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Boosting Software Quality: A Machine Learning Technique for Predicting Defects 6

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

引用

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

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

ISBN: (纸本)9798331522667

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. © 2025 ieee.

关键词： Data reliability

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Equipment System Optimization Method Based on Cost-efficiency Analysis 6

Equipment System Optimization Method Based on Cost-efficienc...

引用

6th international conference on computer Network, Electronic and automation, ICCNEA 2023

作者： Qiao, Hu Lin, Jun Guo, Zhiming Gao, Liang Ye, Jun Xu, Jiawei Xi 'An Technological University School of Mechanical and Electrical Engineering Xi'an710021 China Xi 'An Technological University School of Computer Science and Engineering Xi'an710021 China Academy of Machinery Equipment China Research and Development Beijing100089 China Troop No 31306 of PLA Chengdu610000 China

ISBN: (纸本)9798350305388

The current study of the effectiveness and cost of the equipment system does not involve the analysis from inception to formation and the optimization of the existing equipment system. This paper presents an optimization method of equipment system, which is based on cost-effectiveness analysis. First of all, the components of the equipment system to be studied or formed are defined, the number of factors affecting cost and effectiveness is determined, and the research methods and data processing methods for each factor are determined. Second, if the number of cost influencing factors exceeds the defined value, the influencing factors should be screened. The important influencing factors were obtained by sensitivity analysis. Thirdly, the final cost factor data and efficiency factor data are processed by summation polymerization method to get the final cost and efficiency overall evaluation results. Finally, the cost-effectiveness criterion is selected, the cost-effectiveness ratio is calculated, the cost-effectiveness ratio analysis results are evaluated, and the equipment system is perfected and optimized. This method can understand the optimization method of the equipment system in a more comprehensive way, with integrity, structure and enough flexibility. © 2023 ieee.

关键词： Sensitivity analysis

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Intelligent Pressure Reconstruction of Centrifugal Compressor Based on Gappy POD and Sparse Measurement 22

Intelligent Pressure Reconstruction of Centrifugal Compresso...

引用

22nd ieee international conference on Industrial Informatics, INDIN 2024

作者： Li, Xuhui Hu, Chenxing Peng, Jingyu School of Mechanical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331527471

The capacity to reconstruct the coherent characteristics of fluid flows from limited observational data holds paramount significance for applications across the physical and engineering sciences. The present study conducted steady-state experiments on a centrifugal compressor, utilizing a densely distributed array of static pressure probes to measure the actual static pressure distribution within the flow field, thereby obtaining the aerodynamic performance characteristics of the centrifugal compressor. Furthermore, the study employed randomly distributed sparse pressure sensors and numerical model data of the centrifugal compressor, applying the Gappy POD method to achieve high-fidelity reconstruction of the static pressure distribution on the impeller shroud wall. By comparing the reconstructed results with the experimental measurements, the overall reliability of the data-driven algorithm was validated, and the differences in the subtle flow field characteristics were identified. This not only deepened the understanding of the complex flow dynamics and their underlying patterns within turbomachinery, but provided important theoretical foundations and technical support for the optimization design, performance prediction, and flow control of turbomachinery. © 2024 ieee.

关键词： Centrifugal compressors

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D-Alarm: An Efficient Driver Drowsiness Detection and Alarming System 6

D-Alarm: An Efficient Driver Drowsiness Detection and Alarmi...

引用

6th ieee international conference on Advances in science and Technology, ICAST 2023

作者： Borah, Biju Mukherjee, Shyamapada National Institute of Technology Silchar Department of Electrical Engineering Assam 788010 India National Institute of Technology Rourkela Department of Computer Science and Engineering Odisha 769008 India

ISBN: (纸本)9798350359817

In the coming years, the demand for IoT devices connecting our homes, cities, and industries will grow significantly. We now inhabit a world where interconnected smart devices can be controlled through single commands, fostering an era of automation, particularly in smart homes. IoT is at the forefront of this transformation, with many of these devices relying on machine learning models to decode data and make accurate predictions. However, as the number of connected devices escalates, network congestion becomes a challenge. To address this, incorporating machine learning intelligence into edge devices is essential. Edge computing, which involves processing data closer to its source, reduces latency and enables faster analysis. Consequently, IoT data can be gathered and processed at the edge, reducing the reliance on cloud-based solutions. Yet, deploying machine learning models on small IoT microcontroller units presents memory and computation constraints. This paper introduces a deep learning model for driver engagement recognition in real-time video streams, alongside optimization techniques for deployment on edge devices like Nvidia Jetson. The goal is to facilitate the execution of machine learning models on compact, low-power integrated circuits, addressing this critical bottleneck. © 2023 ieee.

关键词： Internet of things

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Using Fractal Techniques for Evaluation of Qualia in the Brain 24

Using Fractal Techniques for Evaluation of Qualia in the Bra...

引用

24th international conference on Control Systems and computer science, CSCS 2023

作者： Riciu, Ionela Mirela Dobrescu, Radu Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (纸本)9798350313390

This paper discusses various applications of fractals in neurosciences and presents a methodology for their investigation and modeling with appropriate software. It is presented how to use multifractal analysis for characterizing neural signals, with an emphasis on qualia fractality highlighted by the mechanisms proposed for its evaluation. © 2023 ieee.

关键词： Fractals

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ST-SNNCA: Spatio-Temporal Shared Nearest Neighbors Clustering Algorithm Based on Spatio-Temporal Neighbourhood Analysis 4

ST-SNNCA: Spatio-Temporal Shared Nearest Neighbors Clusterin...

引用

4th international conference on computer science, Electronic Information engineering and Intelligent Control Technology, CEI 2024

作者： Zhang, Fengling Huang, Shengqiang Luo, Yonglong The School of Computer Science and Information Technology Anhui Normal University Anhui China

ISBN: (纸本)9798350378542

With the widespread use of mobile devices and GPS technology, the amount of trajectory data has been significantly accumulated. Therefore, how to properly analyze and mine these data has become an essential but challenging problem to be solved. Traditional trajectory clustering algorithms primarily focus on spatial similarity, disregarding the temporal relationship between different trajectories. To tackle this problem, a novel clustering algorithm called Spatio-temporal Shared Nearest Neighbors Clustering Algorithm is proposed in this work. By introducing temporal attributes during the trajectory segmentation stage, this algorithm effectively improves the reliability of segmentation points. Additionally, a Spatio-temporal Shared Nearest Neighbors method is developed by integrating temporal attributes with the Shared Nearest Neighbors approach, and it is found that this method can overcome the existing limitations of distance metrics by constructing the similarity matrix used for clustering. Finally, two different datasets of the hurricane track and animal track, respectively, are considered to verify the clustering validation of our proposed method. © 2024 ieee.

关键词： Spatio-temporal data

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YOLOv5 Small Object Detection Method based on Improved Deformable Convolution 4

YOLOv5 Small Object Detection Method based on Improved Defor...

引用

4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Du, Lixia Chang, Fangzhou Zhang, Zhihao School of Electronic and Information Engineering Lanzhou Jiaotong University Gansu Lanzhou730070 China

ISBN: (纸本)9798331531409

In response to the problems of poor robustness and low accuracy of object detection algorithms in complex scenes, the YOLOv5 model is improved. This method introduces Deformable Convolutional Networks on the basis of the original YOLOv5 algorithm, which can adapt to the geometric changes of data and enhance the network's modeling ability for shape changes;Simultaneously introducing the attention mechanism Squeeze and Excitation reduces the number of parameters, improves the network's feature extraction capability, and enhances the useful information in the feature map;Finally, fully utilizing the GIOU loss function can more comprehensively reflect the similarity between bounding boxes, thereby improving the performance of the object detection model. The experimental results show that the method proposed in this paper has significantly improved the accuracy, recall, and detection precision of detecting graphics compared to the original model, thereby enhancing the accuracy of object detection. © 2024 ieee.

关键词： Deep learning

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction 2

Impurity explicit optimal Machine learning and statistical p...

引用

2nd ieee international conference on Networking and Communications, ICNWC 2024

作者： Nareshkumar, R. Shalinikumari, R. Kowsalya, S. Sri Nandhini Vijay Anand, P. Mohamed, Ajmal Gayathri, S. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India Bharath Institute of Higher Education and Research Selaiyur Department of Architecture Tamilnadu Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamilnadu Chennai India CMR Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350365269

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy. © 2024 ieee.

关键词： Machine learning

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Securing the Industrial IoT: A Novel Network Intrusion Detection Models 3

Securing the Industrial IoT: A Novel Network Intrusion Detec...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Ramaiah, Mangayarkarasi Rahamathulla, Mohemmed Yousuf Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

The growth of automation in the industry makes securing IoT networks a critical priority. A resilient network intrusion detection system (NIDS) can reduce cyber threats. Detecting network traffic irregularities with deep learning (DL) and machine learning (ML) techniques can improve IoT network security and reduce cyber threats. This study analyzes the merits of LSTM and other ML models in mitigating anticipated security breaches in Industrial IoT (IIoT) networks. To design robust NIDS, including an appropriate dataset is essential. Therefore, to identify dynamic threats targeting Industrial Internet of Things (IIoT) networks, deep learning (DL) and machine learning (ML) models underwent training and evaluation utilizing the EdgeIIoT-2021 dataset. The experimental findings demonstrate that the proposed approach surpasses the performance of the current state-of-the-art Network Intrusion Detection Systems (NIDS). The ERT-based IIoT-NIDS achieved a cyber-attack detection accuracy of 99.93%, while the LSTM-based IIoT-NIDS achieved an accuracy of 99.85%. © 2024 ieee.

关键词： Network security

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Sensitivity Analysis and Extended Evaluation of the Two-Level Stochastic Model for the Lateral Movement of Vehicles within their Lane 10

Sensitivity Analysis and Extended Evaluation of the Two-Leve...

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10th international conference on automation, Robotics, and Applications, ICARA 2024

作者： Neis, Nicole Beyerer, Juergen Porsche Engineering Group GmbH Weissach Germany Fraunhofer IOSB and Vision and Fusion Lab Karlsruhe Germany

ISBN: (纸本)9798350394245

The range of vision of vehicle sensors used by auto-mated driving functions is considerably influenced by the lateral movement of vehicles within their lane, both of the recording vehicle and of vehicles around it. To cover this phenomenon in simulations for virtual validation, a two-level stochastic model for the lateral movement of vehicles within their lane under homogeneous traffic conditions has recently been proposed by Neis et al. [1]. It consists of the superposition of a Markov model for the discrete and systematic coarse movement, and a noise model for the stochastically independent fine movement. The general importance of the parameter choice on the performance of the two-level model has already been noted when the model was introduced. More details are added by the present work performing a sensitivity analysis on the model's parameters. Moreover, the evaluation of the model is extended by assessing its capability to describe the lateral speed. The work deepens the understanding of the model's way of working and its sensitivity to the parameters, and motivates the parameter choice in previous work. Besides this, potential for improvement of the model beyond what has already been discussed in the introductory paper is identified based on the extended investigation. © 2024 ieee.

关键词： Stochastic models

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