Journal articles on the topic 'Prediction of quality'
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Liang, Yun-Chia, Yona Maimury, Angela Hsiang-Ling Chen, and Josue Rodolfo Cuevas Juarez. "Machine Learning-Based Prediction of Air Quality." Applied Sciences 10, no. 24 (December 21, 2020): 9151. http://dx.doi.org/10.3390/app10249151.
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Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.
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Muharsyah, Robi, Dian Nur Ratri, and Damiana Fitria Kussatiti. "Improving prediction quality of sea surface temperature (SST) in Niño3.4 region using Bayesian Model Averaging." IOP Conference Series: Earth and Environmental Science 893, no. 1 (November 1, 2021): 012028. http://dx.doi.org/10.1088/1755-1315/893/1/012028.
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Abstract Prediction of Sea Surface Temperature (SST) in Niño3.4 region (170 W - 120 W; 5S - 5N) is important as a valuable indicator to identify El Niño Southern Oscillation (ENSO), i.e., El Niño, La Niña, and Neutral condition for coming months. More accurate prediction Niño3.4 SST can be used to determine the response of ENSO phenomenon to rainfall over Indonesia region. SST predictions are routinely released by meteorological institutions such as the European Center for Medium-Range Weather Forecasts (ECMWF). However, SST predictions from the direct output (RAW) of global models such as ECMWF seasonal forecast is suffering from bias that affects the poor quality of SST predictions. As a result, it also increases the potential errors in predicting the ENSO events. This study uses SST from the output Ensemble Prediction System (EPS) of ECMWF seasonal forecast, namely SEAS5. SEAS5 SST is downloaded from The Copernicus Climate Change Service (C3S) for period 1993-2020. One value representing SST over Niño3.4 region is calculated for each lead-time (LT), LT0-LT6. Bayesian Model Averaging (BMA) is selected as one of the post-processing methods to improve the prediction quality of SEAS5-RAW. The advantage of BMA over other post-processing methods is its ability to quantify the uncertainty in EPS, which is expressed as probability density function (PDF) predictive. It was found that the BMA calibration process reaches optimal performance using 160 months training window. The result show, prediction quality of Niño3.4 SST of BMA output is superior to SEAS5-RAW, especially for LT0, LT1, and LT2. In term deterministic prediction, BMA shows a lower Root Mean Square Error (RMSE), higher Proportion of Correct (PC). In term probabilistic prediction, the error rate of BMA, which is showed by the Brier Score is lower than RAW. Moreover, BMA shows a good ability to discriminating ENSO events which indicates by AUC ROC close to a perfect score.
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Panchal, D. S., M. B. Shelke, S. S. Kawathekar, and S. N. Deshmukh. "Prediction of Healthcare Quality Using Sentiment Analysis." Indian Journal Of Science And Technology 16, no. 21 (June 3, 2023): 1603–13. http://dx.doi.org/10.17485/ijst/v16i21.2506.
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Martens, M., and H. Martens. "Near-Infrared Reflectance Determination of Sensory Quality of Peas." Applied Spectroscopy 40, no. 3 (March 1986): 303–10. http://dx.doi.org/10.1366/0003702864509114.
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Rapid, precise, and relevant methods for predicting the sensory quality of frozen peas were sought. Pea batches chosen to span many different types of quality variations were analyzed by a consumer test, sensory laboratory analysis, and traditional chemical and physical measurements as well as by near-infrared reflectance analysis (NIR). Partial least-squares (PLS) regression was used to reveal the relationships between the different types of measurements. A noise-compensated value, relative ability of prediction (RAP), was used to express the degree of prediction (1.0 = perfect prediction). NIR was found to predict the sensory texture variables (RAP = 0.79) better than the flavor variables (RAP = 0.67). Average consumer preference was less well predicted (RAP = 0.48) by NIR. This was interpretable since NIR gave a better description of the chemical and physical methods relevant for texture (e.g., dry matter (RAP = 0.93)) than the flavor-related variables (e.g., sucrose (RAP = 0.45)) that apparently determine the consumer preference. However, NIR was found to describe the average variation in sensory quality better than the traditional tenderometer value (TV). The highest prediction of sensory variables was obtained by a combination of NIR, TV, and chemical measurements (RAP = 0.87 and 0.80 for texture and flavor variables, respectively). We discuss the predictive validity and the meaning of the present predictive abilities in practice, leading to a conclusion that NIR has a potential for predicting the sensory quality of peas.
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Kim, Donghyun, Heechan Han, Wonjoon Wang, Yujin Kang, Hoyong Lee, and Hung Soo Kim. "Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction." Applied Sciences 12, no. 13 (July 1, 2022): 6699. http://dx.doi.org/10.3390/app12136699.
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Accurate pollutant prediction is essential in fields such as meteorology, meteorological disasters, and climate change studies. In this study, long short-term memory (LSTM) and deep neural network (DNN) models were applied to six pollutants and comprehensive air-quality index (CAI) predictions from 2015 to 2020 in Korea. In addition, we used the network method to find the best data sources that provide factors affecting comprehensive air-quality index behaviors. This study had two steps: (1) predicting the six pollutants, including fine dust (PM10), fine particulate matter (PM2.5), ozone (O3), sulfurous acid gas (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) using the LSTM model; (2) forecasting the CAI using the six predicted pollutants in the first step as predictors of DNNs. The predictive ability of each model for the six pollutants and CAI prediction was evaluated by comparing it with the observed air-quality data. This study showed that combining a DNN model with the network method provided a high predictive power, and this combination could be a remarkable strength in CAI prediction. As the need for disaster management increases, it is anticipated that the LSTM and DNN models with the network method have ample potential to track the dynamics of air pollution behaviors.
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GANESAN, K., TAGHI M. KHOSHGOFTAAR, and EDWARD B. ALLEN. "CASE-BASED SOFTWARE QUALITY PREDICTION." International Journal of Software Engineering and Knowledge Engineering 10, no. 02 (April 2000): 139–52. http://dx.doi.org/10.1142/s0218194000000092.
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Highly reliable software is becoming an essential ingredient in many systems. However, assuring reliability often entails time-consuming costly development processes. One cost-effective strategy is to target reliability-enhancement activities to those modules that are likely to have the most problems. Software quality prediction models can predict the number of faults expected in each module early enough for reliability enhancement to be effective. This paper introduces a case-based reasoning technique for the prediction of software quality factors. Case-based reasoning is a technique that seeks to answer new problems by identifying similar "cases" from the past. A case-based reasoning system can function as a software quality prediction model. To our knowledge, this study is the first to use case-based reasoning systems for predicting quantitative measures of software quality. A case study applied case-based reasoning to software quality modeling of a family of full-scale industrial software systems. The case-based reasoning system's accuracy was much better than a corresponding multiple linear regression model in predicting the number of design faults. When predicting faults in code, its accuracy was significantly better than a corresponding multiple linear regression model for two of three test data sets and statistically equivalent for the third.
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Gonçalves, Mateus Teles Vital, Gota Morota, Paulo Mafra de Almeida Costa, Pedro Marcus Pereira Vidigal, Marcio Henrique Pereira Barbosa, and Luiz Alexandre Peternelli. "Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits." PLOS ONE 16, no. 3 (March 4, 2021): e0236853. http://dx.doi.org/10.1371/journal.pone.0236853.
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The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.
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Kouadri, Wissam Mammar, Mourad Ouziri, Salima Benbernou, Karima Echihabi, Themis Palpanas, and Iheb Ben Amor. "Quality of sentiment analysis tools." Proceedings of the VLDB Endowment 14, no. 4 (December 2020): 668–81. http://dx.doi.org/10.14778/3436905.3436924.
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In this paper, we present a comprehensive study that evaluates six state-of-the-art sentiment analysis tools on five public datasets, based on the quality of predictive results in the presence of semantically equivalent documents, i.e., how consistent existing tools are in predicting the polarity of documents based on paraphrased text. We observe that sentiment analysis tools exhibit intra-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents by the same tool, and inter-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents across different tools. We introduce a heuristic to assess the data quality of an augmented dataset and a new set of metrics to evaluate tool inconsistencies. Our results indicate that tool inconsistencies is still an open problem, and they point towards promising research directions and accuracy improvements that can be obtained if such inconsistencies are resolved.
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Asiah, Mat, Khidzir Nik Zulkarnaen, Deris Safaai, Mat Yaacob Nik Nurul Hafzan, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "A Review on Predictive Modeling Technique for Student Academic Performance Monitoring." MATEC Web of Conferences 255 (2019): 03004. http://dx.doi.org/10.1051/matecconf/201925503004.
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Despite of providing high quality of education, demand on predicting student academic performance become more critical to improve the quality and assisting students to achieve a great performance in their studies. The lack of existing an efficiency and accurate prediction model is one of the major issues. Predictive analytics can provide institution with intuitive and better decision making. The objective of this paper is to review current research activities related to academic analytics focusing on predicting student academic performance. Various methods have been proposed by previous researchers to develop the best performance model using variety of students data, techniques, algorithms and tools. Predictive modeling used in predicting student performance are related to several learning tasks such as classification, regression and clustering. To achieve best prediction model, a lot of variables have been chosen and tested to find most influential attributes to perform prediction. Accurate performance prediction will be helpful in order to provide guidance in learning process that will benefit to students in avoiding poor scores. The predictive model furthermore can help instructor to forecast course completion including student final grade which are directly correlated to student performance success. To harvest an effective predictive model, it requires a good input data and variables, suitable predictive method as well as powerful and robust prediction model.
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Veeramalai, S., Mr T. Praveen, and S. Pradeepa Natarajan. "Cost Based On Product Quality Prediction Using Datamining." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-Active Galaxy (June 30, 2018): 38–42. http://dx.doi.org/10.31142/ijtsrd14564.
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Bao-Wei Zhang, Bao-Wei Zhang, Lin Xu Bao-Wei Zhang, and Yong-Hua Wang Lin Xu. "Prediction of Yarn Quality Based on Actual Production." 網際網路技術學刊 24, no. 4 (July 2023): 871–80. http://dx.doi.org/10.53106/160792642023072404005.
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<p>In recent decades, the neural network approach to predicting yarn quality indicators has been recognized for its high accuracy. Although using neural networks to predict yarn quality indicators has a high accuracy advantage, its relationship understanding between each input parameter and yarn quality indicators may need to be corrected, i.e., increasing the raw cotton strength, the final yarn strength remains the same or decreases. Although this is normal for prediction algorithms, actual production need is more of a trend for individual parameter changes to predict a correct yarn, i.e., raw cotton strength increase should correspond to yarn strength increase. This study proposes a yarn quality prediction method based on actual production by combining nearest neighbor, particle swarm optimization, and expert experience to address the problem. We Use expert experience to determine the upper and lower limits of parameter weights, the particle swarm optimization finds the optimal weights, and then the nearest neighbor algorithm is used to calculate the predicted values of yarn indexes. Finally, the current problems and the rationality of the method proposed in this paper are verified by experiments.</p> <p> </p>
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Wang, Xianhe, Ying Li, Qian Qiao, Adriano Tavares, and Yanchun Liang. "Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods." Entropy 25, no. 8 (August 9, 2023): 1186. http://dx.doi.org/10.3390/e25081186.
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In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in water quality prediction. This approach effectively considers both feature correlation and information content, avoiding excessive reliance on a single criterion for feature selection. Through the utilization of this comprehensive approach, a comprehensive evaluation of the contribution and importance of the features was achieved, thereby minimizing subjective bias and uncertainty. By striking a balance among various factors, features with stronger correlation and greater information content can be selected, leading to improved accuracy and robustness in the feature-selection process. Furthermore, this study explored several machine learning models for water quality prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM). SVM exhibited commendable performance in predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities and high prediction accuracy. MLP demonstrated its strength in nonlinear modeling and performed well in predicting multiple water quality parameters. Conversely, the RF and XGBoost models exhibited relatively inferior performance in water quality prediction. In contrast, the LSTM model, a recurrent neural network specialized in processing time series data, demonstrated exceptional abilities in water quality prediction. It effectively captured the dynamic patterns present in time series data, offering stable and accurate predictions for various water quality parameters.
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Izima, Obinna, Ruairí de Fréin, and Ali Malik. "A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics." Electronics 10, no. 22 (November 19, 2021): 2851. http://dx.doi.org/10.3390/electronics10222851.
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A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.
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Cican, Grigore, Adrian-Nicolae Buturache, and Radu Mirea. "Applying Machine Learning Techniques in Air Quality Prediction—A Bucharest City Case Study." Sustainability 15, no. 11 (May 23, 2023): 8445. http://dx.doi.org/10.3390/su15118445.
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Air quality forecasting is very difficult to achieve in metropolitan areas due to: pollutants emission dynamics, high population density and uncertainty in defining meteorological conditions. The use of data, which contain insufficient information within the model training, and the poor selection of the model to be used limits the air quality prediction accuracy. In this study, the prediction of NO2 concentration is made for the year 2022 using a long short-term memory network (LSTM) and a gated recurrent unit (GRU). this is an improvement in terms of performance compared to traditional methods. Data used for predictive modeling are obtained from the National Air Quality Monitoring Network. The KPIs(key performance indicator) are computed based on the testing data subset when the NO2 predicted values are compared to the real known values. Further, two additional predictions were performed for two days outside the modeling dataset. The quality of the data is not as expected, and so, before building the models, the missing data had to be imputed. LSTM and GRU performance in predicting NO2 levels is similar and reasonable with respect to the case study. In terms of pure generalization capabilities, both LSTM and GRU have the maximum R2 value below 0.8. LSTM and GRU represent powerful architectures for time-series prediction. Both are highly configurable, so the probability of identifying the best suited solution for the studied problem is consequently high.
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Ouenniche, Jamal, Kais Bouslah, Blanca Perez-Gladish, and Bing Xu. "A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction." Annals of Operations Research 296, no. 1-2 (April 9, 2019): 495–512. http://dx.doi.org/10.1007/s10479-019-03223-0.
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AbstractNowadays, business analytics has become a common buzzword in a range of industries, as companies are increasingly aware of the importance of high quality predictions to guide their pro-active planning exercises. The financial industry is amongst those industries where predictive analytics techniques are widely used to predict both continuous and discrete variables. Conceptually, the prediction of discrete variables comes down to addressing sorting problems, classification problems, or clustering problems. The focus of this paper is on classification problems as they are the most relevant in risk-class prediction in the financial industry. The contribution of this paper lies in proposing a new classifier that performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a new VIKOR-based classifier and out-of-sample predictions are devised with a CBR-based classifier trained on the risk class predictions provided by the proposed VIKOR-based classifier. The performance of this new non-parametric classification framework is tested on a dataset of firms in predicting bankruptcy. Our findings conclude that the proposed new classifier can deliver a very high predictive performance, which makes it a real contender in industry applications in finance and investment.
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Sameki, Mehrnoosh, Danna Gurari, and Margrit Betke. "Predicting Quality of Crowdsourced Image Segmentations from Crowd Behavior." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 (September 23, 2015): 30–31. http://dx.doi.org/10.1609/hcomp.v3i1.13260.
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Quality control (QC) is an integral part of many crowd- sourcing systems. However, popular QC methods, such as aggregating multiple annotations, filtering workers, or verifying the quality of crowd work, introduce additional costs and delays. We propose a complementary paradigm to these QC methods based on predicting the quality of submitted crowd work. In particular, we pro- pose to predict the quality of a given crowd drawing directly from a crowd worker’s drawing time, number of user clicks, and average time per user click. We focus on the task of drawing the boundary of a single object in an image. To train and test our prediction models, we collected a total of 2,025 crowd-drawn segmentations for 405 familiar everyday images and unfamiliar biomedical images from 90 unique crowd workers. We first evaluated five prediction models learned using different combinations of the three worker behavior cues for all images. Experiments revealed that time per number of user clicks was the most effective cue for predicting segmentation quality. We next inspected the predictive power of models learned using crowd annotations collected for familiar and unfamiliar data independently. Prediction models were significantly more effective for estimating the segmentation quality from crowd worker behavior for familiar image content than unfamiliar image content.
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Dai, Ning, Haiwei Jin, Kaixin Xu, Xudong Hu, Yanhong Yuan, and Weimin Shi. "Prediction of Cotton Yarn Quality Based on Attention-GRU." Applied Sciences 13, no. 18 (September 5, 2023): 10003. http://dx.doi.org/10.3390/app131810003.
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With the diversification of spinning order varieties and process parameters, the conventional method of determining production plans through trial spinning no longer satisfies the processing requirements of enterprises. Currently, deficiencies exist in predicting spinning quality relying on manual experience and traditional methods. The back propagation (BP) neural network within the realm of deep learning theory faces challenges in handling time series data, while the long short-term memory (LSTM) neural network, despite its intricate mechanism, exhibits an overall lower predictive accuracy. Consequently, a more precise predictive methodology is imperative to assist production personnel in efficiently ascertaining cotton-blending schemes and processing parameters, thereby elevating the production efficiency of the enterprise. In response to this challenge, we propose an attention-GRU-based cotton yarn quality prediction model. By employing the attention mechanism, the model is directed towards the input features most significantly impacting yarn quality. Real-world performance indicators of raw cotton and process parameters are utilized to predict yarn tensile strength. A comparative analysis is conducted against prediction results of BP, LSTM, and gated recurrent unit (GRU) neural networks that do not incorporate the attention mechanism. The outcomes reveal that the GRU model enhanced with the attention mechanism demonstrates reductions of 56.3%, 38.5%, and 36.4% in root mean square error (RMSE), along with 0.367%, 0.158%, and 0.190% in mean absolute percentage error (MAPE), respectively. The model attains a coefficient of determination R-squared of 0.954, indicating a high degree of fitness. This study underscores the potential of the proposed attention-GRU model in refining cotton yarn quality prediction and its consequential implications for process optimization and enhanced production efficiency within textile enterprises.
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Isworo, Slamet, Poerna Sri Oetari, Indah Noor Alita, and Tozan Ajie. "The Prediction of Air Quality Status." International Journal of Applied Science 2, no. 1 (January 29, 2019): p7. http://dx.doi.org/10.30560/ijas.v2n1p7.
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Construction of the National Railway Station Cross Station Kedundang - New Yogyakarta Airport Station is an accelerated development program in supporting the economy of the special region of Yogyakarta. Construction of the railroad as a consequence of infrastructure development that enables potential impacts on the surrounding environment. This study is a predictive study of air quality that might occur after operational construction of a fire pathway with Nitrogen Dioxide (NO2) with the Gas Sampler-Spectrophotometer-Saltzman Method, Carbon Monoxide (CO) with the gas sampler-NDIR analyzer method and dust particles with the dust sample, Hi-Vol gravimetric method. The data obtained is then converted into modeling using Caline 4 software, so that air quality prediction is obtained at the time of operation. Air quality category predictions use the standard air pollution index standard. The results of the analysis of the air quality parameters show a good category, only on the CO2 parameters that address high concentrations. however, based on CO2 conversion using the value of the Air Pollution Standard Index is predicted to remain in the "Good" category at the time of project operation. Therefore an air quality study is needed in the railroad development plan through an analysis study of environmental impacts, so that the management and monitoring of air quality can be carried out properly so as to cause disruption to the environment.
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PALOPOLI, LUIGI, and GIORGIO TERRACINA. "CooPPS: A SYSTEM FOR THE COOPERATIVE PREDICTION OF PROTEIN STRUCTURES." Journal of Bioinformatics and Computational Biology 02, no. 03 (September 2004): 471–95. http://dx.doi.org/10.1142/s0219720004000697.
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Predicting the three-dimensional structure of proteins is a difficult task. In the last few years several approaches have been proposed for performing this task taking into account different protein chemical and physical properties. As a result, a growing number of protein structure prediction tools is becoming available, some of them specialized to work on either some aspects of the predictions or on some categories of proteins; however, they are still not sufficiently accurate and reliable for predicting all kinds of proteins. In this context, it is useful to jointly apply different prediction tools and combine their results in order to improve the quality of the predictions. However, several problems have to be solved in order to make this a viable possibility. In this paper a framework and a tool is proposed which allows: (i) definition of a common reference applicative domain for different prediction tools; (ii) characterization of prediction tools through evaluating some quality parameters; (iii) characterization of the performances of a team of predictors jointly applied over a prediction problem; (iv) the singling out of the best team for a prediction problem; and (v) the integration of predictor results in the team in order to obtain a unique prediction. A system implementing the various steps of the proposed framework (CooPPS) has been developed and several experiments for testing the effectiveness of the proposed approach have been carried out.
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Eskenazi, L., D. G. Childers, and D. M. Hicks. "Acoustic Correlates of Vocal Quality." Journal of Speech, Language, and Hearing Research 33, no. 2 (June 1990): 298–306. http://dx.doi.org/10.1044/jshr.3302.298.
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We have investigated the relationship between various voice qualities and several acoustic measures made from the vowel /i/ phonated by subjects with normal voices and patients with vocal disorders. Among the patients (pathological voices), five qualities were investigated: overall severity, hoarseness, breathiness, roughness, and vocal fry. Six acoustic measures were examined. With one exception, all measures were extracted from the residue signal obtained by inverse filtering the speech signal using the linear predictive coding (LPC) technique. A formal listening test was implemented to rate each pathological voice for each vocal quality. A formal listening test also rated overall excellence of the normal voices. A scale of 1–7 was used. Multiple linear regression analysis between the results of the listening test and the various acoustic measures was used with the prediction sums of squares (PRESS) as the selection criteria. Useful prediction equations of order two or less were obtained relating certain acoustic measures and the ratings of pathological voices for each of the five qualities. The two most useful parameters for predicting vocal quality were the Pitch Amplitude (PA) and the Harmonics-to-Noise Ratio (HNR). No acoustic measure could rank the normal voices.
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Li, Yue, Bin Kong, Weiwei Yu, and Xingliang Zhu. "An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction." Applied Sciences 13, no. 12 (June 10, 2023): 7011. http://dx.doi.org/10.3390/app13127011.
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Wastewater treatment is a pivotal step in water resource recycling. Predicting effluent wastewater quality can help wastewater treatment plants (WWTPs) establish efficient operations so as to save resources. We propose CNN-LSTM-Attention (CLATT), an attention-based effluent wastewater quality prediction model, which uses a convolutional neural network (CNN) as an encoder and a long short-term memory network (LSTM) as a decoder. An attention mechanism is used to aggregate the information at adjacent sampling times. A sliding window method is proposed to solve the problem of the prediction performance of the model decreasing with time. We conducted the experiment using data collected from a WWTP in Fujian, China. Our results show that the accuracy of prediction is improved, with MSE decreasing by 0.25, MAPE decreasing by 5% and LER decreasing by 7%, after using the sliding window method. Compared with other methods, CLATT achieves the fastest prediction speed among all the methods based on LSTM and the most accurate prediction performance, with MSE increasing up to 0.92, MAPE up to 0.08 and LER up to 0.11. Furthermore, we performed an ablation study on the proposed method to validate the rationality of the major part of the model, and the results show that the LSTM significantly improves the predictive performance of the model, and the CNN and the attention mechanism also improve the accuracy of the prediction.
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Lepioufle, Jean-Marie, Leif Marsteen, and Mona Johnsrud. "Error Prediction of Air Quality at Monitoring Stations Using Random Forest in a Total Error Framework." Sensors 21, no. 6 (March 19, 2021): 2160. http://dx.doi.org/10.3390/s21062160.
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Instead of a flag valid/non-valid usually proposed in the quality control (QC) processes of air quality (AQ), we proposed a method that predicts the p-value of each observation as a value between 0 and 1. We based our error predictions on three approaches: the one proposed by the Working Group on Guidance for the Demonstration of Equivalence (European Commission (2010)), the one proposed by Wager (Journal of MachineLearningResearch, 15, 1625–1651 (2014)) and the one proposed by Lu (Journal of MachineLearningResearch, 22, 1–41 (2021)). Total Error framework enables to differentiate the different errors: input, output, structural modeling and remnant. We thus theoretically described a one-site AQ prediction based on a multi-site network using Random Forest for regression in a Total Error framework. We demonstrated the methodology with a dataset of hourly nitrogen dioxide measured by a network of monitoring stations located in Oslo, Norway and implemented the error predictions for the three approaches. The results indicate that a simple one-site AQ prediction based on a multi-site network using Random Forest for regression provides moderate metrics for fixed stations. According to the diagnostic based on predictive qq-plot and among the three approaches used in this study, the approach proposed by Lu provides better error predictions. Furthermore, ensuring a high precision of the error prediction requires efforts on getting accurate input, output and prediction model and limiting our lack of knowledge about the “true” AQ phenomena. We put effort in quantifying each type of error involved in the error prediction to assess the error prediction model and further improving it in terms of performance and precision.
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Maier, Catalina, and Robin Gauthier. "Leveling Quality Prediction Algorithm." Applied Mechanics and Materials 809-810 (November 2015): 235–40. http://dx.doi.org/10.4028/www.scientific.net/amm.809-810.235.
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Roller leveling is a forming process which used to minimize flatness imperfection and residual stresses by repeated forming process of a sheet metal. The determination of the machine settings must be very accurate and ask a precise mechanical study. In order to determine an algorithm which can predict the leveling quality according to the machine settings we start by a theoretical model of stress evolution during the process. The plastification ratio is deducted from this one and the values obtained by this approach are compared whit experimental values. The finite element analysis is performed, in second step in order to assure a good accuracy of the prediction algorithm. Theoretical study determines a minimum of the plastification ratio according to the machine settings. The finite element analysis gives more accurate results due to the consideration of different characteristics of the process, neglected by the theoretical model: cumulative effect of bending/unbending with stretching of the sheet during the passing between each couple of rolls, boundary conditions at the limit of the material deformed by two adjoining couples of rolls, friction force.
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Avvari, Pavithra, Preethi Nacham, Snehitha Sasanapuri, Sirija Reddy Mankena, Phanisree Kudipudi, and Aishwarya Madapati. "Air Quality Index Prediction." E3S Web of Conferences 391 (2023): 01103. http://dx.doi.org/10.1051/e3sconf/202339101103.
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Falling back past few years rapid progress in Air pollution has become a life-threatening concern in many nations throughout the world due to human activity, industrialisation, and urbanisation.. As a result of these activities, sulphur oxides, carbon dioxide (CO2), nitrogen oxides, carbon monoxide (CO), chlorofluorocarbons (CFC), lead, mercury, and other pollutants be emitted into atmosphere. Simultaneously, estimating quality of air is a tough undertaking because of evolution, variability, also unreasonable unpredictability over pollution and particle region and time. In this project we compare the two Algorithms of machine learning in predicting Index of Air Quality and its predominant. Support vector machine (SVM) exists as prominent machine learning method beneficial to forecasting pollutant plus particle levels and predicting the air quality index (AQI), and Random Forest Regression is another. We'll be working with data from India's Open Government Data Platform. This website displays Air Quality Index readings from around India, including Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), and Particulate Matter (PM) are examples of contaminants (PM10 and PM2.5), Carbon Monoxide (CO), and others. The output of the project is the predict of Air Quality index using two different algorithms and the comparison of models using various error metrics.
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Barks, C. Shane. "Adjustment of Regional Regression Equations for Urban Storm-Runoff Quality Using At-Site Data." Transportation Research Record: Journal of the Transportation Research Board 1523, no. 1 (January 1996): 141–46. http://dx.doi.org/10.1177/0361198196152300117.
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Regional regression equations have been developed to estimate urban storm-runoff loads and mean concentrations using a national data base. Four statistical methods using at-site data to adjust the regional equation predictions were developed to provide better local estimates. The four adjustment procedures are a single-factor adjustment, a regression of the observed data against the predicted values, a regression of the observed values against the predicted values and additional local independent variables, and a weighted combination of a local regression with the regional prediction. Data collected at five representative storm-runoff sites during 22 storms in Little Rock, Arkansas, were used to verify, and, when appropriate, adjust the regional regression equation predictions. Comparison of observed values of storm-runoff loads and mean concentrations to the predicted values from the regional regression equations for nine constituents (chemical oxygen demand, suspended solids, total nitrogen as N, total ammonia plus organic nitrogen as N, total phosphorus as P, dissolved phosphorus as P, total recoverable copper, total recoverable lead, and total recoverable zinc) showed large prediction errors ranging from 63 percent to more than several thousand percent. Prediction errors for 6 of the 18 regional regression equations were less than 100 percent and could be considered reasonable for water-quality prediction equations. The regression adjustment procedure was used to adjust five of the regional equation predictions to improve the predictive accuracy. For seven of the regional equations the observed and the predicted values are not significantly correlated. Thus neither the unadjusted regional equations nor any of the adjustments were appropriate. The mean of the observed values was used as a simple estimator when the regional equation predictions and adjusted predictions were not appropriate.
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Han, Jindong, Hao Liu, Hengshu Zhu, Hui Xiong, and Dejing Dou. "Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4081–89. http://dx.doi.org/10.1609/aaai.v35i5.16529.
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Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weather prediction, most of them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On the one hand, the accurate prediction of one task can help improve another task's performance. On the other hand, geospatially distributed air quality and weather monitoring stations provide additional hints for city-wide spatiotemporal dependency modeling. Inspired by the above two insights, in this paper, we propose the Multi-adversarial spatiotemporal recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather prediction. Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations. Then, we develop a multi-adversarial graph learning framework to against observation noise propagation introduced by spatiotemporal modeling. Moreover, we introduce an adaptive training strategy by formulating multi-adversarial learning as a multi-task learning problem. Finally, extensive experiments on two real-world datasets show that MasterGNN achieves the best performance compared with seven baselines on both air quality and weather prediction tasks.
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Bhatta, Madhav, Lucia Gutierrez, Lorena Cammarota, Fernanda Cardozo, Silvia Germán, Blanca Gómez-Guerrero, María Fernanda Pardo, Valeria Lanaro, Mercedes Sayas, and Ariel J. Castro. "Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)." G3: Genes|Genomes|Genetics 10, no. 3 (January 23, 2020): 1113–24. http://dx.doi.org/10.1534/g3.119.400968.
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Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.
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Margaris, Dionisis, Costas Vassilakis, Dimitris Spiliotopoulos, and Stefanos Ougiaroglou. "Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets." Big Data and Cognitive Computing 7, no. 2 (March 24, 2023): 59. http://dx.doi.org/10.3390/bdcc7020059.
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Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered “close” to the user for whom the prediction is being generated have given to the same product or service. In general, “close” users for some user u correspond to users that have rated items similarly to u and these users are termed as “near neighbors”. As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset’s density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method’s efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets.
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Pal, Osim Kumar. "The Quality of Drinkable Water using Machine Learning Techniques." International Journal of Advanced Engineering Research and Science 9, no. 6 (2022): 016–23. http://dx.doi.org/10.22161/ijaers.96.2.
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Predicting potable water quality is more effective for water management and water pollution prevention. Polluted water causes serious waterborne illnesses and poses a threat to human health. Predicting the quality of drinkable water may reduce the incidence of water-related diseases. The latest machine learning approach has shown promising predictive accuracy for water quality. This research uses five different learning algorithms to determine drinking water quality. First, data is gathered from public sources and presented in accordance with World Health Organization (WHO) water quality standards. Several parameters, including hardness, conductivity, pH, organic carbon, solids, and others, are essential for predicting water quality. Second, Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), Deep Neural Network (DNN), and Gaussian Nave Bayes are used to estimate the quality of the drinking water. The conventional laboratory technique for assessing water quality is time-consuming and sometimes costly. The algorithms proposed in this work can predict drinking water quality within a short period of time. ANN has 99 percent height accuracy with a training error of 0.75 percent during the training period. RF has an F1 score of 87.86% and a prediction accuracy of 82.45%. An Artificial Neural Network (ANN) predicted height with an F1 score of 96.51 percent in this study. Using an extended data set could improve how well predictions are made and help stop waterborne diseases in the long run.
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Luo, Zijun, Rui Zeng, and Pan Wang. "Air Quality Prediction Based on Quadratic Prediction Model." Learning & Education 10, no. 5 (March 13, 2022): 52. http://dx.doi.org/10.18282/l-e.v10i5.2669.
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In order to improve the performance of the prediction model of air quality prediction,a secondary prediction mathematical
model is established in this paper.The first is to clean the data and find the potential model relationship between variables through
data mining and correlation methods,so as to establish the limit learning machine model.The model needs to be able to explain
the influence of meteorological index variables on pollutant concentration diffusion to a certain extent.Then,the EML model is
optimized by genetic algorithm,rolling optimization and other methods to reduce noise and make the data as accurate as possible.
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Sandhu, Karansher Singh, Meriem Aoun, Craig F. Morris, and Arron H. Carter. "Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models." Biology 10, no. 7 (July 20, 2021): 689. http://dx.doi.org/10.3390/biology10070689.
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Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year’s dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015–19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45–0.81, 0.29–0.55, and 0.27–0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.
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Iyer, Srinidhi, Simran Kaushik, and Poonam Nandal. "Water Quality Prediction Using Machine Learning." MR International Journal of Engineering and Technology 10, no. 1 (May 11, 2023): 59–62. http://dx.doi.org/10.58864/mrijet.2023.10.1.8.
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This paper shows the use of ML algorithms for the prediction of water quality. The model is trained on Water Quality dataset from Kaggle and it consists of key features such as, pH value, hardness, solids etc. Algorithms used were SVM, Random Forest and Decision Tree. Also, hyperparameter tuning was done in SVM for improving the accuracy using Grid Search technique. The Random Forest algorithm out-performed the others with an accuracy of 68%. Hence, it shows that ML can be used for predicting the quality of water.
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Sajjadian, Mehri, Raymond W. Lam, Roumen Milev, Susan Rotzinger, Benicio N. Frey, Claudio N. Soares, Sagar V. Parikh, et al. "Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis." Psychological Medicine 51, no. 16 (October 12, 2021): 2742–51. http://dx.doi.org/10.1017/s0033291721003871.
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AbstractBackgroundMultiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.MethodsOf 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.ResultsEight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.ConclusionsThe negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.
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Klunder, Jet H., Sofie L. Panneman, Emma Wallace, Ralph de Vries, Karlijn J. Joling, Otto R. Maarsingh, and Hein P. J. van Hout. "Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review." PLOS ONE 17, no. 9 (September 23, 2022): e0275116. http://dx.doi.org/10.1371/journal.pone.0275116.
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Background Identification of community-dwelling older adults at risk of unplanned hospitalizations is of importance to facilitate preventive interventions. Our objective was to review and appraise the methodological quality and predictive performance of prediction models for predicting unplanned hospitalizations in community-dwelling older adults Methods and findings We searched MEDLINE, EMBASE and CINAHL from August 2013 to January 2021. Additionally, we checked references of the identified articles for the inclusion of relevant publications and added studies from two previous reviews that fulfilled the eligibility criteria. We included prospective and retrospective studies with any follow-up period that recruited adults aged 65 and over and developed a prediction model predicting unplanned hospitalizations. We included models with at least one (internal or external) validation cohort. The models had to be intended to be used in a primary care setting. Two authors independently assessed studies for inclusion and undertook data extraction following recommendations of the CHARMS checklist, while quality assessment was performed using the PROBAST tool. A total of 19 studies met the inclusion criteria. Prediction horizon ranged from 4.5 months to 4 years. Most frequently included variables were specific medical diagnoses (n = 11), previous hospital admission (n = 11), age (n = 11), and sex or gender (n = 8). Predictive performance in terms of area under the curve ranged from 0.61 to 0.78. Models developed to predict potentially preventable hospitalizations tended to have better predictive performance than models predicting hospitalizations in general. Overall, risk of bias was high, predominantly in the analysis domain. Conclusions Models developed to predict preventable hospitalizations tended to have better predictive performance than models to predict all-cause hospitalizations. There is however substantial room for improvement on the reporting and analysis of studies. We recommend better adherence to the TRIPOD guidelines.
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GAO, XIN, JINBO XU, SHUAI CHENG LI, and MING LI. "PREDICTING LOCAL QUALITY OF A SEQUENCE–STRUCTURE ALIGNMENT." Journal of Bioinformatics and Computational Biology 07, no. 05 (October 2009): 789–810. http://dx.doi.org/10.1142/s0219720009004345.
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Although protein structure prediction has made great progress in recent years, a protein model derived from automated prediction methods is subject to various errors. As methods for structure prediction develop, a continuing problem is how to evaluate the quality of a protein model, especially to identify some well-predicted regions of the model, so that the structural biology community can benefit from the automated structure prediction. It is also important to identify badly-predicted regions in a model so that some refinement measurements can be applied to it. We present two complementary techniques, FragQA and PosQA, to accurately predict local quality of a sequence–structure (i.e. sequence–template) alignment generated by comparative modeling (i.e. homology modeling and threading). FragQA and PosQA predict local quality from two different perspectives. Different from existing methods, FragQA directly predicts cRMSD between a continuously aligned fragment determined by an alignment and the corresponding fragment in the native structure, while PosQA predicts the quality of an individual aligned position. Both FragQA and PosQA use an SVM (Support Vector Machine) regression method to perform prediction using similar information extracted from a single given alignment. Experimental results demonstrate that FragQA performs well on predicting local fragment quality, and PosQA outperforms two top-notch methods, ProQres and ProQprof. Our results indicate that (1) local quality can be predicted well; (2) local sequence evolutionary information (i.e. sequence similarity) is the major factor in predicting local quality; and (3) structural information such as solvent accessibility and secondary structure helps to improve the prediction performance.
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Aldhyani, Theyazn H. H., Mohammed Al-Yaari, Hasan Alkahtani, and Mashael Maashi. "Water Quality Prediction Using Artificial Intelligence Algorithms." Applied Bionics and Biomechanics 2020 (December 29, 2020): 1–12. http://dx.doi.org/10.1155/2020/6659314.
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During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K -nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient ( RNARNET = 96.17 % and RLSTM = 94.21 % ). This kind of promising research can contribute significantly to water management.
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Sankhye, Sidharth, and Guiping Hu. "Machine Learning Methods for Quality Prediction in Production." Logistics 4, no. 4 (December 21, 2020): 35. http://dx.doi.org/10.3390/logistics4040035.
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The rising popularity of smart factories and Industry 4.0 has made it possible to collect large amounts of data from production stages. Thus, supervised machine learning methods such as classification can viably predict product compliance quality using manufacturing data collected during production. Elimination of uncertainty via accurate prediction provides significant benefits at any stage in a supply chain. Thus, early knowledge of product batch quality can save costs associated with recalls, packaging, and transportation. While there has been thorough research on predicting the quality of specific manufacturing processes, the adoption of classification methods to predict the overall compliance of production batches has not been extensively investigated. This paper aims to design machine learning based classification methods for quality compliance and validate the models via case study of a multi-model appliance production line. The proposed classification model could achieve an accuracy of 0.99 and Cohen’s Kappa of 0.91 for the compliance quality of unit batches. Thus, the proposed method would enable implementation of a predictive model for compliance quality. The case study also highlights the importance of feature construction and dataset knowledge in training classification models.
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Lee, Kyunghwa, Jinhyeok Yu, Sojin Lee, Mieun Park, Hun Hong, Soon Young Park, Myungje Choi, et al. "Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues." Geoscientific Model Development 13, no. 3 (March 10, 2020): 1055–73. http://dx.doi.org/10.5194/gmd-13-1055-2020.
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Abstract. For the purpose of providing reliable and robust air quality predictions, an air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multiscale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic aerosols (OAs) and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O3 and SO2). In this sense, this new air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM10 (index of agreement IOA =0.60; mean bias MB =-13.54) and PM2.5 (IOA =0.71; MB =-2.43) as compared to the BASE RUN for PM10 (IOA =0.51; MB =-27.18) and PM2.5 (IOA =0.67; MB =-9.9). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of −0.27 (BASE RUN) was greatly enhanced to −0.036 (DA RUN). In the cases of O3 and SO2, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO2 mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO3 and peroxyacetyl nitrates – PANs – in the ground-observed NO2 mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLHs) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free-parameter scheme, dust episode prediction and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the 3D variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean air quality prediction system (KAQPS).
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Zhang, Fuhao, Wenbo Shi, Jian Zhang, Min Zeng, Min Li, and Lukasz Kurgan. "PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection." Bioinformatics 36, Supplement_2 (December 2020): i735—i744. http://dx.doi.org/10.1093/bioinformatics/btaa806.
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Abstract Motivation Knowledge of protein-binding residues (PBRs) improves our understanding of protein−protein interactions, contributes to the prediction of protein functions and facilitates protein−protein docking calculations. While many sequence-based predictors of PBRs were published, they offer modest levels of predictive performance and most of them cross-predict residues that interact with other partners. One unexplored option to improve the predictive quality is to design consensus predictors that combine results produced by multiple methods. Results We empirically investigate predictive performance of a representative set of nine predictors of PBRs. We report substantial differences in predictive quality when these methods are used to predict individual proteins, which contrast with the dataset-level benchmarks that are currently used to assess and compare these methods. Our analysis provides new insights for the cross-prediction concern, dissects complementarity between predictors and demonstrates that predictive performance of the top methods depends on unique characteristics of the input protein sequence. Using these insights, we developed PROBselect, first-of-its-kind consensus predictor of PBRs. Our design is based on the dynamic predictor selection at the protein level, where the selection relies on regression-based models that accurately estimate predictive performance of selected predictors directly from the sequence. Empirical assessment using a low-similarity test dataset shows that PROBselect provides significantly improved predictive quality when compared with the current predictors and conventional consensuses that combine residue-level predictions. Moreover, PROBselect informs the users about the expected predictive quality for the prediction generated from a given input protein. Availability and implementation PROBselect is available at http://bioinformatics.csu.edu.cn/PROBselect/home/index. Supplementary information Supplementary data are available at Bioinformatics online.
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Horiguchi, Yuji, Yukino Baba, Hisashi Kashima, Masahito Suzuki, Hiroki Kayahara, and Jun Maeno. "Predicting Fuel Consumption and Flight Delays for Low-Cost Airlines." Proceedings of the AAAI Conference on Artificial Intelligence 31, no. 2 (February 11, 2017): 4686–93. http://dx.doi.org/10.1609/aaai.v31i2.19095.
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Low-cost airlines (LCAs) represent a new category of airlines that provides low-fare flights. The rise and growth of LCAs has intensified the price competition among airlines, and LCAs require continuous efforts to reduce their operating costs to lower flight prices; however, LCA passengers still demand high-quality services. A common measure of airline service quality is on-time departure performance. Because LCAs apply efficient aircraft utilization and the time between flights is likely to be small, additional effort is required to avoid flight delays and improve their service quality. In this paper, we apply state-of-the-art predictive modeling approaches to real airline datasets and investigate the feasibility of machine learning methods for cost reduction and service quality improvement in LCAs. We address two prediction problems: fuel consumption prediction and flight delay prediction. We train predictive models using flight and passenger information, and our experiment results show that our regression model predicts the amount of fuel consumption more accurately than flight dispatchers, and our binary classifier achieves an area under the ROC curve (AUC) of 0.75 for predicting a delay of a specific flight route.
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Li, Pengfei, Tong Zhang, and Yantao Jin. "A Spatio-Temporal Graph Convolutional Network for Air Quality Prediction." Sustainability 15, no. 9 (May 6, 2023): 7624. http://dx.doi.org/10.3390/su15097624.
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Air pollution is a pressing issue that poses significant threats to human health and the ecological environment. The accurate prediction of air quality is crucial to enable management authorities and vulnerable populations to take measures to minimize their exposure to hazardous pollutants. Although many methods have been developed to predict air quality data, the spatio-temporal correlation of air quality data is complex and nonstationary, which makes air quality prediction still challenging. To address this, we propose a novel spatio-temporal neural network, GCNInformer, that combines the graph convolution network with Informer to predict air quality data. GCNInformer incorporates information about the spatial correlations among different monitoring sites through GCN layers and acquires both short-term and long-term temporal information in air quality data through Informer layers. Moreover, GCNInformer uses MLP layers to learn low-dimensional representations from meteorological and air quality data. These designs give GCNInformer the ability to capture the complex and nonstationary relationships between air pollutants and their surrounding environment, allowing for more accurate predictions. The experimental results demonstrate that GCNInformer outperforms other methods in predicting both short-term and long-term air quality data. Thus, the use of GCNInformer can provide useful information for air pollutant prevention and management, which can greatly improve public health by alerting individuals and communities to potential air quality hazards.
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Saiyed, I. M., P. R. Bullock, H. D. Sapirstein, G. J. Finlay, and C. K. Jarvis. "Thermal time models for estimating wheat phenological development and weather-based relationships to wheat quality." Canadian Journal of Plant Science 89, no. 3 (May 1, 2009): 429–39. http://dx.doi.org/10.4141/cjps07114.
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Accurate prediction of crop phenology is a key requirement for crop development models. The prediction of spring wheat yield and quality from meteorological data can be improved by quantifying heat and moisture conditions during specified phenological phases; therefore, accurate prediction of phenological development is important for estimating weather impacts on wheat quality. The objective of this study was to test the accuracy of biometeorological time (BMT), growing degree days (GDD), and physiological days (Pdays) for prediction of wheat phenological stages and impacts of growing season weather during those stages on wheat bread-making quality. Observed crop phenological stages and detailed weather data across 17 site-years in western Canada for six hard spring wheat varieties were collected to assess BMT, GDD and Pdays. Biometeorological time was most consistent for predicting the length of the seeding to jointing and seeding to anthesis growth stages and second most consistent behind GDD for predicting seeding to soft dough and seeding to maturity. The ability of the BMT and GDD models to predict calendar days to anthesis and maturity were further tested using field data from 166 farms across western Canada. Both GDD and BMT models were effective for predicting time from seeding to anthesis (R2 = 0.84 and 0.90, respectively) and seeding to maturity (R2 = 0.62 and 0.66, respectively). BMT- and GDD-predicted wheat growth phases were used to calculate modeled crop water use by growth period for producer fields. Crop water use is significantly correlated to key bread-making quality parameters of flour protein, farinograph dough development time and farinograph stability. Biometeorological time predicted water use was more highly correlated to these quality parameters than GDD predictions. Accordingly, the BMT scale is recommended for estimation of wheat phenological development especially for modeling weather impacts on wheat end-use quality.Key words: Spring wheat, phenological development, biometeorological time, growing degree day, physiological day, wheat quality
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OGAWA, Takahiro, Akira TANAKA, and Miki HASEYAMA. "Wiener-Based Inpainting Quality Prediction." IEICE Transactions on Information and Systems E100.D, no. 10 (2017): 2614–26. http://dx.doi.org/10.1587/transinf.2017edp7058.
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Rodriguez-Fernandez, Nereida, Iria Santos, Alvaro Torrente-Patiño, and Adrian Carballal. "Digital Image Quality Prediction System." Proceedings 54, no. 1 (August 19, 2020): 15. http://dx.doi.org/10.3390/proceedings2020054015.
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“A picture is worth a thousand words.” Based on this well-known adage, we can say that images are important in our society, and increasingly so. Currently, the Internet is the main channel of socialization and marketing, where we seek to communicate in the most efficient way possible. People receive a large amount of information daily and that is where the need to attract attention with quality content and good presentation arises. Social networks, for example, are becoming more visual every day. Only on Facebook can you see that the success of a publication increases up to 180% if it is accompanied by an image. That is why it is not surprising that platforms such as Pinterest and Instagram have grown so much, and have positioned themselves thanks to their power to communicate with images. In a world where more and more relationships and transactions are made through computer applications, many decisions are made based on the quality, aesthetic value or impact of digital images. In the present work, a quality prediction system for digital images was developed, trained from the quality perception of a group of humans.
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Shaw, S. R., C. R. Laughman, S. B. Leeb, and R. F. Lepard. "A power quality prediction system." IEEE Transactions on Industrial Electronics 47, no. 3 (June 2000): 511–17. http://dx.doi.org/10.1109/41.847890.
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Summa, Lori L. "Diagenesis and reservoir quality prediction." Reviews of Geophysics 33 (1995): 87. http://dx.doi.org/10.1029/95rg00739.
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Liu, James (N K. ). "Quality prediction for concrete manufacturing." Automation in Construction 5, no. 6 (March 1997): 491–99. http://dx.doi.org/10.1016/s0926-5805(96)00183-5.
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Kopf, Johannes, Wolf Kienzle, Steven Drucker, and Sing Bing Kang. "Quality prediction for image completion." ACM Transactions on Graphics 31, no. 6 (November 2012): 1–8. http://dx.doi.org/10.1145/2366145.2366150.
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Vijay Anand, M., Chennareddy Sohitha, Galla Neha Saraswathi, and GV Lavanya. "Water quality prediction using CNN." Journal of Physics: Conference Series 2484, no. 1 (May 1, 2023): 012051. http://dx.doi.org/10.1088/1742-6596/2484/1/012051.
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Abstract The interaction of solar radiation with the water level concentration and the elements of the water cause the water to have its characteristic hue. The alteration of the color of the water is reflective of the alteration of the water’s properties and the degree to which it is suitable for use. Due to disasters like floods, tsunami in the last few years and water pollution has been an increasing problem. In world the intake of contaminated water causes 40% of deaths. Drinking unclean water is not safe and in order to reduce the issue to a level of extent, prediction of water quality can be done before consuming. The process used in water plants is based on the parameters pH, turbidity, temperature, hardness etc., of water using filtration and the water quality prediction can also be done using IOT by including both hardware and software. This project mainly comprises the primary level of water prediction using machine learning. Based on the color and quality of water the system predicts whether the given water sample is suitable for drinking or any further use. Tensorflow, Keras and CNN are used to train the model to forecast the water quality prediction. This project is cost-effective and works efficiently and can be used as immediate and initial level of water quality check since image processing tool is used. This model of water quality prediction can be checked using mobile captured and Google earth images of water samples.
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Pintelas, Emmanuel, Meletis Liaskos, Ioannis E. Livieris, Sotiris Kotsiantis, and Panagiotis Pintelas. "Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction." Journal of Imaging 6, no. 6 (May 28, 2020): 37. http://dx.doi.org/10.3390/jimaging6060037.
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Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role. Making decisions on critical issues such as cancer prediction utilizing black box models in order to achieve high prediction accuracy but without provision for any sort of explanation for its prediction, accuracy cannot be considered as sufficient and ethnically acceptable. Reasoning and explanation is essential in order to trust these models and support such critical predictions. Nevertheless, the definition and the validation of the quality of a prediction model’s explanation can be considered in general extremely subjective and unclear. In this work, an accurate and interpretable machine learning framework is proposed, for image classification problems able to make high quality explanations. For this task, it is developed a feature extraction and explanation extraction framework, proposing also three basic general conditions which validate the quality of any model’s prediction explanation for any application domain. The feature extraction framework will extract and create transparent and meaningful high level features for images, while the explanation extraction framework will be responsible for creating good explanations relying on these extracted features and the prediction model’s inner function with respect to the proposed conditions. As a case study application, brain tumor magnetic resonance images were utilized for predicting glioma cancer. Our results demonstrate the efficiency of the proposed model since it managed to achieve sufficient prediction accuracy being also interpretable and explainable in simple human terms.