Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach by Raid Almalki, Mehdi Khaki, Patricia M. Saco, Jose F. Rodriguez

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A machine learning framework for exploring the relationship between supply chain management best practices and agility, risk management, and performance by Tyler Ward, Sam Khoury, Selva Staub, Kouroush Jenab

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SFSCDNet: A Deep Learning Model With Spatial Flow-Based Semantic Change Detection From Bi-Temporal Satellite Images by K. S. Basavaraju, N. Sravya, Vibha Damodara Kevala, Shilpa Suresh, Shyam Lal

Subjects: “…Deep learning…”
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Impact of academic cheating and perceived online learning effectiveness on academic performance during the COVID-19 pandemic among Pakistani students by Aamir Abbas Malik, Mehdi Hassan, Muhammad Rizwan, Iqra Mushtaque, Tauqeer Ahmed Lak, Mussarat Hussain

“…Furthermore, significant gender differences were found on the scales of online learning effectiveness in school, college, and university students (t = 2.3*, p = 0.05 vs. t = 4.32**, p = 0.000 vs. t = −3.3*, p = 0.04). …”
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Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset by Asadi Srinivasulu, Clement Varaprasad Karu, G Sreenivasulu, Gayathri R

“…Existing approaches include machine learning, filtering-based, and model-based methods. …”
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Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging by Abigail Keogan, Thi Nguyet Que Nguyen, Pascaline Bouzy, Nicholas Stone, Karin Jirstrom, Arman Rahman, William M. Gallagher, Aidan D. Meade

“…In this study, Fourier-transform infrared (FTIR) chemical images of BC tissue were used to train deep learning models to predict future disease recurrence. …”
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Evaluation of Machine Learning Algorithms for Classification of Visual Stimulation-Induced EEG Signals in 2D and 3D VR Videos by Mingliang Zuo, Xiaoyu Chen, Li Sui

Subjects: “…machine learning…”
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Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm by Kerem Gencer, Gülcan Gencer

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Comparison of Resampling Methods and Radiomic Machine Learning Classifiers for Predicting Bone Quality Using Dual-Energy X-Ray Absorptiometry by Mailen Gonzalez, José Manuel Fuertes García, María Belén Zanchetta, Rubén Abdala, José María Massa

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Learning from our vulnerabilities: Insights from Octavia E. Butler’s parable of the sower and West Sumatra’s 2024 flood disasters by Sandika Edria, Rizano Gindho, Yeni Marliza

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Risk Prediction for Ship Encounter Situation Awareness Using Long Short-Term Memory Based Deep Learning on Intership Behaviors by Jie Ma, Wenkai Li, Chengfeng Jia, Chunwei Zhang, Yu Zhang

“…To address this issue, we propose a deep learning framework. To represent the temporal dynamics of the encounter process, we use the sliding window technique to generate the sequences of behavioral features. …”
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onEEGwaveLAD: A fully automated online EEG wavelet-based learning adaptive denoiser for artefacts identification and mitigation. by Luca Longo, Richard B Reilly

“…This research contributes to the body of knowledge by introducing onEEGwaveLAD, a novel, fully automated, ONline, EEG wavelet-based Learning Adaptive Denoiser pipeline for artefact identification and reduction. …”
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Enhancing aviation control security through ADS-B injection detection using ensemble meta-learning models with Explainable AI by Vajratiya Vajrobol, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir, Brij B. Gupta, Akshat Gaurav, Kwok Tai Chui

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Machine Learning-Based Integrated Analysis of PANoptosis Patterns in Acute Myeloid Leukemia Reveals a Signature Predicting Survival and Immunotherapy by Lanlan Tang, Wei Zhang, Yang Zhang, Wenjun Deng, Mingyi Zhao

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Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 by Jenberu Mekurianew Kelkay, Deje Sendek Anteneh, Henok Dessie Wubneh, Abraham Dessie Gessesse, Gebeyehu Fassil Gebeyehu, Kalkidan Kassahun Aweke, Mikiyas Birhanu Ejigu, Mathias Amare Sendeku, Kirubel Adrissie Barkneh, Hasset Girma Demissie, Wubshet D. Negash, Birku Getie Mihret

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Machine learning using random forest to model heavy metals removal efficiency using a zeolite-embedded sheet in water by N.D. Takarina, N. Matsue, E. Johan, A. Adiwibowo, M.F.N.K. Rahmawati, S.A. Pramudyawardhani, T. Wukirsari

“…For developing the zeolite-embedded sheet, the methods include fabrication of the zeolite-embedded sheet using a heating procedure and heavy metals adsorption treatment using the zeolite-embedded sheet. The machine learning analysis to model the heavy metal removal efficiency using zeolite-embedded sheet was performed using the random forest method. …”
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A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography by Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, Dong-Hyun Kim

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Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology by Yue Wu, Siyuan Wu, Yinghai Yu, Yinghai Yu, Xiaojun Hu, Ting Zhao, Yan Jiang, Bilian Ke

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Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework by Mingyue Xue, Yinxia Su, Chen Li, Shuxia Wang, Hua Yao

“…Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. …”
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Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder by Yilu Zhao, Zhao Fu, Eric J. Barnett, Ning Wang, Kangfuxi Zhang, Xuping Gao, Xiangyu Zheng, Junbin Tian, Hui Zhang, XueTong Ding, Shaoxian Li, Shuyu Li, Qingjiu Cao, Suhua Chang, Yufeng Wang, Stephen V. Faraone, Li Yang

“…The current study employed a comprehensive approach, combining genome-wide association analyses (GWAS) and deep learning (DL) methods, to elucidate the genetic underpinnings of pharmacological treatment response in ADHD. …”
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