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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 3243 条记录，以下是401-410 订阅

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Research on Network Traffic Feature Map Generation and Evaluation Methods 4

Research on Network Traffic Feature Map Generation and Evalu...

引用

4th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

作者： Jiang, Anxing Shi, Kai Wang, Mengshu Zhang, Zeri Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350329490

With the continuous development of machine learning, it has been widely used in the fields of image processing and computer vision. However, it needs to be transformed into images before feeding network traffic data into a deep learning model for training. The existing methods for directly converting network traffic data into images demonstrate unfavorable results. Therefore, this paper proposes a method for generating network traffic feature maps. This method first uses information entropy and classical k-nearest neighbors estimation algorithm to calculate the entropy values for discrete and continuous variables respectively, then the entropy values are used to filter network traffic. Finally, feature maps are generated through an encoder. Experiments on the UNSW_NB15 and CIC-IDS2017 datasets show that the proposed method outperforms other methods in terms of metrics such as PSNR and SSIM. © 2023 IEEE.

关键词： Intrusion detection

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Robust PETS reinforcement learning algorithm for uncertain environment 2

Robust PETS reinforcement learning algorithm for uncertain e...

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2nd international conference on Cloud Computing, Big Data Application and software engineering, CBASE 2023

作者： Xiong, Jing Zhou, Wei Tang, Dongming Huang, Qiucheng Southwest Minzu University School of Computer Science and Engineering Chengdu China National Observation and Research Station Southwest Minzu University Sichuan Zoige Alpine Wetland Ecosystem Chengdu China

ISBN: (纸本)9798350331448

Reinforcement learning (RL) is a branch of machine learning that learns optimal strategies through the interaction of an intelligent body with its environment. The real and experimental environments are different, which is the problem of environmental parameter uncertainty, which puts high demands on the robustness of RL. In this paper, we propose a robust reinforcement learning algorithm RPETS based on PETS for continuous state and action space. The algorithm makes collective decisions by training multiple PETS models to reduce the sensitivity of policies to model perturbations. In GYM customized environment, compared with classical PPO, TRPO, DDPG, SAC algorithms, this algorithm has better return mean and variance than other algorithms, and for the number of environment change parameters 1, 2, 3, this algorithm outperforms classical algorithms by 78%, 42%, and 23%, which proves that this algorithm has good robustness. © 2023 IEEE.

关键词： Reinforcement learning

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Towards Automated Prediction of software Bugs from Textual Description 18

Towards Automated Prediction of Software Bugs from Textual D...

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Shukla, Suyash Kumar, Sandeep Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9789897586477

Every software deals with issues such as bugs, defect tracking, task management, development issue to a customer query, etc., in its entire lifecycle. An issue-tracking system (ITS) tracks issues and manages software development tasks. However, it has been noted that the inferred issue types often mismatch with the issue title and description. Recent studies showed machine learning (ML) based issue type prediction as a promising direction, mitigating manual issue type assignment problems. This work proposes an ensemble method for issue-type prediction using different ML classifiers. The effectiveness of the proposed model is evaluated over the 40302 manually validated issues of thirty-eight java projects from the SmartSHARK data repository, which has not been done earlier. The textual description of an issue is used as input to the classification model for predicting the type of issue. We employed the term frequency-inverse document frequency (TF-IDF) method to convert textual descriptions of issues into numerical features. We have compared the proposed approach with other widely used ensemble approaches and found that the proposed approach outperforms the other ensemble approaches with an accuracy of 81.41%. Further, we have compared the proposed approach with existing issue-type prediction models in the literature. The results show that the proposed approach performed better than existing models in the literature.

关键词： Issue Tracking System machine learning Term Frequency Inverse Document Frequency Smartshark

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Proceedings - 2023 11th international conference in software engineering Research and Innovation, CONISOFT 2023

Proceedings - 2023 11th International Conference in Software...

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11th international conference in software engineering Research and Innovation, CONISOFT 2023

ISBN: (纸本)9798350328837

The proceedings contain 32 papers. The topics discussed include: on the formal verification of smart contracts;an approach of a migration process from a legacy web management system with a monolithic architecture to a modern microservices-based architecture of a tourism services company;conceptual modeling of the V gene annotation process in antibodies;domain-driven design for microservices architecture systems development: a systematic mapping study;detection of phishing in mobile instant messaging using natural language processing and machine learning;and ensemble classifiers in software defect prediction: a systematic literature review.

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Proceedings - 2023 IEEE international conference on software Services engineering, SSE 2023

Proceedings - 2023 IEEE International Conference on Software...

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2023 IEEE international conference on software Services engineering, SSE 2023

ISBN: (纸本)9798350340754

The proceedings contain 38 papers. The topics discussed include: automated pattern-based recommendation for improving API operation performance and reliability in cloud-based architectures;the ultimate battle against zero-day exploits: toward fully autonomous cyber-physical defense;automatically refactoring application transactions for microservice-oriented architecture;goal-driven adversarial search for distributed self-adaptive systems;analyzing and researching the intermediate layer of alliance medical data combined with edge computing;predicting road traffic risks with CNN-and-LSTM learning over spatio-temporal and multi-feature traffic data;Chat2Code: a chatbot for model specification and code generation, the case of smart contracts;a workflow for the continuous deployment of quantum services;and connecting cloud computing and machine learning through functional situation-awareness: a user-centric smart monitoring application.

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Hybrid Power Optimization at the Libyan Investment Authority: A Synergy of Homer software and machine learning Application 11

Hybrid Power Optimization at the Libyan Investment Authority...

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11th IEEE international conference on Systems and Control, ICSC 2023

作者： Alzarouq, Abdulrahman Arab, Marwa Ben Rekik, Mouna Krichen, Lotfi Electrical Engineering Department Sfax3038 Tunisia

ISBN: (纸本)9798350304886

There is a global demand for clean, safe, reliable, and stable electrical energy. This paper presents an optimal sizing of a photovoltaic generator (PVG), Wind turbines (WT), and an energy storage sources (ESS) integrated in a company using Homer software and machine learning. The case study focuses on the Libyan Investment Authority, which is one of the sovereign and investment funds with significant financial investment assets worldwide the significant work is to find a solution to the problem of electricity supply to the Authority, which is considered completely unacceptable due to the sensitivity of the sovereign fund of the Libyan state. The objective of the study is to cover 250 kW of power demand in the company by integrating renewable energy sources, ESS, and diesel generator. Consequently, the study examines the electric, financial, and environmental aspects of the project. The total system production amounts to 2,398,024 kWh per year, resulting in a total cost of $5.69 million. © 2023 IEEE.

关键词： Investments

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Research on indoor robot navigation algorithm based on deep reinforcement learning 2

Research on indoor robot navigation algorithm based on deep ...

引用

2nd international conference on Image Processing, Computer Vision and machine learning, ICICML 2023

作者： Lei, Yaohua Cheng, Yunli Tan, Xueqin Tan, Yanxian Software Engineering Institute of Guangzhou Guangdong Guangzhou510990 China

ISBN: (纸本)9798350331417

Highly intelligent robot is the trend in the new round of development, and it is also the ultimate goal of robot navigation technology *** ability to learn is an important embodiment of robot *** navigation is the basic function of indoor mobile robots[1]and it is the premise and key technology to realize the autonomy and intelligence of *** the continuous development of robot-related technologies and the continuous expansion of application scope, the tasks faced by robots are more challenging, and the work scenes are increasingly complex and diversified, which puts forward new requirements and challenges for navigation technology. Indoor robot navigation systems need not only to maintain accuracy and stability in fixed scenes, but also to be able to handle dynamic changes in the environment, and to take into account safety and the impact on human comfort in an environment that coexists with *** paper takes the indoor mobile robot platform as the research object[2], introduces the machine learning algorithm based on neural network into the navigation task[3], utilizes the existing research achievements in the field of deep learning to build a robot navigation system based on different learning modes, and endows the robot with intelligent perception ability and navigation strategy learning ability[4],. © 2023 IEEE.

关键词： Imitation learning Indoor robot navigation Navigation algorithms reinforcement learning

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Fault Diagnosis and Prediction Model of Building Lightning Protection System Based on BIM and machine learning 2

Fault Diagnosis and Prediction Model of Building Lightning P...

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2nd international conference on Applied Statistics, Computational Mathematics, and software engineering, ASCMSE 2023

作者： Zhao, Jing Yang, Xiuqian School of Hydraulic Engineering Guangxi Vocational College of Water Resources and Electric Power Guangxi Nanning530023 China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

The current conventional fault diagnosis method of building lightning protection system mainly analyzes the fault diagnosis feature quantity to realize the fault type, and the lack of effective analysis of the fault signal waveform leads to poor diagnosis effect. In this regard, a fault diagnosis and prediction model of building lightning protection system based on BIM and machine learning is proposed. The 3D model of the building lightning protection system is constructed by combining BIM technology, and the vertices in the generated 3D model are reduced by the triangular network simplification method. The current signal flowing through the building lightning protector is collected and amplified by circuit design, and a fault diagnosis and prediction model is constructed based on the support vector machine algorithm. In the experiments, the diagnostic performance of the proposed model is verified. The experimental results show that the fault diagnosis model of the building lightning protection system constructed by the proposed method has a high recall rate and has a more desirable diagnostic effect. © 2023 SPIE.

关键词： Failure analysis

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DETECTION OF SINGULARITIES IN FINITE ELEMENT SIMULATIONS: A 3D DEEP learning APPROACH

DETECTION OF SINGULARITIES IN FINITE ELEMENT SIMULATIONS: A ...

引用

ASME international Design engineering Technical conferences / 43rd Annual Computers and Information in engineering conference (IDETC-CIE)

作者： Bickel, Sebastian Goetz, Stefan Wartzack, Sandro Friedrich Alexander Univ Erlangen Nurnberg Engn Design Erlangen Germany

ISBN: (纸本)9780791887295

The growing digitalization trend has a great influence and impact on the entire product development domain. The new algorithms and methods transform and improve many established processes and workflows. In addition to converting existing know-how into the new processes, available data can also be used to implement various models to enhance the development process. At the same time, the lack of experienced engineers leads to an increasing number of novice engineers from the field of 3D design, who have to perform tasks from the simulation department. Therefore, support for these less experienced users of product development software through newly available methods from the field of data-driven methods is reasonable. Correspondingly, the goal of this paper is to present a method for singularity detection in Finite Element (FE) simulations that detects the presence of a singularity in a computed simulation with the help of Deep learning. For this purpose, a dataset of calculated simulations with several components with and without singularities is first generated. Then, a new approach based on PointNet and labeled datasets of FE simulations is proposed. Afterwards, the new procedure is compared against a classic machine learning approach, whereby different parameter settings were investigated for both approaches.

关键词： Deep learning Finite Element Simulation machine learning Data-Driven Product Development

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ADDITIVELY MANUFACTURED NITIHF SHAPE MEMORY ALLOY TRANSFORMATION TEMPERATURE EVALUATION BY RADIAL BASIS FUNCTION AND PERCEPTRON NEURAL NETWORKS 18

ADDITIVELY MANUFACTURED NITIHF SHAPE MEMORY ALLOY TRANSFORMA...

引用

ASME 18th Int Mfg Sci and Engn conf held jointly with the 51st N Amer Res conf / Japan-Soc-of-Mech-Engineers Int conf on Leading Edge Mfg/Mat and Proc

作者： Abedi, Hossein Abdollahzadeh, Mohammadjavad Almotari, Abdalmageed Ali, Majed Mohajerani, Shiva Elahinia, Mohammad Qattawi, Ala Univ Toledo 2801 W Bancroft St Toledo OH 43606 USA

ISBN: (纸本)9780791887233

Employing Laser Powder Bed Fusion (LPBF) method to manufacture NiTiHf Shape Memory Alloy (SMA) is becoming more common. The major design property for NiTiHf is the transformation temperatures (TTs) which control the activation threshold of the SMA material and enable it to create the shape change due to a microstructure phase transformation. Given the high number of fabrication factors, machine learning (ML) approaches provide a promising approach to the design of SMA to control the TTs. The main obstacle to using ML methods is the need for an established correlation between fabrication features and material properties. The presented work develops an ML approach to enable the prediction of the TTs for additively manufacturing NiTiHf. The work uses all available experimental data on additively and conventionally manufactured NiTiHf. Selected fabrication features included in the ML models consider the elemental compositions of NiTiHf, laser power, laser speed, hatch spacing, and almost all the processing steps historically used to manufacture, or heat treat the NiTiHf for SMA. Multiple models of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Neural Networks (NN) are developed to predict the TTs of LPBF-manufactured NiTiHf. The models successfully predict the TTs for various NiTiHf fabrication conditions.

关键词： Data-driven Material Design Laser Powder Bed Fusion NiTiHf Shape Memory Alloys Transformation Temperatures machine learning Additive Manufacturing

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