Quality Prediction Model Research Articles

Quality prediction of kiwifruit based on transfer learning

Kiwifruit has a high decay rate, in part because quality changes during storage cannot be easily monitored in real time. In order to better monitor the shelf life of kiwifruit and understand the quality changing process during storage, internal quality indexes such as hardness, respiratory intensity and TSS(Total Soluble Solid) were considered into the prediction models. The prediction models were constructed based on BPNN (Back Propagation Neural Network), Random Forest (RF) and XGBoost (eXtreme Gradient Boosting) respectively. And transfer learning algorithm was used to construct the quality prediction models with BPNN, RF, and XGBoost algorithms as the base learner. In the experiments, sample data were augmented by adding Gaussian noise, which effectively prevented the model from over-fitting. The experimental results showed that the prediction accuracy of each index based on transfer learning was better than that of individual BPNN, RF and XGBoost. Moreover, the average prediction accuracy of the models based on transfer learning was 96.2%, and that of respiratory intensity was as high as 99.4%. Therefore transfer learning can be used to effectively analyze and predict changes of kiwifruit quality indexes during storage.

A Water Quality Prediction Method Based on Deep LSTM Network

Water quality parameters are key factors affecting marine ranching. The water quality parameters are not consistent, which are usually complex and variable. The traditional water quality prediction methods have problems such as low long-term prediction accuracy and weak generalization ability. In order to solve the above problems, this paper proposes a multivariate water quality prediction model based on WT-LSTM, which is compared with the BP neural network model for short-term prediction and long-term prediction of dissolved oxygen water quality parameters, and the experimental results show that the WT-LSTM model has good accuracy and generalization in both short-term and long-term prediction, and the short-term prediction accuracy can be up to 98.47% and the long-term prediction accuracy can be up to 98.28%.

Prediction model of health-related quality of life in older adults according to gender using a decision tree model: a study based on the Korea National Health and Nutrition Examination Survey

Purpose: The aim of this study was to predict the subgroups vulnerable to poorer health-related quality of life (HRQoL) according to gender in older adults.Methods: Data from 5,553 Koreans aged 65 or older were extracted from the Korea National Health and Nutrition Examination Survey. HRQoL was assessed using the EQ-5D tool. Complex sample analysis and decision-tree analysis were conducted using SPSS for Windows version 27.0.Results: The mean scores of the EQ-5D index were 0.93 ± 0.00 in men and 0.88 ± 0.00 in women. In men, poorer HRQoL groups were identified with seven different pathways, which were categorized based on participants’ characteristics, such as restriction of activity, perceived health status, muscle exercise, age, relative hand grip strength, suicidal ideation, the number of chronic diseases, body mass index, and income status. Restriction of activity was the most significant predictor of poorer HRQoL in elderly men. In women, the poorer HRQoL groups were identified with nine different pathways, which were categorized based on participants’ characteristics, such as perceived health status, restriction of activity, age, education, unmet medical service needs, anemia, body mass index, relative hand grip, and aerobic exercise. Perceived health status was the most significant predictor of poorer HRQoL in elderly women.Conclusion: This study presents a predictive model of HRQoL in older adults according to gender and can be used to detect individuals at risk of poorer HRQoL.

Developing a dynamic quality prediction model for limited samples target grade based on transfer learning

In response to the dynamic demands of the market, this study addresses the challenge of frequent operational adjustments in a production line to accommodate diverse product grades. The resulting scarcity of data in the new operating conditions (target grade) impedes the development of reliable soft-sensor prediction models. A two-step learning method, termed S2-LGMNSSM-TS-T, is proposed. This method employs a semi-supervised latent Gaussian mixture nonlinear state space model (S2-LGMNSSM-TS) trained on both target and source data, providing a dynamic, one-step-ahead predictive soft sensor. To overcome data scarcity in the target grade, insights from the source are utilized. With a Gaussian mixture prior distribution, S2-LGMNSSM-TS identifies dynamic behaviors in both target and source grades. The enhanced S2-LGMNSSM-TS-T configuration focuses on target-grade predictions, leveraging source knowledge and mitigating data scarcity issues. A numerical example and an industrial case study demonstrate the model's effectiveness in improving target-grade predictions through source knowledge utilization.

TEMPORAL ANALYSIS AND PREDICTIVE MODELING OF AMBIENT AIR QUALITY IN HULU LANGAT DISTRICT, SELANGOR, MALAYSIA: A CHEMOMETRIC APPROACH

One of the most important environmental problems facing the globe today is air pollution. The centre area for the local populace is the Hulu Langat district, which borders Kuala Lumpur, the capital. The purpose of this study is to look at how the ambient air quality varies in Hulu Langat, Selangor. The Air Quality Division of the Malaysian Department of Environment provided five years' worth of secondary data on the air quality at Hulu Langat. The database included five primary air pollutant characteristics sulphur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and particulate matter with a diameter of 10 microns or less (PM10), in addition to data from the Air Pollutant Index (API). Chemometric analysis was used to examine the results. According to the results, SO2, NO2 and PM10 had the greatest correlations with API readings. A statistical process known as statistical control (SPC) showed that certain PM10 values were over national recommendations and control limits. The artificial neural network method's air quality prediction model demonstrated good accuracy with real data (R2 = 0.9). The results of this investigation indicated a strong correlation between the Hulu Langat air quality data. In order to achieve sustainable environmental practices in the future, it is imperative to engage in extensive collaboration across environmental departments and relevant authorities and engage in continuous monitoring of air quality.

The Quality Evaluation of Highland Barley and Its Suitability for Chinese Traditional Tsampa Processing.

The physicochemical traits of highland barley prominently affect the quality of Tsampa. To find out the relevance between the physicochemical properties of raw material and the texture parameters of processed products, twenty-five physicochemical traits and ten quality parameters for seventy-six varieties of highland barley were measured and analyzed. The results showed that there was a significant difference between the physicochemical indexes for highland barleys of various colors. The dark highland barley generally has more fat, protein, total dietary fiber, phenolic, Mg, K, Ca, and Zn and less amylose, Fe, Cu, and Mo than light colored barley. Then, these highland barleys were made into Tsampa. A comprehensive quality evaluation model based on the color and texture parameters of Tsampa was established through principal component analysis. Then, cluster analysis was used to classify the tested samples into three edible quality grades predicated on the above evaluation model. At last, the regression analysis was applied to establish a Tsampa quality predictive model according to the physicochemical traits of the raw material. The results showed that amylose, protein, β-Glucan, and a* and b* could be used to predict the comprehensive quality of Tsampa. The predicted results indicated that 11 of 14 validated samples were consistent with the actual quality, and the accuracy was above 78.57%. Our study built the approach of the appropriate processing varieties evaluation. It may provide reference for processing specific highland barley.

The insightful water quality analysis and predictive model establishment via machine learning in dual-source drinking water distribution system

Dual-source drinking water distribution systems (DWDS) over single-source water supply systems are becoming more practical in providing water for megacities. However, the more complex water supply problems are also generated, especially at the hydraulic junction. Herein, we have sampled for a one-year and analyzed the water quality at the hydraulic junction of a dual-source DWDS. The results show that visible changes in drinking water quality, including turbidity, pH, UV254, DOC, residual chlorine, and trihalomethanes (TMHs), are observed at the sample point between 10 and 12 km to one drinking water plant. The average concentration of residual chlorine decreases from 0.74 ± 0.05 mg/L to 0.31 ± 0.11 mg/L during the water supplied from 0 to 10 km and then increases to 0.75 ± 0.05 mg/L at the end of 22 km. Whereas the THMs shows an opposite trend, the concentration reaches to a peak level at hydraulic junction area (10–12 km). According to parallel factor (PARAFAC) and high-performance size-exclusion chromatography (HPSEC) analysis, organic matters vary significantly during water distribution, and tryptophan-like substances and amino acids are closely related to the level of THMs. The hydraulic junction area is confirmed to be located at 10–12 km based on the water quality variation. Furthermore, data-driven models are established by machine learning (ML) with test R2 higher than 0.8 for THMs prediction. And the SHAP analysis explains the model results and identifies the positive (water temperature and water supply distance) and negative (residual chlorine and pH) key factors influencing the THMs formation. This study conducts a deep understanding of water quality at the hydraulic junction areas and establishes predictive models for THMs formation in dual-sources DWDS.

Prediction Model of Health-Related Quality of Life in Older Adults According to Gender Using a Decision Tree Model: a Study Based on the Korea National Health and Nutrition Examination Survey

Prediction Model of Health-Related Quality of Life in Older Adults According to Gender Using a Decision Tree Model: a Study Based on the Korea National Health and Nutrition Examination Survey

An IoT Real-Time Potable Water Quality Monitoring and Prediction Model Based on Cloud Computing Architecture.

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks.

Advancing air quality prediction models in urban India: a deep learning approach integrating DCNN and LSTM architectures for AQI time-series classification

Advancing air quality prediction models in urban India: a deep learning approach integrating DCNN and LSTM architectures for AQI time-series classification

Analysis of the technical condition of gas turbine plants. Part 3. Application of machine learning methods

A method for estimating the technical condition coefficient of a gas turbine unit for transporting of natural gas using machine learning methods is considered. Archival gas-dynamic parameters recorded by the installation’s automatic control system are used as initial data. The initial data set was created by determining power from the change in enthalpy of natural gas before and after the blower. The software package is implemented in Python; the Scikit-learn library is used for machine learning models. The average absolute error in percentage is chosen as a criterion for the quality of the prediction. The prediction quality of machine learning models is assessed for different sets of parameters-features and sample sizes. Recommendations on the use of models are given. Keywords: gas turbine unit, technical condition, measuring system, data processing, machine learning. v.l.blinov@urfu.ru; deryabingleb.were@gmail.com

A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition

A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

With the development of information technology, a large number of product quality data in the entire manufacturing process is accumulated, but it is not explored and used effectively. The traditional product quality prediction models have many disadvantages, such as high complexity and low accuracy. To overcome the above problems, we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model: radial basis function model optimized by the firefly algorithm with Levy flight mechanism (RBFFALM). First, the new data equalization method is introduced to pre-process the dataset, which reduces the dimension of the data, removes redundant features, and improves the data distribution. Then the RBFFALFM is used to predict product quality. Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India

The importance of water quality models has increased as their inputs are critical to the development of risk assessment framework for environmental management and monitoring of rivers. However, with the advent of a plethora of recent advances in ML algorithms better predictions are possible. This study proposes a causal and effect model by considering climatological such as temperature and precipitation along with geospatial information related to the agricultural land use factor (ALUF), the forest land use factor (FLUF), the grassland usage factor (GLUF), the shrub land use factor (SLUF), and the urban land use factor (ULUF). All these factors are included in the input data, whereas four Stream Water Quality parameters (SWQPs) such as Electrical Conductivity (EC), Biochemical Oxygen Demand (BOD), Nitrate, and Dissolved Oxygen (DO) from 2019 to 2021 are taken as outputs to predict the Godavari River Basin water quality. In the preliminary investigation, out of these four SWQPs, nitrate's coefficient of variation (CV) is high, revealing a close association with climate parameters and land use practices across the sampling stations. In the authors' earlier study, a model using a single-layer Feed-Forward Neural Network (FFNN) showed improved performance in predicting cause and effect factors linked to water quality metrics. To achieve better prediction, a stacked ANN meta-model and nine conventional machine learning (ML) models, including Extreme Gradient Boosting (XGB), Extra Trees (ET), Bagging (BG), Random Forest (RF), AdaBoost or Adaptive Boosting (ADB), Decision Tree (DT), Highest Gradient Boosting (HGB), Light Gradient Boosting Method (LGBM), and Gradient Boosting (GB), were compared in this study. According to the study's findings, Bagging and Boosting models outperformed stand-alone earlier FFNN for the same dataset and showed superior predictive capabilities in terms of accuracy in forecasting the variable of interest. For instance, during testing, the coefficient of determination (R2) of Biochemical Oxygen Demand (BOD) increased from 0.72 to 0.87. Furthermore, a stacked Artificial Neural Network (ANN) meta model that was reinforced using Extreme Gradient Boosting (XGB), Random Forest (RF), and Extra Trees (ET) as base models performed better than the individual ML models (from R2 = 0.87 to 0.91 for BOD in testing). By using this new framework, the effort for hyperparameter tuning can be minimized.

>Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China.

Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics.Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error.

Modeling below-cloud scavenging of size-resolved particles in GEM-MACHv3.1

Abstract. Below-cloud scavenging (BCS) is the process of aerosol removal from the atmosphere between cloud base and the ground by precipitation (e.g., rain or snow), and affects aerosol number or mass concentrations, size distribution, and lifetime. An accurate representation of precipitation phases is important in treating BCS as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. The impact of different representations of BCS on existing model biases was examined through implementing a new aerosol BCS scheme in the Environment and Climate Change Canada (ECCC) air quality prediction model GEM-MACH and comparing it with the existing scavenging scheme in the model. Further, the current GEM-MACH employs a single-phase precipitation for BCS: total precipitation is treated as either liquid or solid depending on a fixed environment temperature threshold. Here, we consider co-existing liquid and solid precipitation phases as they are predicted by the GEM microphysics. GEM-MACH simulations, in a local-area domain over the Athabasca oil sands areas, Canada, are compared with observed precipitation samples, with a focus on the particulate base cation NH4+; acidic anions NO3-, SO4=, HSO3- in precipitation; and observed ambient particulate sulfate, ammonium, and nitrate concentrations. Overall, the introduction of the multi-phase approach and the new scavenging scheme enhances GEM-MACH performance compared to previous methods. Including a multi-phase approach leads to altered SO4= scavenging and impacts the BCS of SO2 into the aqueous phase over the domain. Sulfate biases improved from +46 % to −5 % relative to Alberta Precipitation Quality Monitoring Program wet sulfate observations. At Canadian Air and Precipitation Monitoring Network stations the biases became more negative, from −10 % to −30 % for the tests carried out here. These improvements contrast with prior annual average biases of +200 % for SO4=, indicating enhanced model performance. Improvements in model performance (via scores for correlation coefficient, normalized mean bias, and/or fractional number of model values within a factor of 2 of observations) could also be seen between the base case and the two simulations based on multi-phase partitioning for NO3-, NH4+, and SO4=. Whether or not these improvements corresponded to increases or decreases in NO3- and NH4+ wet deposition varied over the simulation region. The changes were episodic in nature – the most significant changes in wet deposition were likely at specific geographic locations and represent specific cloud precipitation events. The changes in wet scavenging resulted in a higher formation rate and larger concentrations of atmospheric particle sulfate.

A systematic review of hyperparameter tuning techniques for software quality prediction models

BACKGROUND: Software quality prediction models play a crucial role in identifying vulnerable software components during early stages of development, and thereby optimizing the resource allocation and enhancing the overall software quality. While various classification algorithms have been employed for developing these prediction models, most studies have relied on default hyperparameter settings, leading to significant variability in model performance. Tuning the hyperparameters of classification algorithms can enhance the predictive capability of quality models by identifying optimal settings for improved accuracy and effectiveness. METHOD: This systematic review examines studies that have utilized hyperparameter tuning techniques to develop prediction models in software quality domain. The review focused on diverse areas such as defect prediction, maintenance estimation, change impact prediction, reliability prediction, and effort estimation, as these domains demonstrate the wide applicability of common learning algorithms. RESULTS: This review identified 31 primary studies on hyperparameter tuning for software quality prediction models. The results demonstrate that tuning the parameters of classification algorithms enhances the performance of prediction models. Additionally, the study found that certain classification algorithms exhibit high sensitivity to their parameter settings, achieving optimal performance when tuned appropriately. Conversely, certain classification algorithms exhibit low sensitivity to their parameter settings, making tuning unnecessary in such instances. CONCLUSION: Based on the findings of this review, the study conclude that the predictive capability of software quality prediction models can be significantly improved by tuning their hyperparameters. To facilitate effective hyperparameter tuning, we provide practical guidelines derived from the insights obtained through this study.

Contributions of the vegetation index (NDVI) in water quality prediction models in a semi-arid tropical watershed

In this work, a new approach to using NDVI as a predictor of water quality parameters in arid environments is proposed. Our focus was the Araçuaí river basin, which has a predominance of native cerrado vegetation and is subject to seasonal variations in rainfall and vegetation cover. MODIS images (MOD13Q1) from 2000 to 2018 were used to calculate the NDVI of the contributing areas of the water quality monitoring stations and to analyze its relationship with fourteen water quality parameters. The NDVI showed significant seasonality, with high values in the rainy season, and temporal trends of increase in stretches related to the main river. A strong and new relationship was observed between NDVI and six water quality parameters: color, total dissolved solids, total suspended solids, total solids, nitrate and turbidity, this being stronger at the local scale, with better performance at from models that use incremental NDVI, capturing local variations in vegetation cover, instead of regional NDVI, which reflects the general state of vegetation. The results demonstrate the potential of using these indices to develop and improve prediction models of water quality parameters in river basins and to expand the spatial and temporal coverage of water quality monitoring.

Causal Artificial Intelligence Models of Food Quality Data.

Causal Artificial Intelligence Models of Food Quality Data.

A dual-population multiobjective co-evolutionary matching ensemble learning for product multi-indicator prediction in continuous annealing

Achieving simultaneous and precise prediction of multiple product performance indicators is crucial to ensure stable production in the continuous annealing process. However, the majority of existing quality prediction modeling methods concentrate on developing single-output regression models that are limited to single-indicator prediction scenarios. When dealing with the multi-indicator prediction scenarios, these methods usually require the construction of a separate model for each performance indicator, which leads to more computational and storage resources and more complex maintenance in practical applications. To address this issue, a dual-population multiobjective co-evolutionary matching ensemble learning method called DP-CEMEL is developed. It utilizes dual-population co-optimization to enable the joint learning of the algorithm on input–output relationships and inter-indicator correlations. More specifically, a two-stage learning based individual encoding and decoding method and a performance boosting degree (PBD) based individual evaluation strategy are designed to achieve co-optimization of the dual populations to evolve a set of individuals (i.e., base learners) with good accuracy and low complexity. Then, a PBD based matching ensemble method is applied to get the final multi-indicator prediction model by the optimal matching and ensemble of the evolved individuals. Experimental results on both benchmark data and practical strip production data demonstrate that the proposed DP-CEMEL attains competitive or better performance compared to other state-of-the-art learning methods.