Assessing the Performance of a Long Short-Term Memory Algorithm in the Dataset with Missing Values

To solve the problem of high root mean square error (RMSE) caused by the Graph Convolution with Graph Attention Network for Matrix Completion (GCGAT), which cannot fully utilize the node's feature information and temporal information. In this paper, we propose an LSTM-based GCGAT algorithm (GCGAT based on LSTM, GCGAT-LSTM) to solve this problem: the linear processing part of the encoder in GCGAT is replaced by long short-term memory algorithm (LSTM). Two LSTM layers are used to process user's and item's feature information separately, named GCGAT -LSTM. This paper conducted several experiments on MovieLens-100K and MovieLens-1M datasets. The experimental results show that GCGAT -LSTM performs better than the GCGAT algorithm, effectively improving the performance of GCGAT and making more accurate recommendations. Compared with the RMSE in the previous GCGAT algorithm, the RMSE of GCGAT-LSTM is reduced by about 0.03 on both datasets. Therefore, the GCGAT-LSTM method proposed in this paper can better use node's feature information and temporal information to make more accurate predictions and thus obtain better recommendation results. However, the GCGAT-LSTM also has some limitations: the LSTM requires sequential processing over time and therefore has low efficiency and high time complexity for the input sequence.

Estimating the Q-marker concentrations of Salvia miltiorrhiza via a long short-term memory algorithm using climatic factors and metabolic profiling

Abstract. Contamination of surface waters with microbiological pollutants is a major concern to public health. Although long-term and high-frequency Escherichia coli (E. coli) monitoring can help prevent diseases from fecal pathogenic microorganisms, such monitoring is time-consuming and expensive. Process-driven models are an alternative means for estimating concentrations of fecal pathogens. However, process-based modeling still has limitations in improving the model accuracy because of the complexity of relationships among hydrological and environmental variables. With the rise of data availability and computation power, the use of data-driven models is increasing. In this study, we simulated fate and transport of E. coli in a 0.6 km2 tropical headwater catchment located in the Lao People's Democratic Republic (Lao PDR) using a deep-learning model and a process-based model. The deep learning model was built using the long short-term memory (LSTM) methodology, whereas the process-based model was constructed using the Hydrological Simulation Program–FORTRAN (HSPF). First, we calibrated both models for surface as well as for subsurface flow. Then, we simulated the E. coli transport with 6 min time steps with both the HSPF and LSTM models. The LSTM provided accurate results for surface and subsurface flow with 0.51 and 0.64 of the Nash–Sutcliffe efficiency (NSE) values, respectively. In contrast, the NSE values yielded by the HSPF were −0.7 and 0.59 for surface and subsurface flow. The simulated E. coli concentrations from LSTM provided the NSE of 0.35, whereas the HSPF gave an unacceptable performance with an NSE value of −3.01 due to the limitations of HSPF in capturing the dynamics of E. coli with land-use change. The simulated E. coli concentration showed the rise and drop patterns corresponding to annual changes in land use. This study showcases the application of deep-learning-based models as an efficient alternative to process-based models for E. coli fate and transport simulation at the catchment scale.

Software effort estimation (SEE) is an important process for predicting the effort required to develop or maintain software projects. It is considered one of the difficult challenges for software engineering because the more accurate the estimation, the greater the chances of success in developing software projects to complete and deliver them within the specified scheduling and budget. The diversity in software projects led to the use of many techniques to estimate the effort. The main objective of the research is to find the best technique to get the estimated effort as close to the real effort, using Machine learning Algorithms, artificial neural network Long Short-Term Memory(LSTM) and the artificial neural network Stacked Long Short-Term Memory (Stacked_LSTM), with six data sets are China, Kitchenham, Kemerer, Cocomo 81, Albrecht, Decharnais, and comparing the performance of the two machine learning algorithms performance, Neuralnet and Lasso from previous research, used the same data sets and using the same evaluation metrics, which are: the root mean square error (RMSE), mean absolute error (MAE) and R_Squared. The results demonstrated the superiority of the Stacked _LSTM algorithm by all metrics with the dataset of China, Kemerer and Albrecht, while in the data sets, Descharnais and Kitchenham, the LSTM algorithm outperformed the rest of the methods. While in the Cocomo 81 dataset, Neuralnet algorithm outperformed the rest of the techniques used, but the best rating was obtained. It is for Stacked _LSTM algorithm with China data.

Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation

While deep learning (DL) models have shown promise in breast cancer diagnosis using digital breast tomosynthesis (DBT) images, the impact of varying matrix sizes and image interpolation methods on diagnostic accuracy remains unclear. Understanding these effects is essential to optimize preprocessing steps for DL models, which can lead to more efficient training processes, improved diagnostic accuracy, and better utilization of computational resources. Our institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. In this study, 499 patients (29-90 years old, mean age 50.5 years) who underwent breast tomosynthesis were included. We performed downsampling to 256 × 256, 128 × 128, 64 × 64, and 32 × 32 using five image interpolation methods: Nearest (NN), Bilinear (BL), Bicubic (BC), Hamming (HM), and Lanczos (LC). The diagnostic accuracy of the DL model was assessed by mean AUC with its 95% confidence interval (CI). DL models with downsampled images to 256 × 256 pixels using the LC interpolation method showed a significantly lower AUC than the original 512 × 512 pixels model. This decrease was also observed with the 128 × 128 pixels DL models using HM and LC methods. All interpolation methods showed a significant decrease in AUC for the 64 × 64 and 32 × 32 pixels DL models. Our results highlight the significant impact of downsampling size and interpolation methods on the diagnostic performance of DL models. Understanding these effects is essential for optimizing preprocessing steps, which can enhance the accuracy and reliability of breast cancer diagnosis using DBT images.

Development of the HSPF-Paddy model to estimate watershed pollutant loads in paddy farming regions

Air quality in Yogyakarta City is declining along with the increase in air pollution caused by human activities, such as transportation and industry, thus having a serious impact on health and the environment. Some of the parameters are PM10, SO2, Com O3, and NO2 which are based on ISPU (Air Pollution Standard Index) data from the Yogyakarta City Environment Agency from 2020 to 2023. In this study, classification and prediction were carried out using the LSTM (Long Short-Term Memory) algorithm to predict air quality standards with five pollution parameters of PM10, SO2, CO, O3, and NO2. The results of this study on the performance graph showed that the model was likely to be overfitted in some phases of training, although the simulation results on the random data showed consistency in the prediction with the "Good" category for air quality. So it can be concluded that this study using the LSTM (Long Short-Term Memory) model is able to classify and predict air quality effectively with results that are almost in accordance with the actual data and this study will run better with several improvements such as the use of additional parameters such as PM2.5 and increased model accuracy for optimal results.

本文提出了一个改进的藻类生长模型及其新颖的计算机模拟算法.该模型不但考虑了水温、总氮、总磷、浮游动物和辐射等因素对藻类生长率的影响,而且根据水温、总磷和藻类浓度等因素对藻类死亡率进行了修正,从而能更加精确地描述藻类生长的过程.在藻类生长模型计算机模拟中,稳定性和计算精度是至关重要的,因此,本文建立了具有绝对稳定性和二阶精度的数值算法求解藻类生长模型中的偏微分方程组.此外,为了进一步验证该藻类生长模型的性能,本文采用了1998年度太湖中有关采样点总氮、总磷、水温及日辐射量等的实测值进行模拟.由于实测值为每个月中某一天的测量值,为了模拟过程能正确进行,本文采用样条插值的方法估计出每一天的总氮、水温及辐射量等数值.由于在藻类生长模型中,藻类与总磷有直接的耦合,不能简单地用插值等方法.本文提出了一种广义拟合的方法,使得总磷既符合藻类生长模型要求的变化规律,又较好地逼近实测值.计算机模拟结果与测量数值基本符合,而在有明显差异的地方,文中作出了相应的解释.结果表明,本文提出的藻类生长模型及其算法是有效的,各采样点藻类浓度的模拟值能较好地拟合实测值;This paper presents a modified ecological model of algae growth in Taihu Lake and its novel numerical algorithm. In this model, many important factors, such as the water temperature total nitrogen total phosphor, the biomass of the zooplankton radiation etc., are taken into account for the growth rate of the algae. Moreover, the mortality rate of the algae is considered and modified according to the value of water temperature total phosphorus and the biomass of the algae.As a result, a more accurate description of the behavior of the algae is obtained. The performance of stability and accuracy is very important for the correct simulation procedure. A numerical algorithm that has the property of absolute stability and second-order accuracy is proposed and is used to solve the partial differentialequations in the ecological model. In order to perform a further validation of the modeL the field-measured data in Taihu Lake in 1998 are adopted in the simulation, Because only one set of the field-measured data per month i. e., total twelve sets of data are available estimated data of every day are required to perform the simulation correctly, In this paper the estimated data of the water temperature total nitrogen the biomass of the zooplankton and radiation are obtained by interpolation method, In the ecological model there is a direct coupling between the algae and totalphosphorus; therefore the estimated data of phosphorus can not be obtained by simple interpolation, A generalized fitting method is then developed to solve this problem. With this method, the characteristic of the totalphosphorus obeys the ecological model, Meanwhile the values of the simulated total phosphorus are approached to the field-measured data optimally. Basically, the simulated results fairly agree with the field-measured results.Explanations are given to some distinct differences between the simulated results and field-measured results, It is demonstrated that the ecological model and the algorithm are reliable and efficient for the simulation of the actual ecological behavior of algae.

Electrocardiographic Change in V1, V5 and V6 Leads in Patients with Pulmonary Hypertension who are Receiving Effective Treatment

Missing data are nearly always a problem in research, and missing values represent a serious threat to the validity of inferences drawn from findings. Increasingly, social science researchers are turning to multiple imputation to handle missing data. Multiple imputation, in which missing values are replaced by values repeatedly drawn from conditional probability distributions, is an appropriate method for handling missing data when values are not missing completely at random. However, use of this method requires developing an imputation model from the observed data. This is typically a rigorous and time-consuming process. To encourage wider adoption of multiple imputation in social work research, a simple framework for designing imputation models is presented. The framework and its ability to generate unbiased estimates are demonstrated in a simulation study. KEY WORDS: missing data; multiple imputation; nonresponse ********** Missing data are ubiquitous in social research, and missingness or nonresponse can represent a threat to the validity of inferences because of undue effects on efficiency, power, and parameter bias (Shadish, Cook, & Campbell, 2002). Social work researchers are now addressing missing data in a more rigorous manner. Recently, Saunders et al. (2006) and Choi, Golder, Gillmore, and Morrison (2005) described important data imputation methods and dispelled misunderstandings regarding popular imputation methods, such as mean substitution. Recent advances in analytic methods, such as multiple imputation (MI), are taking hold in social work research. With MI, missing values are replaced with values repeatedly drawn from simulated conditional probability distributions (Schafer, 1997), thus creating multiple versions of the data set. Each version of the data set is analyzed according to the data analysis model, and the multiple results are combined into point estimates (Rubin, 1996). A critical task in MI is to devise an imputation model (Allison, 2002) or missing data model (Graham, Olchowski, & Gilreath, 2007), which involves specifying the measures that are putatively associated with the missing values. Although this process adds additional steps, the specification of an imputation model and the creation of multiple data sets can produce less-biased estimates in the presence of missing data across a wide variety of data analysis techniques (Schafer, 1997). Besides MI, there are many other methods for addressing missing data (Schafer, 1999; Schafer & Graham, 2002). An equally rigorous method known as direct or full information maximum likelihood (FIML) estimation can produce unbiased estimates and correct standard errors in the presence of missing data. When the number of imputations is sufficiently large, identical missing data models will produce the same estimates under MI and FIML (Graham et al., 2007). Unlike MI, FIML is limited to maximum likelihood analytic techniques and the missing data model must be included in the analysis model. Although we focus on MI, the steps we describe for developing an imputation model are equally appropriate for use in the missing data model for FIML. On the basis of the MI literature, this article describes a framework for developing an imputation model for use with any free or commercial software package that performs MI. BRIEF REVIEW OF MISSING DATA CONCEPTS Generally, both MI and a broad range of missing data issues have received ample attention in the applied literature (Graham et al., 2007). We briefly discuss the three types of distributions that describe the randomness of nonresponse given that this property has consequences for the development of an imputation model. For a discussion of general missing data concepts that are not critical to understanding our discussion of imputation model development, we refer readers to Schafer and Graham (2002). Distribution of Nonresponse The probability distribution of nonresponse--more frequently referred to as the missing data or nonresponse mechanism (Rubin, 1976)--is both an important factor in the decision to impute with MI and a context for the development of an imputation model. …

Cryptocurrency is a computerized method of money where transactions are done in the cyber space. This is a digital/soft currency, unlike currency notes they are not hard copies. Emphasizing the varieties of currencies that are decentralized and do not have any third party involvement, therefore ensuring that users can get all the services. Because of the high volatility the currency impacts the international trade and relations. Etherium, Ripple, Bitcoin, Litecoin some examples of popular currencies that exists. The study on popular cryptocurrencies Bitcoin, Ripple, Etherium are performed every year. An effective way to improve the method of predictions using deep learning models namely Grated Reccurrent Unit (GRU) and Long Short-Term Memory (LSTM). The research is devoted to the problems related to predicting crypto currency prices using machine learning and data science. The main algorithms used are: RNN and GRU. The data set will include the past price information from the popular crypto currencies for example: Bitcoin, Ethereum and Ripple. LSTM, RNN and GRU algorithms already exist to predict the future price action but the predictability rate is subpar. The main goal is to combine RNN and GRU Algorithms to form a hybrid and possibly increase the accuracy of the predictions.

Restriction site‐associated DNA sequencing (RADseq) has emerged as a useful tool in systematics and population genomics. A common feature of RADseq data sets is that they contain missing data that arise from multiple sources including genealogical sampling bias, assembly methodology and sequencing error. Many RADseq studies have demonstrated that allowing sites (single nucleotide polymorphisms, SNPs) with missing data can increase support for phylogenetic hypotheses. Two non‐mutually exclusive explanations for this observation are that (a) larger data sets contain more phylogenetic information; and (b) excluding missing data disproportionally removes sites with the highest mutation rates, causing the exclusion of characters that are likely variable and informative. Using a RADseq data set derived from the East African banana frog, Afrixalus fornasini (up to 1.1 million SNPs), we found that missing data thresholds were positively correlated with the proportion of parsimony‐informative sites and mean branch support. Using three proxies for estimating site‐specific rate, we found that the most conservative missing data strategies excluded rapidly evolving sites, with four‐state sites present only when allowing ≥60% missing data per SNP. Topological similarity among estimated phylogenies was highest for the data sets with ≥60% missing data per SNP. Our results suggest that several desirable phylogenetic qualities were observed when allowing ≥60% missing data per SNP. However, at the highest missing data thresholds (80% and 90% missing data per SNP), we observed differences in performance between high‐ and mixed‐weight DNA extraction samples, which may indicate there are trade‐offs to consider when using degraded genomic template with RADseq protocols.

In situations with missing data, statistical analyses are usually limited to subjects with complete data. However, such estimates may be biased. The method of 'filling in' missing data is called imputation. This article aimed to present a multiple imputation method. From a data set of 470 surgical patients, logistic models were developed for death as the outcome. Two incomplete data sets were generated: one with 5% and another with 20% of missing data in a single variable. Logistic models were fitted for the complete and incomplete data sets and for the data set completed by multiple imputations. Estimates obtained for the data set with missing data were different from those observed in the complete data set, mainly in the situation with 20% of missing data. The multiple imputation used here appeared efficient, producing very similar results to those obtained with the complete data set. However, one coefficient became non-significant. The analysis using multiple imputations was considered superior to using the data sets that excluded incomplete cases from the analysis.

The success of applications that process data critically depends on the quality of the ingested data. Completeness of a data source is essential in many cases. Yet, most missing value imputation approaches suffer from severe limitations. They are almost exclusively restricted to numerical data, and they either offer only simple imputation methods or are difficult to scale and maintain in production. Here we present a robust and scalable approach to imputation that extends to tables with non-numerical values, including unstructured text data in diverse languages. Experiments on public data sets as well as data sets sampled from a large product catalog in different languages (English and Japanese) demonstrate that the proposed approach is both scalable and yields more accurate imputations than previous approaches. Training on data sets with several million rows is a matter of minutes on a single machine. With a median imputation F1 score of 0.93 across a broad selection of data sets our approach achieves on average a 23-fold improvement compared to mode imputation. While our system allows users to apply state-of-the-art deep learning models if needed, we find that often simple linear n-gram models perform on par with deep learning methods at a much lower operational cost. The proposed method learns all parameters of the entire imputation pipeline automatically in an end-to-end fashion, rendering it attractive as a generic plugin both for engineers in charge of data pipelines where data completeness is relevant, as well as for practitioners without expertise in machine learning who need to impute missing values in tables with non-numerical data.