OccupancySense: Context-based indoor occupancy detection &amp; prediction using CatBoost model

Dutta, Joy; Roy, Sarbani

doi:10.1016/j.asoc.2022.108536

Cited by 33 publications

(7 citation statements)

References 15 publications

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“…The LightGBM models are proven to be more accurate and faster than XGBoost. Data fusion enables stronger forecasting accuracy, according to the integration of gradient boosting based categorical attributes supported by CatBoost algorithm (Dutta & Roy, 2022).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed

Rajković

Jeromela

Pezo

et al. 2023

Journal of Food Composition and Analysis

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Section: Introductionmentioning

confidence: 99%

Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed

Rajković

Jeromela

Pezo

et al. 2023

Journal of Food Composition and Analysis

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“…Compared to XGBoost, LightGBM models have demonstrated superior accuracy and faster performance. Data fusion, incorporating gradient boosting with categorical attributes supported by the CatBoost algorithm, enhances forecasting accuracy [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Sunflower Oil Winterization Using the Cellulose-Based Filtration Aid—Investigation of Oil Quality during Industrial Filtration Probe

Nedić Grujin,

Lužaić,

Pezo

et al. 2023

Foods

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Waxes, phospholipids, free fatty acids, peroxides, aldehydes, soap, trace metals and moisture present in crude sunflower oil have a negative effect on the oil quality and are, therefore, removed during the refining process. Waxes crystallizing at low temperatures are removed during winterization by cooling and filtration. Waxes have poor filtration characteristics and an industrial filtration process must be enhanced by the use of filtration aids, which improve filter cake structure and properties, and consequently prolong the filtration cycle. Today, traditional filtration aids (diatomite, perlite, etc.) being used in the industry are frequently replaced by cellulose-based aids. The aim of this study is to examine the effect of oil filtration assisted by two cellulose-based filtration aids on the chemical parameters (wax, moisture, phospholipids, soaps, and fatty acids), oil transparency, carotenoids, and Fe and Cu content of sunflower oil obtained in an industrial horizontal pressure leaf filter. In order to investigate the mentioned parameters, the following techniques were used: gravimetric (waxes and moisture content), spectrophotometric (phospholipids and carotenoid content and oil transparency), volumetric (soaps and free fatty acids content) as well as inductively coupled plasma mass spectrometry (ICP-MS) for Fe and Cu content. An artificial neural network model (ANN) was employed for the prediction of removal efficiency based on the chemical quality, oil transparency, Fe and Cu content in oils before filtration, as well as filtration aid quantity and filtration time. Cellulose-based filtration aids had multiple beneficial effects; on average, 99.20% of waxes, 74.88% of phospholipids, 100% of soap, 7.99% of carotenoids, 16.39% of Fe and 18.33% of Cu were removed.

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“…CatBoost, which takes its name from the combination of the words Category and Boosting [71], is a gradient-boosting application that uses binary decision trees as key estimators [72] and has been successfully used in both regression and classification problems [45,58,73]. CatBoost, an ensemble-based machine learning algorithm, has a GPU learning algorithm implementation and a CPU scoring algorithm implementation that is significantly faster than other gradient boosting algorithms such as XGB and Light-GBM [74].…”

Section: Catboostmentioning

confidence: 99%

Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

Kabas,

Ercan,

Dinca

2024

Applied Sciences

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Briquetting is a compaction technology that has been used for many years to produce raw materials that are uniform in size and moisture content and are easy to process, transport and store. The physical and chemical properties of the raw material and the briquetting conditions also affect the density and strength of the briquettes. Nonetheless, assessing the quality of briquettes is challenging and extremely expensive, and necessitates lengthy laboratory investigations. In this study, a fast, cost-effective, and simple method using machine learning was used to evaluate the quality characteristics of briquette samples. The deformation energy, one of the most important briquette quality parameters, was predicted by machine learning methods, considering specific compression force, moisture content, compression resistance, briquette density, tumbler index, water resistance, shatter index and compression stress. For this purpose, Random Forest, Extreme Gradient Boosting, and CatBoost methods, which are among the ensemble learning methods, were used. The RMSE, MAE, MAPE, and R2 metrics were used to evaluate the models. With respect to the training data, the model created using the Extreme Gradient Boosting method was successful on all the metrics. However, for test data, the best RMSE (15.69), MAPE (0.0146), and R2 (0.9715) were obtained from the model established with the CatBoost method. The best MAE (10.63) was obtained from the model established with the Random Forest method. The metric results and the graphs obtained from the prediction values of the models revealed that machine learning methods were successfully able to predict briquette deformation energy.

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OccupancySense: Context-based indoor occupancy detection & prediction using CatBoost model

Cited by 33 publications

References 15 publications

Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed

Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed

Sunflower Oil Winterization Using the Cellulose-Based Filtration Aid—Investigation of Oil Quality during Industrial Filtration Probe

Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

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