The Experts below are selected from a list of 87 Experts worldwide ranked by ideXlab platform
Igor Jordanov - One of the best experts on this subject based on the ideXlab platform.
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Monitoring of cellulose oxidation level by electrokinetic phenomena and Numeric Prediction model
Cellulose, 2020Co-Authors: Anita Tarbuk, Katia Grgić, Emilija Toshikj, Daniel Domović, Dejan Dimitrovski, Vesna Dimova, Igor JordanovAbstract:Cellulose with a low level of oxidation is suitable for producing stable long-lasting materials with high added value, while extensively oxidized once is applicable for disposable products. In our previous comprehensive research, the fundamental behavior of the cotton under the action of different oxidants has been explored. Different levels of oxidation, as well as the type of functional groups, have been achieved by properly selected oxidants while controlling their concentration and treatment time. In this research, the electrokinetic ζ-potential of KIO_4 and TEMPO-oxidized cotton and the isoelectric point are measured by the streaming potential method, while the surface charge is calculated from the adsorbed cationic surfactant by the back-titration method. The results of electrokinetic phenomena are compared with the amount of created carboxyl groups determined by the calcium acetate method. The machine learning algorithms Waikato Environment for Knowledge Analysis for regression analysis is employed to develop models that make Numeric Predictions of the ζ-potential values based on the known number of carboxyl groups. The model with the correlation coefficient between the actual and the predicted value of ζ-potential is given for the first time. Graphic abstract
Xu Longqin - One of the best experts on this subject based on the ideXlab platform.
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Numeric Prediction of dissolved oxygen status through two-stage training for classification-driven regression
2020Co-Authors: Guo Pengfei, Liu Han, Liu Shuangyin, Xu LongqinAbstract:Dissolved oxygen of aquaculture is an important measure of the quality of culture environment and how aquatic products have been grown. In the machine learning context, the above measure can be achieved by defining a regression problem, which aims at Numerical Prediction of the dissolved oxygen status. In general, the vast majority of popular machine learning algorithms were designed for undertaking classification tasks. In order to effectively adopt the popular machine learning algorithms for the above-mentioned Numerical Prediction, in this paper, we propose a two-stage training approach that involves transforming a regression problem into a classification problem and then transforming it back to regression problem. In particular, unsupervised discretization of continuous attributes is adopted at the first stage to transform the target (Numeric) attribute into a discrete (nominal) one with several intervals, such that popular machine learning algorithms can be used to predict the interval to which an instance belongs in the setting of a classification task. Furthermore, based on the classification result at the first stage, some of the instances within the predicted interval are selected for training at the second stage towards Numerical Prediction of the target attribute value of each instance. An experimental study is conducted to investigate in general the effectiveness of the popular learning algorithms in the Numerical Prediction task and also analyze how the increase of the number of training instances (selected at the second training stage) can impact on the final Prediction performance. The results show that the adoption of decision tree learning and neural networks lead to better and more stable performance than Naive Bayes, K Nearest Neighbours and Support Vector Machine
Shuangyin Liu - One of the best experts on this subject based on the ideXlab platform.
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ICMLC - Numeric Prediction of Dissolved Oxygen Status Through Two-Stage Training for Classification-Driven Regression
2019 International Conference on Machine Learning and Cybernetics (ICMLC), 2019Co-Authors: Pengfei Guo, Han Liu, Shuangyin LiuAbstract:Dissolved oxygen of aquaculture is an important measure of the quality of culture environment and how aquatic products have been grown. In the machine learning context, the above measure can be achieved by defining a regression problem, which aims at Numerical Prediction of the dissolved oxygen status. In general, the vast majority of popular machine learning algorithms were designed for undertaking classification tasks. In order to effectively adopt the popular machine learning algorithms for the above-mentioned Numerical Prediction, in this paper, we propose a two-stage training approach that involves transforming a regression problem into a classification problem and then transforming it back to regression problem. In particular, unsupervised discretization of continuous attributes is adopted at the first stage to transform the target (Numeric) attribute into a discrete (nominal) one with several intervals, such that popular machine learning algorithms can be used to predict the interval to which an instance belongs in the setting of a classification task. Furthermore, based on the classification result at the first stage, some of the instances within the predicted interval are selected for training at the second stage towards Numerical Prediction of the target attribute value of each instance. An experimental study is conducted to investigate in general the effectiveness of the popular learning algorithms in the Numerical Prediction task and also analyze how the increase of the number of training instances (selected at the second training stage) can impact on the final Prediction performance. The results show that the adoption of decision tree learning and neural networks lead to better and more stable performance than Naive Bayes, K Nearest Neighbours and Support Vector Machine.
Anita Tarbuk - One of the best experts on this subject based on the ideXlab platform.
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Monitoring of cellulose oxidation level by electrokinetic phenomena and Numeric Prediction model
Cellulose, 2020Co-Authors: Anita Tarbuk, Katia Grgić, Emilija Toshikj, Daniel Domović, Dejan Dimitrovski, Vesna Dimova, Igor JordanovAbstract:Cellulose with a low level of oxidation is suitable for producing stable long-lasting materials with high added value, while extensively oxidized once is applicable for disposable products. In our previous comprehensive research, the fundamental behavior of the cotton under the action of different oxidants has been explored. Different levels of oxidation, as well as the type of functional groups, have been achieved by properly selected oxidants while controlling their concentration and treatment time. In this research, the electrokinetic ζ-potential of KIO_4 and TEMPO-oxidized cotton and the isoelectric point are measured by the streaming potential method, while the surface charge is calculated from the adsorbed cationic surfactant by the back-titration method. The results of electrokinetic phenomena are compared with the amount of created carboxyl groups determined by the calcium acetate method. The machine learning algorithms Waikato Environment for Knowledge Analysis for regression analysis is employed to develop models that make Numeric Predictions of the ζ-potential values based on the known number of carboxyl groups. The model with the correlation coefficient between the actual and the predicted value of ζ-potential is given for the first time. Graphic abstract
Witold Pedrycz - One of the best experts on this subject based on the ideXlab platform.
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the modeling and Prediction of time series based on synergy of high order fuzzy cognitive map and fuzzy c means clustering
Knowledge Based Systems, 2014Co-Authors: Jianhua Yang, Xiaodong Liu, Witold PedryczAbstract:The time series Prediction models based on fuzzy set theory have been widely applied to diverse fields such as enrollments, stocks, weather and etc., as they can handle Prediction problem under uncertain circumstances in which data are incomplete or vague. Researchers have presented diverse approaches to support the development of fuzzy time series Prediction models. While the existing approaches exhibit two evident shortcomings: one is that they have low efficiency of development, which is hardly applicable in the Prediction problem involving large-scale time series, and the other is that fuzzy logical relationships mined in an ad hoc way cannot uncover the global characteristics of time series, which reduces accuracy of the resulting model. In this paper, a novel modeling and Prediction approach of time series based on synergy of high-order fuzzy cognitive map (HFCM) and fuzzy c-means clustering is proposed, in which fuzzy c-means clustering algorithm is used to construct information granules, transform original time series into granular time series and generate a structure of HFCM Prediction model in an automatic fashion. Subsequently depending on historical data of time series, the HFCM Prediction model of time series is completely formed by exploiting PSO algorithm to learn all parameters of one. Finally, the developed HFCM Prediction model can realize Numeric Prediction by performing inference in the granular space. Four benchmark time series data sets with different statistical characteristics coming from different areas are applied to validate the feasibility and effectiveness of the proposed modeling approach. The obtained results clearly show the effectiveness of the approach. The developed HFCM Prediction models depend on historical data of time series and is emerged in the form of map, which is simpler, legible and have high-level interpretability. Additionally, the proposed approach also exhibits a clear ability to handle the Prediction problem of large-scale time series.
