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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 3339 条记录，以下是681-690 订阅

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34th international conference on Advanced Information Systems engineering, CAiSE 2022

34th International Conference on Advanced Information System...

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34th international conference on Advanced Information Systems engineering, CAiSE 2022

ISBN: (纸本)9783031074714

the proceedings contain 31 papers. the special focus in this conference is on Advanced Information Systems engineering. the topics include: Causal Reasoning over Control-Flow Decisions in Process models;crop Harvest Forecast via Agronomy-Informed Process Modelling and predictive Monitoring;guiding Knowledge Workers Under Dynamic Contexts;context Knowledge-Aware Recognition of Composite Intents in Task-Oriented Human-Bot Conversations;crowdsourcing Syntactically Diverse Paraphrases with Diversity-Aware Prompts and Workflows;a Subject-aware Attention Hierarchical Tagger for Joint Entity and Relation Extraction;estimating Activity Start Timestamps in the Presence of Waiting Times via Process Simulation;updating Prediction models for predictive Process Monitoring;multi-model Monitoring Framework for Hybrid Process Specifications;inferring a Multi-perspective Likelihood Graph from Black-Box Next Event Predictors;mining Valuable Collaborations from Event Data Using the Recency-Frequency-Monetary Principle;querying Temporal Property Graphs;a Supervised Learning Community Detection Method Based on Attachment Graph Model;soundness of Data-Aware Processes with Arithmetic Conditions;narration as a Technique to Improve Process Model Comprehension: Tell Me What I Cannot See;analyzing Enterprise Architecture models by Means of the Meta Attack Language;enhancing Semantics-Driven Recommender Systems with Visual Features;assisting Mentors in Selecting Newcomers’ Next Task in software Product Lines: A Recommender System Approach;Towards Interoperable Metamodeling Platforms: the Case of Bridging ADOxx and EMF;situation Awareness for Autonomous Vehicles Using Blockchain-Based Service Cooperation;bootstrapping Generalization of Process models Discovered from Event Data;how Big Service and Internet of Services Drive Business Innovation and Transformation;time-Cost Tradeoffs for Composed Services;multi-perspective Process Analysis: Mining the Association Between Control Flow and Data Obje

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Wall thickness Estimation Based on LSTM for through-Wall Radar Imaging

Wall Thickness Estimation Based on LSTM for Through-Wall Rad...

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Tianfang Ma Zhaodong Wang Qingjie Qi School of Emergency Management and Safety Engineering China University of Mining and Technology Beijing China Emergency Science Research Institute Chinese Institute of Coal Science Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Prior knowledge of the wall's thickness is crucial for precisely pinpointing the location of the target in through-wall radar imaging. We suggest a method to estimate wall thickness using Long Short-Term Memory (LSTM) for the radar echo matrix that penetrates through walls. LSTM has demonstrated a prediction accuracy over 96% according to test findings. the model's prediction accuracy is unaffected by alterations in the target's position located behind the wall.

关键词： Accuracy Computational modeling Estimation Radar Radar imaging predictive models Signal processing Distance measurement Long short term memory

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A Brief Review Particle Swarm Optimization on Intrusion Detection System 1

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Nandana, G.M. Yadav, Ashok Kumar Department of Computer Science and Engineering ASET Amity University Noida India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

there has been an improvement in recent years towards increase in volume for remarkable data in the field of Internet and computer networks. this brings up significant security-related challenges. In the past, a number of systems that provide host or network-level security have emerged. Many conventional security measures, such as, firewalls, spyware, authentication methods and antivirus software provide some way towards ensuring security, but they are still vulnerable to cyber-attacks and other system vulnerabilities. Some noteworthy alternatives are discovered, namely Intrusion Detection & Prevention Systems are an intriguing development, but they also have significant drawbacks, such as the inability to identify new assaults and respond to them in real time. Overtime, many researchers have proposed to develop a robust as well as intuitive infrastructure intrusion detection systems (IDS) utilising a variety of neural network models, i.e. support vector machines (SVM), rough sets, Particle Swarm Optimization (PSO) and many other versions. In this paper, we discuss all proposed models have been implemented by researchers in improving IDS performance with PSO model. Also, considering different IDS implemented, we can derive various IDS datasets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Network security

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Experimentation of Code Smells Using Deep Learning Techniques

Experimentation of Code Smells Using Deep Learning Technique...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Omeir Fawaz Mohammad Amaan Suryansh Sahu Mohd Adnan Aakanshi Gupta Amity School of Engineering & Technology Amity University Noida Uttar Ptardesh India

A code smell is a measurable indicator that highlights significant issues in the software development process caused by inadequate programming practices. these are usually introduced in a software program during the design or implementation phases, and may lead to more significant problems during software maintenance. the existing methods to detect smells based on metrics, heuristics or machine learning have certain limitations. In this paper, we suggest two models built on deep learning for predicting code smells: convolutional neural network (CNN) and recurrent neural network (RNN). these models use features extracted from source code to detect six different code smells. We then evaluate and compare their results. the proposed approach performs remarkably well for single code smell identification according to the experimental results obtained.

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Time Series Fusion Model for Predicting Electric power tools and equipment

Time Series Fusion Model for Predicting Electric power tools...

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

作者： Liang Xue Xinghong Ling Lei Yang Yang Yang Computing Science and Artificial Intelligence College Suzhou City University Suzhou China College of Computer Science and Software Engineering Hohai University Nanjing China Suzhou Key Lab of Multi-Modal Data Fusion and Intelligent Healthcare Suzhou City University Suzhou China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

To effectively address the issue of inadequate accuracy and timeliness in industrial data classification, this paper proposes a fusion classification model based on time series (DT-RF-MLP) for loss prediction of electrical work equipment. Firstly, data information is extracted from the dataset and features are constructed based on the data information to enhance the diversity of data features. Secondly, redundant and highly correlated features are removed by quantifying the correlation degree between the features to simplify the model inputs. On this basis, a fusion classification model is constructed and comparative experiments are conducted with a single model to validate the advantages of the fusion model in terms of prediction performance. the experimental results demonstrate that the model markedly enhances prediction accuracy while concurrently reducing the duration of the model run.

关键词： Accuracy Correlation Service robots Time series analysis predictive models Feature extraction Data models Power systems Decision trees Random forests

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Exploration and Practice of Object-Oriented Course Reform Integrating Macro and Micro Learning Methods

Exploration and Practice of Object-Oriented Course Reform In...

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Computer Science, engineering, and Education (CSEE), international conference on

作者： Ben Wang Chunyan Ma Yimeng Wang School of Software Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331505165

ISBN: (纸本)9798331505172

In response to the disconnection between theoretical knowledge acquisition and the ability to solve complex practical problems in object-oriented programming and design courses, this paper analyzes the deficiencies in teaching content and practice based on educational objectives and proposes a blended teaching model that integrates macro and micro learning methods. Macroscopically, a teaching method is constructed that combines “example guidance with virtual and practical integration, theory with practice, practice with experimentation, and experimentation with experience”. Microscopically, a classroom teaching model is established that is guided by “layered and associated” classroom objectives, forming a cascading model for classroom teaching progression, resulting in a teaching method that fosters “capability growth step by step: macro and micro integration”.

关键词： Learning systems Industries Analytical models Object oriented modeling Education Systems thinking Object oriented programming System analysis and design Programming profession software development management

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Load Adjustment Prediction within a Month Based on Federated Learning

Load Adjustment Prediction within a Month Based on Federated...

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international conference on Power and Energy Technology (ICPET)

作者： Pengyang Yan Jie Yu School of Software Southeast University Nanjing China School of Electrical Engineering Southeast University Nanjing China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

In the monthly trading market, electricity retailers participate in intra-month adjustment volume transactions with trading cycles ranging from ten days, weekly, to daily, where the traded electricity volume is based on the remaining days or specific days within the month. Considering user data security and privacy protection, this paper proposes a horizontally federated learning mechanism based on load decomposition and privacy protection for intra-month adjustment volume load prediction analysis. Firstly, each user locally extracts temporal features from the data using trend decomposition algorithms, decomposing the original load data into trend, periodic, and random components. Secondly, based on federated learning, prediction models are separately constructed for the trend, periodic, and random components. Finally, the electricity retailer aggregates the predicted components from each user to obtain the final prediction result. the proposed algorithm is validated using load data from users of a certain electricity retailer in China. the results demonstrate that the algorithm achieves excellent prediction accuracy and generalization ability while protecting user data privacy.

关键词： Data privacy Privacy Federated learning Power supplies Electricity predictive models Feature extraction Prediction algorithms Market research Protection

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Research on Precipitation Forecasting models Utilizing Intermediate Fusion Techniques

Research on Precipitation Forecasting Models Utilizing Inter...

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

作者： Bin Wang Jin Tian School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Multimodal meteorological data offers a comprehensive perspective on the evolution of individual meteorological elements. Exploiting this data effectively is crucial for enhancing the accuracy of short-term (0–4 hours) precipitation forecasts. this study introduces a recurrent neural network model designed for multimodal fusion and spatiotemporal forecasting, named MFSFN. the model utilizes GPM precipitation data and reanalysis data as inputs, providing forecasts for precipitation volume and intensity over a 0–4 hour timeframe. MFSFN integrates these three data sources on a spatiotemporal scale, effectively preserving their flow of temporal and spatial information. Experimental validation on the ERA5 and GPM datasets demonstrates that MFSFN excels in predicting light, moderate, and heavy precipitation events, particularly showing superior performance in early-stage forecasts of light and moderate precipitation compared to other methodologies.

关键词： Measurement Solid modeling Precipitation Recurrent neural networks Accuracy Weather forecasting predictive models

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Path Complexity Analysis for Interprocedural Code

Path Complexity Analysis for Interprocedural Code

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international conference on software engineering - Companion, ICSE, Companion

作者： Mira Kaniyur Ana Cavalcante-Studart Yihan Yang Sangeon Park David Chen Duy Lam Lucas Bang Harvey Mudd College Claremont California USA

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Symbolic execution's path explosion is a critical issue in software testing, quantified by Asymptotic Path Complexity (APC) [3]. APC, more precise than cyclomatic [6] or NPAth [7] complexities, measures the effort to cover paths in code analysis [1]. It's vital for testing, setting limits on path growth for tools like Klee [4], focusing previously on intraprocedural code [2], [8]. Our advancement, APC-IP, extends APC to interprocedural analysis, enhancing scalability and encompassing earlier models.

关键词： software testing Analytical models Codes Scalability Focusing Explosions Complexity theory

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A Verilog-based Decision Tree-driven Adaptive Control System for Traffic Light Duration

A Verilog-based Decision Tree-driven Adaptive Control System...

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Electrical, Electronic Information and Communication engineering (EEICE), international conference on

作者： Jiabei Liu Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China

ISBN: (数字)9798331532598

ISBN: (纸本)9798331532604

As urbanization progresses, the challenge of traffic congestion has escalated, emphasizing the need for the optimization of traffic control systems. this research presents a Verilog-based, decision tree model driven adaptive control system for traffic light duration, which involves the construction and refinement of a decision tree utilizing a traffic flow dataset, followed by optimization through a post-pruning technique. the optimized decision tree model, along with the adaptive traffic light system, is subsequently implemented using Verilog hardware description language. Results from simulation validation and testing on an experimental box demonstrate that the system accurately predicts current traffic flow based on varying inputs and adjusts traffic light durations in real time. this study underscores the potential for integrating machine learning algorithms with programmable logic devices for dynamic traffic signal control and related applications, while also demonstrating opportunities for future improvements.

关键词： Adaptation models Machine learning algorithms Heuristic algorithms Traffic control predictive models Real-time systems Decision trees Hardware design languages Optimization Testing

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