Search model of customer's optimal route in the store based on algorithm of machine learning A*

Narushynska, Olga; Teslyuk, Vasyl; Vovchuk, Bohdan-Dmytro

doi:10.1109/stc-csit.2017.8098787

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…of artificial intelligence [9,10] increases the efficiency of the procedure for developing or designing new materials. Traditional approaches allow obtaining all necessary information about the material properties [7], and the usage of powerful [11], modern ML algorithms [12][13] makes this process easier, shorter and cheaper. This is due to solving prediction and regression tasks, classification or clustering on a small experimental data sample and extrapolation of the results to the new material.…”

Section: на основі експериментально встановлених даних щодо параметріmentioning

confidence: 99%

Development of machine learning method of titanium alloy properties identification in additive technologies

Tkachenko¹,

Duriagina²,

Lemishka³

et al. 2018

EEJET

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Section: на основі експериментально встановлених даних щодо параметріmentioning

confidence: 99%

Development of machine learning method of titanium alloy properties identification in additive technologies

Tkachenko¹,

Duriagina²,

Lemishka³

et al. 2018

EEJET

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“…Manual labeling is critical in training AI models, especially when automated techniques are insufficient. Understanding how data sorting strategies impact the quality of manual labeling is essential in order to optimize the labeling process and ensure that the labeled data captures the actual underlying characteristics of the short texts [10]. This knowledge can lead to improved training data, subsequently enhancing the performance of hierarchical classification models [11].…”

Section: Introductionmentioning

confidence: 99%

Data Sorting Influence on Short Text Manual Labeling Quality for Hierarchical Classification

Narushynska,

Teslyuk,

Doroshenko

et al. 2024

BDCC

Self Cite

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The precise categorization of brief texts holds significant importance in various applications within the ever-changing realm of artificial intelligence (AI) and natural language processing (NLP). Short texts are everywhere in the digital world, from social media updates to customer reviews and feedback. Nevertheless, short texts’ limited length and context pose unique challenges for accurate classification. This research article delves into the influence of data sorting methods on the quality of manual labeling in hierarchical classification, with a particular focus on short texts. The study is set against the backdrop of the increasing reliance on manual labeling in AI and NLP, highlighting its significance in the accuracy of hierarchical text classification. Methodologically, the study integrates AI, notably zero-shot learning, with human annotation processes to examine the efficacy of various data-sorting strategies. The results demonstrate how different sorting approaches impact the accuracy and consistency of manual labeling, a critical aspect of creating high-quality datasets for NLP applications. The study’s findings reveal a significant time efficiency improvement in terms of labeling, where ordered manual labeling required 760 min per 1000 samples, compared to 800 min for traditional manual labeling, illustrating the practical benefits of optimized data sorting strategies. Comparatively, ordered manual labeling achieved the highest mean accuracy rates across all hierarchical levels, with figures reaching up to 99% for segments, 95% for families, 92% for classes, and 90% for bricks, underscoring the efficiency of structured data sorting. It offers valuable insights and practical guidelines for improving labeling quality in hierarchical classification tasks, thereby advancing the precision of text analysis in AI-driven research. This abstract encapsulates the article’s background, methods, results, and conclusions, providing a comprehensive yet succinct study overview.

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Machine Learning Aspects and its Applications Towards Different Research Areas

Kumar

Kaur

Singh

2020

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM)

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Search model of customer's optimal route in the store based on algorithm of machine learning A*

Cited by 4 publications

References 6 publications

Development of machine learning method of titanium alloy properties identification in additive technologies

Development of machine learning method of titanium alloy properties identification in additive technologies

Data Sorting Influence on Short Text Manual Labeling Quality for Hierarchical Classification

Machine Learning Aspects and its Applications Towards Different Research Areas

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