Learning Analytics for Bridging the Skills Gap: A Data-Driven Study of Undergraduate Aspirations and Skills Awareness for Career Preparedness by Joel Weijia Lai, Lei Zhang, Chun Chau Sze, Fun Siong Lim

“…Using machine learning methods such as hierarchical clustering and <i>k</i>-nearest neighbors for classification, coupled with non-parametric statistical analysis such as the Mann–Whitney <i>U</i> and Chi-squared (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula>) tests to understand students’ perceptions of their career preparedness, the findings from this study provide valuable insights into how higher education institutions can prepare students for the workforce and highlight areas where improvements are needed to better support students in achieving their career goals.…”
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An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift. by Yansong Liu, Shuang Wang, He Sui, Li Zhu

“…Learning nonstationary data streams for anomaly detection has been well studied in recent years. …”
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Understanding ChatGPT adoption for data analytics learning: A UTAUT perspective among social science students in Oman by Suliman Zakaria Suliman Abdalla

“…However, the tool received lower ratings for tasks such as data pre-processing and cleaning, suggesting some limitations in its effectiveness in these aspects of data analytics learning. …”
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Refining daily precipitation estimates using machine learning and multi-source data in alpine regions with unevenly distributed gauges by Huajin Lei, Hongyi Li, Hongyu Zhao

“…Study focus: Reliable high-spatiotemporal-resolution and long-term precipitation data are critical for agriculture, hydrology, and climate change impact analysis. …”
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Enhanced HER-2 prediction in breast cancer through synergistic integration of deep learning, ultrasound radiomics, and clinical data by Meijuan Hu, Lianying Zhang, Xiao Wang, Xuehua Xiao

“…The LightGBM and ResNet101 models showed high performance, but the combined model achieved the highest AUC values in both training and testing, demonstrating the effectiveness of integrating diverse data sources. The study successfully demonstrates that the fusion of deep learning with Radiomics analysis significantly improves the prediction accuracy of HER-2 status, offering a new strategy for personalized breast cancer treatment and prognostic assessments.…”
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A Study on Using Transfer Learning to Utilize Information From Similar Systems for Data-Driven Condition Diagnosis and Prognosis by Marcel Braig, Peter Zeiler

“…The present study analyzes the effectiveness of transfer learning on similar system applications. A rolling bearing and a filter degradation data set are used to evaluate the diagnostic and prognostic performance. …”
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Machine learning-based prediction of anxiety disorders using blood metabolite and social trait data from the UK Biobank by Annabel Smith, Jack J. Miller, Daniel C. Anthony, Daniel E. Radford-Smith

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Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data by Gulhan Kilicarslan, Dilber Cetintas, Taner Tuncer, Muhammed Yildirim

“…<b>Methods</b>: In this study, a deep learning-based hybrid model using multiphase magnetic resonance imaging (MRI) data is proposed to provide accurate classification of RCC subtypes and to provide a decision support mechanism to radiologists. …”
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Future-Ready Skills Across Big Data Ecosystems: Insights from Machine Learning-Driven Human Resource Analytics by Fatih Gurcan, Beyza Gudek, Gonca Gokce Menekse Dalveren, Mohammad Derawi

“…This study aims to analyze online job postings using machine learning-based, semantic approaches and to identify the expertise roles and competencies required for big data professions. …”
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Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations by Hisham AbouGrad, Lakshmi Sankuru

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Enhancing game outcome prediction in the Chinese basketball league through a machine learning framework based on performance data by Yuhua Zhong

“…Abstract Basketball remains among the most globally popular sports, with its various competitions drawing substantial attention. The analysis and modeling of basketball game data have long been central topics in sports analytics. …”
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Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design by Muhammad Arslan Masood, Samuel Kaski, Tianyu Cui

“…Experiments on Tox21 and ClinTox datasets demonstrate that our approach achieves equivalent toxic compound identification with 50% fewer iterations compared to conventional active learning. Analysis reveals that pretrained BERT representations generate a structured embedding space enabling reliable uncertainty estimation despite limited labeled data, confirmed through Expected Calibration Error measurements. …”
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Predictive model of malignancy probability in pulmonary nodules based on multicenter data by Yuyan Huang, Yong Chen, Fang He, Li Jiang

“…Multiple machine learning classification models were employed for analysis, with the optimal model ultimately selected. …”
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Large Scale Mowing Event Detection on Dense Time Series Data Using Deep Learning Methods and Knowledge Distillation by T. Moumouris, V. Tsironis, A. Psalta, K. Karantzalos

“…This study presents a novel approach for large-scale mowing event frequency detection using dense time series data and deep learning (DL) methods. Leveraging Sentinel-2 and Landsat data, we developed a benchmark dataset of over 1,600 annotated parcels in Greece, capturing mowing events through photo-interpretation and Enhanced Vegetation Index (EVI) analysis. …”
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Mapping sugarcane plantations in Northeast Thailand using multi-temporal data from multi-sensors and machine-learning algorithms by Savittri Ratanopad Suwanlee, Surasak Keawsomsee, Emma Izquierdo-Verdiguier, Jaturong Som-Ard, Alvaro Moreno-Martinez, Vorraveerukorn Veerachit, Jantima Polpinij, Kanokporn Rattanasuteerakul

“…The effectiveness of machine learning algorithms and the limited reference data introduce uncertainness for sugarcane classification. …”
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A stacked learning framework for accurate classification of polycystic ovary syndrome with advanced data balancing and feature selection techniques by Heba M. Emara, Walid El-Shafai, Naglaa F. Soliman, Abeer D. Algarni, Reem Alkanhel, Fathi E. Abd El-Samie

“…The methodology incorporates stacked learning and depends on the Adaptive Synthetic (ADASYN) algorithm, Synthetic Minority Over-sampling Technique (SMOTE), and random oversampling methods for addressing data imbalances. …”
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Identification of Maize Kernel Varieties Using LF-NMR Combined with Image Data: An Explainable Approach Based on Machine Learning by Chunguang Bi, Xinhua Bi, Jinjing Liu, He Chen, Mohan Wang, Helong Yu, Shaozhong Song

“…Before modeling, principal component analysis (PCA) was used to determine the distribution features of the internal components for each maize variety. …”
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Inflow Prediction for Agricultural Reservoirs Using Disaster Prevention Measurement Data: A Comparison of TANK Model and Machine Learning by Bong-Kuk Lee, Joonyoung Choi, Kyoung Jae Lim, Jeongho Han

“…Through analysis of rainfall-inflow relationships, 4-hour moving average rainfall for Baekrok Reservoir and 8-hour moving average rainfall for Nangye Reservoir were selected as optimal input data for the RidgeCV model. …”
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Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub by Thirunavukarasu Balasubramaniam, Wathsala Anupama Mohotti, Kenneth Sabir, Richi Nayak

“…Utilizing remote sensing and climate data, covering 196 farms (and 6885 paddocks) across Australia, we applied several machine learning techniques, including XGBoost, random forest, linear regression, deep neural networks, stacking, and bootstrapping. …”
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Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield by Danial Shamsuddin, Monica F. Danilevicz, Hawlader A. Al-Mamun, Mohammed Bennamoun, David Edwards

“…In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. …”
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