High Quality Dataset for Machine Learning in the Business Intelligence Domain

Franchina, Luisa; Sergiani, Federico

doi:10.1007/978-3-030-29516-5_31

Cited by 6 publications

(7 citation statements)

References 3 publications

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“…Different scenarios can be compared, such as different numbers of concentrations and replicates, and designs may then be revised accordingly. When an acceptable design has been generated, it can be used to drive liquid handling instruments, such as automated pipette robots (6). After the experiment is performed and analysed, a decision can be made on subsequent experiments e.g.…”

Section: Discussionmentioning

confidence: 99%

“…In the era of data-driven life science, the amounts of data produced are continuously expanding, and artificial intelligence techniques such as machine learning algorithms and constraint programming [1] are seeing adoption for many applications in order to convert the data into actionable insights [2, 3, 4, 5, 6]. While in many applications the primary focus has been to obtain as much data as possible, the importance of having data of high quality cannot be understated [7, 8, 9]. For large-scale biological experiments, many issues related to data quality pertaining to human operations can be effectively reduced or eliminated by using automated setups and robotised equipment [10].…”

Section: Mainmentioning

confidence: 99%

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Designing microplate layouts using artificial intelligence

Rodríguez

Carreras-Puigvert

Spjuth

2022

Preprint

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Microplates are indispensable in large-scale biological experiments but the layout of samples and controls can have a large effect on results. Here we introduce an artificial intelligence based method for designing microplate layouts that reduces unwanted bias and limits the impact of batch effects, leading to more accurate and reliable experimental results. The method relies on constraint programming, and produces effective multiplate layouts for different experimental settings, while at the same time remaining flexible and modifiable to take into account particular laboratory settings. First we discuss the desired properties of effective microplate layouts, which we then implement as a constraint model. We show that our method produces layouts that lead to smaller errors in dose response experiments when estimating EC50/IC50 values, to the point of frequently obtaining smaller errors even when using fewer doses or replicates. We also show how effective layouts lead to more robust results in high-throughput screening experiments. Finally, we make our method easily accessible by providing a suite of tools, an online service (PLAID), a data format to enable automated construction of designs programmatically, and notebooks to evaluate and compare designs aiding decisions on the number of doses and replicates when planning microplate experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Mainmentioning

confidence: 99%

Designing microplate layouts using artificial intelligence

Rodríguez

Carreras-Puigvert

Spjuth

2022

Preprint

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“…Under these circumstances, the use of data-driven tools (commonly known as blackbox models) have been consolidated as a flexible and efficient tool to achieve accurate predictions [15][16][17]. The requirements to construct appropriate models consist of an adequate structure and a healthy dataset (i.e., no empty fields or useless data) [18]. The application of the technology of Internet of Things (IoT) provides a lot of information about the building conditions that can be especially useful to characterize its functions and construct a wide variety of scenarios [19,20].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks

Pensado-Mariño

Garrido²,

Pérez-Iribarren

et al. 2021

Energies

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Accurate forecasting of a building thermal performance can help to optimize its energy consumption. In addition, obtaining the Heat Loss Coefficient (HLC) allows characterizing the thermal envelope of the building under conditions of use. The aim of this work is to study the thermal inertia of a building developing a new methodology based on Long Short-Term Memory (LSTM) neural networks. This approach was applied to the Rectorate building of the University of Basque Country (UPV/EHU), located in the north of Spain. A comparison of different time-lags selected to catch the thermal inertia has been carried out using the CV(RMSE) and the MBE errors, as advised by ASHRAE. The main contribution of this work lies in the analysis of thermal inertia detection and its influence on the thermal behavior of the building, obtaining a model capable of predicting the thermal demand with an error between 12 and 21%. Moreover, the viability of LSTM neural networks to estimate the HLC of an in-use building with an error below 4% was demonstrated.

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“…Indeed, the sequence labelling task for entity extraction runs a supervised learning algorithm to further automatize this task. The learning corpus, including the labelling used, is essential and influences the quality of sequence labelling [15,38].The labelling of the learning corpus is typically performed manually by experts in the field [20,11]. Yet, this task is costly and time-consuming, and it is constrained by the need for such experts, who may not be available.…”

mentioning

confidence: 99%

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confidence: 99%