scholarly journals AHMoSe: A knowledge-based visual support system for selecting regression machine learning models

2021 ◽  
Vol 187 ◽  
pp. 106183
Author(s):  
Diego Rojo ◽  
Nyi Nyi Htun ◽  
Denis Parra ◽  
Robin De Croon ◽  
Katrien Verbert
Keyword(s):  
Machine Learning ◽  
Support System ◽  
Learning Models ◽  
Knowledge Based ◽  
Visual Support ◽  
Machine Learning Models

scholarly journals Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations

2021 ◽  
Vol 13 (3) ◽  
pp. 408
Author(s):  
Charles Nickmilder ◽  
Anthony Tedde ◽  
Isabelle Dufrasne ◽  
Françoise Lessire ◽  
Bernard Tychon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Support System ◽  
Cross Validation ◽  
Learning Models ◽  
Data Transformations ◽  
Independent Validation ◽  
Sward Height ◽  
Machine Learning Models ◽  
Sentinel 2

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass expressed as compressed sward height (CSH) from readily accessible meteorological, optical (Sentinel-2) and radar satellite data (Sentinel-1). This study assumed that combining heterogeneous data sources, data transformations and machine learning methods would improve the robustness and the accuracy of the developed models. A total of 72,795 records of CSH with a spatial positioning, collected in 2018 and 2019, were used and aggregated according to a pixel-like pattern. The resulting dataset was split into a training one with 11,625 pixellated records and an independent validation one with 4952 pixellated records. The models were trained with a 19-fold cross-validation. A wide range of performances was observed (with mean root mean square error (RMSE) of cross-validation ranging from 22.84 mm of CSH to infinite-like values), and the four best-performing models were a cubist, a glmnet, a neural network and a random forest. These models had an RMSE of independent validation lower than 20 mm of CSH at the pixel-level. To simulate the behavior of the model in a decision support system, performances at the paddock level were also studied. These were computed according to two scenarios: either the predictions were made at a sub-parcel level and then aggregated, or the data were aggregated at the parcel level and the predictions were made for these aggregated data. The results obtained in this study were more accurate than those found in the literature concerning pasture budgeting and grassland biomass evaluation. The training of the 124 models resulting from the described framework was part of the realization of a decision support system to help farmers in their daily decision making.

scholarly journals Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

2018 ◽  
pp. 55-74 ◽  
Author(s):  
Hao Li ◽  
Zhijian Liu
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Solar Water Heater ◽  
Learning Method ◽  
Optimization Strategy ◽  
Learning Models ◽  
Water Heater ◽  
Solar Water ◽  
Knowledge Based ◽  
Machine Learning Models

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost, and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately, a knowledge-based machine learning method can provide a promising prediction and optimization strategy for the performance of energy systems. In this chapter, the authors show how they utilize the machine learning models trained from a large experimental database to perform precise prediction and optimization on a solar water heater (SWH) system. A new energy system optimization strategy based on a high-throughput screening (HTS) process is proposed. This chapter consists of: 1) comparative studies on varieties of machine learning models (artificial neural networks [ANNs], support vector machine [SVM], and extreme learning machine [ELM]) to predict the performances of SWHs; 2) development of an ANN-based software to assist the quick prediction; and 3) introduction of a computational HTS method to design a high-performance SWH system.

scholarly journals Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 203 ◽  
Author(s):  
Martin Sarnovsky ◽  
Jan Paralic
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Learning Models ◽  
Knowledge Model ◽  
Detection Systems ◽  
Knowledge Based ◽  
Knowledge Models ◽  
Multi Stage ◽  
Machine Learning Approach ◽  
Machine Learning Models

Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensemble models combining a variety of different machine learning models proved to be efficient in this domain. On the other hand, knowledge models have been explicitly designed for the description of the attacks and used in ontology-based IDS. In this paper, we propose a hierarchical IDS based on the original symmetrical combination of machine learning approach with knowledge-based approach to support detection of existing types and severity of new types of network attacks. Multi-stage hierarchical prediction consists of the predictive models able to distinguish the normal connections from the attacks and then to predict the attack classes and concrete attack types. The knowledge model enables to navigate through the attack taxonomy and to select the appropriate model to perform a prediction on the selected level. Designed IDS was evaluated on a widely used KDD 99 dataset and compared to similar approaches.

Implementation of a decision support system for prediction of the total soluble solids of industrial tomato using machine learning models

2022 ◽  
Vol 193 ◽  
pp. 106688
Author(s):  
Christoforos-Nikitas Kasimatis ◽  
Evangelos Psomakelis ◽  
Nikolaos Katsenios ◽  
Giannis Katsenios ◽  
Marilena Papatheodorou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Soluble Solids ◽  
Learning Models ◽  
Total Soluble Solids ◽  
Machine Learning Models

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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