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检索条件"任意字段=Proceedings of the 9th Knowledge-Based Software Engineering Conference"

共 20366 条记录，以下是691-700 订阅

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Dronexscan: Detection and Identification of Vehicles in Aerial Images Captured by Unmanned Aerial Vehicles(UAVs) 9

Dronexscan: Detection and Identification of Vehicles in Aeri...

引用

9th International conference on Communication and Electronics Systems, ICCES 2024

作者： Anchitaalagammai, J.V. Kavitha, S. Murali, S. Sarvesh, Rlk. Seshanand, S. Javanesh, A. Velammal College of Engineering and Technology Madurai India Madurai India

ISBN: (纸本)9798350377972

this research study proposes Drone-TOOD, the low-power consumption task-aligned object detection algorithm for vehicle detection that was designed from the images captured by UAVs. In the aim of bringing low-power, real-time use into this methodfrom the ESP32-CAM image-processing module, it brings the principles of TOOD into it to balance precision and velocity while detecting vehicles through aerial views and challenges such as variation in altitude, lighting conditions, and orientations of the vehicles. In this regard, the low computational capabilities of ESP32-CAM are supplemented by optimized deep learning methods offering high performance in effective detection with minimal power consumption. Drone-TOOD has performed outstandingly well within the domain of UA V-based vehicle detection and hence asserts suitability for surveillance, traffic monitoring, and search-and-rescue applications. © 2024 IEEE.

关键词： Drones

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Comparative Analysis of Classifiers for Cyber Attack Prediction on LUflow Dataset with Explainability 9

Comparative Analysis of Classifiers for Cyber Attack Predict...

引用

9th IEEE International conference for Convergence in Technology, I2CT 2024

作者： Gunti, Geethika Sree, Kuruva Divya Gupta, Deepa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350394474

As the cyber threats areevolving, intrusions exploit vulnerabilities in software or systems and can occur 24/7, targeting individuals and *** Intrusion detection system (IDS) is designed to help in the detection of cyberattacks as a part of cybersecurity. Researchers are developing machine learning(ML) based IDS to detect the attacks in a network, and this has proven to be very effective. In this work, we propose a framework using various machine learning classifiers to classify different types of attack classes include benign, malicious and outlier on different sets of LUFlow intrusion detection data. Random Forest and Decision tree have outperformed with an accuracy of 91% on combined year data whileconsidering the individual years data, logistic regression performed less well than all other algorithms including ensemble *** study includes the examination of precision, recall, and F1 score metrics for each of the employed classification and ensemble methods. Additionally, the most accurate model has been subjected to model interpretability procedures, which have shown the notable characteristics that have a major impact on the categorization of classes. © 2024 IEEE.

关键词： Decision trees

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Design and Development of an Apple Fruit Disease Identification Methodology based on Enhanced Deep Learning Strategy 9

Design and Development of an Apple Fruit Disease Identificat...

引用

9th International conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Mallikarjuna, G. Parivazhagan, A. Vasumathi, G. Hemajothi, S. Sabitha, R. Madanapalle Institute of Technology & Science Department of Eee Andhra Pradesh Madanapalle India Kalasalingam Academy of Research And Education Department of Cse Krishnankoil India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Ece Chennai India Rajalakshmi Engineering College Department of Cse Chennai Thandalam India

ISBN: (纸本)9798350365092

Apple Fruit Disease Identification plays a pivotal role in ensuring optimal crop health and yield in agricultural settings. In this study, we propose a novel approach leveraging Enhanced Deep Learning Strategy (EDLS) with Cascaded ShuffleNet architecture for accurate and efficient detection of apple fruit diseases. the proposed methodology integrates state-of-the-art deep learning techniques with a lightweight and efficient architecture to achieve high accuracy in disease identification. through extensive experimentation and validation on a diverse dataset of apple fruit images, our approach demonstrates exceptional performance, with an accuracy of 97%. the utilization of Cascaded ShuffleNet enables effective feature extraction while minimizing computational resources, making it suitable for deployment in resource-constrained environments. Our findings underscore the efficacy of the proposed methodology in addressing the challenges of apple fruit disease identification, thereby contributing to enhanced crop management practices and sustainable agriculture. © 2024 IEEE.

关键词： Deep learning

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Electronic Element Parameter Estimation of Amplifier Circuit using Regression Algorithms 9

Electronic Element Parameter Estimation of Amplifier Circuit...

引用

9th International conference on Integrated Circuits, Design, and Verification, ICDV 2024

作者： Chen, Xuxin Cai, Cheng Wang, Huapei Huo, Fang Shanghai DianJi University School of Electronic Information Engineering Shanghai China Technologies Co. Ltd Manufacturing Business Division Shanghai China

ISBN: (纸本)9798350371864

this paper discusses the pivotal role of integrated circuits (ICs) in contemporary electronic devices and the component error issues they encounter. ICs facilitate the rapid advancement of electronic technology through a high degree of integration;however, the intricate fabrication process and delicate operations during manufacturing lead to component errors, which have become an urgent challenge in the testing domain. To address this, the paper proposes a novel test scheme based on S-parameters and regression algorithms. this approach measures S-parameters through computer-aided virtual simulation technology and a network analyzer, and combines various common regression algorithms to predict the parameters of the circuit components. A comparative analysis of these algorithms is conducted, laying the foundation for subsequent testing of circuit components. © 2024 IEEE.

关键词： Integrated circuit testing

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Energy Efficiency in Manufacturing Process on CNC Machine - A SME Case Study 9

Energy Efficiency in Manufacturing Process on CNC Machine - ...

引用

9th International conference on Business and Industrial Research, ICBIR 2024

作者： Setiawan, Rizal Justian Chen, Yu-Tzu Ma'ruf, Khakam College of Engineering Yuan Ze University Department of Industrial Engineering and Management Taoyuan Taiwan Yogyakarta State University Faculty of Engineering Department of Mechanical Engineering Education Yogyakarta Indonesia

ISBN: (纸本)9798350383010

this study focuses on optimizing the use of a coolant pump to clean the CNC machine holder while conserving electrical energy. Coolant pumps serve several functions, including cleaning, lubrication, and cooling. there are three types of cooling pumps available for CNC machines such as shower coolant, through spindle coolant, and bed flushing coolant. the bed flushing coolant pump is responsible for cleaning the engine mount of metal scraps. However, this pump is always running, even when the machine is on standby or not in production activity, leading to unnecessary energy waste. the goal of this research is to improve energy efficiency by optimizing the bed flushing coolant pump with TRIZ design analysis. the approach involves adding a timer-based adaptive program to automatically turn off the bed flushing coolant pump 60 seconds after the production process is complete or when the machine is not in operation. based on the six-month implementation, there has been a significant saving of electrical energy in the CNC of 936 kWh, which equates to a reduction in energy consumption of 12.5%. © 2024 IEEE.

关键词： Scrap metal

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Compaction of Spacecraft Operational Models with Metamodeling Domain knowledge 18

Compaction of Spacecraft Operational Models with Metamodelin...

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

作者： Someya, Kazunori Aoki, Toshiaki Ishihama, Naoki Japan Aerosp Explorat Agcy Space Technol Directorate 1 Tsukuba Ibaraki Japan Japan Adv Inst Sci & Technol Sch Informat Sci Nomi Ishikawa Japan Japan Aerosp Explorat Agcy Res & Dev Directorate Tsukuba Ibaraki Japan

ISBN: (纸本)9789897586477

Due to the difficulty of performing repairs during flight, spacecraft is operated according to operational scenarios tested before launch. Operational models, a type of SysML activity diagram, can be used to depict these scenarios. Although it aids in communication between engineers and stakeholders, the activity diagram can rapidly grow rather large. Due to the extensive operational model, it is therefore challenging to review the activity diagram, which could result in serious issues. therefore, to make operational models easier to evaluate, they should be made concise. this study offers a metamodel that offers stereotypes that can succinctly characterize spacecraft operational scenarios. First, a mind map was used to depict the domain knowledge of spacecraft operations. Second, stereotype metamodel was created by extracting common knowledge from the mind map. Utilizing stereotypes, operational models' size can be decreased;however, crucial review-related data could be lost. therefore, shrinking stereotypes assures that crucial information would not be lost. Several trials were conducted and showed that the number of elements of operational models with stereotypes, as well as their size, reduced by almost half, compared with the original ones, allowing for a simplified review process and boosting trust in the accuracy of the operational scenarios.

关键词： Spacecraft Operation Metamodel Compaction Review Model-based Systems engineering

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Analysis of Multi-Object Recognition System Using Novel Faster Region-based Convolutional Neural Network with Accuracy Compared to Naive Bayes Algorithm 9

Analysis of Multi-Object Recognition System Using Novel Fast...

引用

9th International conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Kumar, K. Vishnu Logu, K. Saveetha School of Engineering Simats Dept of Cse Chennai Tamilnadu Thandalam India

ISBN: (纸本)9798350365092

Aim: the mark of this review is to contemplate the introduction of the Faster Region based Novel Convolutional Mind Association (FCNN) and Simple Bayes Estimation (NBA) in multi not set in stone to redesign accuracy. Materials and Systems: the COCO dataset comprises of north of 200,000 pictures that cover an extensive variety of item classes, including individuals, creatures, vehicles, family things, and that's only the tip of the iceberg, as expressed in a report by ('Kaggle' 2022). Because they depict objects in a variety of complex and naturalistic contexts and were taken from a variety of indoor and outdoor scenes, these images are difficult benchmarks for object detection algorithms. With an example size of twenty for every strategy, recreations were completed in this AI study with the Quicker District based Novel Convolutional Brain Organization (FCNN) and Guileless Bayes Calculation (NBA) procedures. To accomplish the most ideal pH reformulation, the boundaries of FCNN and NBA were changed in the reenactments. the adequacy size for the two groups was calculated using forty samples and an 80 percent power. Results: Considering the came by results, the Speedier region based novel convolutional mind network has a truly basic better gathering accuracy of 81.81% diverged from the Guileless Bayes Estimation of 65.05%. there exists no really basic between the Faster locale based convolutional mind association and Guileless Bayes Estimation considering a free model T-test with the value of p=0.000 (Independent model T Test p © 2024 IEEE.

关键词： Machine learning

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Design of Automated Smart Attendance System Using Deep Learning based Face Recognition 9

Design of Automated Smart Attendance System Using Deep Learn...

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9th International conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Mahyudin, Ritonga Sanchez, Domenic T. Mrudula, S. Teena Veluri, Ravi Kishore Malik, Jawarneh Raghuvanshi, Abhishek Universitas Muhammadiyah Sumatera Barat Indonesia Cebu Technological University-NEC Cebu Philippines Velagapudi Ramakrishna Siddhartha Engineering College Ec Department Andhra Pradesh Vijayawada India Aditya Engineering College Department of Cse Andhra Pradesh India Oman College of Management And Technology Muscat Oman Inti International University Malaysia Mahakal Institute of Technology Department of Cse Ujjain India

ISBN: (纸本)9798350365092

the process of taking attendance in the traditional manner is one that is both laborious and time-consuming. Face recognition and identification technology was developed with the primary objective of providing a timesaving automated solution for tracking attendance. this article presents Design of automated smart attendance system using deep learning based face recognition. Camera IoT devices are used to acquire images. these images are stored in cloud via IoT gateway. Images contain many noises. To remove or reduce these noises, adaptive median filter are used. Once noise is removed, then images quality is enhanced by particle swarm optimization. Enhanced images are classified by CNN, CNN VGG 16 and Xception CNN deep learning techniques. Performance is compared on the basis of metrics like- accuracy, specificity and recall. Xception CNN is outperforming other techniques used in the framework. Accuracy, Specificity and Recall rate of Xception CNN is 99.33%, 98.66% and 99.33 percent respectively. © 2024 IEEE.

关键词： Image classification

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Code Defect Detection Method based on BERT and Ensemble 9

Code Defect Detection Method Based on BERT and Ensemble

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9th International conference on Computer and Communications, ICCC 2023

作者： Ye, Jianjie Wei, Le Chengdu University of Information Technology School of Software Engineering ChengDu China Sichuan Manufacturing Information Industry Collaborative Innovation Center ChengDu China

ISBN: (纸本)9798350317251

A code defect detection method based on Bidirectional Encoder Representations from Transformers (BERT) and ensemble learning was proposed to solve the low accuracy and reliability of the existing neural networks in the task of code defect detection due to insufficient feature extraction and weak generalization ability. this method involves extracting the Abstract Syntax Tree (AST) and the Token depth feature information vector of the source code using the BERT model. then, the different feature information expressions of text vectors were learned by establishing the heterogeneous multi-base classifiers of such neural network models as Graph Convolutional Networks (GCN), Convolutional Neural Networks (CNN), and Bidirectional Long Short-term Memory (BiLISM). Subsequently, the different feature information of text vectors was fused via Stacking ensemble learning for training and prediction. this fusion aimed to bolster the model feature extraction and generalization abilities. As demonstrated by the experimental findings, the proposed method exhibits an enhanced accuracy, showcasing improvements of 7.08% and 11.76% when juxtaposed against VulPecker and SySeVr, respectively. © 2023 IEEE.

关键词： Learning systems

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Architectural Design Decisions for Data Communication of Cyber-Physical Systems 30

Architectural Design Decisions for Data Communication of Cyb...

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30th Asia-Pacific software engineering conference (APSEC)

作者： Amiri, Amirali Ntentos, Evangelos Zdun, Uwe Univ Vienna Res Grp Software Architecture Fac Comp Sci Vienna Austria

ISBN: (纸本)9798350344172

Designing Cyber-Physical Systems (CPS) is a complex task involving integrating physical and digital components to achieve specific objectives. this process consolidates data from various Internet of things (IoT) devices and sources to generate meaningful insights and actionable outcomes. IoT-cloud data communication comprises multiple stages, e.g., data collection, processing, analysis, and visualization. Adopting a comprehensive approach that considers physical and digital aspects is essential to ensure effective data communication in CPS. As a result, architectural design choices are crucial in determining CPS functionality and runtime qualities, e.g., performance, security, and reliability. While numerous CPS architectural patterns and practices have been proposed, much of the relevant knowledge remains scattered across various sources, such as practitioner blogs and system documentation. these sources are often based on personal experiences and lack consistency. To address this gap, our study presents the outcomes of an in-depth qualitative investigation into practitioners' descriptions of the best practices and patterns in CPS architecture. We have developed a formal architectural decision model using a model-based qualitative research method. We aim to bridge the division between scientific understanding and practical use cases, enhance comprehension of practitioners' approaches to CPS, and provide decision-making support for designing CPS applications.

关键词： Architectural Design Decisions Cyber-Physical Systems Edge-Cloud Data Communication software Architecture Grounded theory Gray Literature CPS Practitioners

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