Self-supervised feature learning for acoustic data analysis by Ahmet Pala, Anna Oleynik, Ketil Malde, Nils Olav Handegard

Get full text

Applications of Entropy in Data Analysis and Machine Learning: A Review by Salomé A. Sepúlveda-Fontaine, José M. Amigó

Subjects: Get full text

Analysis of Machine Learning Performance in Spatial Interpolation of Rainfall Data by Alexandre E. L. Nobrega, Itamir M. Barroca Filho

“…The accuracy of the models was evaluated using the coefficient of determination, root mean square error, and concordance index, to confirm the effectiveness of these methodologies for maximum daily annual precipitation data. After testing different models for data spatialization and conducting a thorough statistical analysis, we conclude that machine learning models outperformed the Inverse Distance Weighting method, yielding greater variability in accumulated Annual Maximum Daily Precipitation values and improved overall results.…”
Get full text

Information Assisted Dictionary Learning for fMRI Data Analysis by Manuel Morante, Yannis Kopsinis, Sergios Theodoridis, Athanassios Protopapas

Get full text

Automated analysis of high‐content microscopy data with deep learning by Oren Z Kraus, Ben T Grys, Jimmy Ba, Yolanda Chong, Brendan J Frey, Charles Boone, Brenda J Andrews

“…Abstract Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. …”
Get full text

Risks in Work-Integrated Learning: A Data-Driven Analysis by Xiao Xu

“…This study employs advanced data-driven and machine learning techniques to critically assess the integration of Work-Integrated Learning (WIL) into academic programs, with a focus on psychological well-being, financial, and equity and inclusion risks. …”
Get full text

Topological Data Analysis and Graph-Based Learning for Multimodal Recommendation by Khalil Bachiri, Ali Yahyaouy, Maria Malek, Nicoleta Rogovschi

Subjects: Get full text

Exploratory Data Analysis of the Monkeypox Virus Using Machine Learning by Kiran Dhanaji Kale, Pranav More, Prabhdeep Singh

“…The paper proposes the exploratory data analysis (EDA) of Monkeypox disease using machine learning approaches. …”
Get full text

Machine learning of time series data using persistent homology by Takashi Ichinomiya

Get full text

Quantitative Assessment of Data Volume Requirements for Reliable Machine Learning Analysis by Xukuan Xu, Jinghou Bi, Michael Moeckel, Hajo Wiemer, Steffen Ihlenfeldt

“…Applying machine learning (ML) techniques in the context of limited data remains a challenge of practical importance. …”
Get full text

Threat analysis and defense methods of deep-learning-based data theft in data sandbox mode by Hezhong PAN, Peiyi HAN, Xiayu XIANG, Shaoming DUAN, Rongfei ZHUANG, Chuanyi LIU

“…The threat model of deep-learning-based data theft in data sandbox model was analyzed in detail, and the degree of damage and distinguishing characteristics of this attack were quantitatively evaluated both in the data processing stage and the model training stage.Aiming at the attack in the data processing stage, a data leakage prevention method based on model pruning was proposed to reduce the amount of data leakage while ensuring the availability of the original model.Aiming at the attack in model training stage, an attack detection method based on model parameter analysis was proposed to intercept malicious models and prevent data leakage.These two methods do not need to modify or encrypt data, and do not need to manually analyze the training code of deep learning model, so they can be better applied to data theft defense in data sandbox mode.Experimental evaluation shows that the defense method based on model pruning can reduce 73% of data leakage, and the detection method based on model parameter analysis can effectively identify more than 95% of attacks.…”
Get full text

COVID-19 Data Analysis: The Impact of Missing Data Imputation on Supervised Learning Model Performance by Jorge Daniel Mello-Román, Adrián Martínez-Amarilla

Get full text

Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions by Aikaterini Sakagianni, Christina Koufopoulou, Petros Koufopoulos, Sofia Kalantzi, Nikolaos Theodorakis, Maria Nikolaou, Evgenia Paxinou, Dimitris Kalles, Vassilios S. Verykios, Pavlos Myrianthefs, Georgios Feretzakis

Subjects: Get full text

Learning stream plot: classroom activity visualization with daily learning log data by Izumi Horikoshi, Yuko Toyokawa, Kohei Nakamura, Rwitajit Majumdar, Hiroaki Ogata

Get full text

Big data technologies in e-learning by Gyulara A. Mamedova, Lala A. Zeynalova, Rena T. Melikova

Subjects: Get full text

Learning by precedents based on the analysis of the features properties by V. V. Krasnoproshin, V. G. Rodchanka

“…The issue of learning by precedents is studied. A method of learning based on the analysis of the properties of feature combinations and building feature subspaces where classes do not intersect is proposed. …”
Get full text

Applying Machine Learning on Big Data With Apache Spark by Elias Dritsas, Maria Trigka

Subjects: Get full text

Overcoming the Barriers That Obscure the Interlinking and Analysis of Clinical Data Through Harmonization and Incremental Learning by Vasileios C. Pezoulas, Konstantina D. Kourou, Fanis Kalatzis, Themis P. Exarchos, Evi Zampeli, Saviana Gandolfo, Andreas Goules, Chiara Baldini, Fotini Skopouli, Salvatore De Vita, Athanasios G. Tzioufas, Dimitrios I. Fotiadis

Get full text

Optimization of Quantitative Financial Data Analysis System Based on Deep Learning by Meiyi Liang

“…In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. …”
Get full text

Using topological data analysis and machine learning to predict customer churn by Marcel Sagming, Reolyn Heymann, Maria Vivien Visaya

“…The availability of stored customer data in the form of big data, together with the use of advanced and tuned machine learning (ML) algorithms, have paved the way for the realisation and extraction of useful features associated with customer behaviour and consequently the prediction of customer churn. …”
Get full text