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检索条件"主题词=software algorithms"

共 12509 条记录，以下是381-390 订阅

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A software Designed to Generate Transistor Models with Interconnections

A Software Designed to Generate Transistor Models with Inter...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Timur I. Tretyakov Ilya I. Nikolayev Anastasia A. Drozdova dept. of Television and Control Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

In this paper, an algorithm is developed to generate a transistor model that takes into account parasitic parameters of its interconnections. The algorithm includes the following steps: determining parasitic parameters of the transistor footprint and interconnections on the PCB and inside its enclosure, and setting the parameters of the SPICE-model of the transistor crystal. Based on this algorithm, a graphical interface of the program module for creating augmented transistor models has been developed. The models take into account parasitic parameters of its interconnections that depend on a particular radio-electronic device to be developed. To verify the model, we created an augmented model of the BFR91A bipolar transistor. Applying this model, we built a model of an oscillator circuit and simulated this circuit using the transistor model provided by its manufacturer. The augmented model significantly reduced the voltage amplitude at the output of the oscillator (by 7.38 dB) and increased the oscillation start-up time (from 2 to 9 µs), This indicates that the augmented transistor model provides simulation results that reflect its behavior under real-world operating conditions.

关键词： Simulation software algorithms Integrated circuit interconnections Voltage Crystals software Manufacturing

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Towards a Unified Approach for software Architecture Recovery

Towards a Unified Approach for Software Architecture Recover...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Hasan Sözer Ozyegin University İstanbul Türkiye

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large scale software systems are decomposed into gross-level components to reduce maintenance efforts. software Architecture Recovery (SAR) approaches have been introduced to automatically cluster software modules for identifying modular components. In our previous work, we analyzed and hybridized various algorithms for clustering software modules. In addition to the employed clustering algorithm, SAR approaches are subject to many variations from multiple perspectives like the types of input and recovered artifacts. In this paper, we systematically review these variations. We aim at extending our work towards a unified approach for SAR in the future.

关键词： Analytical models software architecture Reviews Computational modeling software algorithms Clustering algorithms Computer architecture

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A Novel Approach for software Defect Prediction Through Relational Association Rules Based on Cost-Sensitive Learning

A Novel Approach for Software Defect Prediction Through Rela...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Meng Tian Shihai Wang Wentao Wu Wandong Xie School of Reliability and System Engineering Beihang University Beijing China Information Center of China North Industries Group Corporation Beijing China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

software defect prediction (SDP) can predict software modules with potential defect risks before software testing, thereby optimizing the allocation of testing resources. Relational association rules can characterize the relations between data attributes and reveal relevant patterns in complex data. We propose a software defect prediction model using relational association rules based on cost-sensitive learning (CSLRAR). To address the inherent class-imbalance problem of defect data, CSLRAR employs one-class classification strategy to separately mine relational association rules for the defective class and non-defective class using Apriori. Furthermore, we use all training data to construct a feature relational association rule selection mechanism, which which serves as the basis for defective relational association rules set $(RAR_{+})$ and non-defective relational association rules set $(RAR_{-})$ to determine whether the rule is retained. The feature relational association rule selection mechanism can improve the quality of the rules set obtained during the rule generation stage. In addition, we conducted experimental evaluations on nine publicly available datasets from the PROMISE database. By comparing and analyzing five baseline models, it has been proven that CSLRAR is significantly better than the baseline in terms of Balance, MCC, and Gmean.

关键词： Training Databases software algorithms Training data Predictive models Prediction algorithms Feature extraction software Data models Association rule learning

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Deep Learning — Based Feature Fusion and Estimation for Ensuring software Resuability

Deep Learning — Based Feature Fusion and Estimation for Ens...

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Progressive Innovations in Intelligent Systems and Data Science (ICPIDS), International Conference on

作者： Cheripalli Lavanya Gurram Prasanna Nyakapu Rajender V.Shobha Rani Remidi Sravani Department of CSE – Data Science Malla Reddy Engineering College(A) Secunderabad Department of CSE MRECW Secunderabad Department of CSE KLEF (Deemed To Be University) Vijayawada Andhra Pradesh Department of CS&AI SR University Warangal India Department of CSE-AIML MLR Institute of Technology Secundrabad India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

The document addresses the challenges and objectives in the field of software reusability estimation, highlighting limitations of existing techniques and proposing a novel approach using a Deep Neuro-Fuzzy Network (DNFN) optimized by a Competitive Tunicate Swarm Optimization (CTSO) algorithm. Key challenges identified include insufficient static coupling measures for object-oriented metrics, difficulties in predicting software aging and cost for mobile applications, and the dynamic nature of decision-making processes affecting reusability. Additionally, current methods like integrated random forest and gradient boosting techniques lack meta-heuristic approaches and deep learning integration for better accuracy. The proposed methodology involves extracting significant reusability metrics, transforming data using the box-cox model, and performing feature fusion with Levenshtein distance and Deep Residual Network. The CTSO algorithm, integrating Competitive Multi-Verse Optimizer and Tunicate Swarm Algorithm, is employed to train the DNFN. Through a comparative analysis, the proposed CTSO-based DNFN demonstrates superior performance in terms of MAE, MMRE, and SEM when compared to traditional neural networks and fuzzy inference systems, indicating more accurate and reliable software reusability estimation.

关键词： Accuracy software algorithms Estimation Fuzzy neural networks Feature extraction Inference algorithms software reliability software measurement software reusability Residual neural networks

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A Novel software Test Data Generation Framework based on Multi-level Fuzzy Clustering Algorithm

A Novel Software Test Data Generation Framework based on Mul...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： Hongjie Lin School of Economics and Management Xiamen University of Technology Xiamen Fujian China

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

software testing plays a crucial role in ensuring the quality and reliability of software products. This study presents a novel software test data generation framework based on the multi-level fuzzy clustering algorithm. The framework aims to optimize the testing process by efficiently generating test cases and prioritizing them for execution. The proposed methodology integrates hierarchical reinforcement learning and hierarchical clustering techniques to improve the effectiveness and comprehensiveness of software testing. A detailed review of recent advances in software testing methodologies is provided, focusing on differential regression testing for REST APIs, test case prioritization, program repair using neural translation models, and other key areas. The proposed framework is evaluated through experiments on real-world software datasets, demonstrating its superiority in terms of non-redundancy rate and vulnerability count detection compared to existing methods. The results highlight the effectiveness and relevance of the proposed framework in improving the efficiency and reliability of software testing.

关键词： software testing Reviews Image processing software algorithms Refining Clustering algorithms Reinforcement learning

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Towards Classical software Verification using Quantum Computers

Towards Classical Software Verification using Quantum Comput...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Sebastian Issel Kilian Tscharke Pascal Debus Fraunhofer AISEC Garching Germany

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

We explore the possibility of accelerating the formal verification of classical programs with a quantum computer.A common source of security flaws stems from the existence of common programming errors like use after free, null-pointer dereference, or division by zero. To aid in the discovery of such errors, we try to verify that no such flaws *** our approach, for some code snippet and undesired behavior, a SAT instance is generated, which is satisfiable precisely if the behavior is present in the code. It is in turn converted to an optimization problem, that is solved on a quantum computer. This approach holds the potential of an asymptotically polynomial *** examples of common errors, like out-of-bounds and overflows, but also synthetic instances with special properties, specific number of solutions, or structure, are tested with different solvers and tried on a quantum *** use the near-standard Quantum Approximation Optimization Algorithm, an application of the Grover algorithm, and the Quantum Singular Value Transformation to find the optimal solution, and with it a satisfying assignment.

关键词： Quantum computing Codes software algorithms Noise Approximation algorithms Hardware Encoding software Optimization Formal verification

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Developing a New MapReduce Framework with SVM-based software Solution for Lyme Lesion Impact on Alzheimer's Disease Prevention

Developing a New MapReduce Framework with SVM-based Software...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Saravanan. M.S G. Sreehitha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences and Assistant Professor Sri Padmavati Mahila Visvavidyalayam Tirupathi Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Alzheimer's disease has many symptoms to occur, one of the latest research studies identified that Lyme disease, caused by “Borrelia burgdorferi” also one of the reasons indirectly affect the neuroinflammation. The dataset used for this research study is supplied by many web sources, that are involved in feature extraction to apply on machine learning techniques for extracting meaningful knowledge. This work proposing a new MapReduce framework-based Support Vector Machine (SVM) model for better prediction of impact of Lyme on Alzheimer's disease. The MapReduce based framework using SVM model creates more impact on prediction of Alzheimer's disease at the earliest. The clinical data with images are collected from distributed environments and applied on preprocessing and feature extraction stages then applied to proposed MapReduce based SVM model for better accuracy and optimal classification for complex patters on Lyme image dataset. From these results the Alzheimer's risk factors are predicted for early treatment process. The results obtained by this study will give early decision making for highly available sources of treatment procedures and gives more insight on preventive care on each and every patient involved in diagnosis of Lyme disease prediction, lead to identify the Alzheimer's disease prediction.

关键词： Support vector machines Accuracy Machine learning algorithms software algorithms Machine learning Predictive models Feature extraction software Classification algorithms Alzheimer's disease

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Development of Signal Detection and Recognition Algorithm Using software-Defined Radio

Development of Signal Detection and Recognition Algorithm Us...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Anastasia O. Khodakova Yulia D. Naumova Sergey A. Chechenya Sergey V. Soldatkin Anton Y. Boev Konstantin V. Vorontsov Department of Formation and Processing of Radio Frequency Signals National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The article presents an analysis of the existing algorithms used in the implementation of a receiver of multi-position signals based on SDR technology. As a result, a comprehensive approach has been developed to solve the extended task of radio monitoring, which includes not only scanning the spectrum, detecting radio emissions, determining the type of modulation and the location of the source, but also decoding the data contained in the message. To implement the SDR-based algorithm, the GNU Radio program was used, which is convenient because it allows you to build circuits using software modules.

关键词： software algorithms Circuits Signal processing algorithms Receivers software Decoding Synchronization Monitoring Signal detection software radio

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Design and Implementation of Digital Language Translation software based on LSTM Algorithm

Design and Implementation of Digital Language Translation So...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Lei Yue Xuchang Vocational College of Ceramic Xuchang Henan China

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Translation software is one of the important bridges for global exchange and cooperation in the context of globalization. This study builds a Long Short-Term Memory (LSTM) model with Encoder-Decoder based architecture. The encoder converts the source language text into a single fixed length vector representation. The input sequence is translated into a set of vectors and the final vector are used to provide the context for decoder which translates the vectors into the target language text. In this article, we also introduced the attention mechanism for the model, so that the model can focus on the information it needs to solve the current translation problem. The experimental data analysis results show that the LSTM model based translation software designed in this paper improves the performance of multiple language pairs. Compared with traditional RNN and GRU models, the LSTM model reaches 95% of the Metric for Evaluation of Translation with Explicit Ordering (METEOR) maximum value, with the maximum translation time of 104ms, and the maximum stability of 99%.

关键词： Training Analytical models software algorithms Network security software Vectors Data models Stability analysis Long short term memory Optimization

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Research on Adaptive software Development System Driven by ODBAM Tools and Network software Analysis

Research on Adaptive Software Development System Driven by O...

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Electronic Technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Yifan Lan School of Computing Beijing Jiaotong University Weihai China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

This paper proposes an implementation method of software interface based on functional modeling. Then according to the characteristics of SVG, the corresponding interface is automatically generated. It can effectively improve the work efficiency of developers. The generated SVG interface and JavaScript script work together to realize the full capabilities of the entire software. The main research contents include the abstraction and construction of network structure software based on agents, the adaptive and self-evolution mechanism of dynamic binding, the design and implementation of ODAM software based on organizational abstraction, the strategic expression language SADL based on self-adaptation and self-evolution and other core technologies. The system consists of ODBAM Tools, a network software analysis and development tool set, a programming tool set, and an operation support platform. An abstract interface designer allows users to intuitively specify the functional features of a software interface; it can automatically generate interfaces that match the display features of various terminals based on the functional features of the user interface. This paper uses SVG files to show the appearance of the final application interface, and uses JavaScript scripts to complete specific processing logic. With this tool, developers can write one program at a time. In this way, program interfaces are automatically generated multiple times for different devices.

关键词： Analytical models Adaptation models Adaptive systems software algorithms User interfaces Programming software systems

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