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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3232 条记录，以下是321-330 订阅

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Intelligent Deposit Product Recommendation for Flexible Employees of Housing Provident Fund Based on Random Forest 2

Intelligent Deposit Product Recommendation for Flexible Empl...

引用

2nd international conference on Frontiers of Artificial Intelligence and machine learning, FAIML 2023

作者： Chen, Xue Hu, Yuxing Hong, Xiaoxia Zhu, Zhaohan Song, Yang Liu, Juan Song, Peng Xiong, Wei Yan, Meng Chongqing Housing Provident Fund Management Center Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Eletrical Engineering Southwest Jiaotong University Sichuan China

ISBN: (纸本)9798400707544

With the rapid development of big data and cloud computing, digital technology has been widely used to develop provident funds. We innovatively apply data mining and machine learning methods to analyze the impact factors of provident fund deposits and the recommendation model of the deposit products for flexible employees. After experiments, our model performs well in the performance evaluation index (such as Accuracy, Recall, and F1-score). Through our model and research analysis, the housing provident fund center can comprehensively analyze and mine the real data, promote more popular customer policies, and provide better customer service. © 2023 Copyright held by the owner/author(s).

关键词： machine learning

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RLOP: A Framework Design for Offset Prefetching Combined with Reinforcement learning 1

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13th international conference on Computer engineering and Networks (CENet)

作者： Huang, Yan Wang, Zhanyang Zhengzhou Univ Light Ind Coll Software Zhengzhou 450001 Peoples R China

ISBN: (数字)9789819992478

ISBN: (纸本)9789819992492;9789819992478;9789819992461

Previous prefetching schemes have been found to be very effective at enhancing the performance of computers. However, speculative prefetching requests can have negative effects on computers, such as increased memory bandwidth consumption and cache pollution. To address the deficiencies of previous prefetching schemes, we propose the Reinforcement learning Based Offset Prefetching Scheme (RLOP), an offset prefetching scheme based on reinforcement learning. As with previous offset prefetching schemes, RLOP evaluates multiple offsets and enables offsets that qualify to issue prefetching requests. RLOP, however, selects appropriate prefetch offsets through reinforcement learning, and the reinforcement learning reward scheme determines the goal of the prefetcher;we divide the rewards into four different rewards-accurate and timely prefetch, accurate but delayed prefetch, inaccurate prefetch, and no prefetch operation-and by increasing or decreasing the reward value, we facilitate or inhibit RLOP from future environments to collect such rewards, which enables or inhibits RLOP from collecting such rewards, which enables We evaluated and contrasted RLOP with various advanced data prefetchers and demonstrated that our scheme resulted in a 25.26% increase in system performance over systems without data prefetchers and a 3.8% increase over the previous best performing data prefetcher.

关键词： Cache optimization Data prefetching Offset prefetching machine learning Reinforcement learning

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An Investigation for Cursive Context-Specific Printed Script Recognition Techniques 20

An Investigation for Cursive Context-Specific Printed Script...

引用

20th international Multi-conference on Systems, Signals and Devices, SSD 2023

作者： Rafique, Humera Javid, Tariq Hamdard University Graduate School of Engineering Sciences & Information Technology Dept. of Electrical Engineering Karachi Pakistan

ISBN: (纸本)9798350332568

Selecting suitable techniques and datasets for a machine learning problem is a great challenge. This research investigates the available machine learning architectures, datasets, and software tools, for cursive context-specific printed text. Despite the existence of millions of speakers around the globe and rich literary history of more than thousands of years, it is hard to find computational linguistic works related to the Punjabi Shahmukhi script, a member of Perso-Arabic context-specific script - a low-resource language family. An investigation of related work through collected statistics inspired by the popularity and success of artificial neural networks and associated techniques have presented. The historical development of the script follows it in a literary sense and the importance and motivation in the development of optical character recognition systems, algorithms, and techniques. The paper discussed the available datasets and the need for a custom dataset. Based on recent research trends in machine learning frameworks, the work presents the most popular deep learning techniques, convolutional neural networks, and recursive neural networks for cursive context-specific scripts. This paper incorporates actual knowledge to promote research in machine learning and natural language processing disciplines and the selection of algorithms, architectures, and resources. © 2023 IEEE.

关键词： Network architecture

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A Systematic Literature Review on Test Case Prioritization and Regression Test Selection 21

A Systematic Literature Review on Test Case Prioritization a...

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21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

作者： Xiao, Zhengxinchao Xiao, Lei Xiamen University of Technolog School of Computer and Information Engineering Xiamen China

ISBN: (纸本)9798350345889

Regression testing is a crucial component of software testing and a crucial tool for ensuring the quality of software. An appropriate optimization method is essential for maximizing productivity and reducing expenses in regression testing. Test case prioritization (TCP) and regression test selection (RTS) are two popular methods in regression testing. This paper provides a qualitative analysis of 18 TCP and 17 RTS publications from the last five years. This paper presents four main issues. The first covers the most popular TCP techniques, the second covers the most popular RTS methods, the third covers the most popular metrics for measuring TCP and RTS, and the fourth covers data sources. Based on this study, we draw the following conclusions: (1) Defect prediction and machine learning-based TCP methods, machine learning, multi-objective, and model-based RTS methods will receive additional attention in future. (2) Defects4J is the most commonly used data set in TCP in the past five years. SIR and GitHub are the most commonly used datasets in RTS. (3) The most widely used measurement methods in TCP and RTS are APFD and cost, respectively. In future, researchers will use these two indicators to conduct a more comprehensive evaluation together with cost, fault detection capability, and test coverage. © 2023 IEEE.

关键词： software testing

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machine learning Method for Recognizing Human Eye Pupil Region 2

Machine Learning Method for Recognizing Human Eye Pupil Regi...

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2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Rajkumar, R. Gokulavani, D. Rajesh, K. Apuri, Nagaiah Chandrasekar, T. Begam, R Benazir Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Tamilnadu Chennai India Knowledge Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Salem India Telangana Medchal India Velammal College of Engineering and Technology Department of Electrical and Electronics Engineering Madurai India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350325799

This research study describes how machine learning techniques can be employed to segment human pupils. The objective is to recognize and isolate the pupil region in ocular images. The aspects of a methodology are data set preparation, feature extraction, machine learning model selection, model training, model optimization, model testing, model evaluation, and finally post-processing. During preprocessing, this identifies and designates pupil regions in the dataset to obtain high-quality eye images. This compares two machine learning models, the Support Vector machine (SVM) and the Random Forest, after identifying the most significant image characteristics. After being trained and fine-tuned using the dataset, models are evaluated based on metrics such as precision, recall, F1 score, and accuracy. In post-processing, the segmented pupil regions are modified after the initial segmentation. Random Forest attained 96.27 accuracy, while Support Vector machine (SVM) obtained 95.17. This research contributes to the advancement of computer vision and is beneficial for software that employs human vision. © 2023 IEEE.

关键词： Biometrics

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Application of machine learning and Deep learning Algorithms in Predicting Virtual Network Functions for Network Function Virtualization 14

Application of Machine Learning and Deep Learning Algorithms...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Spandana, Chereddy Srisurya, Ippatapu Venkata Priyadharshini, A.R. Krithika, S. Sridevi, S. Prasanna Kumar, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

Network functions virtualization (NFV) is a methodology that replaces hardware middleboxes with more flexible software alternatives called virtualized network functions (VNFs). These VNFs need to be created and their resource allocation needs to be changed as per traffic demands to dynamically adapt to changes. However, determining the appropriate number of resources to be allotted for each VNF instance is challenging, as fixed resource consumption is often assumed in existing optimization methods. This can lead to either resource wastage or poor service quality. A novel machine learning algorithm trained on actual VNF data, which includes performance measures and resource needs is proposed to address this issue. These trained models can accurately predict the number of resources needed for each VNF to handle a specific traffic load. These machine learning models are incorporated into an algorithm for simultaneous VNF placement and scaling, and their effect on the final VNF placements is evaluated. © 2023 IEEE.

关键词： Network function virtualization

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18th international conference on Evaluation of Novel Approaches to software engineering, ENASE 2023

18th International Conference on Evaluation of Novel Approac...

引用

18th international conference on Evaluation of Novel Approaches to software engineering, ENASE 2023

ISBN: (纸本)9783031641817

The proceedings contain 15 papers. The special focus in this conference is on Evaluation of Novel Approaches to software engineering. The topics include: Impact Analysis of Disruptions on Composite Resources;Requirements Elicitation in the Age of AI: A Tool’s Multi-system Journey;A Deep Contextual Product Recommender System for SO-DSPL Framework;Beyond the Systematic: Forecasting Importance and Emergence of Research Areas in Applications of software Traceability Using NLP;a Method for Model-Driven IoT Application Development;operationalizing Decision Tables: A Step-by-Step Framework for Efficient software Product Line Customization;singleton and Factory Design Patterns Detection Based on Features and machine learning;future Vision of Dynamic Certification Schemes for Autonomous Systems;advancing Code Smell Detection: Live Global Data Detection and Data Clumps Testcases;dynamic Data-Flow Analysis with Dacite: Evaluating an Integrated Data-Flow Visualization Approach;enhancing software Defect Prediction: Exploring the Predictive Power of Two Data Flow Metrics;adaptive Strategies Metric Suite;Enhancing Collaboration and Agility in Data-Centric AI Projects.

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Driving Behavior Classification Using Smartphone Sensor Data 8

Driving Behavior Classification Using Smartphone Sensor Data

引用

8th international conference on Computer Science and engineering, UBMK 2023

作者： Dikbiyik, Deniz Alagoz, Fatih Boǧaziçi University Dept. of Software Engineering İstanbul Turkey Boǧaziçi University Dept. of Computer Engineering İstanbul Turkey

ISBN: (纸本)9798350340815

Given the substantial number of accidents because of human error, the development of automatic driver behavior monitoring systems has become a pressing need. By providing real-time monitoring and analysis of driving behavior, these systems have the potential to decrease the incidence of accidents and improve overall road safety. This study presents a novel analysis framework for classifying driving behavior based on data gathered from passengers' smartphones. The data were collected using our mobile application installed on the smartphones and processed using machine learning algorithms. The study utilized several machine learning classification t echniques, w ith a focus on developing a Long Short Term Memory (LSTM) algorithm for improved accuracy. A Federated learning algorithm was also developed to collect the data not into one area and train a global model to apply for labeling the driving behavior. The results show the efficacy of the proposed approach in accurately classifying driving behavior based on data obtained from smartphones. © 2023 IEEE.

关键词： Smartphones

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Multiscale Spectral–Spatial Capsule Neural Network for Hyperspectral Image Classification 1

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UNIfied conference of international Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, international conference on Maintenance engineering, IncoME-V 2023, international conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Wang, Weiye Xu, Yuanping Xu, Zhijie Kong, Chao Niu, Xuemei Huang, Jian School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield United Kingdom School of Management Chengdu University of Information Technology Chengdu China

ISBN: (数字)9783031494130

ISBN: (纸本)9783031494123

Deep learning models have recently shown good performance in the hyperspectral remote sensing image classification tasks. In particular, a capsule network (CapsNet) was introduced as a powerful alternative to convolutional neural networks (CNNs). The CapsNet adopts vector neuron and encode the spatial relationship of features in an image, which exhibits encouraging performance. Motivated by CapsNet, this paper presents a novel capsule network based on multiscale spectral–spatial features to improve the performance for hyperspectral images (HSIs) classification. First, multi-scale features are extracted from the hyperspectral data in 1D spectral, 2D spatial and 3D spatial-spectral cubes, and their primary capsule neurons are constructed separately. Then, a multi-head attention mechanism is introduced to model the association of all primary capsule neurons from multiple dimensions to efficiently extract finer-grained multi-scale spatial-spectral information. Finally, the multiscale capsule neurons are updated using a dynamic routing agreement to obtain more discriminative high-level capsule features thus improving the classification accuracy. Experimental results on two commonly used HSIs datasets (Indian Pines, Pavia University) demonstrate that the proposed model can achieve better performance compared with other state-of-the art deep-learning-based approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neurons

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Design of the control circuit of C523 vertical lathe on PLC 3

Design of the control circuit of C523 vertical lathe on PLC

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

作者： Wang, Yaocheng Wang, Xiaoqing Shenyang Ligong University Shenyang China

ISBN: (纸本)9781510664814

C523 vertical lathe control circuit uses a relay control method, which has some disadvantages including large size, complex wiring, high failure rate, and maintenance difficulties. To solve these problems, a C523 vertical lathe control circuit based on PLC is designed. The electronic requirements of the control circuits, basis of PLC selection, the hardware connection and software design are described. Practice has shown that the PLC control circuit is simple, reliable and can highly improve the production efficiency. © 2023 SPIE.

关键词： software design

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