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Electricity Bill Recovery Risk Prediction Based on machine learning algorithm

Procedia Computer Science 2025年 262卷 1267-1273页

作者： Tao Zhu Yao Zhou Zuoyun Dai Wenxin Tang Ming Zheng Zichao Zhu Ruoxuan Ding State Grid Jurong County Electric Power Supply Company Zhenjiang 212400 China

In order to reduce the risk of electricity fee recovery for power companies and improve the accuracy of electricity fee recovery risk prediction. This paper firstly analyzes and collates the existing electricity rate recovery data comprehensively, and obtains a large number of historical electricity rate payment records and user credit information from the power company database. In the process of index selection, the key indicators that are highly related to the risk of electricity cost recovery are selected by combining the expert experience and data characteristics. Based on these key indicators, the paper constructs a machine learning algorithm model, compares the performance of multiple algorithms, and selects the algorithm with the best performance in prediction accuracy and generalization ability. Experiments show that LSTM neural network model is superior to logistic regression model in hit rate, coverage rate, promotion degree, accuracy rate, recall rate and F1 value, showing higher prediction accuracy and effectiveness.

关键词： Electricity bill recovery Risk prediction machine learning algorithm

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MEMS fault-tolerant machine learning algorithm assisted attitude estimation for fixed-wing UAVs

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 129卷

作者： Pasha, Amjad Ali Sankaralingam, L. Rahman, Mustafa Mutiur Alam, Mohammad Irfan Juhany, Khalid Ahmad King Abdulaziz Univ Aerosp Engn Dept Jeddah 21589 Saudi Arabia Hindustan Inst Technol & Sci Sch Aeronaut Sci Chennai 603103 India Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada Konkuk Univ Konkuk Aerosp Design Airworthiness Res Inst KADA Seoul South Korea

The utilization of Micro-electromechanical Systems (MEMS) sensors is widespread for directly detecting attitude angles, such as Accelerometer, Gyro, and Magnetometer readings. However, these MEMS sensors are prone to flaws, leading to inaccurate estimates of attitude angles and, consequently, causing UAVs to lose control. Given that UAVs are operated remotely over long distances, ensuring accurate attitude estimates becomes crucial. This study aims to address this issue by employing machine learning algorithms (MLA). These algorithms were trained and evaluated to overcome the problem by predicting missing data from a malfunctioning MEMS sensor using the available data from other MEMS sensors. To calculate the attitude angles, the study utilizes the Extended Kalman Filter (EKF) technique. Furthermore, a novel fault-tolerant machine learning-aided estimation algorithm has been proposed specifically for estimating the attitude angles (phi, theta, psi) of fixed-wing UAVs. The significance of this research becomes even more prominent when considering the occurrence of MEMS sensor failure. In such cases, the machine learning algorithm plays a crucial role as it has been pre-trained specifically to handle these scenarios. The algorithm is equipped with the ability to effectively address and mitigate the challenges posed by MEMS sensor failures. By leveraging its pre-existing knowledge and learned patterns, the algorithm can accu-rately predict missing data caused by malfunctioning MEMS sensors. This capability proves invaluable in ensuring the reliable estimation of attitude angles, even in the face of sensor failures. Thus, the integration of machine learning into the estimation process enhances the resilience and robustness of the system, making it well-equipped to handle the uncertainties introduced by MEMS sensor failures.

关键词： Fault-tolerant machine learning algorithm Attitude angles estimation Kalman filter

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A machine learning algorithm-enhanced mathematical simulation based on Carrera unified formulation to estimate fundamental frequency of the FG origami-enabled metamaterial composite system surrounded by auxetic concrete foundation

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年

作者： Wei, Qiujie Li, Jialing Cheng, Xi Rehman, Ateekh Ur Chengdu Polytech Chengdu Peoples R China Chongqing Youth Vocat & Tech Coll Sch Artificial Intellegence Chongqing Peoples R China Zhuzhijian Technol Chongqing Co Ltd Chongqing Peoples R China King Saud Univ Coll Engn Dept Ind Engn Riyadh Saudi Arabia

This study investigates the nonlinear dynamic behavior and natural frequencies of skew plates integrated with FG (Functionally Graded) origami-enabled metamaterials and supported by auxetic concrete foundations. The analysis is performed using the Carrera unified formulation (CUF), which provides a robust and accurate method for assessing the flexural response of complex plate systems. The formulation accounts for geometric nonlinearity, material gradation, and skew angles, capturing both the nonlinear natural frequencies and dynamic deflections. The inclusion of auxetic concrete foundations enhances the structural performance due to their negative Poisson's ratio, which offers improved energy absorption and deformation characteristics. To further validate the accuracy and reliability of the proposed method, the results are verified through comparison with existing literature and are supplemented by a novel fuzzy decision tree algorithm as a machine learning tool. The fuzzy algorithm aids in automating the verification process and identifying patterns in the dynamic response, ensuring computational efficiency and robustness. Results demonstrate the significant influence of skew angles, and metamaterial properties on the dynamic behavior of skew plates. The study highlights the advantages of FG origami-enabled metamaterials in enhancing structural stiffness and reducing dynamic deflections, making them suitable for advanced engineering applications. This research bridges the gap between computational mechanics and machine learning, providing a comprehensive approach to analyzing and verifying the nonlinear dynamic performance of skew plates with auxetic foundation support.

关键词： Carrera unified formulation skew plate FG origami-enabled metamaterials auxetic concrete foundation nonlinear natural frequency and dynamic deflection machine learning algorithm

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Integrating machine learning algorithm with sewer process model to realize swift prediction and real-time control of H2S pollution in sewer systems

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WATER RESEARCH X 2024年 23卷 100230页

作者： Liang, Zhensheng Xie, Wenlang Li, Hao Li, Yu Jiang, Feng Sun Yat Sen Univ Sch Environm Sci & Engn Guangdong Prov Key Lab Environm Pollut Control & R Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Prov Int Joint Res Ctr Urban Water Manag Guangzhou 510275 Peoples R China South China Normal Univ Sch Environm Guangdong Prov Key Lab Chem Pollut & Environm Safe Guangzhou 510006 Peoples R China South China Normal Univ MOE Key Lab Theoret Chem Environm Guangzhou 510006 Peoples R China

The frequent occurrence of safety incidents in sewer systems due to the emergency toxicity of hydrogen sulfide (H2S) necessitate timely and efficient prediction, early warning and real-time control. However, various factors influencing H2S generation and emission leads to a substantial computational burden for the existing dynamic sewer process models and fails to timely control the H2S exposure risk. The present study proposed a swift prediction model (SPM) that combined the validated dynamic sewer process model (the biofilm-initiated sewer process model, BISM) with a high-speed machine learning algorithm (MLA), achieving accurately and swiftly predict the dissolved sulfide (DS) concentration and H2S concentration in a specific sewer network. Based on Gradient Boosting Decision Tree -based SPM, the simulated concentrations of DS and H2S are 1.95 mg S/L and 214 ppm, respectively, which are closely to the field -measured values of 1.82 mg S/L and 219 ppm. Notably, SPM achieved a computation time of less than 0.3 s, and a significant improvement over BISM ( > 5000 s) for the same task. Moreover, the real-time and dynamic dosing scheme facilitated by SPM outperformed the conventional constant dosing scheme provided by dynamic sewer process model, which significantly improved the H2S control completion rate from 69 % to 100 %, and achieved a significant reduction in chemical dosage. In conclusion, the integration of dynamic sewer process models with MLA addresses the inadequacy of monitoring data for MLA training, and thus pursues swift prediction of H2S generation and emission, and achieving real-time, effective, and economic control of H2S in complex sewer networks.

关键词： Sulfide pollution in sewers Validated dynamic sewer process model machine learning algorithm Swift prediction model Real-time control

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Detection of Unfocused EEG Epochs by the Application of machine learning algorithm

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SENSORS 2024年第15期24卷 4829页

作者： Akhter, Rafia Beyette, Fred R. Univ Georgia Coll Engn Dept ECE Athens GA 30602 USA

Electroencephalography (EEG) is a non-invasive method used to track human brain activity over time. The time-locked EEG to an external event is known as event-related potential (ERP). ERP can be a biomarker of human perception and other cognitive processes. The success of ERP research depends on the laboratory conditions and attentiveness of the test subjects. Specifically, the inability to control experimental variables has reduced ERP research in the real world. This study collected EEG data under various experimental circumstances within an auditory oddball paradigm experiment to enable the use of ERP as an active biomarker in normal laboratory conditions. Then, ERP epochs were analyzed to identify unfocused epochs, affected by typical artifacts and external distortion. For the initial comparison, the ability of four unsupervised machine learning algorithms (MLAs) was evaluated to identify unfocused epochs. Then, their accuracy was compared with the human inspection and a current EEG analysis tool (EEGLab). All four MLAs were typically 95-100% accurate. In summary, our analysis finds that humans might miss subtle differences in the regular ERP patterns, but MLAs could efficiently identify those. Thus, our analysis suggests that unsupervised MLAs perform better for detecting unfocused ERP epochs compared with the other two standard methods.

关键词： electroencephalography distraction artifacts machine learning algorithm oddball paradigm human visual inspection EEGLab

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Research on wind turbine icing prediction data processing and accuracy of machine learning algorithm

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RENEWABLE ENERGY 2024年 237卷

作者： Zhang, Lidong Zhao, Yuze Guo, Yunfeng Hu, Tianyu Xu, Xiandong Zhang, Duanmei Song, Changpeng Guo, Yuanjun Ma, Yuanchi Northeast Elect Power Univ Sch Energy & Power Engn Jilin 132012 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Peoples R China Univ Manchester Sch Engn Manchester M13 9PL England Key Lab Smart Energy & Informat Technol Tianjin Mu Tianjin 300072 Peoples R China Changchun Inst Technol Coll Jilin Emergency Management Changchun 130021 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China

Studying the icing problem of wind turbine blades is crucial for optimizing wind farm operation and maintenance. Traditional machine learning algorithms like Random Forest and Support Vector machines have limitations in handling complex time series data and capturing long-term dependencies, leading to insufficient accuracy and generalization. To address these gaps, this paper proposes a comprehensive approach involving advanced machine learning frameworks and feature engineering techniques. This paper based on the original SACDA dataset, four distinct types of processing are performed on the prediction data via PCA dimensionality reduction and the introduction of new features;meanwhile, the four types mentioned above of datasets are trained and predicted using the Gated Recycling Unit model (GRU), Random Forest (RF), GA-BP Neural Network (BP), and Extreme learning machine (ELM) models. The results indicate that the prediction accuracies of all four types of models are more satisfactory, with the RF model having the highest prediction accuracy and the overall accuracy remaining above 99 percent, the dataset + RF model after dimensionality reduction of the original sensitive features having the highest prediction accuracy and speed.

关键词： Wind turbine Icing prediction PCA dimension reduction machine learning algorithm

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Reliability Analysis and Optimization of Computer Communication Network Based on machine learning algorithm

Reliability Analysis and Optimization of Computer Communicat...

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International Conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Liu, Dai-xiong Wuhan Polytech Coll Comp Technol & Software Engn Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781665473644

ISBN: (纸本)9781665473644

machine learning is to find laws from observed data and use these laws to predict future data or unobservable data. Network measurement and routing optimization strategy are critical components in the routing optimization problem. Due to the continuous progress of information technology, computer information technology is widely used in various fields, so its security and reliability will be paid more and more attention. The unsupervised learning classification is carried out through the fast density clustering algorithm to classify the importance of nodes, which can be effectively applied to the important evaluation of communication network nodes and support the planning of the communication network. Given the progress of communication technology, optical fiber technology and computer internet technology, the network's functions have been strengthened daily, and the research on the reliability of computer communication networks has been promoted to develop in depth. Furthermore, optimization theory can realize the bandwidth allocation of a communication network. The important is that computer communication network reliability based on machine learning algorithm has great economic value, social value and social benefit.

关键词： machine learning algorithm Computer communication network Reliability Optimization

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Smart Tourism Route Planning System Based on machine learning algorithm

Smart Tourism Route Planning System Based on Machine Learnin...

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International Conference on Computers and Artificial Intelligence Technologies (CAIT)

作者： Yu, Zhenzhen Wang, Shan Shandong Inst Commerce & Technol Jinan 250103 Shandong Peoples R China

ISBN: (纸本)9798350396171

With the development of economy and the improvement of life quality, people have a deeper pursuit. Therefore, tourism has developed into one of the pillar industries of China's economy. Tourism has gradually become an important part of people's life and entertainment, but people's tourism consumption behavior has undergone significant changes, and they have a higher standard for tourism quality, which is the background of the birth of the concept of smart tourism. This paper analyzes the current situation and existing problems of tourism market route planning, and constructs an intelligent tourism route planning system based on machine learning algorithm to meet the diversified needs of tourists and promote the high-quality development of tourism Through the analysis of the tourism situation of a province from 2016 to 2020, the response time of the system was tested after the intelligent tourism route planning system was put into use. It was found that the response time of the system would increase 0.0005 seconds for every 100000 additional passengers. The increase of people flow will lead to system interface congestion, but the time is very short and can be basically ignored. The research shows that the design of the system can still bring convenience to passengers.

关键词： machine learning algorithm smart tourism route planning system tourism industry

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Sentimental analysis over twitter data using clustering based machine learning algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年 1-12页

作者： Jacob, Sharon Susan Vijayakumar, R. Mahatma Gandhi Univ Sch Comp Sci Kottayam Kerala India

Using Internet technologies, people often share their thoughts, feedback, news and information with others. Owing to social media sites, speed and ease of contact improved. User posts their views on public sentiment web sites like Facebook, Instagram, Twitter, blogs, WhatsApp, Snapchat, LinkedIn, etc. Thousands of posts, millions of tweets and thousands of letters are posted each day. Twitter is one of them that is now being widely popular in these social media sites. It offers an easy and quick way to evaluate the opinions of consumers on a product or service. The approach to be used to mark customer's impressions or opinions of a product is to establish an sentimental analysis system. Twitter is a microblogging platform where users can submit feedback to a community of followers in the form of ratings or tweets in bigdata. A tweet may be defined as positive, negative or neutral depending on the viewpoint shared. Here we investigate the sentiment of Twitter messages in this paper using the clustering approach based on machine learning (ML) algorithm. Our tests are carried out in a qualified and test collection composed of large data from tweets from one lakh of results, showing our work to determine if a tweet is positive or negative.

关键词： Twitter Sentiment analysis Clustering Big data machine learning algorithm

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Model construction and verification of Financial Management Based on machine learning algorithm

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Procedia Computer Science 2025年 262卷 1274-1280页

作者： Yuchan Chen Hainan Technology and Business College Haikou 570203 China

With the rapid development of big data and artificial intelligence technology, machine learning algorithms are increasingly widely used in the field of financial management. This paper first summarizes the basic principles of machine learning algorithms, and then deeply analyzes their specific applications in financial management. By comparing traditional financial management methods with those based on machine learning algorithms, this paper reveals the advantages of machine learning algorithms in improving the efficiency, accuracy and intelligence of financial management. On this basis, this paper verifies the effectiveness and reliability of the proposed financial management model based on machine learning algorithm through experiments. The model based on machine learning algorithm can more accurately predict the financial situation of enterprises, timely detect potential financial risks, and provide strong support for corporate decision-making.

关键词： Financial management machine learning algorithm Artificial intelligence

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