Quality of Data in Machine Learning

Kariluoto, Antti; Pärnänen, Arto; Kultanen, Joni; Soininen, Jukka; Abrahamsson, Pekka

doi:10.1109/qrs-c55045.2021.00040

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…However, refs. [21][22][23] caution against overreliance on these methods, as their accuracy is heavily dependent on the quantity and quality of data used in the training process of the neural nets, which is not always guaranteed since one cannot cover all the driving scenarios in an exhaustive way. Moreover, refs.…”

Section: The Recent Work On Estimating Vehicle Sideslip Anglementioning

confidence: 99%

Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm

Atheupe,

El Mrhasli,

Emabou

et al. 2023

Sensors

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This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach.

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Section: The Recent Work On Estimating Vehicle Sideslip Anglementioning

confidence: 99%

Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm

Atheupe,

El Mrhasli,

Emabou

et al. 2023

Sensors

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“…Effort series forecasting in software estimation is a challenge for ML models [118]. Furthermore, determining the right model for different types of datasets also remains a challenge [119], [120].…”

Section: ) Gap Analysismentioning

confidence: 99%

Software Cost and Effort Estimation: Current Approaches and Future Trends

Rashid,

Shafi,

Ahmad

et al. 2023

IEEE Access

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Software cost and effort estimation is one of the most significant tasks in the area of software engineering. Research conducted in this field has been evolving with new techniques that necessitate periodic comparative analyses. Software project success largely depends on accurate software cost estimation as it gives an idea of the challenges and risks involved in the development. The great diversity of ML and Non-ML techniques has generated a comparison and progressed into the integration of these techniques. Based on varying advantages it has become imperative to work out preferred estimation techniques to improve the project development process. This study aims to present a systematic literature review (SLR) to investigate the trends of the articles published in the recent one and a half decades and to propose a way forward. This systematic literature review has proposed a three-stage approach to plan (Tollgate approach), conduct (Likert type scale), and report the results from five renowned digital libraries. For the selected 52 articles, artificial neural network model (ANN) and constructive cost model (COCOMO) based approaches have been the favored techniques. The mean magnitude of relative error (MMRE) has been the preferred accuracy metric, software engineering, and project management are the most relevant fields, and the promise repository has been identified as the widely accessed database. This review is likely to be of value for the development, cost, and effort estimations.

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“…With more data, the model has a greater opportunity to learn the underlying patterns and relationships in the data, which can lead to better predictions and generalization with new, unseen data, while data quality means that the data are free of errors, noise, and bias, which can improve the accuracy and reliability of machine learning models. Furthermore, data quality is related to how well the features describe the problem [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory

Nogales

Benalcázar

2023

BDCC

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Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensionality of the problem is directly related to the performance of machine learning models. The dimensionality problem can be addressed through feature selection and feature extraction. In this sense, the evaluation of a model with manual feature extraction and automatic feature extraction was proposed. The manual feature extraction was performed using the statistical functions of central tendency, while the automatic extraction was performed by means of a CNN and BiLSTM. These features were also evaluated in classifiers such as Softmax, ANN, and SVM. The best-performing model was the combination of BiLSTM and ANN (BiLSTM-ANN), with an accuracy of 99.9912%.

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Quality of Data in Machine Learning

Cited by 7 publications

References 3 publications

Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm

Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm

Software Cost and Effort Estimation: Current Approaches and Future Trends

Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory

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