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

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Research on the application of data mining technology in software engineering 3

Research on the application of data mining technology in sof...

引用

3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Yitao, Lin School of Software Heilongjiang University Heilongjiang Province Harbin450000 China

The role of data mining in software engineering is obvious, but there is a lack of mining depth. In the past, engineering layout and function distribution problems in software development, and the framework construction was unreasonable. Therefore, this paper proposes a software system based on data mining technology. First, the software engineering project is divided, and the software engineering project design is carried out according to the interactive requirements to realize the preprocessing of software engineering. Then, according to the design criteria, the design collection of software engineering is formed, and the information system is analyzed in depth. MATLAB simulation shows that the design interactivity and accuracy of data mining are superior to traditional design methods under the condition that software engineering requirements are consistent. © 2023 Elsevier B.V.. All rights reserved.

关键词： Data mining

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AI-Based development on software ecosystem platforms 23

AI-Based development on software ecosystem platforms

引用

37th Brazilian Symposium on software engineering, SBES 2023, held in conjunction with the Brazilian conference on software: Theory and Practice, CBSoft 2023

作者： Silva, Izaque Gomes, Jonas Braga, Regina David, José Maria N. Stroele, Victor Soares, Rômulo Oliveira, Vitória De Oliveira, André Luiz Computer Science Department Federal University of Juiz de Fora Minas Gerais Brazil

ISBN: (纸本)9798400707872

Recent advances in machine learning have stimulated interest in integrating Artificial Intelligence (AI) capabilities into large-scale software and services. However, the support of software engineering (SE) methods and tools did not go along with this evolution. Therefore, the SE community needs improved methods and tools to support this new reality. The development of AI-based applications involves many challenges. One of these challenges is to deal with data management. In this paper, we discuss some of the challenges faced by our group in evolving a previous ecosystems platform, to support the development of intelligent applications using machine learning (ML) techniques. Our findings drive providing services for data and intelligent algorithms management. © 2023 ACM.

关键词： Ecosystems

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A Multi-label Support Vector machine-based Transformer Fault Prediction Method 26

A Multi-label Support Vector Machine-based Transformer Fault...

引用

26th ACIS international Winter conference on software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Shao, Zhimin Shao, Qi Wang, Yong Zhang, Junsan State Grid Shandong Electric Power Company State Grid Corporation of China Jinan China Qingdao China

ISBN: (纸本)9798350345865

Large power transformers typically use oil-immersed transformers, and the insulation oil can play a role in heat dissipation and insulation. By analyzing the gas composition in the oil, transformer failures can be predicted. Support vector machines are a data mining algorithm suitable for small sample data, but they are aimed at binary classification problems. However, there are multiple types of transformer failures. Therefore, a multi-label radial basis support vector machine is proposed for transformer failure prediction. The model analyzes gases with strong correlations among various gas content to construct a transformer failure prediction model. machine learning algorithms can quickly mine potential rules in data, providing references for reasonable maintenance plans for transformers. © 2023 IEEE.

关键词： Support vector machines

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Split and Federated learning with Mobility in Vehicular Edge Computing 21

Split and Federated Learning with Mobility in Vehicular Edge...

引用

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

作者： Moon, Sungwon Lim, Yujin Sookmyung Women's University Dept. of IT Engineering Seoul04310 Korea Republic of

ISBN: (纸本)9798350345889

Vehicular edge computing (VEC) is a promising technology to support vehicular applications that leverage machine learning (ML) technology. Due to limited resources of the vehicle, the vehicle uses Split learning (SL) to split the computation of the ML model and offload it to the VEC server (VECS). Federated learning (FL) is also used for data privacy and parallel training of the vehicles. Therefore, SplitFed learning, which combines SL and FL, enables parallel processing, which is an advantage of FL, and reduces the computational burden on the vehicle through ML model split, which is an advantage of SL. However, the SplitFed learning does not consider the mobility of device/vehicle. Therefore, we propose a SplitFed learning with mobility method to minimize the training time of the model. SplitFed learning with mobility method is a migration method of the ML model when the vehicle moves from the current serving VECS to the target VECS. Through simulations, compared with conventional SplitFed learning where the vehicle travels after 50% and 80% of training is completed, the proposed method can reduce training time by about 19-33% for LeNet and by about 22-44% for VGG16, respectively, and does not degrade accuracy of model. © 2023 IEEE.

关键词： Edge computing

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3rd international Semantic Intelligence conference, ISIC 2023

3rd International Semantic Intelligence Conference, ISIC 202...

引用

3rd international Semantic Intelligence conference, ISIC 2023

ISBN: (纸本)9789819773558

The proceedings contain 36 papers. The special focus in this conference is on Semantic Intelligence. The topics include: On the Potential of Sustainable software Solutions in Smart Manufacturing Environments;technologies and Concepts for the Next-Generation Integrated Energy Services;from Text to Voice: A Comparative Study of machine learning Techniques for Podcast Synthesis;artificial Intelligence and Legal Practice: Jurisprudential Foundations for Analyzing Legal Text and Predicting Outcomes;unveiling the Truth: A Literature Review on Leveraging Computational Linguistics for Enhanced Forensic Analysis;navigating the Digital Frontier: Unraveling the Complexities and Challenges of Emerging Virtual Reality;challenges to Admissibility and Reliability of Electronic Evidence in India in the Age of ‘Deepfakes’;an In-Depth Exploration of Anomaly Detection, Classification, and Localization with Deep learning: A Comprehensive Overview;comparative Analysis of Docker Image Files Across Various Programming Environments;Dimensions of ICT-Based Student Evaluation and Assessment in the Education Sector;effectiveness of Online Education System;a Formula for Effective Evaluation Practice Using Online Education Tool;deciphering the Catalysts Influencing the Willingness to Embrace Digital learning Applications: A Comprehensive Exploration;Pedagogical Explorations in ICT: Navigating the Educational Landscape with Web 2.0, 3.0, and 4.0 for Transformative learning Experiences;admission Prediction for Universities Using Decision Tree Algorithm and Support Vector machine;Visualization and Statistical Analysis of Research Pillar of Top Five THE (Times Higher Education)-Ranked Universities for the Years 2020–2023;assessing machine learning Algorithms for Customer Segmentation: A Comparative Study;genre Classification of Movie Trailers Using Audio and Visual Features: A Comparative Study of machine learning Algorithms.

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Detection of Oil Spill Events at Sea Using machine learning 5

Detection of Oil Spill Events at Sea Using Machine Learning

引用

5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Sandhiya, S. Ramya, R. Sukitha, M. Vikhashni, S. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350321425

Oil spill detection performed by using machine learning (ML) involves building and training a model to identify and classify oil spill images from satellite or drone imagery. Synthetic Aperture Radar (SAR) is a technology that has been extensively used in oil spill detection. It is to detect and monitor tea-based oil pollution by airplanes and satellites furnished with SAR. It is necessary to see and spot oil spills in seas, oceans, rivers, and lakes. The ML model can be trained by images labeled with clean water and water with oil spills gathered from satellite and drone images with high resolution. In this proposed method, AIS data of ships were utilized to predict the oil spill events at sea before it happens. With the parameters like container details, type of oil, dispersion rate, skim rate, etc. The probability of an oil spill is detected by training a machine learning model. The software used for implementing the idea is Spyder IDE and algorithms like Linear Regression, Support Vector machine (SVM), and Decision Tree were employed here. The major challenge in oil spill detection is accuracy and time complexity. © 2023 IEEE.

关键词： Decision trees

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Analyzing a Semantics-Aware Bug Seeding Tool's Efficacy: A qualitative study with the SemSeed tool 23

Analyzing a Semantics-Aware Bug Seeding Tool's Efficacy: A q...

引用

37th Brazilian Symposium on software engineering, SBES 2023, held in conjunction with the Brazilian conference on software: Theory and Practice, CBSoft 2023

作者： Martins, ViníCius Ramalho, Camila T. Marques, Lucas Cordeiro Pereira, Juliana Alves Garcia, Alessandro Lucena, Carlos Feijó, Bruno Furtado, Antonio L. Rio de Janeiro Brazil

ISBN: (纸本)9798400707872

software developers can benefit from machine learning solutions to predict bugs. machine learning solutions usually require a lot of data to train a model in order to achieve reliable results. In this context, developers use bug-seeding approaches to generate synthetic bugs, which should be similar to human-made bugs. A recent state-of-the-art tool, called SemSeed, uses a semantics-aware bug seeding approach in order to hopefully achieve more realistic bugs. In this study, we report on the investigation of SemSeed's efficacy. We create a survey that shows developers a bug and asks whether it is a Real or Synthetic bug. We collected and analyzed the answers from 47 developers, and we show that SemSeed can be very accurate in seeding realistic bugs. © 2023 ACM.

关键词： machine learning

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The Assessment of the Design Risk for ORAN

The Assessment of the Design Risk for ORAN

引用

2023 international conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Shiang-Jiun Chang, YaHan Raksasook, Nattapol Wu, Wei-Liang National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan National Taipei University of Technology International Electrical Engineering and Computer Science Taipei Taiwan University of Taipei Department of Mathematics Taiwan

ISBN: (纸本)9798350324174

The Open Radio Access Network (O-RAN) uses a virtualization architecture to disaggregate components and connect them through open interfaces, and also uses software defined networking (SDN) technology for decoupling. The O-RAN architecture is an innovative telecommunication architecture, which also brings some potential problems for security, including but not limited to the followings: new architecture (disaggregation, Real Time RAN intelligent controllers (Near-RT RIC), virtualization), the open interfaces (O1, A1, and E2, etc.), artificial intelligent and machine learning (AIML) workflow, and standardization issues. Facing the new structure, in addition to the original risks, new risks also need to be considered. "Secure in design"is imperative to mitigate the information security risks. This study will look at the risk assessment of O-RAN from the perspective of testing and do security analysis. © 2023 IEEE.

关键词： machine learning

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Design of Advanced Privacy Preserving Model for Protecting Privacy within a Fog Computing Scenario 10

Design of Advanced Privacy Preserving Model for Protecting P...

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Jain, Pinkal Thada, Vikas Lala, Ajay Amity University Department of Cse Gwalior India Gyan Ganga College of Technology Department of Cse M.P. Jabalpur India

ISBN: (纸本)9798350382471

Due to the ongoing development of computer and communication abilities, the IoT is becoming more and more significant in many smart applications. As a result, IoT devices produce plenty of data every day, which provides a strong basis for ML to succeed. The IoT data's strict privacy standards, however, make its machine learning extremely challenging. Numerous privacy-based ML strategies have been developed to safeguard the privacy of data. The majority of current schemes do not provide generic answers and only focus on specific models, which is not the best option for engineering practice. To solve this issue, we proposed a powerful machine learning model that protects privacy within a fog computing scenario. The software service provider (SSP) can train models while protecting the privacy of the data on the fog nodes using the APPML (Advanced Privacy Preserving machine learning) framework. Only SSP may access the model parameters, and it is possible to maintain the privacy of data stored at the fog nodes. Experimental results demonstrates that, when compared to the existing systems, our approach lowers the processing and communication overhead and provides the highest privacy preservation. © 2023 IEEE.

关键词： APPML (Advanced Privacy Preserving machine learning) Fog nodes Internet of things machine learning Privacy Privacy Preservation

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machine learning in the Quantum Era 2

Machine Learning in the Quantum Era

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2nd IEEE international conference on Futuristic Technologies, INCOFT 2023

作者： Khurana, Sachin Nene, Manisha J SoCE&MS Defence Institute of Advanced Technology Pune India

ISBN: (纸本)9798350308846

machine learning (ML) has been extensively utilized in various scientific and engineering domains. But the inherent constraints and computational complexity that arise in classical machine learning are particularly evident when dealing with large-scale, high-dimensional datasets or when attempting to solve intricate optimisation problems. Quantum systems exhibit unconventional patterns that are typically not effectively generated by traditional systems and can also tackle high dimensional and large size datasets efficiently. Therefore, it is believed that quantum computing systems may outperform traditional computing devices in machine learning tasks. Quantum machine learning (QML) has emerged as a rapidly developing field that combines the concepts of quantum mechanics and machine learning to solve complex problems across various disciplines. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. However, the major challenges encountered are hardware limitations, noise, and development of quantum algorithms. The objective of this paper is to provide an insight on the foundational principles, essential algorithms, various associated platforms, potential applications and challenges being faced by QML. As data continues to grow in size and complexity, together with the emergence of nonlinear and high-dimensional real-world situations, the computing benefits offered by QML have the potential to facilitate significant advancements in the fields of healthcare, finance, robotics, logistics, communication, and cyber security. This paper presents the basis for future research work related to development of quantum algorithms in the domain of machine learning. © 2023 IEEE.

关键词： Algorithms machine learning Quantum Computing

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