Performance Comparison Between Deep Learning Models for Fault Classification in Transmission Lines Using Time Series Data by Nadeem Ahmed Tunio, Mohsin Ali Tunio, Muhammad Amir Raza, Muhammad Faheem, Ashfaque Ahmed Hashmani, Rumaisa Nadeem

“…ABSTRACT Deep learning has become a vital tool for addressing complex challenges in power systems, particularly fault detection and classification in transmission lines. …”
Get full text

Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models. by Yubo Zhao, Mo Chen

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”
Get full text

Comprehensive VR dataset for machine learning: Head- and eye-centred video and positional dataThe Adaptive Mind Datahub by Alexander Kreß, Markus Lappe, Frank Bremmer

“…Researchers can leverage this extensive dataset to develop and refine algorithms requiring comprehensive and annotated video and positional data. By providing a well-organized and detailed dataset, it serves as an invaluable resource for advancing machine learning research in VR and fostering the development of innovative VR technologies.…”
Get full text

A hybrid deep learning framework for short-term load forecasting with improved data cleansing and preprocessing techniques by Muhammad Sajid Iqbal, Muhammad Adnan, Salah Eldeen Gasim Mohamed, Muhammad Tariq

“…Subsequently, a hybrid deep learning model, comprising bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and a fully connected layer is trained using the clean data. …”
Get full text

Leveraging Machine Learning for Pediatric Appendicitis Diagnosis: A Retrospective Study Integrating Clinical, Laboratory, and Imaging Data by Mahdi Navaei, Zohre Doogchi, Fatemeh Gholami, Moein Kermanizadeh Tavakoli

“…Methods A retrospective analysis was conducted on 782 pediatric patients from the Regensburg Pediatric Appendicitis Data set. …”
Get full text

Deep Learning‐Based 3D Microseismic Event Direct Location Using Simultaneous Surface and Borehole Data by Yuanyuan Yang, Omar M. Saad, Tariq Alkhalifah

“…It is also able to effectively model the complicated dynamics from the moveout patterns of microseismic energy to responsible source locations, and the concerted interactions of these moveout signatures across various sensor layouts. The statistical analysis on results from synthetic passive seismic data demonstrates the efficacy of the method. …”
Get full text

BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation by Mohammad Rahman, Shyh Wei Teng, Manzur Murshed, Manoranjan Paul, David Brennan

“…Hyperspectral band selection algorithms are crucial for processing high-dimensional data, which enables dimensionality reduction, improves data analysis, and enhances computational efficiency. …”
Get full text

Assessing Driving Risk Level: Harnessing Deep Learning Hybrid Model With Intercity Bus Naturalistic Driving Data by Wei-Hsun Lee, Che-Yu Chang

“…This study advances the field by developing a deep learning hybrid model for time series analysis to categorize driving risks into low, moderate, and high levels. …”
Get full text

Inhibiting Factors of Dance Learning in the Higher Grades of Elementary School Students by Deasylina Da Ary, Markamah

“…The data analysis technique used is the Miles and Huberman model. …”
Get full text

A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data by Fengchun Ren, Xiao Zhao, Qin Yang, Huaqiang Liao, Yudong Zhang, Xuemei Liu

“…Cognitive status was classified using data-driven quartile thresholds on the Digit Symbol Substitution Test, CERAD Word-Learning Test, and Animal Fluency tests. …”
Get full text

PassAI: An Explainable Machine Learning Framework for Predicting Soccer Pass Outcomes Using Multimodal Match Data by Ryota Takamido, Jun Ota, Hiroki Nakamoto

“…As sports data, increasingly shaped by recent advances in in-game data acquisition technologies, become more complex and high-dimensional, analyzing such multimodal datasets presents challenges in both predictive performance and interpretability. …”
Get full text

Soil Organic Carbon Assessment Using Remote-Sensing Data and Machine Learning: A Systematic Literature Review by Arthur A. J. Lima, Júlio Castro Lopes, Rui Pedro Lopes, Tomás de Figueiredo, Eva Vidal-Vázquez, Zulimar Hernández

“…This SLR was conducted using Systematic Review and Meta-Analysis (PRISMA) methodology, highlighting the use of Deep Learning (DL), traditional neural networks, and other machine-learning models, and the input data were used to estimate SOC. …”
Get full text

Decentralized Grant-Free mMTC Traffic Multiplexing With eMBB Data Through Deep Reinforcement Learning by Giovanni Di Gennaro, Amedeo Buonanno, Gianmarco Romano, Stefano Buzzi, Francesco A. N. Palmieri

Get full text

An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugal by Silvia García-Méndez, Francisco de Arriba-Pérez, Fátima Leal, Bruno Veloso, Benedita Malheiro, Juan Carlos Burguillo-Rial

“…This work contributes to a real-time data-driven predictive maintenance solution for Intelligent Transportation Systems. …”
Get full text

Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets by Yusuf Aliyu, Aliza Sarlan, Kamaluddeen Usman Danyaro, Abdullahi Sani abd Rahman, Aminu Aminu Muazu, Mustapha Yusuf Abubakar

“…Prior research has predominantly concentrated on sentiment analysis within single-language data rather than code-switched data. …”
Get full text

Prediction on Permeability Coefficient of Continuously Graded Coarse-Grained Soils: A Data-Driven Machine Learning Method by Jinhua Wang, Haibin Ding, Lingxiao Guan, Yulin Wang

“…Gray relational analysis (GRA) revealed that all input parameters (α, β, d_max, n) significantly influence <i>k</i> (R > 0.6). …”
Get full text

Real-Time Detection, Evaluation, and Mapping of Crowd Panic Emergencies Based on Geo-Biometrical Data and Machine Learning by Ilias Lazarou, Anastasios L. Kesidis, Andreas Tsatsaris

“…In this paper, we propose a real-time system for detecting and mapping crowd panic emergencies based on machine learning and georeferenced biometric data from wearable devices and smartphones. …”
Get full text

Data driven tensile strength prediction for fiber-reinforced rubberized recycled aggregate concrete using machine learning by Avijit Pal, Khondaker Sakil Ahmed, Nur Yazdani

Get full text

Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data by Bappa Roy, Shuma Adhikari, Subir Datta, Kharibam Jilenkumari Devi, Aribam Deleena Devi, Taha Selim Ustun

“…This paper introduces a novel deep learning-based MPPT algorithm that leverages a Long Short-Term Memory (LSTM) deep neural network (DNN) to effectively track maximum power from solar PV panels, utilizing real-world data. …”
Get full text

Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…Also, challenges such as data imbalance and high-dimensional feature sets often hinder model performance. …”
Get full text