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检索条件"主题词=software algorithms"

共 12479 条记录，以下是31-40 订阅

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Application of Data Mining algorithms in Financial Analysis software

Application of Data Mining Algorithms in Financial Analysis ...

引用

Communications and Information Technologies (GCCIT), Global Conference on

作者： Fang Xiao School of Communication And Management China

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Risk management plays an important role in the application of financial analysis software, but there is the problem of inaccurate management positioning. Traditional iterative algorithms cannot solve the risk management problems in the application of financial analysis software, and the effect is not satisfactory. Therefore, this paper proposes the application of financial analysis software based on data mining algorithm and analyzes the application in financial analysis software. Firstly, the statistical set theory is used to locate the influencing factors, and the indicators is divided according to the requirements of risk management to reduce the interference factors in risk management. Then, the statistical set theory is used to form a risk management scheme of data mining algorithm, and the risk management results is comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, the data mining algorithm is better than the traditional iterative algorithm in terms of risk management accuracy and risk management influencing factor time.

关键词： Simulation software algorithms Interference Financial management Set theory software Risk management Data mining Iterative methods MATLAB

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Alternative Quadrant Representations with Morton Index and AVX2 Vectorization for AMR algorithms within the p4est software Library

Alternative Quadrant Representations with Morton Index and A...

引用

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Mikhail Kirilin Carsten Burstedde Institute for Numerical Simulation University of Bonn Bonn Germany

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

We present a technical enhancement within the p4est software for parallel adaptive mesh refinement. In p4est mesh primitives are stored as octants in three and quadrants in two dimensions. While, classically, they are encoded by the native approach using its spatial coordinates and refinement level, any other mathematically equivalent encoding might be used instead. Recognizing this, we add two alternative representations to the classical, explicit version, based on a long monotonic index and 128-bit AVX quad integers, respectively. The first one requires changes in logic for low-level quadrant manipulating algorithms, while the other exploits data level parallelism and requires algorithms to be adapted to SIMD instructions. The resultant algorithms and data structures lead to higher performance and lesser memory usage in comparison with the standard baseline. We benchmark selected algorithms on a cluster with two Intel(R) Xeon(R) Gold 6130 Skylake family CPUs per node, which provides support for AVX2 extensions, 192 GB RAM per node, and up to 512 computational cores in total.

关键词： Three-dimensional displays software algorithms Memory management Random access memory Clustering algorithms Prediction algorithms software

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Comparative Analysis of software Reliability Prediction and Optimization using Machine Learning algorithms

Comparative Analysis of Software Reliability Prediction and ...

引用

Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Prashanth Kumar Bolisetty Midhunchakkaravarthy Faculty of AI AI Computing and Multimedia Lincoln University College Selangor Darul Eshan Malaysia Faculty of AI Computing and Multimedia Lincoln University College Selangor Darul Eshan Malaysia

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

software reliability plays a crucial role in assessing the overall quality of software systems. Developing highly reliable software presents challenges, including ensuring usability, maintaining consistent performance, offering reliable service, and facilitating smooth maintenance. To overcome these limitations, researchers have turned to intelligent prediction and optimization algorithms. This paper presents a comprehensive review of software reliability prediction, emphasizing the application of machine learning techniques. It also provides an analytical evaluation of existing methods to identify areas for improvement and future research opportunities in software reliability prediction.

关键词： Machine learning algorithms Reviews software algorithms Machine learning software systems Prediction algorithms software reliability Maintenance Usability Optimization

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Integration of Large Language Models and Computer Vision algorithms in LMS: A Methodology for Automated Verification of software Tasks and Multimodal Analysis of Educational Data

Integration of Large Language Models and Computer Vision Alg...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： E. I. Markin V. V. Zuparova A. I. Martyshkin Penza state technological University PSTU Penza Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This paper presents a study on the design and development of a learning management system (LMS) integrated with large language models (LLMs) and image recognition/generation algorithms to automate software task validation and multimodal educational data analysis. The purpose of the development is to improve the quality of feedback for learners, deepen the understanding of errors made and expand the capabilities of the system by synthesizing formal LLM models and visual processing algorithms. The system is based on the use of domestic LLMs such as GigaChat and YandexGPT, Retrieval-Augmented Generation (RAG) technology, and modern computer vision algorithms. The system architecture is described, including GitVerse for code storage, GitVerse Actions for automated testing of solutions in Docker containers, and image processing modules for analyzing and generating visual data in the educational process. The conducted experiment demonstrates the effectiveness of the integrative approach, which allows to improve the quality of feedback and increase the performance of students. Prospects for further research include optimization of computational resources and development of techniques for integrated analysis of multimodal educational data.

关键词： Visualization Computer vision Codes Image recognition Costs Large language models Computational modeling software algorithms Trajectory Testing

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Search-Based software Testing Driven by Bioinspired algorithms: A Systematic Literature Mapping

Search-Based Software Testing Driven by Bioinspired Algorith...

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International Conference in software Engineering Research and Innovation (CONISOFT)

作者： Joshua E. Hernández-Suárez Mario A. Jiménez-Jiménez Saúl Domínguez-Isidro Jorge Octavio Ocharán-Hernández Facultad de Estadistíca e Informática Universidad Veracruzana Xalapa México

ISBN: (数字)9798331532116

ISBN: (纸本)9798331532123

Bioinspired algorithms have gained relevance in Search-Based software Testing (SBST) mainly because they can find multiple solutions to difficult-to-solve problems. Exploring the usage trends and elements of its implementation is necessary for software engineering since it allows for understanding the area's current state and proposing guidelines for innovation. We proposed a Systematic Literature Mapping to analyze the canonical bioinspired algorithms used in SBST. During the mapping conduction, 60 studies were analyzed, in which bio-inspired algorithms were used in different software testing activities, such as test data generation, test case generation, test case prioritization, and others. The practical implications of our research are significant. The most commonly applied bioinspired algorithms in these activities are the Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization. Concerning problem types, most of these algorithms are focused on single-objective, with or without constraints, and, to a lesser extent, multi-objective and phased problems. According to the results, SBST driven by bio-inspired algorithms favors variables such as execution time and computational cost. Code coverage is usually used as a performance metric to evaluate its effectiveness. The application of canonical bioinspired algorithms mainly seeks to solve single-objective problems.

关键词： software testing Measurement Technological innovation Systematics Codes software algorithms Data collection Computational efficiency Genetic algorithms software engineering

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Enhancing software Test Effort Estimation using Ensemble Learning algorithms

Enhancing Software Test Effort Estimation using Ensemble Lea...

引用

Intelligent Technologies (CONIT), International Conference on

作者： K Venkatraman M Akashvarma S Siddharth Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In software engineering, accurate test effort prediction is critical to project schedule and resource efficiency. Conventional approaches, which depend on past performance or expert opinion, frequently fail to provide a reliable estimate of the testing effort. These approaches might offer some insight at first, but as project complexity rises, they become less scalable and more error-prone. This study explores the use of machine learning to improve the estimation of software test effort in response. This work intends to overcome the shortcomings of conventional methods and create more accurate and scalable solutions by utilising the power of machine learning algorithms. The goal of the research is to determine the best machine learning strategy for software test effort estimation through extensive testing and comparative analysis. The results of this study are compared using metrics such as RMSE, MSE, MAE, and R2 score, with Random Forest algorithm demonstrating superior performance over other algorithms.

关键词： Schedules Machine learning algorithms Accuracy software algorithms Estimation software software reliability

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Applying Genetic algorithms for Optimizing Program Increment Planning in software Development Teams

Applying Genetic Algorithms for Optimizing Program Increment...

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International IEEE Conference on Intelligent Systems, IS

作者： Dimitar Lyubchev Angel Marchev Department of Management University of National and World Economy Sofia Bulgaria

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

Effective and efficient Program Increment (PI) planning plays a pivotal role in enhancing collaboration and productivity within software development teams following the Agile way of working. This paper investigates the integration of Genetic algorithms (GAs) into the PI planning process to address the challenge of optimizing resource allocation. PI planning can be a complex and time-consuming process, requiring teams to balance numerous interdependent factors such as resource constraints, task urgency and importance, as well as team member availability. Genetic algorithms, inspired by the principles of natural selection, provide an alternative optimization technique that can explore a vast solution space and propose a number of possible and more efficient PI plans. By modeling the PI planning problem as a genetic algorithm, teams can leverage the algorithm's ability to rapidly converge on near-optimal solutions, leading to improved resource utilization, reduced planning time, and enhanced team alignment.

关键词： Decision making software algorithms Collaboration Manuals Planning Resource management Stakeholders Security Genetic algorithms software development management

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Improvement of Learning Ability and Prediction Accuracy of Intelligent software Through Machine Learning and Deep Learning algorithms

Improvement of Learning Ability and Prediction Accuracy of I...

引用

International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Xue Yu Harbin Institute of Information Technology Harbin China

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

With the increasing demand for software, the learning ability and prediction accuracy of software can no longer meet people's needs, but there is currently no good solution. To address this issue, this study utilizes machine learning and deep learning algorithms to improve these issues. A literature review method is adopted to investigate and summarize the research on machine learning and deep learning algorithms in improving the learning ability and prediction accuracy of intelligent software. The learning ability of software is explored by collecting relevant literature, analyzing and comparing the characteristics and application scenarios of different algorithms, and a deep comparison is conducted on the prediction accuracy and learning ability of the software through experiments. The research results indicate that the prediction accuracy of the algorithm in this article is between 91% and 99%, and machine learning and deep learning algorithms have significant effects on improving the learning ability and prediction accuracy of intelligent software.

关键词： Deep learning Machine learning algorithms Accuracy software algorithms Predictive models Prediction algorithms Feature extraction software Data mining Systematic literature review

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Research on Malicious software Detection and Clearing algorithms Based on Artificial Intelligence

Research on Malicious Software Detection and Clearing Algori...

引用

Electrical Drives, Power Electronics & Engineering (EDPEE), International Conference on

作者： Shanshan Lei Jindong He Huimin Ye State Grid Fujian Electric Power Research Institute Fuzhou Fujian China

ISBN: (数字)9798350395631

ISBN: (纸本)9798350395648

The proliferation of malicious software poses extreme security threats to network users, enterprises, industrial facilities, networks, and information devices. With the rapid growth of artificial intelligence (AI), machine learning (ML) and deep learning (DL) technologies are gradually being introduced into the detection of malicious software, which is called intelligent malware detection technology. Malicious software detection is based on malicious software analysis technology to obtain program information and conduct statistical analysis to construct a data source. Finally, a set method is used to classify and identify malicious software. Traditional malware detection methods, faced with the difficulty of quickly identifying a large number of malicious samples, propose a DL based malware detection and removal algorithm. Using DL algorithm, train a malware detection model by extracting prominent features of malware, and develop malware detection tools to detect and classify malware, improving the efficiency and precision of malware detection. The experimental results show that the algorithm designed in this article can effectively detect and clear malicious software.

关键词： Text recognition Statistical analysis Soft sensors software algorithms Internet security Feature extraction Industrial facilities

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software Fault Detection algorithms Using Artificial Intelligence: A Review and Classification

Software Fault Detection Algorithms Using Artificial Intelli...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Lungisani Ndlovu Bheki Cossa Caroline Makokoe Lethukuthula Khumalo Ntomfuthi Ntshangase Siphelele Myaka Nomalisa Ndhlovu Mamello Mtshali Information and Cyber Security Centre Council for Scientific and Industrial Research (CSIR) Pretoria South Africa

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

With the rise of software applications, software development has become a rapidly evolving field, propelled by technological advancements and the increasing demand for innovative solutions to sustain our digital era. Detecting software faults has emerged as a critical attribute in the software development cycle to ensure system reliability, quality, and user satisfaction. However, traditional fault detection methods often suffer from drawbacks such as time consumption and error proneness, particularly in the context of large and complex software systems. In response to these hurdles, artificial intelligence (AI) presents itself as a promising methodology, with the aim of improving accuracy, scalability, automation, and proactiveness in fault detection. This paper systematically reviews the literature on AI-based software fault detection algorithms. Leveraging the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a curated set of 50 peer-reviewed research publications was identified in Google Scholar and subsequently analyzed using a classification coding framework. The findings reveal that AI-based software fault detection algorithms played an important role in improving software system reliability and performance.

关键词： Fault detection Scalability software algorithms Propulsion software systems Classification algorithms software reliability Internet Artificial intelligence software development management

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