruiqi Ji, Ang Li, Tao Sun, Zhanbo Fu, Zhijian Southwest Jiaotong Univ Sch Transportat & Logist Natl Engn Lab Integrated Transportat Big Data Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350399462

The paper introduces an interpretable machine learning technique SHAP (SHapley Additive exPlanation) to analyze the vehicle yielding behaviors during pedestrian-vehicle interactions at unsignalized intersections. The study first extracts trajectory data from drone videos and then exploits machine learning methods to construct the yielding classification model. The results indicate that Random Forest (RF) outperforms Support Vector machine (SVM), Gradient Boosting machine (GBM), and eXtreme Gradient Boosting (XGBoost), achieving the best classification performance with an area under the ROC curve (AUC) of 0.934. Finally, the SHAP algorithm is fused with RF to improve the model interpretability. The analysis reveals that the distances between vehicles and pedestrians make the most significant impact on vehicle yielding behavior. Furthermore, it is found that traffic-related variables exhibit non-linear and threshold effects on vehicle yielding.

关键词： Vehicles

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A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement learning

A Real-World Quadrupedal Locomotion Benchmark for Offline Re...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Zhang, Hongyin Yang, Shuyu Wang, Donglin Westlake Univ Sch Engn Hangzhou 310024 Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Online reinforcement learning (RL) methods are often data-inefficient or unreliable, making them difficult to train on real robotic hardware, especially quadruped robots. So learning robotic tasks from pre-collected data is a promising direction. Agile and stable legged locomotion remains an open issue in its general form. Analogous to the rapid progress of supervised learning in recent years, the combination of offline reinforcement learning (ORL) and realistic datasets has the potential to make breakthroughs in this challenging field. To facilitate the ORL research for real-world applications, we benchmark ten ORL algorithms in the realistic quadrupedal locomotion dataset. The dataset is collected by the classical model predictive control (MPC) method, rather than the online RL method commonly utilized by previous ORL benchmarks. Extensive experimental results show that the best-performing ORL algorithms can achieve competitive performance compared with the online RL, and even surpass it in some tasks. However, there is still a gap between the learning-based methods and classical MPC, especially in terms of stability and task response accuracy. Our benchmark can provide a fertile ground for future application-oriented ORL research.

关键词： Adversarial machine learning

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Analyzing various machine learning Algorithms with SMOTE and ADASYN for Image Classification having Imbalanced data

Analyzing various Machine Learning Algorithms with SMOTE and...

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2022 IEEE international conference on Current Development in engineering and Technology, CCET 2022

作者： Yakshit Kaur, Gagandeep Kaur, Veerpal Sharma, Yashika Bansal, Vishnu Chitkara University Institute of Engineering and Technology Punjab India Lovely Professional University Department of Computer Science and Engineering Phagwara Punjab India

ISBN: (纸本)9781665454155

Oversampling is a strategy employed in machine learning to handle imbalanced datasets by creating copies of the minority class instances to balance the dataset, thus reducing bias and enhancing the accuracy of the model. The work presents various machine learning techniques to classify images from an imbalanced dataset. In this paper various oversampling techniques such as ADASYN and SMOTE are blended with the classification algorithms i.e., SVM and CNN with SVM in order to balance imbalanced datasets. The experimentation is performed on the Google Colab using different machine learning techniques: SVM, and (CNN and SVM). The results of the experimentation suggests that the amalgamation of SVM and CNN is better than the SVM and SMOTE is better than ADASYN on the basis of performance matrices such as recall, precision, F1 score. © 2022 IEEE.

关键词： Support vector machines

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Applying Enhanced machine learning Mechanisms to Improve Performance of Statistical data Processing over Cloud System 2

Applying Enhanced Machine Learning Mechanisms to Improve Per...

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2nd IEEE international conference on Intelligent Technologies, CONIT 2022

作者： Shukla, Amogh Nagar, Rishabh School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

A computer system is trained to make right predictions by giving it data through a process known as machine learning. The phrase 'cloud computing' refers to on-demand internet access to computer resources kept at a remote data center under the administration of a cloud services provider, such as programs, servers (both real and virtual), data storage, development tools, networking capabilities etc. In order to better understand their target audience, automate aspects of their manufacturing process, and develop better goods in response to market demand, organizations utilize machine learning and analytic. All of these elements contribute to a company's enhanced profitability, providing it an advantage over its rivals. The idea of machine learning is founded on the concept of learning from experience. The cloud computing approach can alter the technological infrastructure by incorporating this technology-based idea. An 'intelligent cloud' can be built by merging machine learning with cloud computing. © 2022 IEEE.

关键词： machine learning

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