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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751-760 订阅

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Hybrid Demand Forecasting Model for Big Marts Using Seasonal Decomposition, ARIMA, and Gradient Boosting Regressor

Hybrid Demand Forecasting Model for Big Marts Using Seasonal...

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

作者： S. Balaji D. Manikavelan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Demand forecasting is a pivotal factor of the retail enterprise, permitting businesses to successfully optimize stock management and meet client demand. this paper affords a hybrid model that integrates 3 key methodologies-Seasonal Decomposition (SD), Auto Regressive integrated shifting common (ARIMA), and Gradient Boosting Regressor (GBR)-to decorate forecasting accuracy for large marts. the version is constructed on ancient income facts, where Seasonal Decomposition first separates the time collection into seasonal, trend, and residual components, revealing underlying patterns. ARIMA is then implemented to the detrended records, accounting for time-dependent autocorrelations and shifting averages, while GBR is hired to model and predict the residuals, thereby refining the overall forecast. the performance of the SD-ARIMA-GBR version is empirically tested on income statistics from 1559 products throughout 10 stores, yielding an impressively low implied Mean Absolute percentage error (MAPE) of 3.0%. When in comparison to standard models like ARIMA and Exponential Smoothing, the hybrid technique proves substantially extra correct. the combination of machine mastering, especially GBR, performs a crucial function in improving predictive precision. this complete version gives huge marts an effective tool for awaiting calls for fluctuations, using more accurate stock-making plans, and enhancing operational efficiency.

关键词： Adaptation models Accuracy Social networking (online) Demand forecasting predictive models Inventory management Boosting Market research Standards Business

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Simulation Method for Reliability Prediction of Switching Power Supply injected with PoF Model

Simulation Method for Reliability Prediction of Switching Po...

引用

System Reliability and Safety engineering (SRSE), international conference on

作者： Kaiwen Xiao Cen Chen Weiming Liu Shujuan Wang Xiaoying Gao Guofu Zhai Department of Electrical Engineering & Automation Harbin Institute of Technology Harbin China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

Switching power supplies are widely used in aerospace, new energy and many other fields. During long-term operation, various electronic components in these supplies experience performance degradation due to continuous electrical and thermal stress, leading to changes in output characteristics. this is the fundamental reason affecting the reliability of switching power supplies. However, the current combination of physics of failure (PoF) models and simulation technique is imperfect, and most methods cannot achieve batch injection of multiple device failures. this paper proposes a reliability prediction simulation method for switching power supplies based on PoF model injection. By focusing on a switching circuit, the approach involves digital twin modeling and validation, failure mechanism and sensitivity analysis, key failure components degradation modeling, and batch degradation simulation. this method achieves continuous degradation data of multiple components based on the PoF model through batch injection, improving the efficiency and accuracy of reliability prediction for switching power supplies.

关键词： Degradation Power supplies Switches predictive models Reliability engineering Mathematical models Data models Integrated circuit modeling thermal stresses Stress

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Real-World Bug Hunting Techniques for Advanced Real-Time IP Address Redirection

Real-World Bug Hunting Techniques for Advanced Real-Time IP ...

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

作者： R. Umesh Akchara TD G. Naglakshmi Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research study builds the state-of-art cyber security platform that eventually relied on the internet. It focuses on building this from high-end approaches with practical bug-hunting awareness in its aim toward effective threat detection. It continuously monitors network traffic through several algorithm models for analyzing and stopping malicious activities at real time. In doing this, it blocks and dynamically changes IP addresses in such a way that unwanted access cannot happen. Key Features: Anomaly detection through User and Entity Behavior Analytics, Security Adaptation, Automation, and Response for effective threat management, a threat intelligence feed keeping it abreast of new attack vectors, and the platform comes with deep logging for forensic investigations purposes. predictive analytics, along with its continuous learning module, allows the detection algorithms to get optimized. Its friendly user interface promotes teamwork, data aggregation, and collective protection and, henceforth, provides the all-encompassing and flexible cybersecurity solution that can help to strengthen the businesses' defenses against constantly changing threats.

关键词： Cloud computing Analytical models Biological system modeling Buildings Organizations Data aggregation threat assessment Real-time systems IP networks predictive analytics

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Semi − Disambiguated Partial Label Learning by Mixing Disambiguation and Non-disambiguation Strategies

Semi − Disambiguated Partial Label Learning by Mixing Disam...

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software engineering and Computer Science (CSECS), international conference on

作者： Guangkui Lei Jing Chai School of information Science and Engineering Yunnan University Yunnan Kunming China

Partial Label Learning (PLL) is employed to tackle the issue of ambiguous labeling, where each instance in the training set is associated with a set of labels, but only one label in the associated set is the ground-true label. Two common strategies, disambiguation and non-disambiguation, both achieve some improvements in tackling the PLL problem. However, they each have their own drawbacks. In this work, we develop a novel PLL method, namely, Semi-Disambiguated Partial Label Learning (SDPLL), to mix the two strategies with the goal of enhancing the strengths and making up for the weaknesses of both strategies. In specific, first we adapt label propagation to operate a coarse-level disambiguation, by which we eliminate low-confidence labels from the candidate label set to get the shrinking set of candidate labels. Next, we employ binary decomposition to conduct non-disambiguation treatment on the shrinking version of the dataset. Empirical experiments on both controlled UCI datasets and real-world datasets justify the superiority (at least the competitiveness) of SDPLL over several representative competing models.

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Enhancing Chatbot User Satisfaction: A Machine Learning Approach Integrating Decision Tree, TF-IDF, and BERTopic

Enhancing Chatbot User Satisfaction: A Machine Learning Appr...

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

作者： Jiaxin Lu Trine University Phoenix USA

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Ensuring that large language models (LLMs) generate responses that resonate with users is critical for successful interactions. this study leverages machine learning techniques to improve chatbot interactions by aligning responses more closely with human preferences. By analyzing the Chatbot Arena dialogue dataset, we identified key features influencing user satisfaction and employed an ensemble model combining Decision Tree, TF-IDF, and BERTopic to enhance prediction accuracy. Our results demonstrate significant improvements in both accuracy and user satisfaction metrics compared to traditional models. this approach addresses the complexities of human-computer dialogue, providing a robust framework for future enhancements. the integration of advanced feature engineering and topic modeling techniques enables the chatbot to generate more contextually relevant and engaging responses, setting a new standard in the field of conversational AI.

关键词： Measurement Deep learning Accuracy Computational modeling Large language models predictive models Chatbots Feature extraction Decision trees Standards

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AI-based Repeat Buyers Prediction System using Deep Learning 6

AI-based Repeat Buyers Prediction System using Deep Learning

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6th IEEE international conference on Intelligent Computing and Signal Processing, ICSP 2021

作者： Huang, Shan Xi'an Jiaotong University School of Software Engineering China

ISBN: (纸本)9780738143705

Accurate prediction of repeat buyers can effectively help e-commerce companies adjust their business strategies accordingly. this study proposes an AI-based predictive model decision system for identifying. In the system, a complete machine learning and deep learning model comparison and automatic selection mechanism are established through the analysis of performance parameters and time efficiency. this study proposes an AI-based predictive model decision system, in which Support Vector Machine (SVM), Random Forest, Light Gradient Boosting Machine (LightGBM), Light Gradient Boosting and Factorization Machine (LightGBM+FM), and Deep Factorization Machine (DeepFM) are performed in order to determine the optimal predictive model for each e-commerce platform. Based on the parameters of model performance and time efficiency, including accuracy, Area under the ROC Curve (AUC score), Log Loss score, model fitting time and prediction time, the best models for Taobao and Tmall platforms are tested to be the Deep Factorization Machine with a 76.17% accuracy and a 0.7615 AUC score, and Light Gradient Boosting and Factorization Machine with a 61.90% accuracy and a 0.6063 AUC score. the results indicate that this model decision system can help build reliable and economical model strategies for industrial companies based on the repurchase prediction of their e-commerce platforms. © 2021 IEEE.

关键词： Forecasting

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Forecasting of photovoltaic power generation leveraging multi-view attention model

Forecasting of photovoltaic power generation leveraging mult...

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international conference on Power and Energy Technology (ICPET)

作者： Hanjing Liu Qiankun Su College of Computer Engineering Jimei University Xiamen China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

Forecasting of photovoltaic (PV) power generation is a challenging task since it requires to consider many factors synthetically, such as historical power generation data, meteorological data. the unstable and nonlinear nature of PV power generation contributes to the difficulty. Most existing predictive models rarely focus on the hidden information in the data and fail to capture spatio-temporal dependencies in the data. To address these challenges, we propose a hybrid deep learning framework that combines multi-view time-series representation with an attention mechanism, named Multi-View Attention model for PV Power Generation Prediction (PV-MVAT). Our model improves the accuracy of its predictions by the following two techniques: i) capture key features and information through the multi-view time-series representation technique; ii) leverage the attention mechanism to dynamically adjust the attention weights for different time points and features. Real data simulations from the Elia Group in Belgium demonstrate the viability of our design choices. In particular, compared with the best performance among ten baseline models, our model achieves 16.06%, 8.11% and 7.73% improvement in MSE, MAE and CVRMSE respectively.

关键词： Photovoltaic systems Deep learning Attention mechanisms Accuracy Time series analysis predictive models Data models Robustness Hybrid power systems Forecasting

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LQR Control of Two-Wheeled Self-Balancing Vehicle Based on ARX Model

LQR Control of Two-Wheeled Self-Balancing Vehicle Based on A...

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international conference on Electronic engineering and Informatics (EEI)

作者： Wei Zhou Jun Wu Ziqiang Yin Xi Liu Changsha University of Science and Technology Changsha China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

In order to solve the problem of inaccurate parameters of the mechanism model of the two-wheeled self-balancing car, the ARX model was selected to describe the dynamic characteristics of the object near the working point, the modeling method based on system identification technology was studied, and the one-step prediction error and long-term prediction error were used as performance indicators, and the modeling performance of the mechanism model was compared, and the LQR control strategy based on the ARX model was designed, and compared with the double closed-loop control strategy based on PID control. the effectiveness and feasibility of the proposed modeling and control scheme are verified in real-time control.

关键词： PI control Regulators Battery powered vehicles predictive models Real-time systems System identification PD control Automobiles Vehicle dynamics Informatics

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Cooperative Adaptive Cruise Control for Vehicles Under False Data Injection Attacks

Cooperative Adaptive Cruise Control for Vehicles Under False...

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Industrial Cyber-Physical Systems (ICPS)

作者： Feng Zhongwei Keyun Qin Xiaohang Jiao Feifei Du Dongshen Li Department of Electrical Engineering and Automation Northeast Agricultural University Harbin Heilongjiang Department of Packaging Engineering Northeast Agricultural University Harbin Heilongjiang Department of Agricultural Mechanization and Automation Northeast Agricultural University Harbin Heilongjiang

this paper studies the problem of vehicle cooperative adaptive cruise control (CACC) under false data injection attacks on vehicle communications, and an intrusion detection mechanism is designed. When a vehicle is detected to be attacked in CACC, the vehicle will switch from CACC mode to ACC mode. Firstly, the vehicle dynamics model including the acceleration disturbance of the preceding vehicle is established. then, based on the relative distance, relative velocity and relative acceleration, the quadratic multi-objective optimization performance index function and multi-parameter constraint conditions are established under model predictive control (MPC) framework, and the vehicle predictive control optimization proposition is constructed to obtain the optimal control quantity. Furthermore, the false data injection attack model is set up and an intrusion detection mechanism is presented. Finally, the effectiveness of the intrusion detection mechanism is verified by comparative simulation.

关键词： Adaptation models Adaptive systems Intrusion detection Switches Data models Performance analysis Vehicle dynamics

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Application of Soft Computing Techniques for predictive Analytics in Financial Markets

Application of Soft Computing Techniques for Predictive Anal...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Bhavya K R Dharmaraj A Prakash Pareek Manoj Ashok Sathe Mohit Tiwari Geetha Manoharan Commerce and Management Dr. N.S.A.M First Grade College Bangalore Karnataka India Department of Management Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of ECE Vishnu Institute of Technology Bhimavaram A.P. India PES Modern Institute Of Business Studies Pune India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India School of Business SR University Warangal Telanagana India

Successful applications of predictive analysis can be found in finance. For these goals, modern soft computing theories are applied. In contrast to other apps, financial applications have unique characteristics. Forecasting is crucial, particularly in the financial industry because it lowers expenses, which can increase revenues and help businesses win the competition. Due to inescapable changes and expansion in every aspect of life, almost every organization on earth is operating in an unpredictable environment. Forecasting becomes increasingly difficult as a result of these developments, which either directly or indirectly affect stock market values. the demand for trustworthy, economical, and efficient forecasting models is therefore great in order to reduce uncertainty as well as risk in investing in the stock market. Academics and information researchers have developed a variety of time series models for the most precise and perfect future prediction. Financial autoregressive time series models have produced precise prediction-capable predictive models like the autoregressive moving average and autoregressive integrated moving average. To predict the closing value of the BSE100 S&P Sensex record every week and every day, discrete wavelet change and wavelet denoising soft computing models are combined with autoregressive models in the continuing work.

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