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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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Heterogeneous Ensemble Imputation for software Development Effort Estimation 17

Heterogeneous Ensemble Imputation for Software Development E...

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17th ACM international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Abnane, Ibtissam Idri, Ali Hosni, Mohamed Abran, Alain Mohammed V Univ Software Project Management Res Team ENSIAS Rabat Morocco Mohammed VI Polytech Univ MSDA Ben Guerir Morocco Moulay Ismail Univ MOSI L2M3S ENSAM Meknes Meknes Morocco Univ Quebec Dept Software Engn & Informat Technol ETS Montreal PQ Canada

ISBN: (纸本)9781450386807

Choosing the appropriate Missing Data (MD) imputation technique for a given software development effort estimation (SDEE) technique is not a trivial task. In fact, the impact of the MD imputation on the estimation output depends on the dataset and the SDEE technique used and there is no best imputation technique in all contexts. thus, an attractive solution is to use more than one single imputation technique and combine their results for a final imputation outcome. this concept is called ensemble imputation and can help to significantly improve the estimation accuracy. this paper develops and evaluates a heterogeneous ensemble imputation whose members were the four single imputation techniques: K-Nearest Neighbors (KNN), Expectation Maximization (EM), Support Vector Regression (SVR), and Decision Trees (DT). the impact of the ensemble imputation was evaluated and compared with those of the four single imputation techniques on the accuracy measured in terms of the standardized accuracy criterion of four SDEE techniques: Case Based Reasoning (CBR), Multi-Layers Perceptron (MLP), Support Vector Regression (SVR) and Reduced Error Pruning Tree (REPTree). the Wilcoxon statistical test was also performed in order to assess whether the results are significant. All the empirical evaluations were carried out over the six datasets, namely, ISBSG, China, COCOMO81, Desharnais, Kemerer, and Miyazaki. Results show that the use of heterogeneous ensemble-based imputation instead single imputation significantly improved the accuracy of the four SDEE techniques. Indeed, the ensemble imputation technique was ranked either first or second in all contexts.

关键词： software development effort estimation missing data imputation heterogeneous ensemble standardized accuracy machine learning

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Robotic Arm Automatic Placement Workstation with AI Pneumatic Tube Fault Diagnosis

Robotic Arm Automatic Placement Workstation with AI Pneumati...

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international conference on Control and Robots (ICCR)

作者： Jeng-Dao Lee Hsu-Lun Lin Yu-Cheng Ye Ying-Hsiu Hung Department of Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Yunlin County Taiwan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

Traditional equipment failures are often identified only when they manifest during production, leading to costly downtime, increased defect rates, and safety hazards. this study focuses on the progressive stages and severity of AI pneumatic tube fault diagnosis, a critical component in industrial operations. By leveraging a sensor and a microphone to collect vibration and audio data, we developed a deep learning-based system to accurately classify and predict the progression of these failures. Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM) networks were trained to identify anomalies in the collected data, effectively distinguishing between different fault stages, from minor issues to major failures. TCN models, with their dilated convolutions, capture long-term dependencies in the data, allowing for early detection of subtle anomalies. LSTM models, using their gated architecture, retain and utilize critical historical information, enabling more precise fault classification. A comparative analysis of various equipment condition monitoring methods demonstrates the superiority of our proposed approach in terms of signal detection and classification accuracy. Additionally, system integration of a Robotic Arm Automatic Placement Workstation and a user-friendly Node-RED-based interface supports real-time monitoring and operator intervention. this integrated system allows seamless communication between hardware components and software, enhancing overall operational efficiency. this study highlights the potential of AI and deep learning to enhance predictive maintenance and operational efficiency in industrial settings, particularly for critical equipment like pneumatic tubes.

关键词： Fault diagnosis Vibrations Service robots System integration Pneumatic systems Manipulators Data models Workstations Artificial intelligence Long short term memory

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Formal Specification and Verification of API Security Based on Design by Contract

Formal Specification and Verification of API Security Based ...

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IEEE international conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Qing Tang Zetong Zhou Zhaoxiong Bu Li Ma Yong Zhong Guangdong Chankong Big Data Technology Co. Ltd. Foshan China School of Management Guangdong University of Technology Foshan China School of Electronic and Information Engineering Foshan University Foshan China School of Electronic and Information Engineering Foshan University Zhixin Urban Big Data Technology Research Institute Foshan China Foshan Data Science Association School of Electronic and Information Engineering Foshan University Zhixin Urban Big Data Technology Research Institute Foshan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

the exploitation and hijacking of API vulnerabilities are becoming increasingly prominent issues in software supply chain security. this paper proposes an Object-Z based formal verification method for APIs in the supply chain, aiming to address the security risks associated with API applications. By incorporating the concept of design by contract and using Object-Z for formal modeling, a dynamic defense against API threats is implemented. Finally, based on a classification analysis of API threats in the supply chain, this paper presents a ase study through modeling and application analysis.

关键词： Analytical models Computational modeling Supply chains software Security Formal specifications Contracts Formal verification

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Research on Prediction Model Based on Improved LSTM

Research on Prediction Model Based on Improved LSTM

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international conference on Communications, Information System and Computer engineering (CISCE)

作者： Lingfei Lin Yuan Tan Guangjun Zeng Ming Zhou Wenlu Zhou College of Information and Communication National University of Defense Technology Wuhan China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In this study, a prediction model based on improved Long Short-term memory Network (LSTM) is developed to improve the accuracy of time series data analysis. the model is particularly suitable for areas that require analysis and prediction of time series data, such as financial markets, weather forecasts, energy consumption, etc. It takes into account many factors such as traffic flow, meteorological conditions and historical data, and verifies and optimizes the simulation image data set. the experimental results show that the improved LSTM model has excellent performance in predicting traffic situation, especially when dealing with problems with obvious time characteristics. this model is not only suitable for traffic flow forecasting and management, but also provides new theoretical insights and practical tools for urban traffic management, and provides an important reference for researchers who study traffic forecasting technology. It is also useful in areas where similar data sets can be acquired or generated, and where traffic situation analysis is required. the excellent performance of the model is of great significance to the decision-makers and researchers who pay attention to the development trend of future traffic forecasting technology, indicating the possible far-reaching impact on the development of future traffic forecasting technology.

关键词： Analytical models Energy consumption Time series analysis Weather forecasting predictive models Market research Data models

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XANNLight: A Novel Hybrid Ensemble Model for Accurate Health Insurance Prediction and Trend Forecasting

XANNLight: A Novel Hybrid Ensemble Model for Accurate Health...

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

作者： Kafila Sajja Suneel Jayendra Gopal thatipudi M Jini Kumari K. Penyameen Anubhav Sharma School of Business SR University Warangal Telangana India Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Dept. of Computer Science Jayendra Gopal Thatipudi Student University of North Texas USA Department of ECE Chennai Institute of Technology Velammal College Of Engineering And Technology Madurai Department of CSE IES institute of Technology and management IES University Bhopal Mp India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Health insurance is a critical industry that requires precise data analysis and predictive capabilities to ensure operational efficiency and cost management. In an era where large datasets are readily available, machine learning models have become indispensable for predicting claims and identifying trends in insurance data. the rapid growth of the health insurance industry has increased the need for accurate prediction models to optimize claim management and trend forecasting. this study presents XANNLight, a novel hybrid ensemble model that integrates Artificial Neural Networks (ANN) and LightGBM to predict health insurance claims with high precision. the proposed model handles complex, non-linear relationships using ANN while LightGBM provides fast and accurate predictions for structured data. the dataset used for model training and evaluation consists of 1338 records, with features including age, sex, BMI, number of children, smoker status, and region. the model's performance is evaluated based on accuracy, precision, recall, and F1 score. the XANNLight model achieves an accuracy of 98.12%, outperforming nine existing models, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, which achieved accuracy levels between 85% and 96%. Furthermore, XANNLight demonstrates superior precision (97.83%), recall (97.90 % ), and F1 score (97.86 % ). these results highlight the model's efficiency in processing large datasets and predicting insurance claims, making it an effective tool for health insurance companies aiming to enhance operational efficiency and cost management.

关键词： Industries Accuracy Costs Computational modeling Insurance Artificial neural networks predictive models Market research Data models Forecasting

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A Machine Learning Study on the Model Performance of Human Resources predictive Algorithms 4

A Machine Learning Study on the Model Performance of Human R...

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4th international conference on Applied Machine Learning, ICAML 2022

作者： Shi, Yong School of Computer Science Huainan Normal University Huainan China School of Information Technology and Engineering St. Paul University Philippines Tuguegarao Philippines

ISBN: (纸本)9781665462655

A good ecological environment is crucial to attracting talents, cultivating talents, retaining talents and making talents fully effective. this study provides a solution to the current mainstream problem of how to deal with excellent employee turnover in advance, so as to promote the sustainable and harmonious human resources ecological environment of enterprises with a shortage of *** study obtains open data sets and conducts data preprocessing, model construction and model optimization, and describes a set of enterprise employee turnover prediction models based on RapidMiner workflow. the data preprocessing is completed with the help of the data statistical analysis software IBM SPSS Statistic and *** charts, scatter plots and boxplots for analysis are generated to realize data visualization analysis. Machine learning, model application, performance vector, and cross-validation through RapidMiner's multiple operators and workflows. Model design algorithms include support vector machines, naive Bayes, decision trees, and neural networks. Comparing the performance parameters of the algorithm model from the four aspects of accuracy, precision, recall and F1-score. It is concluded that the performance of the decision tree algorithm model is the highest. the performance evaluation results confirm the effectiveness of this model in sustainable exploring of enterprise employee turnover prediction in human resource management. © 2022 IEEE.

关键词： Decision trees

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Deep Learning-Assisted MRI Image Segmentation and Classification for Precise Brain Tumor Analysis

Deep Learning-Assisted MRI Image Segmentation and Classifica...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Shuvro Chandra Das Md.Ahiya Sarder Srejon Das Deluwar Hussen Tanvir Sheikh Taraque Aziz Ashraful Islam Dept. of Electrical and Electronic Engineering Sylhet Engineering College Sylhet Bangladesh Dept. of Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh

For determining the appropriate treatment for brain tumors, an accurate diagnosis is necessary. Many studies have focused on the deep learning-based classification of brain tumors. this study employed a comprehensive approach using deep learning techniques to improve diagnostic accuracy and treatment planning for brain tumors. the Random Forest algorithm, SVM, YOLOv5, and Neural Network classifier were utilized to classify brain tumors and different planes of brain MRI images. To segment the tumors, the U-Net architecture with various neural network backbones (VGG16, Resnet34, and InceptionV3) was implemented, and metrics such as intersection over union (IOU) and dice coefficient were used to assess the segmentation outcome. the U-Net model achieved an IOU of 0.73 and a dice coefficient of 0.84. the Random Forest algorithm achieved 99 percent and 95 percent classification accuracy in image planes and tumor types, respectively. the SVM, YOLOv5, and Neural Network Classifier provided classification accuracies of 91 percent, 95 percent, and 96 percent, respectively, in classifying brain tumors. the ensemble models predicted the final result, demonstrating the potential of this approach to improve brain tumor diagnosis and treatment planning.

关键词： Support vector machines Image segmentation Magnetic resonance imaging predictive models Brain modeling Planning Classification algorithms

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Entity relation extraction based on candidate relation attention mechanism and interaction graph

Entity relation extraction based on candidate relation atten...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Shiyong Xiong Erpu Deng Huajian Xie Renfei Zhou School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Research Office Chongqing Education Science Research Institute Chongqing China School of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

A fundamental task in information extraction is Relation Extraction (RE). However, existing methods often struggle with overlapping relations and nested entity extraction, and the modeling of entity-relation interactions is weak. In order to tackle these challenges, this study presents the CraRel model, an entity-relation extraction model based on candidate relation attention mechanisms and interaction graphs. By enhancing the interaction between entities and relations, the model effectively tackles overlapping relations and nested entities, improving the accuracy and robustness of relation extraction. the experimental findings indicate that this approach surpasses currently popular models across various public datasets.

关键词： Attention mechanisms Computational modeling Bidirectional control Information retrieval Boosting Robustness Natural language processing Encoding Data mining Faces

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Optimised Classification of Android Malware Using ML Classifiers and Stacking Ensemble Algorithm

Optimised Classification of Android Malware Using ML Classif...

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

作者： Preet Singh Taniya Hasija KR Ramkumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

One of the biggest threats that malicious software, or malware, poses to cybersecurity is exploiting vulnerabilities to infect computer systems. this research is based on malware classification using ensemble-based and kernel-specific machine-learning classification techniques. Advanced cyber threat scenarios have a critical need for solutions that balance accuracy, precision, recall, and F1 score while providing successful malware identification. the diverse base models integrated into the proposed methodology consisted of SVM with linear, polynomial, and RBF kernels, and a stacking classifier that utilizes the base model's strengths. the results showed that, compared with various single models, a stacking classifier achieved quite a high accuracy of 99.14% and maintained high precision and recall percentages for all evaluation metrics. It draws attention to hybrid and ensemble approaches in cybersecurity and the chances to raise detection rates at the cost of reduced false classifications. Advanced ensemble strategies, new deep learning models, as well as XAI will be used for the best enhancement of performance, adaptability, and transparency against evolving malware threats.

关键词： Support vector machines Deep learning Accuracy Computational modeling Stacking Malware Polynomials Kernel Computer security Tuning

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Comparative Study of Different Intent Classification Algorithms for Agriculture Question and Answer Task

Comparative Study of Different Intent Classification Algorit...

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international conference on Electrical, Control and Instrumentation engineering (ICECIE)

作者： Pratijnya Ajawan Veena Desai Supriya Shanbhag Nikita Hemant Kodkany KLS Gogte Institute of Technology Belagavi Karnataka India

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

this research examines the effectiveness of various machine learning models in accurately classifying the intent of agricultural queries using fundamental machine learning techniques. the study analyzes two curated datasets: the QUERY dataset, containing 2,853 queries and their corresponding intents, and the ANSWERS dataset, which includes crops and their associated intents, covering 21 intent categories across 30 different crops. the main objective is to evaluate the performance of traditional machine learning models on this dataset to enable accurate intent classification, facilitating responsive interactions via the QA system, which delivers data-driven answers to farmers' queries. Notably, the Support Vector Classifier demonstrated the best performance with an accuracy of 92 % , closely followed by the Linear Discriminant Analysis Classifier. Using the Support Vector Classifier, the QA system efficiently retrieves responses from the ANSWERS dataset. this paper provides a comprehensive analysis of the performance of various algorithms for the question-answering (QA) task in the agricultural domain. the evaluation is based on pre-modelled classification reports from Sklearn's metrics package, ensuring rigorous analysis and detailed insights into the effectiveness of each approach.

关键词： Machine learning algorithms Accuracy Intent recognition Crops Static VAr compensators Machine learning predictive models Prediction algorithms Vectors Classification algorithms

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