Arrival Rate Research Articles

Optimisation of medication reconciliation using queueing theory: a computer experiment.

Medication reconciliation (MedRec) in hospitals is an important tool to enhance the continuity of care, but completing MedRec is challenging. The aim of this study was to investigate whether queueing theory could be used to compare various interventions to optimise the MedRec process to ultimately reduce the number of patients discharged prior to MedRec being completed. Queueing theory, the mathematical study of waiting lines or queues, has not been previously applied in hospital pharmacies but enables comparisons without interfering with the baseline workflow. Possible interventions to enhance the MedRec process (replacing in-person conversations with telephone conversations, reallocating pharmacy technicians (PTs) or adjusting their working schedule) were compared in a computer experiment. The primary outcome was the percentage of patients with an incomplete discharge MedRec. Due to the COVID-19 pandemic, it was possible to add a real-life post hoc intervention (PTs starting their shift later) to the theoretical interventions. Descriptive analysis was performed. The queueing model showed that the number of patients with an incomplete discharge MedRec decreased from 37.2% in the original scenario to approximately 16% when the PTs started their shift 2h earlier and 1 PT was reassigned to prepare the discharge MedRec. The number increased with the real-life post hoc intervention (PTs starting later), which matches a decrease in the computer experiment when started earlier. Using queueing theory in a computer experiment could identify the most promising theoretical intervention to decrease the percentage of patients discharged prior to MedRec being completed.

Well-posedness of stochastic multi-weighted complex networks with regime-switching diffusions

In this paper, a stochastic multi-weighted complex network with regime-switching diffusions (SMCNRSD) is considered. Here, the jump and the diffusion processes are interdependent, which generalizes the most existing network model. In the study of stochastic multi-weighted complex networks, stability analysis is an important topic. But so far, some work on stability requires stochastic multi-weighted complex networks to satisfy the global linear growth condition, which cannot be applied to some high-order or nonlinear networks. Therefore, one of the research contents of this study is to obtain the global existence and uniqueness of SMCNRSD under local conditions. Besides, the SMCNRSD usually does not stabilize spontaneously for reasons such as stochastic disturbance, multiple weights and nonlinear coupling. To stabilize SMCNRSD, periodic intermittent control is introduced to obtain the stability criterion. In application, stochastic multi-weighted Chua’s circuits with regime-switching diffusions are taken into account. The corresponding existence and uniqueness as well as stability criteria are given under local conditions. To further illustrate the validity of theoretical results, numerical simulations are given.

The Determining Role of Covariances in Large Networks of Stochastic Neurons.

Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity.

Semiglobal fixed/preassigned-time synchronization of stochastic neural networks with random delay via adaptive control

This paper presents two kinds of novel results about semiglobal synchronization in probability for stochastic systems. To this end, new criteria of semiglobal fixed-time synchronization in probability (SFXTSp) and semiglobal preassigned-time synchronization in probability (SPATSp) are proposed. In contrast to the existing results of semiglobal synchronization in probability, our criteria have wider applicability in SFXTSp and SPATSp due to their flexible forms. Meanwhile, based on the properties of convex functions and Jensen’s inequality, our approaches overcome the issue arising from the non-equivalence between the Lyapunov function and its expectation, making our conclusions more rigorous. In addition, to achieve SFXTSp of stochastic neural networks with random delay (RDSNNs), a unified adaptive controller is developed. By modifying the parameters of the unified controller, RDSNNs reach SPATSp in a predetermined time. Ultimately, numerical simulations are conducted to confirm the practicality and accuracy of the derived outcomes.

A novel stochastic configuration network with enhanced feature extraction for industrial process modeling

Stochastic configuration networks (SCNs), possessing sound generalization performance and low computational burden, have been extensively investigated in data analysis field. But in practice, it performs poor. This is mainly caused by two aspects: (1) the increment of random hidden nodes with incompact constraints may bring the emergence of redundant nodes, thereby resulting in poor modeling performance, and (2) SCNs have limited feature extraction capabilities. To address these issues, a novel SCN inserting feature layer, termed as FSCN, is presented in this paper. First, the embedded feature layer establishes a connection between the input and hidden layers, thus mapping the input data into a suitable feature space. Second, an inequality constraint based on Greville iterative method is established. It can not only facilitate the construction of high-quality hidden nodes but also guarantee convergence of the built model. Then, the Greville iterative method is also employed for output weight updating. Finally, comparative experiments on a real-valued nonlinear function, four real-world data sets and the modeling of two real industrial processes are conducted and the comparison results with other modeling methods show that FSCN has superior performance in terms of both model accuracy and compactness.

Synchronization of stochastic complex networks based on privacy protection

As one of the security issues, privacy protection (PP) has become a research hotspot. The paper studies the synchronization problem of stochastic networks based on PP. First, a synchronization standard for stochastic network with delay is given based on PP. Second, a finite‐time (FnT) synchronization criterion for stochastic network without delay is given based on PP. The PP function of continuous time‐varying function is designed to mask the real state of the controlled complex network before converging to the real value in FnT and infinite time. Finally, the effectiveness of the method discussed in this article was verified through simulation examples.

Mathematical Model of the Process of Data Transmission over the Radio Channel of Cyber-Physical Systems

This article introduces a refined mathematical model to evaluate the quality of mobile radio channels within cyber-physical systems, employing the topological transformation of stochastic networks. The operation of the radio channel is conceptualized as a stochastic network, enabling the derivation of critical metrics such as an equivalent function, mathematical expectation, variance, and the time distribution function of the implemented processes. The model uses the Gamma distribution for the initial distribution functions of random variables, enhancing its analytical precision. A significant advancement of this study is the development of a comprehensive model that describes the data transmission process through phases of connection establishment, information transmission, and connection maintenance. The innovative aspect of this research lies in applying an equivalent function to a stochastic network that includes a logical “AND” node with gamma-distributed incoming branches. The stochastic network presented in the article, which includes a logical “AND” node, helps to elucidate the mechanism for obtaining an equivalent function for such networks, allowing the application area of the GERT method to be expanded. This methodological enhancement extends the previously limited scope of topological transformation methods, which only applied to exponential distribution models for the timing of branch inputs. By integrating a Gamma distribution, the model simplifies the equivalent function and reduces the computational complexity required to assess the radio channel’s quality, ensuring the accuracy needed for engineering calculations. Moreover, the proposed method requires 25–40% fewer series members than the traditional Taylor series decomposition, while maintaining comparable computational complexity for the typical series members. Furthermore, the maximum absolute error in the calculations is capped at 9 × 10−3, which is well within acceptable limits for engineering purposes. Primarily designed for radio channels in cyber-physical systems, the model’s applicability extends to wireless communications, providing a valuable tool for evaluating channel efficiency and security in the face of increasing cyber threats.

Existence and uniqueness of solutions for forward and backward nonlocal Fokker-Planck equations with time-dependent coefficients

Based on the Feynman-Kac formula, we develop a new approach to show the global existence and uniqueness of the solutions for the forward and backward time-dependent multidimensional nonlocal Fokker-Planck equations, which are associated with a class of time-dependent stochastic differential equations driven by symmetric (or asymmetric) non-Gaussian Lévy process. First, after deriving the forward time-dependent multidimensional nonlocal Fokker-Planck equation, the Feynman-Kac formula for the forward nonlocal Fokker-Planck equation is established by using Itô's formula and techniques for the backward nonlocal Fokker-Planck equation corresponding to the backward stochastic differential equation driven by Lévy process. Then, from the Feynman-Kac formula, we prove the global existence and uniqueness of the solutions for the forward and backward time-dependent multidimensional nonlocal Fokker-Planck equations by using techniques from stochastic analysis. Finally, the dynamic evolution of the probability density function corresponding to the stochastic model for the MeKS network (reflecting the interactions among the MecA complex, comK, comS) is investigated over an extended period by Feynman-Kac formula and Monte Carlo simulations. In particular, we reveal the influence of symmetric and asymmetric stable noises as well as tempered stable noise on the probability density function corresponding to the stochastic MeKS network model.

An information entropy-based fuzzy stochastic configuration network for robust data modeling

In data-driven modeling, the generalization performance of a neural network based on the minimum mean square error criterion may be affected when the samples contain noise. To handle uncertain data modeling problems, this study contributes a novel robust data modeling method called information entropy-based fuzzy stochastic configuration networks (IEFSCNs), which incorporates the maximum correntropy criterion (MCC) to reflect the squared error of the training sample set and uses the quantized minimum error entropy (QMEE) criterion to mine the distribution structure of the modeling data and suppress outlier data. Fuzzy systems are designed to generate nodes in the fuzzy transformation layer. A nonlinear enhancement layer is established as a hidden layer to enhance the representational capacity of the target system. The fuzzy transformation and nonlinear enhancement layers are fully connected to determine the outputs of the IEFSCNs. In addition, an incremental parameter learning algorithm is developed based on a supervision mechanism for optimizing the cost function, and its convergence property is demonstrated. Owing to the superiorities of the cost function by synthesizing MCC and QMEE, IEFSCNs can improve the modeling accuracy and reduce noise suppression. The performances are evaluated using various regression and classification tasks. Compared with existing methods, including SCNs, RVFL, F-SCNs, RSC-KDE, RSC-MCC, and SM-RSC, the proposed method demonstrates an effective robust modeling performance by reducing network structure redundancy while enhancing its ability to suppress noisy data.

Data-driven traffic signal adaptive control algorithm integrating vehicle perception and traffic flow data

Due to the equipment error, environmental interference and data transmission delay of vehicle flow detection, the accuracy and real-time performance of vehicle perception and traffic flow data will be affected to some extent, resulting in poor traffic signal control effect. Therefore, a data-driven traffic signal adaptive control algorithm is designed by integrating vehicle perception and traffic flow data. To complete the modeling of urban traffic, the discrete distribution and continuous distribution of traffic are obtained. Based on this research environment, the DV-hop localization algorithm is improved to sense the vehicle position. Based on the phase space reconstruction of traffic flow time series and vehicle location information, traffic flow data is predicted. Based on the driving of traffic data, the vehicle types are divided into small, medium and large three categories, and the impact weights are assigned respectively, and the weight values affecting the final allocation of green time are obtained to realize the allocation of green time. The experimental results show that: The research algorithm can not only predict the traffic flow intensity effectively, but also the predicted results are highly coincident with the actual traffic flow intensity. Vehicle arrival rates are higher, vehicle delays are shorter, and vehicles stop fewer times on average.

Modeling of traffic at a road crossing and optimization of waiting time of the vehicles

Traffic management is a critical activity, the population is increasing day by day and so the traffic on the road is also increasing. Traffic jams and long waiting queues of vehicles at the road crossing are now part of everyone's life. The traffic lights used at the crossing to regulate the traffic play a vital role in the smooth functioning of traffic movement. At a crossing of four roads, it has been observed that giving an equal amount of green light to all roads is meaningless since the arrival of traffic on different paths is different. Importantly, the arrival rate is responsible for all traffic jams, long queues, and increased waiting time. Therefore, this paper suggests a green light allocation scheme for all paths i depending on the arrival rate of the vehicles. Thus, the allocation of green light will be dynamic. Further, weight is also computed, where more arrival rate means more weight, thereby assigning more time to the green signal. This will help in reducing the long queue length, residual traffic, and long waiting times. On simulating the traffic with the traffic data, the proposed optimized green light allocation scheme to path i reduces the residue traffic to negligible, allowing smooth traffic flow even during peak hours. The work also provides a proficient optimization of the waiting time of vehicles accumulated during the red light. According to the simulation, the maximum time assigned for the green signal during the peak hour of 9:30 AM to 10:00 AM for paths i, where 1≤i≤4 is 39.96, 33.36, 26.64, and 20.04 seconds respectively. Similarly, during the second rush hour of 5:00 PM to 6:00 PM, the simulation assigns a green signal time of 41.4, 37.2, 24.84, and 16.56 seconds for corresponding paths 1–4. Thus, the proposed work suggests an effective traffic management scheme at the four-road crossing.

Multiple Table Models Based on Queuing Theory for Firefighting Traffic Problems

With the development of global urbanization, urban planning has been a hot spot of most concern. Since the traditional urban fire station setting method has been gradually unable to meet the emergency needs of a city. In this paper, a series of methods are developed to achieve the best balance between economic expenditure and reliability. According to the heat maps about emergency calls in a city in recent years, different types of alarm phones have obvious regional distribution characteristics. Inspired by the Queuing Theory algorithm, we abstract this complex problem into a parallel multi-server waiting queuing model to find each average queue length of three kinds of alarm calls and success in converting the multiplicity problem into solving the vehicle number and location problems respectively. Based on this planning problem, new combinations with different numbers of vehicles can be gotten. Then based on the assignment model, the optimal deployment plan can also be different with different numbers and geographical locations of the three kinds of stations. Our method significantly improves the firefighting traffic problems and can be applied in the actual situation.

Predictive analysis of concrete slump using a stochastic search-consolidated neural network

Attaining a dependable measurement of concrete slump is crucial as it is a valuable indication of concrete workability. On the other hand, complexities associated with costly traditional approaches have driven engineers to use indirect efficient models such as metaheuristic-based machine learning for approximating the slump. While the literature shows promising application of some metaheuristic techniques for this purpose, the large variety of these algorithms calls for evaluating the most capable ones to keep the solution updated. Stochastic fractal search (SFS) is one of the most powerful optimization algorithms in the literature that has not received appropriate attention in analyzing concrete mechanical parameters. In the present research, a multi-layer perceptron neural network (NN-MLP), is enhanced using the SFS. The proposed SFS-NN-MLP model aims to predict the slump based on the amount of ingredients in the mixture, as well as the curing age of specimens. Accuracy assessment revealed that the proposed model can deal with the assigned task with excellent accuracy. It indicates that the SFS could properly tune the parameters required for training the NN-MLP, and consequently, the trained network could reliably calculate the slump of specimens that were not analyzed before. For comparative validation, the SFS was replaced with two similar optimizers, namely elephant herding optimization algorithm (EHO) and slime mould algorithm (SMA). Based on the calculated mean square errors of 5.6526, 6.1129, and 7.3561 along with mean absolute errors of 4.6657, 5.0078, and 6.3066, as well as the percentage-Pearson correlation coefficients of 78.06 %, 73.95 %, and 58.11 %, respectively for the SFS-NN-MLP, EHO–NN–MLP, and SMA–NN–MLP, it was shown that the SFS-NN-MLP is the most accurate predictor. Hereupon, the SFS-NN-MLP model is recommended to be effectively used for obtaining a cost-efficient approximation of concrete slump in real-world projects.

Respiration and Cough in Pompe Disease: Results from a neuromechanical computational model of the respiratory control system

Late Onset Pompe Disease (LOPD) is an incurable, rare progressive genetic disease. It is characterized by glycogen build-up, due to mutations in the gene encoding the lysosomal enzyme acid alpha glucosidase, throughout muscle and neural regions. This accumulation is associated with cell death and muscle fiber swelling, eventually leading to respiratory insuffciency and airway clearance (i.e. cough) deficiency. Impairments in effective airway clearance result from the inability to clear normal pulmonary volume. Emerging evidence indicates significant respiratory neuron dysfunction, altering pulmonary function in humans and animal models. It has been suggested that respiratory and airway clearance insuffciency observed in LOPD may be attributed to medullary motoneurons potentially associated with the respiratory and cough pattern generators (Fuller et al., 2013; DeRuisseau et al., 2009). However, the precise neural mechanisms that suppress breathing and cough are not fully understood. One possible target for LOPD respiratory insuffciencies may be reduced medullary inspiratory neuron activity within this network, which could result in decreased phrenic motoneuron output. Using a stochastic neural network simulator that consisted of discrete “integrate and fire” populations, we simulated a systematic decrease in three medullary inspiratory neuron populations: inspiratory driver (I-Driver), inspiratory decrementing (I-Dec), or inspiratory augmenting (I-Aug) neurons. We simulated breathing and three cough trials. By targeting these populations, we expected global decreased neural bursts, suppressed inspiratory motor drive, and/or suppressed expiratory motoneuron output. Simulations indicated that when the I-Dec motoneuron population decreased by 25%, it resulted in apnea and abolished cough. Reducing the I-Aug motoneuron population by 20% resulted in longer inspiratory phase durations (TI) and a decreased respiratory rate. During cough, there was a decrease in the duration and amplitudes of the expiratory motoneuronal activity associated with decreased cough receptor and pulmonary stretch receptor afferent feedback. When the I-Driver motoneuron population was reduced by 25%, TI decreased and I amplitude increased during respiration. During cough, lumbar and phrenic motoneuron discharge duration and amplitude decreased. These results are consistent with previously reported in vivo data. Overall, our simulation data support the plausibility of glycogen storage dysfunction impairing medullary neuronal and premotoneuronal excitability within the inspiratory network. Our results also suggest that some neurons may be more sensitive to glycogen storage than others within the brainstem inspiratory network. Supported by NHLBI L30HL165496, NIH T32 HL 134621 and 3OT2OD023854. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Mean-squared exponential stability of distributed almost periodic sequence for nonlocal space-time discrete shunting inhibitory cellular neural networks with Brownian motions

The discrete-time and discrete-space model for stochastic nonlocal shunting inhibitory cellular neural networks with reaction diffusions are modelled in the first time. Owing to the consideration of spatial variables, the discrete model derived in this article is more complex than the traditional ordinary difference model. In accordance with the constant-variation-formula for discrete-time and discrete-space model, Banach contracting mapping principle, the method of proof by contradiction and stochastic calculus, we also obtain the existence of a unique bounded almost periodic sequence for the discrete model, which is exponentially stable in the mean-square sense. Noting that it is the first time to consider the dynamics of almost periodicity in distribution of time-space discrete neural network models. The practicability of the present results is demonstrated by means of an illustration.

A stochastic scale conjugate neural network procedure for the SIRC epidemic delay differential system

In this study, a stochastic computing structure is provided for the numerical solutions of the SIRC epidemic delay differential model, i.e. SIRC-EDDM using the dynamics of the COVID-19. The design of the scale conjugate gradient (CG) neural networks (SCGNNs) is presented for the numerical treatment of SIRC-EDDM. The mathematical model is divided into susceptible S ( ρ ) , recovered R ( ρ ) , infected I ( ρ ) , and cross-immune C ( ρ ) , while the numerical performances have been provided into three different cases. The exactitude of the SCGNNs is perceived through the comparison of the accomplished and reference outcomes (Runge-Kutta scheme) and the negligible absolute error (AE) that are performed around 10−06 to 10−08 for each case of the SIRC-EDDM. The obtained results have been presented to reduce the mean square error (MSE) using the performances of train, validation, and test data. The neuron analysis is also performed that shows the AE by taking 14 neurons provide more accurateness as compared to 4 numbers of neurons. To check the proficiency of SCGNNs, the comprehensive studies are accessible using the error histograms (EHs) investigations, state transitions (STs) values, MSE performances, regression measures, and correlation.

Stochastic Game Network-inspired intelligent framework for quality assessment in logistic industry

The widespread utilization of IoT-Fog-Cloud computing has revolutionized customer-oriented service delivery aspects in the global logistics industry. With industrial globalization, the logistics industry is often challenged to address quality issues. Moreover, with improper product management and transportation, the logistics industry has to face considerable financial loss. Conspicuously, a novel Stochastic Game Network (SGN) is proposed in the current study to ensure enhanced service product quality in the logistics industry. Specifically, the smart logistic industry-based decision-making framework is presented by incorporating several parameters of product quality. In the proposed method, each IoT device acts as a game player with a preset action set and strategy. Moreover, each IoT sensor at 4 levels of the logistic industry including Production Management Sensor (IMS), Inventory Management Sensor (IMS), Transportation Management Sensor (TMS), and Delivery Management Sensor (DMS) is incorporated as a game player node. Quality parameters are analyzed using the proposed Bayesian Belief Model (BBM) technique for effective classification. Moreover, Game-theoretic decision-making is used to automate enhanced product quality management and control units in IoT-based logistic industries for effective analysis and customer-oriented service delivery. For validation purposes, experimental results are compared with state-of-the-art decision-making techniques. Based on the results, the proposed model has registered enhanced statistical measures in terms of Precision (92.98%), Specificity (93.78%), Sensitivity (96.78%), Reliability (96.54%), and Stability (78%). To assess the theoretical efficacy of the proposed strategy, mathematical analysis is performed to quantify customer satisfaction. Conclusively, the proposed model presents an effective mechanism for quality assurance by incorporating stochastic behavior in the logistics industry.

Inter-Departure Time Correlations in PH/G/1 Queues

In non-Markovian tandem queueing networks the output process of one site, which is the input process to the next site, is not renewal. Consequently, the correlation analysis of that output processes is essential when studying such networks. A correlation analysis in the M/G/1 queue has been studied in the literature via derivation of the joint Laplace-Stieltjes transform (LST) of the sum of two consecutive inter-departure times. That LST is obtained by considering all possible cases at departure epochs. However, those epochs are expressed via dependent variables. In this paper, we first extend the analysis to the more general PH/G/1 queue, and investigate various queues, such as E2/G/1 and C2/C2/1. Then, we consider the lag-n correlation, which requires derivation of the joint LST of sum of n + 1 consecutive inter-departure times. Yet, deriving this LST by the common approach becomes impractical for n + 1 ≥ 3, as the number of all possible cases at departure epochs increases significantly. To overcome this obstacle, we derive a corresponding single-parameter LST, which expresses the sum of n + 1 consecutive inter-departure times via the (n + 1)-st departure epoch only. Consequently, the latter LST is expressed via a much fewer number of possible cases, and not less important, as a function of independent variables only, eliminating the need to derive the corresponding joint density. Considering the M/G/1 and the E2/G/1 queues, we demonstrate that the joint LST can be reconstructed directly via the corresponding single-parameter LST when n + 1 = 2. We further conjecture that the multi-parameter joint LST can be reconstructed from the corresponding single-parameter LST in more general queues and for values of n + 1 > 2. The conjecture is validated for various PH/G/1 queues and proved for n + 1 = 3 in the M/G/1 case. The new approach facilitates the calculation of lag-n correlation of the departure process from PH/G/1 queue for n + 1 ≥ 3. Our analysis illuminates the cases when using renewal approximation of the output process provides a proper approximation when studying non-Markovian stochastic networks.

Assessing LSAM's ability to account for changes in organ donation and transplant center behavior.

The Liver Simulated Allocation Model (LSAM) is used to evaluate proposed organ allocation policies. Although LSAM has been shown to predict the directionality of changes in transplants and nonused organs, the magnitude is often overestimated. One reason is that policymakers and researchers using LSAM assume static levels of organ donation and center behavior because of challenges with predicting future behavior. We sought to assess the ability of LSAM to account for changes in organ donation and organ acceptance behavior using LSAM 2019. We ran 1-year simulations with the default model and then ran simulations changing donor arrival rates (ie, organ donation) and center acceptance behavior. Changing the donor arrival rate was associated with a progressive simulated increase in transplants, with corresponding simulated decreases in waitlist deaths. Changing parameters related to organ acceptance was associated with important changes in transplants, nonused organs, and waitlist deaths in the expected direction in data simulations, although to a much lesser degree than changing the donor arrival rate. Increasing the donor arrival rate was associated with a marked decrease in the travel distance of donor livers in simulations. In conclusion, we demonstrate that LSAM can account for changes in organ donation and organ acceptance in a manner aligned with historical precedent that can inform future policy analyses. As Scientific Registry of Transplant Recipients develops new simulation programs, the importance of considering changes in donation and center practice is critical to accurately estimate the impact of new allocation policies.

An adaptive strategy for time‐varying batch process fault prediction based on stochastic configuration network

AbstractFault prediction ensures safe and stable production, and cuts maintenance costs. Due to the changing operating conditions that lead to the changes in the characteristics of industrial processes, there is a need to monitor the fault state of batch processes in real‐time and to accurately predict fault trends. An adaptive slow feature analysis‐neighborhood preserving embedding‐improved stochastic configuration network (SFA‐NPE‐ISCN) algorithm for batch process fault prediction is proposed. Firstly, SFA is used to extract the time‐varying features of process data and establish the update index of the NPE model. Then, to extract local nearest‐neighbor features and reconstruct them by the NPE model with adaptive update capability, square prediction error (SPE) statistics are constructed as fault state features based on the reconstructed error. Further, the hunter‐prey optimization (HPO) algorithm optimizes the weights and biases in the stochastic configuration network, and the singular value decomposition (SVD) and QR decomposition of column rotation are introduced to solve the ill‐posed problem of SCN and obtain the prediction model of ISCN. Finally, the obtained statistics SPE is formed into a time series, and the ISCN model is used to predict the process state trend. The effectiveness of the proposed algorithm is verified by case studies of industrial‐scale penicillin fermentation processes and the Hot strip mill process.