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Vincent Margot and George Luta
Interpretability is becoming increasingly important for predictive model analysis. Unfortunately, as remarked by many authors, there is still no consensus regarding this notion. The goal of this paper is to propose the definition of a score that allows f...
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Anne-Sophie Nicolas, Michel Ducher, Laurent Bourguignon, Virginie Dauphinot and Pierre Krolak-Salmon
The evolution of functional autonomy loss leads to institutionalization of people affected by Alzheimer?s disease (AD), to an alteration of their quality of life and that of their caregivers. To predict loss of functional autonomy could optimize preventi...
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Shaw-Hwa Lo and Yiqiao Yin
The field of explainable artificial intelligence (XAI) aims to build explainable and interpretable machine learning (or deep learning) methods without sacrificing prediction performance. Convolutional neural networks (CNNs) have been successful in making...
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Chien-Min Kang, Ming-Chieh Wang and Lin Lin
In response to relatively little evidence on the determinants of the financial distress in cooperative financial institutions (e.g., Credit Unions), this paper proposes a distress indicator of Merton Distance to default (Merton DD), which was constructed...
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Maryam Badar and Marco Fisichella
Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, et...
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Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employ...
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Joan D. Gonzalez-Franco, Jorge E. Preciado-Velasco, Jose E. Lozano-Rizk, Raul Rivera-Rodriguez, Jorge Torres-Rodriguez and Miguel A. Alonso-Arevalo
Improving the quality of service (QoS) and meeting service level agreements (SLAs) are critical objectives in next-generation networks. This article presents a study on applying supervised learning (SL) algorithms in a 5G/B5G service dataset after being ...
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Vincent Schilling, Peter Beyerlein and Jeremy Chien
The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients? outcomes an...
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Qiang Zhu, Zhong Wang, Yunfeng Dou and Jian Zhou
A conversion method based on the inversion of Mel frequency cepstral coefficient (MFCC) features was proposed to convert whispered speech into normal speech. First, the MFCC features of whispered speech and normal speech were extracted and a matching rel...
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Sumayh S. Aljameel
Early diagnosis of thyroid cancer can reduce mortality, and can decrease the risk of recurrence, side effects, or the need for lengthy surgery. In this study, an explainable artificial neural network (EANN) model was developed to distinguish between mali...
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Lena A. Hofmann, Steffen Lau and Johannes Kirchebner
Linear statistical methods may not be suited to the understanding of psychiatric phenomena such as aggression due to their complexity and multifactorial origins. Here, the application of machine learning (ML) algorithms offers the possibility of analyzin...
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Denis Vrazhnov, Anastasia Knyazkova, Maria Konnikova, Oleg Shevelev, Ivan Razumov, Evgeny Zavjalov, Yury Kistenev, Alexander Shkurinov and Olga Cherkasova
In this research, an experimental U87 glioblastoma small animal model was studied. The association between glioblastoma stages and the spectral patterns of mouse blood serum measured in the terahertz range was analyzed by terahertz time-domain spectrosco...
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Chaitanya Joshi, Sophie Curtis-Ham, Clayton D?Ath and Deane Searle
A literature review of the important trends in predictive crime modeling and the existing measures of accuracy was undertaken. It highlighted the need for a robust, comprehensive and independent evaluation and the need to include complementary measures f...
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Guizhe Song and Degen Huang
The massive amount of data generated by social media present a unique opportunity for disaster analysis. As a leading social platform, Twitter generates over 500 million Tweets each day. Due to its real-time characteristic, more agencies employ Twitter t...
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Dian Yu and Shouqian Sun
Subject-independent emotion recognition based on physiological signals has become a research hotspot. Previous research has proved that electrodermal activity (EDA) signals are an effective data resource for emotion recognition. Benefiting from their gre...
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Datao Xu, Yang Song, Yao Meng, Bíró István and Yaodong Gu
Firefighters require a high level of physical fitness to meet the demands of their job. The correlations and contributions of individual physical health parameters to the tasks of firefighting would enable firefighters to focus on the effects of specific...
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Che-Hao Chang, Jason Lin, Jia-Wei Chang, Yu-Shun Huang, Ming-Hsin Lai and Yen-Jen Chang
Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybri...
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Polona Tominc, Dijana Ore?ki, Vesna Cancer and Maja Ro?man
Background: This article delves into an in-depth analysis of the statistically significant differences in AI support levels for project management between SMEs and large enterprises. The research was conducted based on a comprehensive survey encompassing...
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Konstantinos Psychogyios, Andreas Papadakis, Stavroula Bourou, Nikolaos Nikolaou, Apostolos Maniatis and Theodore Zahariadis
The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individual...
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Anibal Pedraza, Lucia Gonzalez, Oscar Deniz and Gloria Bueno
HER2 overexpression is a prognostic and predictive factor observed in about 15% to 20% of breast cancer cases. The assessment of its expression directly affects the selection of treatment and prognosis. The measurement of HER2 status is performed by an e...
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