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检索条件"任意字段=2nd International Conference on Software Engineering and Machine Learning, CONF-SEML 2024"

共 232 条记录，以下是1-10 订阅

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2nd international conference on software engineering and machine learning

2nd International Conference on Software Engineering and Mac...

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2nd international conference on software engineering and machine learning, conf-seml 2024

The proceedings contain 9 papers. The topics discussed include: a recognition algorithm applied to apple orchard picking robot;An underground pipeline relocation decision support system based on BIM and GIS integration;UAV path planning method based on modeling in complex forest environment;analysis and study on path planning algorithms in the further mobile action;optimization of image style transfer algorithm based on CycleGAN;optimization of adaptive mechanism for pipeline robots;Fostc3net: a lightweight YOLOv5 based on the network structure optimization;A review of advanced techniques in simultaneous localization and mapping.

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An underground pipeline relocation decision support system based on BIM and GIS integration 2

An underground pipeline relocation decision support system b...

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2nd international conference on software engineering and machine learning, conf-seml 2024

作者： Ma, Fei Gao, Yang Zhang, Guien Huang, Can Chen, Wenkai CCCC Wuhan Zhixing International Engineering Consulting Co. Ltd. Hanyang District Wuhan430100 China

This study introduces a rapid decision-support system for underground pipeline relocation, leveraging the synergistic capabilities of Building Information Modeling (BIM) and Geographic Information Systems (GIS). The amalgamation of BIM and GIS technologies within the system facilitates comprehensive information management and analysis. To validate the system's practicality, dedicated software applications have been developed to streamline data collection, model generation, and data fusion. In the data collection phase, a mobile application has been designed to efficiently acquire standardized physical exploration data. In the design phase, we have implemented an automated pipeline modeling process, utilizing the physical exploration data through our in-house Revit plug-in. Concurrently, the plug-in enables clash detection and distance analysis. Subsequently, the model can be seamlessly transferred to our custom-built integration platform, which is founded on ArcGIS. Within this platform, the integration of the BIM model with GIS can be visually represented. Furthermore, it provides tools for analyzing project quantity and monitoring project progress. The integrating proposed system and software enhances the efficiency of underground pipeline relocation and augments decision-making capabilities. © Published under licence by IOP Publishing Ltd.

关键词： Data fusion

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Research on the Credibility Test Technology of Intelligent Autonomous System 2

Research on the Credibility Test Technology of Intelligent A...

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2nd IEEE international conference on Sensors, Electronics and Computer engineering, ICSECE 2024

作者： Jiang, Shixian Wu, Lijin Liu, Bojiang Jin, Kuangyu Shen, Xiaomei China Institute of Marine Technology & Economy Beijing China

ISBN: (纸本)9798350373646

With significant breakthroughs in key technologies such as machine learning, computer vision, and natural language processing, artificial intelligence technology is accelerating the profound transformation of the military field from mechanization and informatization to intelligence, and traditional software testing methods are no longer applicable. This article first analyzes the credibility of artificial intelligence software, then elaborates on the testing techniques of artificial intelligence software, and finally summarizes the credibility evaluation system, providing technical support for the validation of artificial intelligence software. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimization of adaptive mechanism for pipeline robots 2

Optimization of adaptive mechanism for pipeline robots

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2nd international conference on software engineering and machine learning, conf-seml 2024

作者： Sun, Yiquan Li, Xuchong Zhao, Tao School of Mechanical Engineering Yanshan University Qinhuangdao066000 China School of Advanced Manufacturing Engineering Hefei University Hefei230041 China School of Electrical Engineering Guangdong University of Technology Guangzhou510006 China

The environment inside the pipeline is complex, and it is now more reasonable to use pipeline robots to work inside the pipeline. This article delves into the adaptive mechanisms of pipeline robots, including their kinematics and dynamics, as well as the analysis using Jacobian matrices. Through a series of analyses, adaptive motion simulation was completed using SolidWorks and Simulink. To ensure the performance of the adaptive mechanism, we conducted simulation analysis on the mechanism. During the simulation process, we simulated various working conditions, such as the challenges faced when working in different terrains and environments, as well as working in pipelines of different diameters. Through simulation analysis, we have obtained important information about the obstacle clearance performance, diameter reduction performance, and energy efficiency of the optimized adaptive mechanism. This information can help us further optimize the design of the organization, making it more efficient, energy-saving, and environmentally friendly. In addition, we also studied the adaptive mechanisms of different types of pipeline robots to determine the optimal parameter design scheme. This will provide better performance and higher reliability for different types of pipeline robots. In order to make pipeline robots more flexible, efficient, and reliable. We have designed an adaptive mechanism on the basis of mechanical analysis. We have completed adaptive motion simulation and have optimized the adaptive mechanism based on the simulation results. © Published under licence by IOP Publishing Ltd.

关键词： machine design

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A review of advanced techniques in simultaneous localization and mapping 2

A review of advanced techniques in simultaneous localization...

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2nd international conference on software engineering and machine learning, conf-seml 2024

作者： Song, Ruojing Zhang, Han Yao, Yutong Ahmed, Dastagir Department of Electrical and Electronic Engineering University College London LondonWC1E 6BT United Kingdom College of Mechanical Engineering Tongji University Shanghai200092 China College of Computer Science and Technology Jiangsu University Zhenjiang212013 China Shanghai Dastagir Enterprise Management Co. Ltd Shanghai China

Simultaneous Localization and Mapping (SLAM), as one of the key elements of robot vision, has become an emerging topic in the past 3 decades. The focus of SLAM is to reconstruct the map surrounding the robot from sensors like camera or LiDAR and meanwhile, find the location of the robot itself inside that map. With the contribution of researchers, many different techniques and algorithms have been developed to improve the accuracy of SLAM. The main difference between those techniques is the choice of sensor to solve the SLAM problem. Some approaches are based on LiDAR sensors, which are LiDAR SLAM. Some of them are based on cameras, e.g.: Monocular, stereo, or RGB-D cameras, which are also known as visual SLAM (VSLAM). We will also review how deep learning methods like CNN and RNN together optimize VSLAM computation and remove some of the old modules from the traditional SLAM framework. By comparing the most recent techniques, we will start with some general differences between these techniques and mention some explicit differences in terms of applications. Finally, we will discuss the advantages and drawbacks of both techniques and propose some challenges and future direction towards both techniques. © Published under licence by IOP Publishing Ltd.

关键词： Mapping

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A Comprehensive Review of SDP Using machine and Deep learning Techniques in software Testing 2

A Comprehensive Review of SDP Using Machine and Deep Learnin...

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2nd international conference on Computing and Data Analytics, ICCDA 2024

作者： Angamuthu, Suguna Maheswari, K.G. Nandha Engineering College Department of Information Technology India Government College of Engineering Department of Information Technology Tamil Nadu India

ISBN: (纸本)9798331542573

software Defect Prediction (SDP) is essential to ensure the high quality and dependability of the software Testing process in the software Development Life Cycle (SDLC). Early identification of defects plays a significant role in minimizing the efforts, resources and time. Conventional manual testing techniques have become insufficient due to the increasing intricacy of software programs and the demand for automated techniques. To evaluate vast amounts of data and find defect patterns, Deep learning (DL) and machine learning (ML) techniques have emerged as promising solutions for accurate SDP. A comprehensive analysis of recent developments in ML and DL techniques for SDP is provided in this review paper. The reviewed SDP methodologies, their advantages and limitations, along with their comparative performance, are discussed. The recent research trends, emerging methods, and promising paths for future research in this field are also identified in this domain. This paper aims to be a beneficial source for researchers to understand the current state of SDP and how to use ML, DL, and ensemble approaches to improve software quality assurance practices for effective software testing performance in SDLC. © 2024 IEEE.

关键词： Contrastive learning

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Research on Data-Driven Decision Making for Multi-Objective Optimisation Based on Genetic Algorithm and ARIMA 2

Research on Data-Driven Decision Making for Multi-Objective ...

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2nd IEEE international conference on Electrical, Automation and Computer engineering, ICEACE 2024

作者： Li, Yingqi Qingdao University of Technology Qingdao China

ISBN: (纸本)9798350368208

This study is based on a multi-objective optimisation model and uses various machine learning algorithms for the optimisation analysis of complex systems. Firstly, a bi-objective optimisation model was constructed using Excel and MATLAB to analyse the data for trend analysis and category correlation calculations and incorporating the cost plus rate. Genetic algorithm was used to solve the model to find the optimal solution and ARIMA model was used for future replenishment prediction. The LINGO software was further used to achieve multi-objective optimisation under a variety of constraints including maximising revenue, unit profit and minimising replenishment loss rate. The results of the study show that the model exhibits high reliability and effectiveness in data-driven decision making, demonstrating the potential of genetic algorithms with machine learning algorithms such as ARIMA for a wide range of applications in multi-objective optimisation. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning Approaches to Securing IoT Networks: A Comprehensive Review of Recent Advances 2

Machine Learning Approaches to Securing IoT Networks: A Comp...

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2nd IEEE international conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Saini, Harnit Prasad, Sanjeev Kumar Galgotias University School of Computer Science and Engineering Greater Noida India Ajay Kumar Garg Engineering College Computer Science and Engineering Department Ghaziabad India

ISBN: (纸本)9798350390179

The IoT is a network of physical objects embedded with sensors and software, connecting and exchanging data over the internet. With over 7 billion connected devices, experts predict growth to 10 billion by 2020 and 22 billion by 2025. machine learning ML technologies are advancing quickly, with new algorithms and approaches emerging regularly. In this paper, a review is carried out that helps provide insights into their effectiveness in the current landscape. Different ML techniques are categorized and evaluated based on how they address diverse challenges. Reviewing current ML techniques helps in understanding their strengths and limitations in combating novel attack vectors. Later on, a comparative analysis of these techniques is conducted based on performance metrics. A comprehensive review identifies gaps in current research and practice, highlighting areas where ML techniques might need further development or improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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2nd international conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

2nd International Conference on Emerging Trends in Informati...

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2nd international conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

ISBN: (纸本)9798350328202

The proceedings contain 306 papers. The topics discussed include: IoT in agriculture 4.0: farmer adoption, barriers, and the path to sustainable farming;an impact of evasion attacks on machine learning algorithms;parallel convolutional neural network for brain tumor using MR images;voice-based common cold detection;review on IOMT security through distributed machine learning;performance analysis for diving sport using YoLoV8, OpenPose and fuzzy logic;reliability analysis of PMSM drives processor for commercial electric vehicle utility;H-Blockenc: a pioneering blockchain integrated cryptographic security model for IoT-healthcare systems;early detection of down syndrome through ultrasound imaging using deep learning strategies — a review;route optimization using clustering and K Opt optimization for garbage collection;and preventing network attacks using support vector machine (SVM) and software defined networking (SDN) integration.

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machine learning Driven Load Balancing in software Defined Networks: Recent Progress and Emerging Challenges 2

Machine Learning Driven Load Balancing in Software Defined N...

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2nd IEEE international conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Sharma, Sonam Mahapatra, Rajendra Prasad Kapil, Manoj Seth, Dambarudhar Srm Institute of Science and Technology Department of Computer Science Engineering Delhi-NCR Campus Ghaziabad201204 India Swami Vivekananda Subharti University Faculty of Engineering and Technology NH-58 Delhi-Haridwar Bypass Road 250005 India

ISBN: (纸本)9798350390179

Load balancing has arisen as an important challenge in case of software Defined Network because of the dynamic patterns in the traffic and expanding demands of network. The centralized framework of SDN delivers unique opportunity. It enhances the performance of network and usage of resources by distributing the load of network dynamically across multiple devices and paths. This paper provides a concise overview of SDN and examines the influence of load balancing within this context. It includes a comprehensive study to analyse the algorithms of load balancing used by researchers for different scenarios and to identify the persistent challenges. Furthermore, a methodology has been proposed to highlight the impact of machine learning techniques to amplify the load balancing decision which can be useful to develop more intelligent and adaptive architectures of SDN. For future research, the study aims to deploy scalable and robust SDN solutions in next-generation network infrastructures. © 2024 IEEE.

关键词： Load Balancing machine learning Networking software Defined Network Traffic

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