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Frontiers in Psychology, 2023 IntroductionWith the continuous expansion of higher education worldwide, the academic performance of first-generation college students has become an essential topic in the scope of international educational research.
Xiaojing Li, Weitong Liu, Ke Hu
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Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning
Frontiers in Big Data, 2020 Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks.
Andrea Galassi +4 more
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Wavelet-Based Classification of Enhanced Melanoma Skin Lesions through Deep Neural Architectures
Information, 2022 In recent years, skin cancer diagnosis has been aided by the most sophisticated and advanced machine learning algorithms, primarily implemented in the spatial domain.
Premaladha Jayaraman +6 more
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Function-Based Search of Place Using Theoretical, Empirical and Probabilistic Patterns
ISPRS International Journal of Geo-Information, 2019 Searching for places rather than traditional keyword-based search represents significant challenges. The most prevalent method of addressing place-related queries is based on place names but has limited potential due to the vagueness of natural language ...
Emmanuel Papadakis +3 more
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Human-Guided Learning for Probabilistic Logic Models
Frontiers in Robotics and AI, 2018 Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role
Phillip Odom, Sriraam Natarajan
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Pattern discovery and disentanglement on relational datasets
Scientific Reports, 2021 Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled ...
Andrew K. C. Wong +2 more
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Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis [PDF]
arXiv, 2021 Statistical depth is the act of gauging how representative a point is compared to a reference probability measure. The depth allows introducing rankings and orderings to data living in multivariate, or function spaces. Though widely applied and with much experimental success, little theoretical progress has been made in analysing functional depths ...
arxiv
A social work study on the impact of age, gender and residential status on drug addiction [PDF]
Management Science Letters, 2013 During the past few years, there have been growing interests on intellectual capital due to industrial changes on the market. Thus, identifying different ways to create, manage, and evaluate the impact of intellectual capital has remained an open area of
Mohammad Reza Iravani +4 more
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A logic-based relational learning approach to relation extraction: The OntoILPER system [PDF]
Engineering Applications of Artificial Intelligence, Elsevier,
2019, 78, pp.142-157, 2020 Relation Extraction (RE), the task of detecting and characterizing semantic relations between entities in text, has gained much importance in the last two decades, mainly in the biomedical domain. Many papers have been published on Relation Extraction using supervised machine learning techniques.
arxiv +1 more source
Scientific Reports, 2022 During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources.
Cornelius Fritz +2 more
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