Pulmonary fibrosis is a progressive disease of the lungs which usually gets worse over time. Once this disease damages the lungs, it cannot be cured totally, but early detection and proper diagnosis can help to keep t...
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ISBN:
(纸本)9798350333046
Pulmonary fibrosis is a progressive disease of the lungs which usually gets worse over time. Once this disease damages the lungs, it cannot be cured totally, but early detection and proper diagnosis can help to keep the disease in control. The Kaggle competition entitled "OSIC Pulmonary Fibrosis Progression Predict lung function decline" ran from July to September 2020 with the goal of early detection of the disease. Our approach achieved a Laplace Log Likelihood score of -6.8590 which was within the bronze medal band. The Kaggle dataset contained CT scans and anonymized demographic and clinical data from multiple patient visits, such as spirometry forced vital capacity (FVC), for 176 unique patients. In our method we predict FVC and a confidence measure using a sigmoid equation. This equation is extracted via a novel transformation using only three of the given parameters. In this way we created a simple but accurate model for the prediction of lung function decline.
This paper covers different methods to evaluate the power consumption of several conveyor belt systems (CBSs) used in the Turkish Mining Industry (TMI). Based on each CBS's operational features, the power consumpt...
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This paper covers different methods to evaluate the power consumption of several conveyor belt systems (CBSs) used in the Turkish Mining Industry (TMI). Based on each CBS's operational features, the power consumption (P-c, kW) was measured directly on motorised head-pulleys. The P-c was investigated through several conventional, statistical, and machine learning methods. This study shows that the DIN 22,101 could be the most convenient conventional method for the investigated CBSs. On the other hand, based on the nonlinear regression (NLR) and genetic expression programming (GEP) models, two new approaches were suggested for the design and optimisation of the P-c.
The weather is the state of atmosphere that can be described with various categorical and quantitative parameters. A lot of everyday operations (ex: logistics) depend on these parameters and meteorologists all over th...
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Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbi...
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Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbial activities, pollutants leaching and energy consumption during thermal treatment. Traditional moisture content measurement methods are time-consuming, cumbersome and destructive to samples. Therefore, a rapid and nondestructive method for determining the moisture content of solid waste has become a key technology. In this work, an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multiple machine learning methods was developed to predict the moisture content of multi-source solid waste (textile, paper, leather and wood waste). A combined model was proposed for moisture content regression prediction, and the applicability of 20 combinations of five spectral preprocessing methods and four regression algorithms were discussed to further improve the modeling accuracy. Furthermore, the prediction result based on the water-band spectra was compared with the prediction result based on the full-band spectra. The result showed that the combination model can efficiently predict the moisture content of multi-source solid waste, and the R-2 values of the validation and test datasets and the root mean square error for the moisture prediction reached 0.9604, 0.9660, and 3.80, respectively after the hyperparameter optimization. The excellent performance indicated that the proposed combined models can rapidly and accurately measure the moisture content of solid waste, which is significant for the existing waste characterization scheme, and for the further real-time monitoring and management of solid waste treatment and disposal process.
Crop yield prediction is a crucial aspect of agricultural planning and decision-making. This study utilizes a Kaggle dataset featuring State, Year, Season, Crop, Area, Production, etc., employing extensive data prepro...
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In the specialized literature, researchers can find a large number of proposals for solving regression problems that come from different research areas. However, researchers tend to use only proposals from the area in...
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In the specialized literature, researchers can find a large number of proposals for solving regression problems that come from different research areas. However, researchers tend to use only proposals from the area in which they are experts. This paper analyses the performance of a large number of the available regression algorithms from some of the most known and widely used software tools in order to help non-expert users from other areas to properly solve their own regression problems and to help specialized researchers developing well-founded future proposals by properly comparing and identifying algorithms that will enable them to focus on significant further developments. To sum up, we have analyzed 164 algorithms that come from 14 main different families available in 6 software tools (Neural Networks, Support Vector Machines, regression Trees, Rule-Based Methods, Stacking, Random Forests, Model trees, Generalized Linear Models, Nearest Neighbor methods, Partial Least Squares and Principal Component regression, Multivariate Adaptive regression Splines, Bagging, Boosting, and other methods) over 52 datasets. A new measure has also been proposed to show the goodness of each algorithm with respect to the others. Finally, a statistical analysis by non-parametric tests has been carried out over all the algorithms and on the best 30 algorithms, both with and without bagging. Results show that the algorithms from Random Forest, Model Tree and Support Vector Machine families get the best positions in the rankings obtained by the statistical tests when bagging is not considered. In addition, the use of bagging techniques significantly improves the performance of the algorithms without excessive increase in computational times.
In a variety of problem domains, including sales, finance, healthcare, the stock market, etc., forecasting techniques are applied. The supermarket sales forecast aids in increasing sales in a professional setting. The...
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It is impossible to imagine humans surviving without oxygen. Due to the recent developments in almost all aspects of civilization, air quality has continually decreased. The day-to-day industrial, transportation, and ...
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In most developing countries like India, Agriculture is seen as one of the most widely followed habitations and contributes majorly to the country’s economy. Agriculture provides the primary source of food, income, l...
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In most developing countries like India, Agriculture is seen as one of the most widely followed habitations and contributes majorly to the country’s economy. Agriculture provides the primary source of food, income, livelihood and employment to the majority of rural populations in India. Many crops are destroyed every year due to a lack of technical knowledge and unpredictable weather patterns such as temperature, rainfall, and other atmospheric parameters, which play a massive role in deciding the crop yield and profit. Therefore, choosing the right crop to grow and enhancing crop yield is an essential aspect of improving real-life farming scenarios. One of the motives is to collect and integrate the agricultural data from specific regions that may be used to analyse the optimal crop and estimate the crop yield. This script is novel by using simple crop, soil and weather parameters like crop, the area under cultivation, nitrogen, phosphorus and potassium content of the soil, season, average rainfall and temperature of a district in Karnataka, India. The user can predict the most suitable crop and its estimated yield for a chosen year. This model uses primary classification, techniques like the random forest, k-NN, logistic regression, decision tree, XGBoost, SVM and gradient boosting classifier for determining the most suitable crop and regression algorithms like Linear regression, k-NN, DBSCAN, Random Forest and ANN algorithm to estimate the yield of the most optimal crop identified earlier. The algorithm that has the least mean error is chosen for prediction and estimation and thus gives better results than the particular machine learning algorithm domain. There is a web interface for ease of use for end-users. Therefore, this project assists the farmers in choosing the suitable crop that can be grown in a particular region during a specific season or specific period and estimate its yield and predict if the recommended crop is profitable. Hence this project helps
In order to improve the modeling accuracy,this paper investigates the problem of the wind power modeling method based on the data augmentation technology and stacking integrated learning ***,since the raw wind power d...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In order to improve the modeling accuracy,this paper investigates the problem of the wind power modeling method based on the data augmentation technology and stacking integrated learning ***,since the raw wind power data are scarce and unevenly distributed,various flexible data augmentation techniques including jittering,scaling,magnitude warping,and time warping are used to transform the original data to expand the ***,in order to build a stacking integrated model of wind power,the Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),and Convolutional Neural Network(CNN)based on time domain are selected as primary learners,and a stable Support Vector regression(SVR) algorithm is used as a secondary ***,the ablation and comparative experiments are conducted according to the measured data of a wind farm in *** experimental results show that the data augmentation technique significantly improves the robustness of each learner,and the proposed stacking integrated model is more effective compared with the single learner model,whose modeling accuracy has been effectively improved.
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