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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 3227 条记录，以下是351-360 订阅

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machine learning-Based Crack Detection Methods in Ancient Buildings 24

Machine Learning-Based Crack Detection Methods in Ancient Bu...

引用

2024 Guangdong-Hong Kong-Macao Greater Bay Area international conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Fang, Tianke Hui, Zhenxing Rey, William P. Yang, Aihua Liu, Bin He, Yuanrong School of Information Technology Mapua University Manila1200 Philippines School of Computing and Information Engineering Xiamen University of Technology Xiamen Fujian361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen Fujian361024 China School of Information Media Zhangzhou Vocational College of Science and Technology Zhangzhou Fujian363200 China College of Software Engineering Xiamen University of Technology Xiamen Fujian361024 China

ISBN: (纸本)9798400717147

This research paper introduces an innovative machine learning-based methodology for detecting cracks in ancient buildings, a vital aspect of architectural heritage conservation. Recognizing the limitations of conventional techniques in addressing the complex nature of historical structures, this study explores the integration of deep learning algorithms with enhanced image processing methods tailored for aged and varied materials typical of ancient architecture. The paper includes a comprehensive process involving meticulous data collection from diverse ancient structures, employing specialized preprocessing methods to handle the unique challenges of old surfaces. The development of a custom machine learning model, designed to adapt to the intricacies of historical construction, is thoroughly detailed. Experimental results demonstrate the model's superior performance in accurately identifying structural cracks compared to traditional methods. This research not only significantly contributes to the preservation of historical buildings but also exemplifies the versatile application of machine learning in the field of architectural conservation. The findings hold promise for revolutionizing the approach to maintaining and restoring our architectural heritage, ensuring longevity and integrity. © 2024 ACM.

关键词： Image enhancement

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Man-hour Prediction for Complex Industrial Products 4

Man-hour Prediction for Complex Industrial Products

引用

4th international Informatics and software engineering conference, IISEC 2023

作者： Unal, Ahmet Emin Boyar, Halit Pak, Burcu Kuleli Cem Yildiz, Mehmet Erten, Ali Erman Cagri Gungor, Vehbi Istanbul Tech. University Dept. of Comp. Engineering Istanbul Turkey Adesso Turkey R&d Department Istanbul Turkey Ermetal Automotive R&d Center Bursa Turkey Erkalip Quotation Department Bursa Turkey Abdullah Gül University Dept. of Comp. Engineering Kayseri Turkey

ISBN: (纸本)9798350318036

Accurately predicting the cost is crucial for the success of complex industrial projects. There can be several sources contributing to the cost. Traditional methods for cost estimation may not provide the required accuracy and speed to ensure the success of the project. Recently, machine learning techniques have shown promising results in improving cost estimation in various industrial products. This study investigates the performance of gradient-boosting machine learning models and feature engineering techniques on a private dataset of metal sheet project man-hour costs. A comparison of distinct models is conducted, key aspects influencing cost are identified, and the implications of incorporating domain-specific knowledge, including its advantages and disadvantages, are assessed based on performance outcomes. Experimental results demonstrate that LightGBM and XGBoost outperform other models, and feature selection and synthetic data generation techniques improve the performance. Overall, this study highlights the potential of machine learning in metal sheet sampling projects and emphasizes the importance of feature engineering and domain expertise for better model performance. © 2023 IEEE.

关键词： Forecasting

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Information Security Control engineering for an Industrial Company with '1C' software

Information Security Control Engineering for an Industrial C...

引用

2023 IEEE international conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Zalozhnev, Alexey Yu. Chistov, Dmitry V. V.A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences Active Systems Laboratory Moscow Russia Financial University Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350327816

The issues related to information security control engineering for an industrial company with '1C' software are considered in this paper. The article discusses the types of threats to the company's information resources, including collective knowledge stored on electronic media. The paper contains standards and other documents governing the development and operation of an industrial company's information security. A brief description of the 1C software platform and 1C ERP is given. To prevent unauthorized access to the IT infrastructure and misappropriation of information important to the client, a set of needed organizational and technical measures with the use of 1C software products is considered. Some practical conclusions are also given. © 2023 IEEE.

关键词： industrial company information security information security control security engineering software software system

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Computational Ethology: Short Review of Current Sensors and Artificial Intelligence Based Methods 1

引用

24th international conference on engineering Applications of Neural Networks (EANN)

作者： Aguilar-Moreno, Marina Grana, Manuel Univ Basque Country UPV EHU Computat Intelligence Grp San Sebastian Spain

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Computational Ethology provides automated and precise measurement of animal behavior. Artificial Intelligence (AI) techniques have also introduced the enhanced capabilities to interpret experimental data in order to extract accurate ethograms allowing the comparison of animal models with high discriminative power. In this short review we introduce the most recent software tools that employ AI tools for this endeavor, including the popular deep learning approaches.

关键词： Computational Ethology Animal Behavior Sensors Tracking machine learning Deep learning

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Classifying Hand Gestures through EMG Data with machine learning 10

Classifying Hand Gestures through EMG Data with Machine Lear...

引用

17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Agarwal, Shweta Chadha, Raman Bhati, Bhoopesh Singh Chandigarh University Computer Science and Engineering Department Punjab Mohali India Indian Institute of Information Technology Computer Science and Engineering Department Haryana Sonepat India

ISBN: (纸本)9789380544472

This study explores the application of machine learning techniques in the classification of hand gestures for controlling upper limb neuroprosthetics. The non-stationary nature of sEMG in practical applications presents a challenge in maintaining consistent performance. To address this issue, we applied five distinct machine learning methods to classify hand gesture data collected from 8 sensors through a Myo wristband. The Kaggle dataset provided over 2900 samples from 4 classes, resulting in over 2900 samples per group. We evaluated the performance of the classification methods based on various metrics such as precision, recall, F1 score, support, and accuracy. Our findings show that QDA performed the best due to its high accuracy. The study identified and classified four hand gestures based on their performance metrics. Our research provides a simple approach to controlling smart equipment like prosthetic limbs, wheelchairs, entertainment software, and human-machine interaction. The use of machine learning in the classification of hand gestures for neuroprosthetics has the potential to significantly enhance the functionality and ease of use of these devices. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Support vector machines

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IoT Flooding Attack Defense in SDN: A Real-Data machine learning Approach 1

IoT Flooding Attack Defense in SDN: A Real-Data Machine Lear...

引用

1st international conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Gonzalez, Carlos Charfadine, Salim Mahamat Lorient France Enastic National Superior School of Tic Tchad N'Djamena Chad

ISBN: (纸本)9798350306484

software-defined networking (SDN) has become a promising paradigm for mitigating the limitations intrinsic to legacy network architectures. By decentralizing the control logic from the underlying data-forwarding plane, SDN radically transforms the fundamental principles and practices of network engineering. This study introduces a comprehensive security framework tailored for SDN networks, leveraging the power of machine learning to detect and prevent flooding attacks initiated by poisoned IoT devices. Although the concept of using machine learning for network security is not entirely novel, what sets this research apart is its reliance on real-time data for training and testing a machine learning model. On one hand, the framework consists of a traffic monitoring component that collects network traffic data and identifies potential anomalous traffic flows based on packet size features. On the other hand, a machine learning component then uses this real-data to train a model that can identify and classify attacks. The efficacy of our framework was assessed using a dataset comprising real-time network-traffic data. These results underscore the high level of accuracy achieved by our framework in the detection and classification of attacks. We also demonstrate that our framework can be used to mitigate attacks by blocking malicious traffic. In the classification model, the best-performing machine-learning algorithm was a neural network with an average accuracy of 95 %. Based on our empirical analysis, it has been established that the application of machine-learning techniques holds substantial promise for accurately identifying and mitigating IoT attacks in real time within the context of SDN. The utilization of our framework offers the potential to improve the security of SDN and make it more resilient to other types of attacks. © 2023 IEEE.

关键词： machine learning

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Energy Management System Using Multi Agent Systems with IoT and machine learning

Energy Management System Using Multi Agent Systems with IoT ...

引用

2023 international conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Raju, Leo Vishwa Raj, V. Vishal, E. Vimalan, K.M. SSN College of Engineering Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350333244

The topic of this study is a Smart Microgrid with Dynamic, Distributed Energy Management. At the moment, a SCADA system is used for energy management (Supervisory Control and Data Acquisition). A distributed energy system (DES) called a 'multi-agent system' (MAS) is made up of several software agents that form a loosely connected network and cooperate to solve issues that are more complex than they can handle alone. By selecting the least expensive renewable resource that is readily available at the time, economic and environmental optimization is performed. As a result, dynamic energy management has been put into practice. The purpose of this research is to demonstrate how an energy management system based on MAS may be utilized to optimize a microgrid. machine learning has been used to forecast load, increase power grid efficiency, and establish a stable operating environment. © 2023 IEEE.

关键词： Multi agent systems

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A Survey on machine learning Techniques for Detecting, Mitigating, and Preventing Intrusions 3

A Survey on Machine Learning Techniques for Detecting, Mitig...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Balyan, Amit Kr. Trivedi, Naresh Kumar Sharma, Sanjeev Kumar Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9798350342338

Modern society relies on networks, making cybersecurity a vital field of study. Intrusion detection systems (IDS) monitor network software and hardware for cyber security. Despite decades of research, IDSs struggle to improve detection accuracy, reduce false alarms, and detect novel assaults. Many academics have developed machine learning based IDSs to address the above challenges. machine learning can automatically and accurately detect normal and abnormal data. machine learning approaches can also detect unknown assaults due to their generalizability. A famous study area is deep learning, a machine learning subfield with extraordinary performance. This investigation utilizes data objects to categorize and condense the existing literature on intrusion detection systems (IDS) that are based on machine learning and deep neural network approaches. We recommend this categorization system for cyber security experts. IDSs are defined and classified to start the survey. This section introduces machine learning techniques, metrics, and benchmark datasets. After introducing the suggested taxonomy approach and citing relevant research, we demonstrate how machine learning and deep learning may tackle key IDS issues. Reviewing recent representative research, difficulties, and future advancements are highlighted. © 2023 IEEE.

关键词： Cybersecurity Intrusion Detection System machine learning Network System

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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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Educational Support for Automated Classification of UML Diagrams Using machine learning 20th

Educational Support for Automated Classification of UML Diag...

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20th international conference on Intelligent Tutoring Systems (ITS)

作者： Nedelcu, Irina-Gabriela Opranescu, Veronica Chiriac, Beatrice-Nicoleta Ionita, Anca Daniela Natl Univ Sci & Technol Politehnica Bucharest Splaiul Independentei 313 Bucharest 060042 Romania

ISBN: (纸本)9783031630309;9783031630316

As engineering is very much based on modeling, this is also important for education in this field, and teachers sometimes have to check a very large number of models and determine if they are valid or not. In software engineering, for modeling in conformity with the standard Unified Modeling Language, attempts have been made to automatically classify diagrams and determine whether they conform to this language. This paper shows an approach based onmachine learning and possible improvements made using a feature-based dataset, with the objective of more accurately categorizing designated labels. Employing a specialized neural network tailored for feature-based learning, the study endeavors to enhance classification accuracy and efficiency. Comparative analysis against a pre-existing model trained on a diagram images dataset reveals better results in predictive outcomes.

关键词： Neural networks Unified Modeling Language Teaching tools

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