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

共 3308 条记录，以下是541-550 订阅

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7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

7th IFIP TC 12 International Conference on Computational Int...

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7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

ISBN: (纸本)9783031699856

the proceedings contain 72 papers. the special focus in this conference is on Computational Intelligence in Data Science. the topics include: Chat Bot in Banking Sector Using Machine Learning and Natural Language Processing;lecter-A Large Language Model Chatbot for Cognitive Behavioral therapy;Evaluating the Language Translation Accuracy of GPT-3.5 Using Prompt engineering;multi-camera Enhanced Real-Time Content-Aware Vehicle Detection;COOL: Classification of Online Offensive Language Using Machine Learning and Deep Learning;improved Evaluator for Subjective Answers Using Natural Language Processing;Self-harm Detection from Texts: A Comparative Study Utilizing BERT, Machine Learning, and Deep Learning Approaches;neuro-Evolution-Based Language Model for Text Generation;offensive Language Detection on Telugu Language;User Story Based Automated Test Case Generation Using NLP;anticipating Future College Admission Cutoffs: An Innovative predictive Model Incorporating Student Reviews and Historical Admissions Cutoff Data Using Machine Learning;sentiment Analysis for Stock Prediction Using Mass Media Sources;CADFRA: Coronary Artery Disease Feature Reduction with Autoencoder for Optimistic and Effective Classification;machine Learning Based Alzheimer’s Disease Detection: A Comprehensive Approach;supervised Learning of Procedures from Tutorial Videos;De-noising of Low Dose CT Liver Images Using Improved Discrete Wavelet Transform;a-Eye Tracker: Human Eye Defect Tracker and Analyzing software;design and Analysis of Structural Health Monitoring System for the Diagnosis of Morphological Deformities of Bolted Structures;Empowering Medical Image Analysis: Unveiling Anomalies through GANs and BiGAN’s models;pneumonia Detection Using Chest X-Rays: A Comprehensive Review;realistic Avatar Control through Video-Driven Animation for Augmented Reality;CNN-Based Skin Lesion Classification for Melanoma Detection.

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A Transformer-Based Model for Assisting Dockerfile Revising

A Transformer-Based Model for Assisting Dockerfile Revising

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international conference on software engineering - Companion, ICSE, Companion

作者： Yiwen Wu Yang Zhang Tao Wang Huaimin Wang China State Key Laboratory of Complex & Critical Software Environment National University of Defense Technology China

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Dockerfile plays an important role in the containerized software development process since it specifies the structure and function-ality of the built Docker image. Currently, Dockerfile writing and modification still rely on manual operations which can be time-consuming. thus, there is a need for automation tools to support the Dockerfile revising process. In this study, we focus on utilizing pretraining techniques for the tasks in the Dockerfile revising scenario. We propose a Transformer-based model and pretrain it with an instruction-aware objective. Furthermore, we fine-tune our model in two downstream tasks, including revision opportunity estimation and revision activity prediction. the experimental results show that our model outperforms the baseline models.

关键词： Automation Estimation Writing predictive models Transformers Task analysis software engineering

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Classical Mechanics-based Kinematic Analysis of a Specific Microrobot

Classical Mechanics-based Kinematic Analysis of a Specific M...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Xiao Zhang Jingqi Wang Kaiyun Zhu Haiwang Li Yanxin Zhai Tiantong Xu Research Institute of Aero Engine Beihang University Beijing China Huawei Technologies Co. Ltd China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Detailed kinematic analysis is a necessary part of microrobot research, which is an important reference for the selection and determination of robot structural parameters, the optimisation analysis of motion performance, and the selection of robot control strategies. In this paper, we derive detailed equations of motion for a microrobot with bionic inchworm motion based on the theory of classical mechanics, thus obtaining a theoretical design method for a microrobot based on low-frequency motion performance. Comparison of the predicted kinematic data of the microrobot with the experimental data using the theoretical formulas revealed an error of less than 6.1%. this results in a theoretical kinematic predictive design method for microbots, which can provide a reference for the design of such inchworm-type microbots.

关键词： Analytical models Design methodology Robot control Kinematics software Structural engineering software reliability Robots Optimization Load modeling

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software Package for Assessing the Parameters of Intermittent Unbalance in a Network with Traction Load

Software Package for Assessing the Parameters of Intermitten...

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international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Pavel Bordadyn Vladimir Korolev Department of Theoretical Foundations of Electrical Engineering National Research University “Moscow Power Engineering Institute” Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

the paper is devoted to the software package development that allows modeling a network with an alternating current variable traction load. the main computer models of network elements and electric rolling stock for modeling have been developed. A static model has been calculated to verify the program operation using Matlab Simulink software. Dynamic modeling has been performed during the electric rolling stock movement and time dependencies of the intermittent unbalance parameters in network nodes have been obtained.

关键词： software packages Computational modeling Power system dynamics Interference software systems Mathematical models Data models Rail transportation MATLAB Load modeling

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35th international conference on Advanced Information Systems engineering, CAiSE 2023

35th International Conference on Advanced Information System...

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35th international conference on Advanced Information Systems engineering, CAiSE 2023

ISBN: (纸本)9783031345593

the proceedings contain 36 papers. the special focus in this conference is on Advanced Information Systems engineering. the topics include: Request Relaxation Based-on Provider Constraints for a Capability-Based NaaS Services Discovery;configuration Optimization with Limited Functional Impact;modeling Context-Aware Events and Responses in an IoT Environment;modular Quality-of-Service Analysis of software Design models for Cyber-Physical Systems;simulating and Analyzing Crowdsourcing Impacts in Flood Management: A Geo-spatial Agent-Based Approach;a Data-Centric Approach for Reducing Carbon Emissions in Deep Learning;unsupervised Task Recognition from User Interaction Streams;preface;comparing User Perspectives in a Virtual Reality Cultural Heritage Environment;revealing the Importance of Setting Parameters in Declarative Discovery Algorithms: An Evolutionary-Based Methodology;why Am I Waiting? Data-Driven Analysis of Waiting Times in Business Processes;extracting Event Data from Document-Driven Enterprise Systems;event Data-Driven Feasibility Checking of Process Schedules;comparing Trace Similarity Metrics Across Logs and Evaluation Measures;where Are the Readings Behind Your Concept Maps? Annotation-driven Concept Mapping;Ontological Representation of FAIR Principles: A Blueprint for FAIRer Data Sources;enabling Representation Learning in Ontology-Driven Conceptual Modeling Using Graph Neural Networks;DOML: A New Modelling Approach to Infrastructure-as-Code;task Completeness Assessments in the Evolution of Domain-Specific Modelling Languages;can Information System engineering Make Cyber-Human Systems Smarter?;lessons Learned in Model-Based Reverse engineering of Large Legacy Systems;design and Evaluation of a User Interface Concept for Prescriptive Process Monitoring;learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning;model-Agnostic Event Log Augmentation for predictive Process Monitoring;detect

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Optimizing the classroom Space Design for Intellectually Disabled Students Based on the Analysis of the Acoustic Environment

Optimizing the classroom Space Design for Intellectually Dis...

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international conference on Communication engineering and Technology (ICCET)

作者： Tawfiq Jebril Yang Chen Heena M Shirahatti Syed Far Abid Hossain School of Human Settlements and Civil Engineering Xi’an Jiaotong University Xi’an China KLE Technological University Hubli Karnataka BRAC Business School BRAC University Dhaka BD.

the present study focuses primarily on the effects of designing a rounded corner classroom for intellectually disabled students in primary schools based on the reverberation time and noise level. Usually, straight 90-degree edges are used for classroom corners, but rounded edges for walls and furniture are more comfortable and help to ensure students safety. therefore, this study examines how a rounded-edge classroom design for walls and furniture affects additive babble and environmental noise level conditions in primary school classrooms for intellectually disabled students. the study compares the two classroom designs (rounded and right-angle corners) covering the same area with different variable factors to improve students’ performance. the computer software program ODEON and the MATLAB programming language were primarily used to investigate the two models. these models were used to predict five design scenarios’ impact to assess acoustic behavior with and without furniture on intellectually disabled students learning. the findings reveal that the proposed model with rounded corner walls and furniture and a high and sloped ceiling performs better than the regular classroom model. the study also confirms that a better study environment can be created for intellectually disabled students to study alongside regular students.

关键词： Analytical models Computational modeling Working environment noise Simulation predictive models software Behavioral sciences

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Brain tumor image segmentation using Deep learning 6

Brain tumor image segmentation using Deep learning

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6th international conference on Devices, Circuits and Systems, ICDCS 2022

作者： Armstrong, T Richie Emmanuel Manimegalai, P. Abinath, A. Pamela, D. Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9781665480949

Commonly MRI scans are used by the radiologists to conclude the presence or absence of tumor. Most of these imaging techniques are non-invasive and non-ionizing radiation. Basically, a magnetic and ratio field extraction. By this, Doctor can know whether the patient is responding (or) reacting the medication or *** MRI scans are collected from the internet sources like Kaggle and *** these scans are from various hospitals and sources,It has to be preprocessed. In order to match their histogram and remove unwanted images. Followed by data visualization where large quantity of data is translated to the desired form. In data augmentation, the scanned images are selected and collected for *** order to augment, a radiologist effort,deep learning program,that can effectively segment the Gliomas,which can be very *** or delineating of pixels corresponding to tumor is an important task. Segmentation is used to extract features each pixel. In fact, for each pixel input there are many different outputs in segmentation the intensity of tumor and point of site are predicted. MRI image volumes are acquire as a 3D array. Here 2D patches of MRI are used to predicts the class of center pixel of the ***(Convolutional Neural Network) is a part of neural network which is the advanced topic in deep learning.U-Net and VGG-19 are used here which is a pretrained model that is a model which was already trained with more than ten thousand images. © 2022 IEEE.

关键词： Image segmentation three-dimensional displays Magnetic resonance imaging predictive models Feature extraction Brain modeling Task analysis

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Enhancing Sales Forecasting Accuracy in the Presence of Missing Data 17

Enhancing Sales Forecasting Accuracy in the Presence of Miss...

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IEEE Congress on Cybermatics: 17th IEEE international conference on Internet of things, ithings 2024, 20th IEEE international conference on Green Computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE international conference on Smart Data, SmartData 2024

作者： Tang, Jun Sun, Jingpeng Guo, Bing Shen, Yan Dai, Shengxin Wang, Peng College of Computer Science Sichuan University Chengdu China College of Computer Science University of Birmingham Dubai United Arab Emirates School of Computer Science Chengdu University of Information Technology Chengdu China College of Software Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350351637

Accurate sales forecasting serves as a critical foundation for informed business decision-making and numerous methodologies have been advanced by scholars predominantly rely on complete datasets, however, in practice, uncontrollable variables often result in the presence of missing data, diminishing the applicability of established techniques, so the sales forecasting research based on missing data is of great significance. Some researchers have resorted to employing deletion or interpolation techniques to pre-process incomplete data before conducting sales forecasts, but such completion methods tend to introduce distributional biases, subsequently leading to forecast inaccuracies. In light of these challenges, this study introduces a novel computational framework combining Convolutional Neural Network (CNN), Self-Attention, and Sequence-to-Sequence (Seq2Seq) models, where global features are first extracted via the synergistic application of CNN and Self-Attention mechanisms, then the extracted features are seamlessly integrated with the original dataset, supplanting conventional interpolation strategies, and finally the amalgamated data are inputted into the Seq2Seq model for sales prediction. the proposed methodology is empirically evaluated using sales data from a real-world company, demonstrating commendable predictive performance and underscoring the efficacy of the proposed approach in enabling accurate sales forecasts within the context of missing data scenarios. © 2024 IEEE.

关键词： Data accuracy

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Construction of PCOS Prediction Model Based on BP Neural Network

Construction of PCOS Prediction Model Based on BP Neural Net...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Xianqing Hu Amit Yadav Asif Khan Abhishek Pratap Sah Sami Azam School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP N eural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients.

关键词： Training Adaptation models Accuracy Biological system modeling Computational modeling Neural networks predictive models Data models Robustness Tuning

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Analysis of Honey Production with Environmenta Variables 6

Analysis of Honey Production with Environmenta Variables

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6th international conference on Computer Science and engineering, UBMK 2021

作者： Atagün, Ercan Aalbayrak, Ahmet Department of Computer Engineering Duzce University Duzce Turkey

ISBN: (纸本)9781665429085

Regression algorithms are included in the supervised learning techniques of machine learning. Regression covers the operations of estimating the variable with the class label (output variable) by using the numerical values in a data with regression algorithms. When the desired performances cannot be achieved with the existing regression algorithms for a problem, Ensemble Learning models are applied. In the Ensemble Learning model, multiple predictive algorithms come together and aim to achieve a higher success than the success of an algorithm alone. In this study, honey production problem was estimated with Support vector machines. Multi-layer Perceptron Regressor, KNeighborsRegressor, Voting Regressor, RandomForestRegressor, AdaBoostRegressor, BaggingRegressor, GradientBoostingRegressor and the results were compared. It was observed that the ensemble learning models increased the prediction success with the regression processes. © 2021 IEEE

关键词： Food products

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