Utilizing Machine Learning for Continuous Process Improvement in Lean Six Sigma

There are many influencing factors in the entrepreneurial process, which lead to a variety of unknown situations in the entrepreneurial process and affect the entrepreneurial process. In order to improve the effect of entrepreneurial analysis, this paper improves the traditional machine learning algorithm and proposes an entrepreneurial dynamic model based on improved machine learning. Aimed at the opportunistic behavior of the following venture capital institution under joint investment, this paper constructs an evolutionary game model between the leading venture capital institution and the following venture capital institution under joint investment and constructs an industrial cluster knowledge fusion and entrepreneurial innovation model. Moreover, this paper confirms through the structural equation model that the use of information fusion technology can improve the effectiveness and comprehensiveness of internal and external knowledge of industrial clusters. In addition, this paper uses experimental analysis methods to evaluate the performance of the entrepreneurial dynamic model constructed in this paper. From the research results, it can be seen that the system constructed in this paper has a certain effect.

Using machine learning for process improvement in sepsis management

The 21st century is an era of rapid development of the Internet. Internet technology is widely used in various fields. With the rapid development of network, the importance of network information security is also highlighted. The traditional network information security technology has been difficult to ensure the security of network information. Therefore, we mainly study the application of machine learning feature extraction method in situational awareness system. A feature selection method based on machine learning is proposed to extract situational features.By analyzing whether the background of network information is safe or not, and according to the current research situation at home and abroad and the trend of Internet development, this paper tries out the practical application of machine learning feature extraction method in a certain perception system. Based on the above points, a selection method based on machine learning is proposed to extract situational features. The accuracy and timeliness of situational awareness system detection are seriously affected by the high dimension, noise and redundant features of massive network traffic data.Therefore, it is of great value to further study network intrusion detection technology on the basis of machine learning.

In order to improve the teaching effect of intelligent education, this paper combines machine learning and fuzzy algorithm to improve the application effect of educational information technology and the use of information platform. Moreover, this paper collects the user’s behavior information in the process of using the platform as the user’s interest description and selects personalized upgrade resources for the user. In addition, this paper introduces the similarity as a personalized recommendation evaluation standard and calculates the average similarity between the recommended related resources and the user’s interest. Finally, this paper constructs an educational information system based on improved machine learning and fuzzy algorithms. The experimental research results show that the intelligent system proposed in this paper can play an important role in educational information processing and teaching quality improvement.

There are currently few studies on the stress of athletes, so it is impossible to provide effective stadium guidance for athletes. Based on this, this study combines machine learning algorithms to identify athletes’ pre-game emotions. At the same time, this study obtains the data related to the research through the survey access form and obtains the physiological parameters of the athletes under stress in the experimental way and processes the physiological parameters of the athletes with the machine learning algorithm. In order to improve the efficiency of data processing, this study improves the traditional machine learning algorithm, and combines the particle optimization algorithm with the support vector machine to realize the effective recognition of the athlete’s physiological state. In addition, through the experimental method combined with the contrast method, this paper compares the performance of the improved algorithm with the traditional algorithm and combines the data analysis to analyze the test results. Finally, this study analyzes the effectiveness of the proposed algorithm by example analysis. The research shows that the proposed algorithm has better performance than the traditional algorithm and has certain practical significance and can provide theoretical reference for subsequent related research.

Abstract. The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans and is a critical region for the ocean uptake of anthropogenic CO2. However, estimates of the Southern Ocean air–sea CO2 flux are highly uncertain due to limited data coverage. Increased sampling in winter and across meridional gradients in the Southern Ocean may improve machine learning (ML) reconstructions of global surface ocean pCO2. Here, we use a large ensemble test bed (LET) of Earth system models and the “pCO2-Residual” reconstruction method to assess improvements in pCO2 reconstruction fidelity that could be achieved with additional autonomous sampling in the Southern Ocean added to existing Surface Ocean CO2 Atlas (SOCAT) observations. The LET allows for a robust evaluation of the skill of pCO2 reconstructions in space and time through comparison to “model truth”. With only SOCAT sampling, Southern Ocean and global pCO2 are overestimated, and thus the ocean carbon sink is underestimated. Incorporating uncrewed surface vehicle (USV) sampling increases the spatial and seasonal coverage of observations within the Southern Ocean, leading to a decrease in the overestimation of pCO2. A modest number of additional observations in Southern Hemisphere winter and across meridional gradients in the Southern Ocean leads to an improvement in reconstruction bias and root-mean-squared error (RMSE) of as much as 86 % and 16 %, respectively, as compared to SOCAT sampling alone. Lastly, the large decadal variability of air–sea CO2 fluxes shown by SOCAT-only sampling may be partially attributable to undersampling of the Southern Ocean.

This study develops an innovative methodology for CO 2 flooding optimization in tight oil reservoirs through a novel hybrid analytical framework that effectively combines Grey Relational Analysis (GRA), Entropy Weight Method (EWM), and Analytic Hierarchy Process (AHP) in a logically structured decision-making process. The research achieves significant methodological advancement by intelligently integrating type-2 fuzzy logic with artificial neural networks to establish robust high-precision predictive models, while developing a comprehensive optimization system that innovatively correlates geological-fluid characteristics with engineering parameters through systematic parameter coupling. The proposed framework introduces a new classification-based approach to formulate specific optimization criteria for different reservoir grades. Detailed comparative studies confirm that the staggered well-fracture network configuration provides optimal performance, offering clear technical advantages over alternative patterns in terms of both productivity enhancement and recovery efficiency. The framework's distinctive analytical capability enables a thorough evaluation of key controlling factors, with rigorous sensitivity analysis identifying porosity, oil saturation, effective reservoir thickness, crude oil viscosity, and crude oil density as dominant geological controls in Block W, while precisely quantifying the impact of operational parameters including fracturing clusters, horizontal interval length, and total gas injection volume. The optimization process employs an enhanced Particle Swarm-Genetic Hybrid System, where 151 iterations of intelligent algorithm refinement yield field-validated performance improvements. The resulting scheme demonstrates consistent effectiveness across reservoir grades, delivering 10%–25% cumulative production increase and over 8% recovery factor enhancement. This multidisciplinary approach provides a technically sound and practically viable solution for tight oil reservoir management, establishing a new methodological standard for CO 2 flooding optimization that successfully overcomes several key limitations of traditional analytical approaches through its integrated, data-driven methodology. • Machine learning-based multi-parameter coupling analysis boosts CO 2 flooding optimization in tight reservoirs. • Production and efficiency prediction by type-2 fuzzy neural network system. • Formation-specific CO 2 flooding strategies based on reservoir classification-driven optimization.

In-situ grown carbon nanotubes on waste glass powder: resource-efficient preparation and machine-learning based dispersion evaluation

A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems

Contemporary classroom teaching requires the combination of students’ classroom behavior and their psychological activities and appropriately changes the teaching mode according to students’ psychological characteristics. This paper analyzes the traditional characteristic recognition algorithm, and after improving its deficiencies, an improved characteristic extraction algorithm is proposed, based on the actual situation of classroom learning. This new algorithm can effectively improve the students’ psychological feature prediction; with the support of this algorithm, a comprehensive analysis model with classroom behavior recognition and psychological feature recognition is constructed; also, the functional structure of the system is built up. Through experimental research, the model proposed in this paper is analyzed, and the experimental data has approved that the systemic model could play an important role in classroom teaching.

Humans’ most powerful tool is their mental wellness. Individuals’ well-being can be impacted by poor mental health. This paper focuses on a smart technical solution to the problem of mental health issues detection related to the stress, sadness, depression, anxiety, etc. which if not handled efficiently may further lead to a severe problem. The paper deals with the designing of an automated smart system using social media posts that will help mental health experts to successfully identify and understand about the mental health condition of social media users. That can be done based on text analysis of rich social media resources such as Reddit, Twitter posts. The implementation of the system is done using Natural Language Processing (NLP) methods, machine learning and deep learning algorithms. The models are trained using a prepared dataset of social media postings. With this automated system the mental health experts can able to detect the stress or some other emotions of social media uses in a very earlier as well as faster way. The proposed system can predict five emotional categories: ‘Happy’, ‘Angry’, ‘Surprise’, ‘Sad’, ‘Fear’ based on machine learning (Logistic Regression, Random Forest, SVM), deep learning Long Short-Term Memory (LSTM) and BERT transfer learning algorithms. All the applied algorithms are evaluated using confusion matrix, the highest accuracy and f1 score achieved is more than 85%, which is better than the existing human emotion detection systems.KeywordsNatural language processing (NLP)Text analysisMachine learningDeep learningTransfer learningText2emotionSocial media postsLSTMBERT

This paper proposes a new prognostics analysis approach for power electronics by combining physics-based and data-driven techniques. Starting with Weibull degradation model, machine learning (ML) techniques are applied to degradation data progressively for continuous reliability monitoring and predictive maintenance decision-making. No prior knowledge of components or mission profiles is required for model training and prediction. Extracted features from analysis can be used to cluster the iteration-based predictions effectively. Another advantage is abrupt change of operation condition can be captured through machine learning for potential lifetime improvement through predictive maintenance. Proposed method can be generalized to other hardware components beyond power electronics.

We apply both the unsupervised and supervised machine learning (ML) methods, in particular, the k-means clustering and support vector machine (SVM) to improve the performance of the optical communication system based on the nonlinear Fourier transform (NFT). The NFT system employs the continuous NFT spectrum part to carry data up to 1000 km using the 16-QAM OFDM modulation. We classify the performance of the system in terms of BER versus signal power dependence. We show that the NFT system performance can be improved considerably by means of the ML techniques and that the more advanced SVM method typically outperforms the k-means clustering.

Accurate modeling of GNSS measurement errors is imperative for an effective computation of GNSS position and velocity using raw measurements. This is because the model allows us to quantitatively define the accuracy of each measurement. There are multiple different ways to model the errors including the theoretical approaches using elevation angles and signals’ Carrierto-Noise-Ratio-Densities (C/No). Also, in the case of Android devices, the embedded Application-Specific Integrated Circuit (ASIC) chipsets also provide the expected measurement noises. Furthermore, there are empirical methods where the errors can be modeled using training data. The questions that may be raised include which theoretical model will provide the most accurate position, velocity, and time (PVT) solution, whether the chipset reported noises can be trusted, and how the empirical models should be trained. The novelty of the paper lies in addressing all these pending questions. The paper assesses the positional accuracies from each model and shows that the theoretical model, incorporating both elevation angles and C/No features, achieves the highest accuracy. Also, for the chipsets, Samsung and Qualcomm provide noise values that are representative of the expected measurement residuals, while MediaTek and Broadcom provide over-bounding estimates. Finally, the paper discusses the empirical modeling approach, detailing how an Extended Kalman Filter (EKF) can be applied to estimate and eliminate receiver clock bias and drift terms from the residuals before using them as model labels. For the model features, elevation angles, C/No, and user heading are utilized. A tree-based machine learning (ML) regression model is employed. The results indicate that when test environments closely resemble the training environments, the empirical models outperform the theoretical ones; however, when the environments differ, there is a decline in performance. All data for this study comes from the publicly available Google Smartphone Decimeter Challenge (GSDC) 2023 dataset, making the proposed methodologies and analysis easily replicable.

This paper introduces a novel approach for optimizing Internet of Things (IoT) network performance by employing machine learning techniques to predict network types based on measurable properties of network traffic. While traditional studies have primarily focused on either reducing latency or enhancing network redundancy, our work integrates these aspects by dynamically predicting network conditions that facilitate the selection of optimal communication pathways. This methodology not only significantly reduces latency and improves redundancy but also enhances the overall efficiency and reliability of IoT networks. By analyzing a synthesized dataset reflective of real-world IoT environments, our model achieves high accuracy in network-type prediction, demonstrating its potential for real-time network management applications. Furthermore, the study delves into error analysis and feature sensitivity, providing insights into the reliability of network predictions and their implications for network design and operation. This paper aims to serve as a cornerstone for future explorations into optimizing IoT networks, setting the stage for more resilient, efficient, and responsive IoT ecosystems.