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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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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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ElecDaug: Electromagnetic Data Augmentation for Model Repair based on Metamorphic Relation 7

ElecDaug: Electromagnetic Data Augmentation for Model Repair...

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37th IEEE/ACM international conference on Automated software engineering (ASE)

作者： He, Jiawei Bao, Zhida Zhang, Quanjun Sun, Weisong Liu, Jiawei Fang, Chunrong Lin, Yun Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Harbin Engn Univ Harbin Peoples R China

ISBN: (纸本)9781450394758

With the application of deep learning (DL) in signal detection, improving the robustness of classification models has received much attention, especially in automatic modulation classification (AMC) of electromagnetic signals. A large amount of electromagnetic signal data is required to obtain robust models in the training and testing process. However, the high cost of manual collection and the issue of low quality of automatically generated data contribute to the AMC model's defects. therefore, it is essential to generate electromagnetic data by data augmentation. In this paper, we propose a novel electromagnetic data augmentation tool, namely ElecDaug, which directs the metamorphic process by electromagnetic signal characteristics to achieve automatic data augmentation. Based on electromagnetic data pre-processing, transmission or time-frequency domains characteristic metamorphic, ElecDaug can augment the data samples to build robust AMC models. Preliminary experiments show that ElecDaug can effectively augment available data samples for model repair. the video is at https://***/x5g6IVX_Q3s. Documentation and source code can be found here: https://***/ehhhhjw/tool_***.

关键词： Model Repair Data Augmentation Metamorphic Relation Automatic Modulation Classification

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Bearing Fault Diagnosis Utilizing S transform Signal Processing with Fisher score and Ant colony optimization Method 6

Bearing Fault Diagnosis Utilizing S transform Signal Process...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Dave Vipul, K. Patel Jatin, J. Dabhi Vipul, M. Khyati, Zalawadia Aeronautical Engineering Parul Institute of Engineering and Technology Parul University Vadodara India Hansaba College of Engineering and Technology Gokul Global University Siddhpur India Parul Institute of Engineering and Technology Parul University Vadodara India Parul Institute of Engineering and Technology Parul University Vadodara India

ISBN: (纸本)9798350369748

Vibration analysis is an essential tool for comprehending and diagnosing problems in mechanical systems. Rotating machinery heavily relies on bearings, making them critical components. Bearings guide and support shafts, and any faults in them can result in production losses and equipment issues. Bearing fault diagnosis is, therefore, of utmost importance. Effective vibration signal processing is necessary to obtain insightful information and spot hidden patterns. In this study, the paper explores the application of the S transform signal processing technique for fault diagnosis in a test rig. the results obtained from the experimental setup of CWRU showcase the effectiveness of this approach, particularly when combined with machine learning tools. the paper introduces a signal analysis technique for monitoring machine health, employing the S transform signal processing technique. Fisher score evaluates how well features arrange data and benefits in selecting the most relevant ones. Ant colony optimization searches through several feature sets to identify effective methods for data organization and sorting. Both approaches improve the models' comprehension of data and their ability to make decisions in data analysis and machine learning. To enhance the process, the Fisher Score and Ant Colony optimization are utilized for feature ranking and selection. the training and tenfold cross-validation results indicate an acceptable level of accuracy across various classifiers, highlighting the effectiveness of the proposed method. © 2024 IEEE.

关键词： Ant colony optimization

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Virtual Soccer Champions: A Case Study on Artifact Reuse in Soccer Robot Digital Twin Construction 22

Virtual Soccer Champions: A Case Study on Artifact Reuse in ...

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ACM/IEEE 25th international conference on Model Driven engineering Languages and Systems (models)

作者： Walravens, Gijs Muctadir, Hossain Muhammad Cleophas, Loek Eindhoven Univ Technol Dept Math & Comp Sci Software Engn & Technol Cluster Eindhoven Netherlands

ISBN: (纸本)9781450394673

A Digital Twin (DT) can be described as a pairing of a physical and virtual entity, in which the latter 'mimicks' the former in some sense, using data from its physical counterpart-whether real-time or historical-, for various purposes including better understanding of the physical entity's behaviour, predictive maintenance, and what-if scenario exploration. Here, we focus on the development of the virtual part of the DT in a context where the physical entity already exists, offering the potential for design and engineering artifacts from the physical entity to be reused. We performed a case study on the Turtle soccer robots developed by Eindhoven University of Technology's Tech United [6] team, to explore the possibilities of reusing artifacts for the development of the corresponding virtual entity. In this paper, we present our experiences from this case study and discuss the benefits, drawbacks, and challenges we faced.

关键词： digital twin development turtle soccer robot artifact reuse

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An investigation of ML techniques to detect Phishing Websites by complexity reduction 6

An investigation of ML techniques to detect Phishing Website...

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6th IEEE international conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

作者： Bin Karim, Md. Faiyed Hasan, Tasnimul Tazreen, Nushera Hakim, Safayat Bin Tarannum, Samiha Islamic University of Technology Electrical and Electronic Engineering Gazipur Bangladesh

ISBN: (数字)9781665497428

ISBN: (纸本)9781665497428

In today's digital age, one of the predominant causes of the security breaches is phishing web sites that disguise them-selves as legitimate web sites and trick unsuspecting users into revealing sensitive information. With the proliferation of high-speed internet and the popularization of IT education, there is an increase in unscrupulous actors on the web who are always ready to counterfeit a legitimate website and use it to deceive and ma-nipulate users. software and non-software-based techniques have been used to try to unmask the phishers. Phishing web sites have many characteristics in them. thus, classifying and detecting those is unavoidably time-consuming and complex. Our research analyzed several hybrid machine learning models, including a bespoke preprocessing step of reducing minimally correlated features and then training with four boosting algorithms and three SVM models for classification. these models have also been trained after hyperparameter tuning. Among the investigated models, XGBoost brought the highest accuracy of 97.0455% after the hyperparameter tuning. © 2022 IEEE.

关键词： Websites

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Mechanization of a Large DSML: An Experiment with AADL and Coq 20

Mechanization of a Large DSML: An Experiment with AADL and C...

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20th ACM-IEEE international conference on Formal Methods and models for System Design (MEMOCODE)

作者： Hugues, Jerome Wrage, Lutz Hatcliff, John Stewart, Danielle Carnegie Mellon Univ Inst Software Engn Pittsburgh PA USA Kansas State Univ Dept Comp & Informat Manhattan KS USA Galois Inc Minneapolis MN USA

ISBN: (数字)9798350331905

ISBN: (纸本)9798350331905

Domain-Specific Modeling Languages (DSMLs) rely on model-based techniques to deliver tailored languages to meet specific needs, such as system modeling, formal verification, and code generation. A DSML has specific static and dynamic behavior rules that must be properly assessed before processing the model. the definition of these rules remains a challenge. Meta-modeling techniques usually lack the foundational elements required to fully express behavioral semantics. In this context, using an interactive theorem prover provides a mathematical foundation with which the semantics of a DSML can be defined. this includes an abstract syntax tree, typing rules, and derivation of an executable simulator.

关键词： Codes Program processors Semantics Metamodeling Systems modeling Syntactics Real-time systems

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Analysis of movement of an elbow joint with a wearable robotic exoskeleton Using OpenSim software 44

Analysis of movement of an elbow joint with a wearable robot...

引用

44th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Noei, Vahid Lakany, Heba Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Human body movement occurs as a result of a coordinated effort between the skeleton, muscles, tendons, ligaments, cartilage, and other connective tissue. the study of movement is crucial in the treatment of some neurological and musculoskeletal diseases. the advancement of science and technology has led to the development of musculoskeletal model simulation software such as OpenSim that plays a very significant role in tackling complex bioengineering challenges and assists in our understanding of human movement. Such biomechanical models of musculoskeletal systems may also facilitate medical decision- making. through fast and accurate calculations, OpenSim modelling enables prediction and visualisation of motion problems. OpenSim has been used in many studies to investigate and assess movements of the upper limb under various scenarios. this work investigates elbow movement of a paretic arm wearing a myoelectric robotic exoskeleton. the simulation focuses on the exoskeleton elbow joint with one degree of freedom for individuals that we have developed to support and rehabilitate a weakened/paretic arm due to a spinal cord injury for example. Accordingly, it simulates the kinematic characteristics of the human arm whilst the exoskeleton assists the arm flexion/extension to maximise its range of motion. To obtain the motion data required for this study, a forward dynamics method must be implemented. Firstly, inverse kinematics is applied to the joint angles, and then, the torque and force required for angular motion of the elbow joint are calculated using forward dynamics. the results show that the muscle forces required to generate an elbow flexion are considerably less when the exoskeleton is worn.

关键词： Torque Biological system modeling Exoskeletons Dynamics Kinematics Muscles predictive models

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Improving Prediction Accuracy in IoT Network Security Against DDoS Attacks by Combining RIPPER and KNN Algorithms 8

Improving Prediction Accuracy in IoT Network Security Agains...

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8th international conference on Electrical, Telecommunication and Computer engineering, ELTICOM 2024

作者： Juliansyah, Handrino Sihombing, Poltak Fahmi, Fahmi Universitas Sumatera Utara Faculty of Computer Science and Information Technology North Sumatera Medan Indonesia Universitas Sumatera Utara Faculty of Engineering Electrical Engineering Dept North Sumatera Medan Indonesia

ISBN: (纸本)9798331531249

the Internet of things (IoT) is a network of linked devices comprising interconnected devices that communicate through internet, integrating sensors, software and other technologies. As the number of connected devices grows, ensuring IoT security has become increasingly critical, with cyber threats and other related risks arising. A prevalent threat is the Distributed Denial of Service (DDoS) attack, which seeks to incapacitate server systems by overwhelming the network with excessive traffic. this study explores machine learning approach to improve intrusion detection systems (IDS) performance, such as accuracy in identifying and mitigating DDoS attacks. Specifically, it employs a hybrid approach combining the well-known RIPPER algorithm or Repeated Incremental Pruning to Produce Error Reduction and the K-Nearest Neighbors (KNN) algorithm as detection models. the study utilizes the Network Security Layer-Knowledge Discovery in Database (NSL-KDD) dataset, which contains 41 features for classifying DDoS attacks. To optimize the model's performance, the data underwent preprocessing to eliminate irrelevant and redundant features while retaining essential information. the classification results demonstrate a peak accuracy of 97.32% with k=6 and an average accuracy of 96.30% across 15 trials with varying k values from 1 to 15. these findings highlight the effectiveness of the RIPPER-KNN approach in improving the prediction accuracy of DDoS attack detection in IoT networks. © 2024 IEEE.

关键词： Denial-of-service attack

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Diabetes Types Classification through Federated Learning and Adversarial Robustness 6

Diabetes Types Classification through Federated Learning and...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Charu, Krity Haque Nafis, Kh. Fardin Zubair Reza, Md. Tanzim Computer Science And Engineering BRAC University Dhaka Bangladesh

ISBN: (纸本)9798350369748

In many parts of the world, diabetes is a widespread and serious concern affecting both young and older populations. It not only poses a threat to lives but also gives rise to a cascade of additional health complications. What makes it even more challenging is that there are different types of diabetes - type-1 and type-2 - each with its own set of health risks. this underscores the importance of accurate classification for tailored treatment. Despite the many machine learning models used for diabetes classification, the utilization of federated learning and its advancements remains an unexplored frontier in the realm of distinguishing between different types of diabetes. Hence, this research aims to develop a Federated Learning (FL) based diabetes type prediction model and establish it against adversarial attacks by producing adversarial examples using the Fast Gradient Sign Method (FGSM), departing from the traditional methods of Machine Learning. this approach not only keeps medical data private and is good for refining how we classify diabetes but also defends from intentionally manipulating inputted data that can cause incorrect decisions. In our research, we used a custom neural network sequential model to address this. After developing the model, we generated adversarial examples using the FGSM. this method, which is frequently applied to image processing, was modified for our numerical dataset to examine its influence on model stability. We have selected crucial features from our dataset according to the health experts to figure out if it’s type-1 or type-2. Our study’s accuracy rate of 93% is comparable to that of any other traditional centralized model. © 2024 IEEE.

关键词： Federated learning

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Adaptive Treatment Planning via Multi-class Segmentation of GI Tract Tumours 6th

Adaptive Treatment Planning via Multi-class Segmentation of ...

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6th international conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

this study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. the primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. this substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. the overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. the successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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