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检索条件"主题词=software algorithms"

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Method and Process Mining Based software for ECG Analysis and Arrhythmia Detection

Method and Process Mining Based Software for ECG Analysis an...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Kseniya Demushkina Andrey Kuzmin Information and Computing Systems Penza state University Penza Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Currently, the use of neural network based solutions in medical decision support systems remains popular. In order to overcome the hidden decision-making process called “black box”, the authors propose to use the “white box” principle based on the process mining approach. The aim of the work is to develop and implement a process mining based method of electrocardiosignal analysis and arrhythmia detection. Pan-Tompkins algorithm for electrocardiosignal signal analysis and ProM 6.11 framework for software implementation were used. As a results a new intelligent process mining based electrocardiosignal analysis method is proposed. It consists of conversion of digital data of cardiac signal into event log, construction of Petri net and construction of diagnostic decision tree. The original plugin generates the event log in the .xes format using Pan-Tompkins algorithm. ProM 6.11 AlphaMiner plugin generates a Petri net that reflects the relationship of events in the log. The resulting event log allows building a diagnostic decision tree using ProM 6.11 discovery of the process data-flow plugin. The study demonstrates the applicability of the proposed method using normal and arrhythmic cardiac signal records from PhysioNet database. The proposed solution on the base of process mining technology and ProM 6.11 framework allows implementing software for transparent analysis of cardiac signals data for medical decision support systems. The next step is to conduct statistical assessments of the effectiveness of the proposed solution and to expand the number of informative parameters used for analysis.

关键词： Process mining Decision support systems Arrhythmia PROM software algorithms Signal processing algorithms Electrocardiography software Decision trees Medical diagnostic imaging

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Boosting software Quality: A Machine Learning Technique for Predicting Defects

Boosting Software Quality: A Machine Learning Technique for ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Akilan E. Daniel Alwin Infant P Jainish G R Computer science & Engineering Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Computer Science and Engineering Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges.

关键词： Computational modeling software algorithms software quality Predictive models Prediction algorithms Data models software reliability Decision trees software measurement Embedded software

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A New Approach for Early Detection of Vulnerabilities Across the Automotive software Development

A New Approach for Early Detection of Vulnerabilities Across...

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2024 International Symposium on Computer Science and Educational Technology (ISCSET)

作者： Dimitrios Datsogiannis Ariane Heller Wolfram Hardt Professorship of Computer Engineering TU Chemnitz Chemnitz Germany

ISBN: (数字)9798350390803

ISBN: (纸本)9798350390810

This paper presents a new approach of evaluating automotive development processes, starting from the early stages of development to the final product. The main objective of this paper is to ensure transparency among stakeholders and provide accurate feedback throughout the development cycle, with the ultimate goal of preventing software defects and conflicts that can be reproduced. The concept aims to enable stakeholders to efficiently evaluate the status and quality of software releases. The latter can be achieved by an intelligent recommendation system that selects and poses a few precise questions from a large database during each development cycle, minimizing time demands on development stakeholders while obtaining important results. To accomplish this, we have extracted the most crucial development metrics from the literature and transformed them into questions. To each question is assigned a weight based on its importance, and each answer is given a corresponding value. Through this process, we have created a dynamic pool of potential questions that can be selected using the reinforcement learning algorithm of Contextual Multi-Armed Bandits (CMAB), which serves as a recommendation system to seven key roles among development stakeholders. The software implementation of this method enables not only the escalation of the number of components under evaluation but also the continuous improvement of its performance over time.

关键词： Heuristic algorithms software algorithms software quality Reinforcement learning Stakeholders software measurement Vehicle dynamics Recommender systems software development management Testing

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AI in software Patenting: Jurisprudential Analysis under the European Patent Convention, UK, and US Legal Frameworks

AI in Software Patenting: Jurisprudential Analysis under the...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Ahtshamuddin Ansari Rajneesh Arshi Ansari Mithilesh Kumar Yadav Vishva Gaurav Ashutesh Anand College of Law IIMT University Meerut U.P. School of Arts and Humanities IIMT University Meerut U.P.

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Artificial intelligence in software patenting presents unique problems and creates opportunities across a wide variety of legal jurisdictions. Drawing on jurisprudential analysis under the European Patent Convention, the United Kingdom, and the legal frameworks of the United States of America, this paper sheds lighter on these intricacies. It provides, among other things, the requirements of patentability with an emphasis on the inventive step and novelty in AI-driven inventions. Most importantly, it signals divergences of the legal interpretation and practice of such within these regions and gives space to such polemics as questions about algorithm patentability or human contribution and all this means for innovation policy. The comparative legal analysis herein offers insight into a fast-changing landscape of AI software patenting. Finally, it provides recommendations on ways and means to harmonize further laws regulating patents in light of rapid technological development in AI.

关键词： Training Patents Technological innovation Law software algorithms Europe software Artificial intelligence Protection Standards

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XANDAR: An X-by-Construction Framework for Safety, Security, and Real-Time Behavior of Embedded software Systems

XANDAR: An X-by-Construction Framework for Safety, Security,...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Tobias Dörr Florian Schade Juergen Becker Georgios Keramidas Nikos Petrellis Vasilios Kelefouras Michail Mavropoulos Konstantinos Antonopoulos Christos P. Antonopoulos Nikolaos Voros Alexander Ahlbrecht Wanja Zaeske Vincent Janson Phillip Nöldeke Umut Durak Christos Panagiotou Dimitris Karadimas Nico Adler Clemens Reichmann Andreas Sailer Raphael Weber Thomas Wilhelm Wolfgang Gabler Katrin Weiden Xavier Anzuela Recasens Sakir Sezer Fahad Siddiqui Rafiullah Khan Kieran McLaughlin Sena Yengec Tasdemir Balmukund Sonigara Henry Hui Esther Soriano Viguer Aridane Alvarez Suarez Vicente Nicolau Gallego Manuel Muñoz Alcobendas Miguel Masmano Tello Karlsruhe Institute of Technology Germany University of Peloponnese Greece Aristotle University of Thessaloniki Greece German Aerospace Center (DLR) Institute of Flight Systems Germany AVN Innovative Technology Solutions Limited Cyprus Vector Informatik GmbH Germany Bayerische Motoren Werke Aktiengesellschaft Germany Queen's University Belfast UK Fent Innovative Software Solutions SL Spain

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The safe and secure implementation of increasingly complex features is a major challenge in the development of autonomous and distributed embedded systems. Automated design-time procedures that guarantee the fulfillment of critical system properties are a promising approach to tackle this challenge. In the European project XANDAR, which took place from 2021 to 2023, eight partners developed an X-by-Construction (XbC) design framework to support developers in the creation of embedded software systems with certain safety, security, and real-time properties. The design framework combines a model-based toolchain with a hypervisor-based runtime architecture. It targets modern high-performance hardware, facilitates the integration of machine learning applications, and employs a library of trusted safety and security patterns to reduce the implementation and verification effort. This paper describes the concepts developed during the project, the prototypical implementation of the design framework, and its application in both an automotive and an avionics use case.

关键词： Technical requirements Embedded systems Runtime software architecture software algorithms Real-time systems Hardware

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An Efficient DDOS Attack Detection in software-Defined Networks Using Enhanced Long Short-Term Memory

An Efficient DDOS Attack Detection in Software-Defined Netwo...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： S. Nivethithai B. Hariharan Department of computer science SRM Institute of Science and Technology Kattankulathur India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

software-defined networking (SDN) offers enhanced control over network infrastructure through a central controller, but it remains vulnerable to security threats like DDoS attacks. This paper proposes an automatic attack detection system for SDN, comprising pre-processing, feature selection using Adaptive Walrus Optimization Algorithm (AWaOA), and classification using the enhanced long short-term memory (ELSTM) model. Pre-processing eliminates redundant and missing data, while feature selection optimizes the important features for classification. The ELSTM model then classifies traffic as malicious or legitimate. Performance evaluation includes metrics like accuracy, precision, and recall to assess the effectiveness of the proposed approach in detecting and mitigating DDoS attacks in SDN environments.

关键词： Adaptation models Accuracy software algorithms Denial-of-service attack Feature extraction Security software defined networking Computer crime Optimization Long short term memory

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Enhancing software Quality with Fault Detection and Prediction based on AI-Driven Model

Enhancing Software Quality with Fault Detection and Predicti...

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

作者： Ramesh Bishukarma Sumeet Mathur Puneet Matapurkar Sandeep Gupta University of Waikato NZ – Joint Institute at Zhejiang University Hangzhou China Department of Mathematical Sciences & Computer Application Bundelkhand University Jhansi India Department of Computer Science Engineering SATI Vidisha India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Early defect detection and prediction are essential in software engineering to reduce costs and improve quality. This study presents an AI-driven approach for fault detection and prediction employing ML techniques on the CM1 dataset from NASA’s software metrics program. The data preprocessing steps include normalisation, feature selection, and the use of the ADASYN algorithm to enhance minority class detection. Faults are predicted using ML classifiers: Extra Trees (ET), Support Vector Machine (SVM), KNN, RF, MLP, and NB. F1 score, recall, accuracy, and precision are a metrics utilised to assess a models. When it comes to defect prediction, the Extra Trees Classifier (ETC) model outperforms all of the others with an accuracy91.51%, precision91.76%, and recall99%. Although the Naive Bayes model shows lower accuracy (77.61%) and efficiency, it offers a simpler approach for fault detection. Future research in this area should be guided by these findings, which highlight the promise of AI-driven approaches to enhance frameworks for software defect prediction and quality assurance.

关键词： Support vector machines Accuracy Fault detection software algorithms software quality Predictive models Nearest neighbor methods software measurement Classification tree analysis software engineering

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HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning software Defect Prediction Framework

HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Software...

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International Conference on Information Networking

作者： Madhusmita Das Biju R. Mohan Ram Mohana Reddy Guddeti Department of Information Technology National Institute of Technology Karnataka Surathkal Mangalore India

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Defect prediction plays a crucial role for any software system across various domains, as its failure may cause unavoidable and undeniable scenarios. For reliable software, defect-free is considered as one of the most important criteria. This research aims to design a hybrid swarm-optimized machine learning software defect prediction (HSoMLSDP) framework to predict software defects. We strive to do this by designing a swarm-optimized machine learning defect prediction (SoMLDP) model within the HSoMLSDP framework. In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper introduces a hybrid swarm optimization algorithm (SOA) referred to as the gravitational force Lévy flight grasshopper optimization algorithm-artificial bee colony (GFLFGOA-ABC) algorithm. By combining the enhanced exploration feature of the gravitational force Lévy flight grasshopper optimization algorithm (GFLFGOA) with the robust exploitation capacity of the artificial bee colony (ABC), the GFLFGOA-ABC algorithm is proposed. Prior to validating the HSoMLSDP framework, the LFGFGOA-ABC algorithm’s performance is first confirmed by experiments on 6 benchmark functions (BFs) to assess its mean and convergence rate. Following BF verification, the second experiment tunes the hyperparameters of ML models (ANN, GB, XGB) to improve the defect accuracy of the SoMLDP model. As an enhancement of accuracy justifies the correctness of the SoMLDP model, thus validating the HSoMLSDP framework.

关键词： Machine learning algorithms Accuracy software algorithms Machine learning Predictive models Benchmark testing Prediction algorithms software systems Optimization Gravity

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Integrating AI into software Engineering: A Critical Review and Future Directions

Integrating AI into Software Engineering: A Critical Review ...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Aditya Pai H Sharath K R Kavita Babalad Guru Prasad M S Chandrappa S Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India Department of Computer Science and Engineering Graphic Era (Deemed-to-be University) Dehradun India School of Computing and Information Technology REVA University Bangalore India School of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Department of Computer Applications Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The field of Artificial Intelligence (AI) has been witnessing a huge demand in the field of research, tools development, and applications of deployment. There are multiple software companies which are shifting their focus to developing AI systems, many are also deploying the AI paradigms to their current system. Correspondingly, the academic research community has also used AI paradigms to solve the traditional software engineering problems. AI has become very useful to the software engineering community. The paper also uses a survey of the most commonplace methods of using AI into the software Engineering, that covers the methods for SE phases such as Requirements, Design, Development, Testing, Release, and Maintenance. The paper also aims in answering to the sufficient intelligence in the SE lifecycle, this evaluates the overlapping of the SE and AI domains, and challenge the current conventional wisdom of the state-of-the-art.

关键词： software testing Surveys software maintenance Reviews software algorithms Prediction algorithms Requirements engineering Artificial intelligence software engineering software development management

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A Comprehensive Evaluation of Machine Learning Techniques for Predicting software Bugs

A Comprehensive Evaluation of Machine Learning Techniques fo...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Fahim Faysal Sakib Md. Sajjadul Islam Juel Zinniya Tafannum Jihad Shahriar Joy Razuan Karim Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

software Bug Prediction (SBP) is essential to control overall software quality. As systems become complex, faults increase, and early prediction is needed to enhance quality and reduce expenses. This paper provides a technical study of thirteen Machine Learning (ML) techniques for predicting software bugs by utilizing NASA's Promise Repository datasets. The study examines the effectiveness of these classification techniques focusing on evaluation metrics such as Precision, Recall, F-measure, Accuracy, Error Rate, FPR, FNR, ROC, and MCC. The results indicate that Random Forest (RF) achieved the highest accuracy on PC1 (94.96%) and CM1 (96.60%), while Multilayer Perceptron (MLP) scored 85.6% on KC1, and Logistic Regression (LR) got 86.25% on KC2. Regarding error rates, RF had the lowest on PC1 (0.050), LR on KC2 (0.137). For FPR, SVM, MLP, LR, RBF, ANN, and AdaBoost excelled on PC1 (0.00). For FNR, Decision Tree (DT) performed best on PC1 (0.611), KC2 (0.518), and LDA on CM1 (0.500).

关键词： Radio frequency Support vector machines Measurement Accuracy Error analysis Computer bugs software algorithms Signal processing algorithms Prediction algorithms Random forests

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