A Data-Driven Machine Learning Framework Proposal for Selecting Project Management Research Methodologies by Otniel Didraga, Andrei Albu, Viorel Negrut, Diogen Babuc, Ovidiu Dobrican

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Lessons from the Classroom: MEI for Data Scientists by Richard Freedman, Daniel Russo-Batterham

“…The article shows some of the methods we use to help students understand XML as a hierarchical network of elements, how to traverse this network in search of relevant data, and how to harvest XML elements and attributes as tabular data for further analysis. …”
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Data science and automation in the process of theorizing: Machine learning's power of induction in the co-duction cycle. by Daan Kolkman, Gwendolyn K Lee, Arjen van Witteloostuijn

“…Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. …”
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Learning based lower limb joint kinematic estimation using open source IMU data by Benjamin Hur, Sunin Baek, Inseung Kang, Daekyum Kim

“…While deep learning methods avoid sensor drift, extensive calibration, and complex setup procedures, they require substantial data. …”
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Analyzing risk factors and handling imbalanced data for predicting stroke risk using machine learning by Adiwijaya Adiwijaya, Nur Ghaniaviyanto Ramadhan

“…Effective preventive measures are crucial to minimize stroke risk, and using predictive methods based on data analysis from the clinical examination dataset over the last three years (2019-2021), known as the general checkup (GCU) dataset, presents an innovative approach. …”
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Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin by Chandni Thakur, Venkatesh Budamala, KS Kasiviswanathan, Claudia Teutschbein, Bankaru-Swamy Soundharajan

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Data Elements Marketization and Corporate Investment Efficiency: Causal Inference via Double Machine Learning by Yeteng Ma, Zhuo Li, Li He

“…Based on data from Chinese A-share listed companies spanning 2007 to 2023, this study systematically evaluates the impact of data element marketization on corporate investment efficiency using a Double Machine Learning approach. …”
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Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk by Mohamed Chaouch, Omama M. Al-Hamed

“…This paper introduces novel nonparametric supervised learning techniques for classifying massive datasets, addressing key limitations of existing methods in Big and Streaming Data framework. …”
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Utilizing machine learning and digital twin technology for rock parameter estimation from drilling data by Abdullah Khan, Yiming Li, Muhammad Shoaib, Umair Sajjad, Fuxin Rui

“…The integration of the DT technology with numerical methods, including finite element analysis (FEA) and discrete element model (DEM), facilitates real-time monitoring, simulation, and optimization of rock parameters extracted from drilling, which can enhance accuracy and robustness even with limited empirical data, representing a notable solution. …”
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High-Resolution Geochemical Data Mapping With Swin Transformer-Convolution-Based Multisource Geoscience Data Fusion by Ye Yuan, Shuguang Zhou, Jianhua Bian, Jinlin Wang, Wei Han, Jining Yan

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Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data by Md Ariful Islam Mozumder, Tagne Poupi Theodore Armand, Rashadul Islam Sumon, Shah Muhammad Imtiyaj Uddin, Hee-Cheol Kim

“…There is very little research on cat activity and cat disease analysis based on real-time data. Although previous studies have made progress, several key questions still need addressing: What types of data are best suited for accurately detecting activity patterns? …”
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Discovering action insights from large-scale assessment log data using machine learning by Minyoung Yun, Minjeong Jeon, Heyoung Yang

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Electronic Health Data Records for Diabetes Patients Based on Deep Learning Models: A Review by Dalal Hamid, Mohammed Younis

“…The use of deep learning models for analyzing electronic health records (EHR) data in diabetes management has grown significantly. …”
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Deep Learning Model for Feature Extraction and Anomaly Recognition in High-Dimensional Energy Metering Data by Huakun Que, Zetao Jiang, Zhifeng Zhou, Yongsheng He, Xin Liu

“…Objectives: This study aims to develop a deep learning-based method to detect anomalies in high-dimensional energy metering data, overcoming the limitations of existing techniques that struggle with data complexity and lack effective contextual analysis. …”
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Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy by Tamás Ungvári, Döme Szabó, András Győrfi, Zsófia Dankovics, Balázs Kiss, Judit Olajos, Károly Tőkési

“…The aim of this study was to determine whether planning CT scans can predict which patients are more likely to develop lung lesions after treatment. A retrospective analysis of 242 patient records was performed using different machine learning models. …”
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A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data by Shahid Ahmad Wani, SMK Quadri, Mohammad Shuaib Mir, Yonis Gulzar

“…Most methods demonstrated robust capabilities in annotating major cell types and identifying rare cell populations, though naive Bayes was the least effective due to its inherent limitations in handling high-dimensional and interdependent data. This study provides valuable insights into the relative strengths and weaknesses of machine learning methods for single-cell annotation, offering guidance for selecting appropriate techniques in scRNA-seq analyses.…”
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Optimisation of Residual Network Using Data Augmentation and Ensemble Deep Learning for Butterfly Image Classification by Diniati Ruaika, Shofwatul Uyun

“…Convolutional Neural Networks (CNN) have proven effective for image analysis. However, problems arise due to factors such as the model’s quality, unbalanced training data, overfitting, and layers’ complexity. …”
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A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data by Giovanni Ludeno, Giuseppe Esposito, Claudio Lugni, Francesco Soldovieri, Gianluca Gennarelli

“…In the context of sea state monitoring, reconstructing the wave field and estimating the sea state parameters from radar data is a challenging problem. To reach this goal, this paper proposes a fully data-driven, deep learning approach based on a convolutional neural network. …”
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Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning by Sarah Dandou, Kriti Amin, Véronique D’Hondt, Jérôme Solassol, Olivier Dereure, Peter J. Coopman, Ovidiu Radulescu, Holger Fröhlich, Romain M. Larive

“…We used machine learning algorithms and pre-processed genomic data to test whether they could contain useful information to improve the progression-free survival (PFS) prediction. …”
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Predicting tennis match outcomes mid-game using machine learning on psychological and physical data by Boyuan Li, Zihui Deng, Gaurav Gupta, Jinger Li, Yixuan Miao

“…The role of player performance is integral in-game analytics, impacting the interpretation and results of the analysis. Our work presents an AI for Science (AI4Sci) method to use real-time data from each game point to determine essential feature values, formulate and assess the impact of psychological momentum, and employ machine learning methodology on mid-match data for predicting the game’s victor. …”
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