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共 66536 条记录，以下是111-120 订阅

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An Efficient Algorithm for software Reliability Prediction via Harris Hawks Optimization 2nd

An Efficient Algorithm for Software Reliability Prediction v...

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2nd international conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Kong, Li Sheng Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

software reliability has become an increasingly important characteristic as software systems continue to be integrated into all parts of our lives. While the occurrence of failure after a period of time is within the nature of all software systems, the ability to estimate and predict failure is greatly beneficial to software development and maintenance. Hence, software Reliability Growth Models (SRGMs) were developed to fulfill this requirement. Conventional parameter estimation methods of SRGM functions were complex and left more to be desired. Consequently, metaheuristics such as Swarm Intelligence algorithms were introduced to estimate and optimize the parameters of SRGM functions. Likewise, Swarm Intelligence algorithms were found to be exceptionally effective in estimating and optimizing the parameters of SRGM functions. This paper presents a novel approach to parameter estimation on three different SRGM functions through Harris’ Hawks Optimization (HHO). The proposed HHO design is compared against numerous different variants of Swarm Intelligence algorithms and is shown to be significantly more efficient than the majority of the compared designs. Moreover, as the proposed design is a base variant of Swarm Intelligence algorithms, various avenues of future improvements have also been presented to improve its overall prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harris’ Hawks Optimization Parameter Estimation software Reliability Growth Models software Reliability Prediction Swarm Intelligence

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Astronomical Image Processing by the Lemur software 24

Astronomical Image Processing by the Lemur Software

引用

6th Asia conference on Machine Learning and computing, ACMLC 2024

作者： Khlamov, Sergii Savanevych, Vadym Briukhovetskyi, Oleksandr Vlasenko, Vladimir Dikov, Yevhen Dmytrenko, Artem Department of Media Systems and Technologies Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of systems engineering Kharkiv National University of Radio Electronics Kharkiv Ukraine Western Center of Radio Engineering Surveillance National Space Facilities Control and Test Center Kyiv Ukraine Space Research and Communication Center National Space Facilities Control and Test Center Kyiv Ukraine Department of Software Engineering GlobalLogic Incorporation Kharkiv Ukraine Institute of Astronomy V. N. Karazin Kharkiv National University Kharkiv Ukraine

ISBN: (纸本)9798400710018

The astronomical images are made by cameras with the charge-coupled device (CCD). They can be received from the different sources, like servers, clusters, predefined series, archives or "live" (online) data streams. The astronomical images processing is focused on but not limited to the following tasks: data mining, knowledge discovery, big astronomical data processing, filtering, background alignment, brightness equalization, segmentation, classification, image recognition, object's image detection, object's astrometry and photometry, moving object detection, parameters determination of the object's image and apparent motion, reference objects selection and others. The modern Lemur software of the Collection Light Technology (CoLiTec) project (https://***) was developed using the described above technologies and approaches. The Lemur software is designed to perform a sequence of the following main steps: pre-processing (astronomical information collection -> worst data rejection -> useful data extraction -> data mining -> classification -> background alignment -> brightness equalization), image processing (segmentation -> typical form analysis -> recognition patterns applying -> detection of the object's image -> astrometry -> photometry -> objects identification -> tracks detection), knowledge discovery (Solar System objects or artificial satellites to be discovered, tracks parameters for the investigation, light curves of the variable stars, scientific reports in the international formats). The paper describes the modern features for the astronomical image processing implemented in the Lemur software. It has assisted in making over 1700 discoveries of asteroids, including 5 NEOs, 21 Trojan asteroids of Jupiter, 1 Centaur. In total it has been used in about 800 000 observations, during which five comets were discovered. All such observations and discoveries have official observational circulares from the Minor Planet Center (MPC) of the international Ast

关键词： Asteroids

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

引用

31st international conference on multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Empirical Validation of software Defect Prediction Models Using Grid Search Gray Wolf Optimization 3rd

Empirical Validation of Software Defect Prediction Models Us...

引用

3rd international conference on computing and Communication Networks, ICCCN 2023

作者： Malhotra, Ruchika Meena, Shweta Department of Software Engineering Delhi Technological University Delhi110042 India

ISBN: (纸本)9789819726707

software engineering principles deal with the quality of software in consideration of various attributes. software defect prediction is one of the key domains in the field of software engineering. software defect prediction aims at providing non-defective softwares to the end user. In order to ensure the reliability and robustness of software, defects must be identified at each stage by conducting various review activities. In order to analyze the quality of software various defect prediction models are developed. In this study, a defect prediction model is modeled using Traditional Gray Wolf Optimization (TGWO) and Grid Search GWO (GSGWO). The performance of defect prediction models is analyzed on the basis of Within-Project Defect Prediction (WPDP_GWO), tenfold_GWO, TGWO, and GSGWO. Further, homogeneous cross-project defect prediction model is also developed under the consideration of predictive modeling. The performance of prediction models analyzed using AUC performance metric. Statistical tests are used for the validation of prediction models. Thus, RFGWO performed best for CPDP and GSGWO performed best for WPDP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Compact and Biocompatible Microstrip Antenna for Non Invasive Dental Biosensing 6

Compact and Biocompatible Microstrip Antenna for Non Invasiv...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Ashok Reddy, B. Nooka Appa Rao, B. Rexiline Sheeba, I. Sharath Kumar Reddy, C. Praveen, D. Hitesh, T. Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

Designing a compact, efficient, and biocompatible microstrip antenna for non invasive dental biosensing, operating at 2.4 GHz (Industrial Scientific Medical band). Microstrip antenna is optimized for miniaturization and performance using suitable substrate materials and advanced simulation software (HFSS). Various feeding techniques are explored for optimal impedance matching and bandwidth. This project paves the way for wireless dental technology advancements in diagnostics, treatment monitoring, and patient comfort. © 2025 IEEE.

关键词： Antenna feeders

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Cloud computing technology and its application in simulation and analysis of large-scale LNG storage tanks 24

Cloud computing technology and its application in simulation...

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2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Chen, Tuanhai Wang, Xuefei Wang, Bendong Ji, Ningning Zhen, Cong Xiang, Hua CNOOC Gas and Power Group Co. Ltd. Beijing China Beijing Internet Based Engineering Application Co. Ltd. Beijing China School of Mechanical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400709869

LNG storage tank is one of the most core equipments of liquefied natural gas (LNG) receiving station, and with the increase of natural gas demand, the development trend of LNG storage tank is large-scale and super-large-scale. Due to the complex structure, high cost, harsh working conditions and high safety requirements of large-scale LNG storage tanks, a large number of finite element analysis calculations must be carried out during the design process, and the feasibility of the design scheme is verified based on the results of the simulation calculations. The finite element simulation technology of large-scale LNG storage tanks involves a series of complex modeling, load solving, normalization and other technologies, which is a great challenge to the tool software and the engineer's personal technical ability and experience, and it is difficult to meet the requirements of rapid program design and optimization and improvement of the traditional single person - single tool work mode, so my team in the large-scale LNG storage tanks for many years, on the basis of the accumulation of experience in the simulation technology combined with Therefore, based on years of experience in large-scale LNG storage tank simulation technology, my team combines cloud computing technology with the bottom software and hardware resources, technical experience, industry specifications and other unified planning and construction to form a simulation cloud platform for LNG storage tanks, and releases the simulation function to the application engineers in the form of professional APP service to realize the rapid promotion and application of technical experience, and this paper makes a systematic elaboration on the key technology and application effect. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cloud platforms

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Proceedings - 5th IEEE international conference on Mobile Cloud computing, Services, and engineering, MobileCloud 2017

Proceedings - 5th IEEE International Conference on Mobile Cl...

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5th IEEE international conference on Mobile Cloud computing, Services and engineering, MobileCloud 2017

ISBN: (纸本)9781509063253

The proceedings contain 33 papers. The topics discussed include: Cowbird: a flexible cloud-based framework for combining smartphone sensors and IoT;HVC: a hybrid cloud computing framework in vehicular environments;MEC-ConPaaS: an experimental single-board based mobile edge cloud;an efficient double auction mechanism for on-demand transport services in cloud-based mobile commerce;social media intermediation robot for elderly people using external cloud-based services;cost efficient repository management for cloud-based on-demand video streaming;augmented resource allocation framework for disaster response coordination in mobile cloud environments;mobility prediction for efficient resources management in vehicular cloud computing;ROSAC: a round-wise fair scheduling approach for mobile clouds based on task asymptotic complexity;cloudlet-assisted heuristic offloading for mobile interactive applications;decision tree-based approaches for handling offloading decisions and performing adaptive monitoring in MCC systems;inferring mobile apps from resource usage patterns;extensible orchestration of elastic IP multimedia subsystem as a service using open bat;SoftAccess: cloud-based software defined virtualized wireless mobile access networks;HSC-IoT: a hardware and software co-verification based authentication scheme for Internet of Things;and a mobile cloud hierarchical trust management protocol for IoT systems.

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Analysis of Key Parameters in software Defined Network for Optimizing the Performance 5

Analysis of Key Parameters in Software Defined Network for O...

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5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Chandra, Pushpendra Kumar Sharma, Suraj Ray, Niranjan Kumar Negi, Satish Kumar Guru Ghasidas Vishwavidyalaya Dept. of Cse Bilaspur India Kiit Deemed to be University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331522100

SDN is a novel approach to networking by decoupling the control and data planes and allowing for centralized management and dynamic configuration. The paper looks into the core performance parameters critical to SDN functionality, focusing on their role in ensuring efficient, scalable, and reliable network operations. Additionally, it highlights optimization strategies and tools that address the challenges posed by these parameters. This research study aims to provide the desired broad perspective in development of innovative solutions to improve SDN performance in meeting the requirements of modern and emerging networking environments. © 2025 IEEE.

关键词： performance parameters software-Defined Networking (SDN)

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Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in software Logs 3

Leveraging Sequential Deep Learning Models for Robust Compil...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 IEEE.

关键词： Application programs

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Two-Dimensional Multi-Deep CNNs for Accurate and Robust software Defect Detection: A Performance-Driven Approach 5

Two-Dimensional Multi-Deep CNNs for Accurate and Robust Soft...

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5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Veeramachaneni, Vinod Mallick, Pradeep Kumar Rout, Subhashree Pareek, Piyush Kumar Colorado Technical University Department of Information Technology United States Kiit Deemed to be University School of Computer Engineering India Nalanda Institute of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Nitte Meenakshi Institute of Technology Yelahanka Karnataka Bengaluru560064 India

ISBN: (纸本)9798331522100

software systems are growing in size and complexity. These properties make software defect prevention challenging. Thus, automatically predicting software module defects can help engineers conserve resources. Several ways have been presented to discover and fix these issues cheaply. However, these solutions may need a performance bump. This paper introduces the Multi-Deep Convolutional Neural Network to improve binary and multi-class software classification. Instead of one-dimensional CNNs, the proposed model captures n-gram characteristics inside and between datasets using a two-dimensional multi-scale convolutional technique. Advanced crow search optimisation algorithm (ACSOA) is used to optimally select model parameters. Besides Apache Active MQ, the study employed Eclipse data sets to evaluate the recommended deep learning method. F1-score, recall, accuracy, and precision were utilized to validate the model. The results show that the suggested strategy finds software vulnerabilities better and more reliably than current models. © 2025 IEEE.

关键词： Search engines

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