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检索条件"任意字段=Proceedings of the 9th International Conference on Predictive Models in Software Engineering"

共 2518 条记录，以下是171-180 订阅

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Bug predictive models based on Data Analytics and Soft Computing Techniques: A Survey 10

Bug Predictive Models based on Data Analytics and Soft Compu...

引用

17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Jadhav, Veena Devale, Prakash Jadhav, Rohini Molawade, Mayuri Mohite, Sagar Bidwe, Ranjeet Vasant College of Engineering Department of Computer Engineering Pune India Lavale Pune India

ISBN: (纸本)9789380544472

As software projects become larger and more complex, bugs pose a significant threat to their consistency and efficiency. During early phases of software development, bug prediction essentially enhances vital aspects such as software efficiency reliability quality, cost of development. Hence, various software bug prediction techniques have been presented previously but there is no research that focuses on surveying logical analysis and Just-In-Time (JIT) bug predictions with the consideration of data analytics and soft computing techniques. Hence this research investigates Soft computing and hybrid logistic model by determining imperfect range of data, class labels with heuristics prediction of bugs. Also, survey data analytics techniques such as clustering and oversampling techniques with JIT prediction for forecasting software bug in future. the study also compares various software bug prediction techniques to determine their effectiveness. then, mentioned the basic benefits and drawbacks of the various bug prediction techniques. Moreover, future directions are provided to overcome the existing impact of various techniques in software bug prediction. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Data Analytics

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A Server Anomaly Detection Method Based on EWC and LSTM-GAN 9

A Server Anomaly Detection Method Based on EWC and LSTM-GAN

引用

9th international conference on Computer and Communications, ICCC 2023

作者： Yu, Lei Le, Wei Chengdu University of Information Technology School of Software Engineering Chengdu China Chengdu University of Information Technology Sichuan Manufacturing Information Industry Collaborative Innovation Center School of Software Engineering Chengdu China

ISBN: (纸本)9798350317251

In the realm of server operations and maintenance, anomaly data is often characterized by its rarity, evolving over time with the influx of new data. models require continuous parameter updates to adapt to these shifting data distributions. To address these challenges, this paper introduces a method for detecting anomalous server data based on Adversarial Learning and Elastic Weight Consolidation (EWCAD-GAN). Firstly, this method employs stacked Long Short-Term Memory networks (LSTM) as both the generator and discriminator, using adversarial training principles to obtain an initial model. Secondly, the Elastic Weight Consolidation (EWC) algorithm is applied to fine-tune the model obtained after adversarial training, bolstering the model's ability to remember temporal patterns within time- series data. this approach alleviates the difficulties associated with training models on rare anomalous data and facilitates online learning through the use of the EWC algorithm. Experimental evaluations of this method were conducted on two publicly available datasets for intelligent operations and maintenance anomaly detection. the results clearly demonstrate that EWCAD- GAN outperforms other similar methods, achieving significantly higher anomaly detection accuracy while simultaneously addressing the challenges associated with model training and online learning. © 2023 IEEE.

关键词： Anomaly detection

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Critical Wind Speed Prediction in Tunnel Fire by Attention-Based Neural Network 9

Critical Wind Speed Prediction in Tunnel Fire by Attention-B...

引用

9th international conference on Mechanical and Electronics engineering, ICMEE 2023

作者： Xu, Quan He, Jian Zhang, Xueguang Xu, Xiaobo Liang, Huagang Li, Qiulu Wu, Yiyan Zhejiang Highway Operation Management Co. Ltd. Highway Hangzhou South Management Hangzhou China School Of Electronic And Control Engineering Chang'an University Xi'an China

ISBN: (纸本)9798350307146

Tunnel fires are one of the serious threats to public safety and infrastructure. When a tunnel fire occurs, the emission of smoke is crucial for controlling the loss of the tunnel fire. In order to better control the tunnel fire and reduce casualties and financial losses, it is necessary to accurately predict the critical wind speed. the neural network prediction model can play a good predictive role. Although long short-term memory (LSTM) can achieve good prediction results in dealing with sequential data, as the length of the input sequence increases, the model tends to forget the previous information seriously. this article combines the attention mechanism with the LSTM network to improve information transmission and solve long-sequence modeling problems. In this study, a dual attention mechanism-based LSTM neural network is proposed, with attention weights introduced to the encoder and the decoder. the LSTM and the CNN+LSTM models are also compared to our proposed one. It is shown that the loss of our proposed model is the smallest, indicating the best prediction performance among the three models. this proves that adding attention to the LSTM encoding-decoding architecture improves the prediction accuracy of tunnel fire critical wind speed. © 2023 IEEE.

关键词： Long short-term memory

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Improved Job Scam Detection Methods Using Machine Learning and Resampling Techniques 9

Improved Job Scam Detection Methods Using Machine Learning a...

引用

9th international conference on Systems and Informatics, ICSAI 2023

作者： Yang, Yang Zhang, Yi Zhu, Chengli Tsinghua University Department of Industrial Engineering Beijing China

in recent years, job scams, especially online job scams, have become one of the most serious social problems worldwide and have brought great threats to the safety of people9;s lives and property. this study aimed ... 详细信息

ISBN: (纸本)9798350383706

in recent years, job scams, especially online job scams, have become one of the most serious social problems worldwide and have brought great threats to the safety of people's lives and property. this study aimed to develop a machine learning predictive model for the authenticity of job postings to help people identify job scams and get access to real job opportunities. In this study, we considered the significant class imbalance in the dataset and used the TF-IDF method to extract text features from the text fields. the performance of various machine learning models was evaluated using balanced data generated through sampling techniques. the results show that the random forest model performs exceptionally well in handling balanced data and maintaining better accuracy (AC) and recall (RC) for both regular and generated balanced data. It also proved that TF-IDF is an effective method to extract features from text data. © 2023 IEEE.

关键词： Data handling

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19th international conference on software Technologies, ICSOFT 2024

19th International Conference on Software Technologies, ICSO...

引用

19th international conference on software Technologies, ICSOFT 2024

ISBN: (纸本)9789897587061

the proceedings contain 57 papers. the special focus in this conference is on software Technologies. the topics include: Product-Line engineering for Smart Manufacturing: A Systematic Mapping Study on Security Concepts;diagnosis Automation Using Similarity Analysis: Application to Industrial Systems;improving Robustness of Satellite Image Processing Using Principal Component Analysis for Explainability;multimodal Approach Based on Autistic Child Behavior Analysis for Meltdown Crisis Detection;Towards Accurate Cervical Cancer Detection: Leveraging Two-Stage CNNs for Pap Smear Analysis;feature Extraction, Learning and Selection in Support of Patch Correctness Assessment;Accurate Recommendation of EV Charging Stations Driven by Availability Status Prediction;RLHR: A Framework for Driving Dynamically Adaptable Questionnaires and Profiling People Using Reinforcement Learning;optimizing Intensive Database Tasks through Caching Proxy Mechanisms;An Evaluation of Risk Management Standards and Frameworks for Assuring Data Security of Medical Device software AI models;a Systematic Mapping Study on Impact Analysis;HybridCRS-TMS: Integrating Collaborative Recommender System and TOPSIS for Optimal Transport Mode Selection;automated Generation of Web Application Front-end Components from User Interface Mockups;a Webcam Artificial Intelligence-Based Gaze-Tracking Algorithm;an Empirical Examination of the Technical Aspects of Data Sovereignty;asmDocGen: Generating Functional Natural Language Descriptions for Assembly Code;Integrating a LLaMa-based Chatbot with Augmented Retrieval Generation as a Complementary Educational Tool for High School and College Students;artificial Intelligence-Based Detection and Prediction of Giant African Snail (Lissachatina Fulica) Infestation in the Galapagos Islands;six-Layer Industrial Architecture Applied to predictive Maintenance;Smart Blockchain-Based Information Flow Control for SOA;logging Hypercalls to Learn About the Behavior of Hyper-V.

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software Testing Based on software Product Quality Metrics (SPQM) 9

Software Testing Based on Software Product Quality Metrics (...

引用

9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Sivakrishna, Vijay Kumar, Pasala Saketh Jhaveri, Jhansi Akanksha, Lakshmi Sai Rao, P. Venkateswara Sai, N. Raghavendra Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

Producinghigh-quality products has emerged-as a crucial component of corporate success in today9;s marketplace. In this regard, establishing and holding to software product to assess the present quality standardto ... 详细信息

ISBN: (纸本)9798350397376

Producinghigh-quality products has emerged-as a crucial component of corporate success in today's marketplace. In this regard, establishing and holding to software product to assess the present quality standardto environment and promote system to improvethe quality of the productit significance. Testing software is an essential step in the software development process. this technique is more accurate and effective when it is automated. Emerged new testing tools for automated testing. the selection of the proper tools has become a challenging and confusing activity as a result of the abundance and variety of testing instruments. the current study attempts to assess the current research areas and trends on this subject that have appeared in the literature over the previous twelve years. Based on their titles and abstracts, 50 conference and article papers on SPQM published in the interval 2010 and 2022 were subjected to Systematic Mapping (SM) research. the findings are presented using a combination of diagrams, written explanations, and mind-mapping techniques, which include a trend map for the period from 2010 to 2022, details about the field and measurement techniques, identified areas for improvement, and consistency across sources such as conference papers, journal articles, and internationally recognized quality models. Future research that aims to advance this important topic may build on the outcome of this study. this research may provide a starting point for further investigations meant to advance this vital area of study. © 2023 IEEE.

关键词： software testing

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BAMTGAN: A Balanced Augmentation Technique for Tabular Data 9

BAMTGAN: A Balanced Augmentation Technique for Tabular Data

引用

9th international conference on Applied System Innovation, ICASI 2023

作者： Jeong, Jueun Jeong, Hanseok Kim, Han-Joon University of Seoul Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350398380

this paper presents BAMTGAN, a novel data augmentation technique that addresses the class imbalance problem and prevents mode collapse by utilizing a modified DCGAN model and a new similarity loss to generate diverse and realistic tabular data. BAMTGAN encodes each column to produce a feature map for each record, which is then converted back to its original tabular form an intermediate image format. Experimental results demonstrate that BAMTGAN provides a more substantial improvement in developing high-quality predictive models than existing augmentation methods. © 2023 IEEE.

关键词： Deep learning

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A Combined Learning Based Classifier Using Decision Tree and Logistic Regression 9

A Combined Learning Based Classifier Using Decision Tree and...

引用

9th international conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Kim, Seong-Jun Kim, Jihee Industrial Engineering GW Wonju Korea Republic of

ISBN: (纸本)9798350311402

Overweight and obesity are pointed out as a serious threat to the health of people. According to a 2016 report by WHO, 1.9 billion people worldwide are overweight and 650 million are obese. Because the obesity causes various cardiovascular diseases, diabetes, hypertension, and musculoskeletal disorders, it is important to assess the risk of obesity in order to prepare its prevention policies. To deal with this problem, logistic regressions and decision trees are widely applied and then employed as primary classifiers. this paper intends to present a combined learning approach to classifying the risk of overweight and obesity. In our framework, logistic regression models are cascaded onto decision tree classifiers. this feature would be applicable to improving the predictive capability of primary classifiers. Experimental results, using an open dataset provided by Korea Disease Control and Prevention Agency, show that the proposed scheme improves the classification accuracy by 5% to 27%. © 2023 IEEE.

关键词： Disease control

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ValidGen: A Tool for Automatic Generation of Validation Scripts to Support Rapid Requirements Validation 24

ValidGen: A Tool for Automatic Generation of Validation Scri...

引用

46th international conference on software engineering: Companion, ICSE-Companion 2024

作者： Pan, Hongyue Yang, Yilong School of Software Beihang University State Key Laboratory of Complex & Critical Software Environment Beijing China

ISBN: (纸本)9798400705021

Rapid prototyping is an effective way for requirement validation in the earliest stages of software development. Our previous work, RM2PT, can automatically generate software prototypes from requirements models to support incremental and rapid requirements validation. this paper proposes a CASE tool named ValidGen based on RM2PT, which can automatically generate validation scripts to execute the prototype. thanks to these validation scripts, the stakeholder only needs to monitor the execution process without selecting the system operation and typing the input parameters, which significantly reduces the time and effort needed for validating requirements. We adopted three case studies to evaluate the tool, and the results show that the tool requires only about 60% of the time for requirements validation compared to traditional methods. Overall, the results were satisfactory. the proposed tool can be further extended and applied for requirements validation in the software industry. the tool can be downloaded at https://***/advs/validgen/, and a demo video is at https://***/AP9Ymg1ewIA. © 2024 IEEE Computer Society. All rights reserved.

关键词： software prototyping

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Efficient Automated Modeling of Lattice Structures for 3D Printing using ABAQUS 9

Efficient Automated Modeling of Lattice Structures for 3D Pr...

引用

9th international conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Hoang, Trong Hieu Nguyen, Cong Hanh Nguyen, Dinh Son Van, thien An Nguyen Nguyen, Phu Duy The University of Danang-Univesity of Technology and Education Faculty of Mechanical Danang Viet Nam The University of Danang-Univesity of Science and Technology Faculty of Transportation and Mechanical Engineering Danang Viet Nam

ISBN: (纸本)9798331505042

In recent years, 3D printing technology has seen significant advancements and has attracted substantial attention from both the scientific community and industry. Its ability to fabricate complex structures and surfaces, which are often unattainable with conventional machining methods such as milling, turning, and casting, sets it apart. the incorporation of lattice structures into mechanical components is becoming increasingly common as 3D printing technology progresses, allowing for optimized designs in terms of shape and weight. In this context, geometric modeling is crucial, acting as the link between design, simulation, and manufacturing. However, the process typically involves designers using CAD tools to create large geometric models, which can lead to challenges related to processing time, memory consumption, and the need to switch between different computational software packages. therefore, there is a pressing need for more efficient and effective methods to create and represent various lattice structure configurations, thereby fully leveraging the design freedom afforded by 3D printing technology. this paper provides an overview of lattice structures and introduces an automated modeling approach for various lattice types using ABAQUS finite element analysis software, tailored for 3D printing applications. © 2024 IEEE.

关键词： ABAQUS

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