Initial Data Research Articles

Multi-factorial methodology for wind power plant repowering optimization: A Spanish case study

Due to the climate change, there have been changes in the rhythm of nature over the last decades. In addition, there has been an increasing social consciousness regarding decarbonization and emissions, promoting governments new policies and economic support measures for renewable integration. In this scenario, the participation of the onshore and offshore wind sectors play a crucial role, expecting that more than 6000 GW will be globally installed in 2050. In parallel, the useful life of other wind power plants is coming to their end. Therefore, some countries will face in the coming years the decision of repowering, decommissioning, installing new turbines, or a combination of them. In general, the areas with the greatest wind potential are already occupied by relatively old wind turbines. Subsequently, a complex group of factors influences on the most appropriate solution decision-making, involving climatic, technological, environmental, social or economic aspects. This paper proposes an optimized methodology based on multi-criteria decision-making to estimate the most appropriate solution for extending the useful-life of wind power plants. An initial group of 26 factors were identified, corresponding to seven categories: technical, geographic, socio-environmental, location, economic, political and climate. The methodology is divided into three stages: (i) data, (ii) alternatives, and (iii) optimization. It was assessed with a Spanish onshore wind power plant connected to the grid. From the initial data and factors, 11 alternatives were designed based on a selection of 10 most relevant factors. The results provide an optimal solution, combining both repowering and installation of new wind turbines. From the results, this methodology would allow reducing the LCOE of the new wind plants installed in the European Union by more than 50%.

An Approach for Evaluation and Recognition of Facial Emotions Using EMG Signal

Background: Facial electromyography (fEMG) records muscular activities from the facial muscles, which provides details regarding facial muscle stimulation patterns in experimentation. Objectives: The Principal Component Analysis (PCA) is mostly implemented, whereas the actual or unprocessed initial fEMG data are rendered into low-spatial units with minimizing the level of data repetition. Methods: Facial EMG signal was acquired by using the instrument BIOPAC MP150. Four electrodes were fixed on the face of each participant for capturing the four different emotions like happiness, anger, sad and fear. Two electrodes were placed on arm for grounding purposes. Results: The aim of this research paper is to propagate the functioning of PCA in synchrony with the subjective fEMG analysis and to give a thorough apprehension of the advanced PCA in the areas of machine learning. It describes its arithmetical characteristics, while PCA is estimated by implying the covariance matrix. Datasets which are larger in size are progressively universal and their interpretation often becomes complex or tough. So, it is necessary to minimize the number of variables and elucidate linear compositions of the data to explicate it on a huge number of variables with a relevant approach. Therefore, Principal Component Analysis (PCA) is applied because it is an unsupervised training method that utilizes advanced statistical concept to minimize the dimensionality of huge datasets. Conclusion: This work is furthermore inclined toward the analysis of fEMG signals acquired for four different facial expressions using Analysis of Variance (ANOVA) to provide clarity on the variation of features.

National Vascular Quality Initiative Data Finds Disparities in Aortic Aneurysm Care in Rural Patients

National Vascular Quality Initiative Data Finds Disparities in Aortic Aneurysm Care in Rural Patients

Characterization of clinical data for patient stratification in moderate osteoarthritis with support vector machines, regulatory network models, and verification against osteoarthritis Initiative data

Knee osteoarthritis (OA) diagnosis is based on symptoms, assessed through questionnaires such as the WOMAC. However, the inconsistency of pain recording and the discrepancy between joint phenotype and symptoms highlight the need for objective biomarkers in knee OA diagnosis. To this end, we study relationships among clinical and molecular data in a cohort of women (n = 51) with Kellgren–Lawrence grade 2–3 knee OA through a Support Vector Machine (SVM) and a regulation network model. Clinical descriptors (i.e., pain catastrophism, depression, functionality, joint pain, rigidity, sensitization and synovitis) are used to classify patients. A Youden’s test is performed for each classifier to determine optimal binarization thresholds for the descriptors. Thresholds are tested against patient stratification according to baseline WOMAC data from the Osteoarthritis Initiative, and the mean accuracy is 0.97. For our cohort, the data used as SVM inputs are knee OA descriptors, synovial fluid proteomic measurements (n = 25), and transcription factor activation obtained from regulatory network model stimulated with the synovial fluid measurements. The relative weights after classification reflect input importance. The performance of each classifier is evaluated through ROC-AUC analysis. The best classifier with clinical data is pain catastrophism (AUC = 0.9), highly influenced by funcionality and pain sensetization, suggesting that kinesophobia is involved in pain perception. With synovial fluid proteins used as input, leptin strongly influences every classifier, suggesting the importance of low-grade inflammation. When transcription factors are used, the mean AUC is limited to 0.608, which can be related to the pleomorphic behaviour of osteoarthritic chondrocytes. Nevertheless, funcionality has an AUC of 0.7 with a decisive importance of FOXO downregulation. Though larger and longitudinal cohorts are needed, this unique combination of SVM and regulatory network model shall help to stratify knee OA patients more objectively.

Dynamic Survival Prediction Using Sparse Longitudinal Images via Multi-Dimensional Functional Principal Component Analysis

Our work is motivated by predicting the progression of Alzheimer’s disease (AD) based on a series of longitudinally observed brain scan images. Existing works on dynamic prediction for AD focus primarily on extracting predictive information from multivariate longitudinal biomarker values or brain imaging data at the baseline; whereas in practice, the subject’s brain scan image represented by a multi-dimensional data matrix is collected at each follow-up visit. It is of great interest to predict the progression of AD directly from a series of longitudinally observed images. We propose a novel multi-dimensional functional principal component analysis based on alternating regression on tensor-product B-spline, which circumvents the computational difficulty of doing eigendecomposition, and offers the flexibility of accommodating sparsely and irregularly observed image series. We then use the functional principal component scores as features in the Cox proportional hazards model. We further develop a dynamic prediction framework to provide a personalized prediction that can be updated as new images are collected. Our method extracts visibly interpretable images of the functional principal components and offers an accurate prediction of the conversion to AD. We examine the effectiveness of our method via simulation studies and illustrate its application on the Alzheimer’s Disease Neuroimaging Initiative data. Supplementary materials for this article are available online.

On dynamical system modeling of Learned Primal-Dual with a linear operator $\mathcal{K}$: Stability and convergence properties

Abstract Learned Primal-Dual (LPD) is a deep learning based method for composite optimization problems that is based on unrolling/unfolding the primal-dual hybrid gradient algorithm. While achieving great successes in applications, the mathematical interpretation of LPD as a truncated iterative scheme is not necessarily sufficient to fully understand its properties. In this paper, we study the LPD with a general linear operator. We model the forward propagation of LPD as a system of difference equations and a system of differential equations in discrete- and continuous-time settings (for primal and dual variables/trajectories), which are named discrete-time LPD and continuous-time LPD, respectively. Forward analyses such as stabilities and the convergence of the state variables of the discrete-time LPD to the solution of continuous-time LPD are given. Moreover, we analyze the learning problems with/without regularization terms of both discrete-time and continuous-time LPD from the optimal control viewpoint. We prove convergence results of their optimal solutions with respect to the network state initialization and training data, showing in some sense the topological stability of the learning problems. We also establish convergence from the solution of the discrete-time LPD learning problem to that of the continuous-time LPD learning problem through a piecewise linear extension, under some appropriate assumptions on the space of learnable parameters. This study demonstrates theoretically the robustness of the LPD structure and the associated training process, and can induce some future research and applications.

Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR.

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Monitoring and analysis of physical activity and health conditions based on smart wearable devices

The rapid growth of the Internet of Things (IoT) brings sweeping changes in various industries. Healthcare industries have become a prime example where the Internet of Healthcare Things (IoHT) is making significant progress, particularly in how we approach real-time patient care. Traditional systems for monitoring older people and people with special needs are frequently expensive, require a large workforce, and fall short of providing real-time data. This paper introduces the “3-Tier Health Care Architecture,” an integrated approach to mitigating these issues. This architecture capitalizes on IoHT technologies and is constructed around three principal tiers: Sensor, Fog, and Cloud. The Sensor Tier employs Health Metrics Acquisition Units (HMAUs) fitted with an nRF5340 Development Kit, capturing an extensive range of health-related metrics via wearable sensors. These metrics are then relayed to the Local Processing Units (LPUs) in Fog Tier, which operates on Raspberry Pi Zero 2 W microprocessors for the initial data processing before forwarding to the cloud. The Cloud Tier uses a hybrid CNN-LSTM Machine Learning (ML) model to perform Real-Time Healthcare Monitoring (RTHM) status assessments and includes an Early Warning System for immediate alert issuance. The proposed architecture is resilient, scalable, and efficient, serving as a fortified and all-encompassing solution for RTHM. This enables quick medical interventions, thus elevating healthcare quality and potentially life-saving.

Track initialisation for multiple formations based on neutrosophic Hough transform

AbstractTrack crossing is a major issue in the initialisation of multiple‐flight trajectories. To solve this problem, the authors propose a track initialisation algorithm. It uses characteristics of formation flight to find centroids of each formation with DBSCAN algorithm. Then, it initialises the track based on the centroid. The author use a Neutrosophic Hough Transform (NHT) method to improve accuracy and computational speed. That helps address errors caused by the approximation of straight lines between true points resulting from the clustering algorithm. The authors made three experiments using track initialisation data from two flight formations with five target aircraft each, over a span of three frames. NHT, Fuzzy HT, Improved Hough Transform (HT) and HT are compared. Results revealed that the average runtime of NHT was 10.2153 s. The F‐measure of NHT was 100.00%, while that of Fuzzy HT was 9.8347 s. The F‐measure of Fuzzy HT was 80.00%. The Improved HT was 12.0723 s. The F‐measure of Improved HT was 11.76% and HT was 13.783 s. And the F‐measure of HT was 6.87%. The authors lost some computation speed to achieve higher prediction accuracy. The accuracy of the NHT is higher than other methods.

Fuzzy expert system design for detecting stunting

Stunting is a chronic nutritional problem that occurs in toddler due to lack of nutritional intake which results in impaired growth toddler. Usually, toddler who experience stunting are characterized by not increasing weight over a long period of time. Application utilization health which makes it easier for users to access information, one of which can be used to identify toddler who are stunted by selecting symptoms. The symptoms experienced by toddlers go through a system known as the system expert. In this research an expert system will be developed that is capable of early detection developmental disorders in toddlers using the Mamdani fuzzy method. The results obtained from this research are an expert system design for early detection of stunting using the Mamdani fuzzy method. The Mamdani fuzzy method was implemented to group the criteria for toddlers who fall into the stunting category or not from the initial data which is still gray because they are still unsure whether to categorize the toddler as having stunting or not. The detection accuracy rate using the Mamdani fuzzy method is 80.87% compared to expert diagnosis.

Вопросы управления траекторией электронного обучения с применением нейросетевых технологий

This article discusses the application of neural networks in managing the educational trajectory in electronic educational systems. A model of learning management in an electronic information and educational environment is built, and an algorithm for managing adaptive learning is described. A test example of a neural network is constructed, showing the capabilities and problems of applying neural networks to the tasks of constructing trajectories of students’ adaptive personalized training. The following stages of constructing a neural network model are implemented: determining the structure of a neural network and its software implementation using the PyTorch deep machine learning library; preparing a test training sample for training the neural network; importing the initial data for training the model using the modules of the Pandas library; training the model using torch.optim Adam, Adamax, and Rprop optimizers; visualizing the obtained data in the form of graphs based on the matplotlib library; exporting the obtained numerical results and analysing the data. The paper also states the problems of preparing a training data set for teaching a neural network. The paper concludes that using neural networks in the field of building personalized adaptive educational trajectories has great prospects and will be one of the urgent tasks in the coming years.

The Association Between Diet Quality and Glycemic Outcomes Among People with Type 1 Diabetes

BackgroundThe amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, reflected in diet quality, may acutely impact glycemic levels and insulin needs. ObjectiveType 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise. MethodsUsing the Remote Food Photography Method, ≤8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI (≤57) or high HEI (>57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t-test and robust regression models. ResultsTwo hundred twenty-three adults (76% female) with mean ± SD age, HbA1c, and body mass index (BMI) of 37 ± 14 y, 6.6% ± 0.7%, and 25.1 ± 3.6 kg/m2, respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 ± 4) compared with low HEI days (mean, 47 ± 5), total time in range (70–180 mg/dL) was greater (77.2% ± 14% compared with 75.7% ± 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% ± 14% compared with 21% ± 15%, respectively, P = 0.004), mean glucose (143 ± 22 compared with 145 ± 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 ± 0.18 compared with 0.54 ± 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not significant. ConclusionsHigher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise.

Application of Ensemble Learning and Feature Selection Models in Energy Expenditure Estimation of Fitness Tracking Devices

The use of fitness tracking devices to monitor energy expenditure (EE) is becoming increasingly popular among consumers. However, the accuracy of EE estimation still needs to be improved due to the lack of accurate data filtering and more efficient data analysis models. To address this issue, we designed an energy expenditure estimation (EEE) model based on smart tracking devices. This paper proposes an algorithm for data reconstruction called PC-DF-RFECV: firstly, Pearson correlation analysis (PC) is used for initial data screening, then Feature Derivation (DF) is applied to reconstruct new features, and finally, the Recursive Feature Elimination algorithm based on cross-validation (RFECV) is employed to select the new features. Furthermore, the EEE model adopts a weighted average (Wei-Voting) strategy to enhance the robustness of the model by integrating the predictions of multiple base learners (XGBoost, LightGBM, Random Forest). During algorithm optimization, a Bayesian hyperparameter optimization technique is employed to fine-tune the model's hyperparameters. Empirical findings indicate that the EEE model, when applied to the dataset collected from motion tracking devices, attains an score of 0.9985, of 1.033, and of 0.865. These results demonstrate superior performance compared to current state-of-the-art research and conventional algorithms including random forests, gradient boosting, and bagging.

Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive.

Objective. Neural interfacing via decomposition of high-density surface electromyography (HD-sEMG) should be robust to signal non-stationarities incurred by changes in joint pose and contraction intensity.Approach. We present an adaptive real-time motor unit decoding algorithm and test it on HD-sEMG collected from the extensor carpi radialis brevis during isometric contractions over a range of wrist angles and contraction intensities. The performance of the algorithm was verified using high-confidence benchmark decompositions derived from concurrently recorded intramuscular electromyography.Main results. In trials where contraction conditions between the initialization and testing data differed, the adaptive decoding algorithm maintained significantly higher decoding accuracies when compared to static decoding methods.Significance. Using "gold standard" verification techniques, we demonstrate the limitations of filter re-use decoding methods and show the necessity of parameter adaptation to achieve robust neural decoding.

Boundedness in a chemotaxis-May–Nowak model with exposed state

This paper is concerned with the Neumann initial–boundary value problem of the chemotaxis-May–Nowak model with exposed state for virus dynamics. It is proved that the problem admits a unique global classical solution which is uniformly bounded for all sufficiently smooth initial data in smoothly bounded domains Ω⊂Rn, n≤3.

Predicting the onset of end-stage knee osteoarthritis over two- and five-years using machine learning

ObjectiveIdentifying participants who will progress to advanced stage in knee osteoarthritis (KOA) trials remains a significant challenge. Current tools, relying on total knee replacements (TKR), fall short in reliability due to the extraneous factors influencing TKR decisions. Acknowledging these limitations, our study identifies a critical need for a more robust metric to assess severe KOA. The end-stage KOA (esKOA) measure, which combines symptomatic and radiographic criteria, serves as a solid indicator. To enhance future trials that use esKOA as an endpoint, our study focuses on developing and validating a machine-learning tool to identify individuals likely to develop esKOA within 2 to 5 years. DesignUtilizing the Osteoarthritis Initiative (OAI) data, we trained models on 3,114 participants and validated them with 606 participants for the right knee, and similarly for the left knee, with external validation from the Multicentre Osteoarthritis Study (MOST) involving 1,602 participants. We aimed to predict esKOA onset at 2-to-2.5 years and 4-to-5 years, defining esKOA by severe radiographic KOA with moderate/severe symptoms or mild/moderate radiographic KOA with persistent/intense symptoms. Our analysis considered 51 candidate predictors, including demographics, clinical history, physical examination, and X-ray evaluations. An online tool predicting esKOA progression, based on models with ten and nine predictors for the right and left knees, respectively, was developed. ResultsExternal validation (MOST) for the right knee at 2.5 years yielded an Area Under Curve (AUC) of 0.847 (95 % CI 0.811 to 0.882), and at 5 years, 0.853 (95 % CI 0.823 to 0.881); for the left knee at 2.5 years, AUC was 0.824 (95 % CI 0.782 to 0.857), and at 5 years, 0.807 (95 % CI 0.768 to 0.843). Models with fewer predictors demonstrated comparable performance. The online tool is available at: https://eskoa.shinyapps.io/webapp/. ConclusionOur study unveils a robust, externally validated machine learning tool proficient in predicting the onset of esKOA over the next 2 to 5 years. Our tool can lead to more efficient KOA trials.

Diseño de un sistema de agitación para un biorreactor de 1m3 de capacidad para la producción de ácido glicérico a partir de glicerol por fermentación microbiana de Acetobacter tropicalis

This work presents the design of an agitation system for a 1m3 capacity bioreactor intended to produce glyceric acid (GA) through microbial fermentation of A. tropicalis using 20% v/v glycerol as the primary substrate. Building on previous studies that evaluated ideal conditions for glyceric acid production, initial data were utilized to identify the impeller type and perform subsequent calculations, including impeller Reynolds number, power consumption, and mixing time. Once the agitator type was specified, standard geometric relationships were employed to size components of the agitation system, such as tank size, baffle plates, and agitator dimensions. Power consumption calculations were compared with results from other studies on fermentation media with similar rheological characteristics. The agitator's power consumption for mixing 1m3 of medium was determined to be 8.35 kW, achieving 95% homogenization in 5.14 seconds, with a mixing time error of approximately 10%, operating at an agitation speed of 500 rpm. Keywords: agitation system; glyceryc acid; bioreactor

Streamlining Telecommunications Center and Interfacility Patient Throughput to a Pediatric Emergency Department by Utilizing an Electronic Handoff: A Quality Improvement Initiative.

Effective handoffs are critical for patient safety and high-quality care. The pediatric emergency department serves as the initial reception for patients where optimal communication is crucial. The complexities of interfacility handoffs can result in information loss due to lack of standardization. The aim of our project was a 50% reduction in monthly calls routed through the communication center from 157 to 78, for interfacility transfers to the emergency department from outpatient sites within our organization over a 1-year period, through utilization of an electronic handoff activity. We designed a quality improvement project in a tertiary care pediatric hospital to improve the process of interfacility transfer. The initiative aimed to streamline the transfer of patients from ambulatory, urgent care, and nurse triage encounters to the pediatric emergency department by using the electronic health record. The primary outcome measure was number of monthly calls received by the telecommunications center for these transfers.Our process measure was tracked by measuring the utilization of the electronic handoff. In addition, the number of safety events reported because of information lost through using the electronic handoff served as a balancing measure. One year after the enterprise-wide rollout of the handoff, the telecommunications center was receiving an average of 29 calls per month versus 157 at time of study initiation, a decrease of 81.5%. Monthly usage increased from zero to an average of 544 during the same period. The project was continued after the initial 12-month data collection and demonstrated stability. Our initiative facilitated the safe and efficient transfer of patients and streamlined workflows without sacrificing quality of patient care. Our telecommunications center has been freed up for other tasks with fewer interruptions during patient throughput. Next steps will analyze the encounters of transferred patients to further optimize patient flow at our organization.

Panaxynol improves crypt and mucosal architecture, suppresses colitis-enriched microbes, and alters the immune response to mitigate colitis.

Ulcerative colitis (UC) is an idiopathic inflammatory disease of the large intestine, which impacts millions worldwide. Current interventions aimed at treating UC symptoms can have off-target effects, invoking the need for alternatives that may provide similar benefits with less unintended consequences. This study builds on our initial data, which showed that panaxynol-a novel, potent, bioavailable compound found in American ginseng-can suppress disease severity in murine colitis. Here we explore the underlying mechanisms by which panaxynol improves both chronic and acute murine colitis. Fourteen-week-old C57BL/6 female mice were either given three rounds of dextran sulfate sodium (DSS) in drinking water to induce chronic colitis or one round to induce acute colitis. Vehicle or panaxynol (2.5 mg/kg) was administered via oral gavage three times per week for the study duration. Consistent with our previous findings, panaxynol significantly (P < 0.05) improved the disease activity index and endoscopic scores in both models. Using the acute model to examine potential mechanisms, we show that panaxynol significantly (P < 0.05) reduced DSS-induced crypt distortion, goblet cell loss, and mucus loss in the colon. 16S Sequencing revealed panaxynol altered microbial composition to suppress colitis-enriched genera (i.e., Enterococcus, Eubacterium, and Ruminococcus). In addition, panaxynol significantly (P < 0.05) suppressed macrophages and induced regulatory T-cells in the colonic lamina propria. The beneficial effects of panaxynol on mucosal and crypt architecture, combined with its microbial and immune-mediated effects, provide insight into the mechanisms by which panaxynol suppresses murine colitis. Overall, this data is promising for the use of panaxynol to improve colitis in the clinic.NEW & NOTEWORTHY In the current study, we report that panaxynol ameliorates chemically induced murine colitis by improving colonic crypt and mucosal architecture, suppressing colitis-enriched microbes, reducing macrophages, and promoting the differentiation of regulatory T-cells in the colonic lamina propria. This study suggests that this novel natural compound may serve as asafe and effective treatment option for colitis patients.

Sentiment Analysis on the Quality of Public Services with User Satisfaction Prediction of YuhSinau Application Managed by BKPSDM Kabupaten Kebumen Using LSTM Method

The quality of public services is critical in providing effective and responsive governance in an increasingly digital society. The development of the YuhSinau application by the Personnel and Human Resource Development Agency (BKPSDM) of Kebumen Regency offers an innovative response to the growing need for e-learning solutions for local government civil servants (Pegawai Negeri Sipil or PNS). However, determining the app’s effectiveness and user satisfaction is critical. This demands a thorough sentiment analysis in order to acquire insights into users’ thoughts and opinions about the quality of public services supplied by YuhSinau. The Long Short-Term Memory (LSTM) approach is used in this article to examine feelings and forecast customer pleasure. Data collection from multiple sources, initial data preprocessing, LSTM model construction, training, validation, and prediction are all part of the process. The results show that the model has some drawbacks, most notably its failure to appropriately explain variation in target data due to a negative R-squared value. Enhancements to the LSTM architecture, hyperparameter adjustment, and the use of more diverse and representative training data are proposed to improve the model. Continuous review and responsiveness to user comments are critical for improving the quality of public services via the YuhSinau application.&#x0D; Keywords: sentiment analysis, public service quality, user satisfaction, YuhSinau application,LSTM method