Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans by Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque

“…Key water quality parameters—Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH—were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. …”
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Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study by Amirreza Sadeghinasab, Jafar Fatahiasl, Marziyeh Tahmasbi, Sasan Razmjoo, Mohammad Yousefipour

“…Results The Naïve Bayes classifier demonstrated the highest performance among the trained models, achieving an AUC (area under the receiver operating characteristic curve) of 0.86 ± 0.13 when trained on the seven features selected by the forward sequential algorithm and an AUC of 0.84 ± 0.14 when trained using the five features chosen by the LASSO algorithm. …”
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High Seroprevalence of Autoantibodies Typical of Autoimmune Liver Disease in Eastern Ethiopia: Is Chewing of Khat (Catha edulis) a Triggering Factor? by Stian Magnus Staurung Orlien, Tekabe Abdosh Ahmed, Nejib Yusuf Ismael, Nega Berhe, Trine Lauritzen, Svein Gunnar Gundersen, Asgeir Johannessen

“…However, the seroprevalences of AMA and SMA were strikingly high in this Ethiopian population compared to global estimates, suggesting that diagnostic algorithms for autoimmune liver diseases developed in Europe and North America might lead to misdiagnosis of patients on the African continent.…”
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Auditory attention decoding based on neural-network for binaural beamforming applications by Roy Gueta, Elana Zion-Golumbic, Jacob Goldberger, Sharon Gannot

“…This led to the development of several algorithms addressing the auditory attention decoder (AAD) task. …”
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Predictive model for determining the indications for automated 3D ultrasound for screening patients at low risk of developing breast tumors by A. E. Garanina, A. V. Kholin

“…The developed algorithms will help optimize screening and referral for additional examinations, which is of practical importance for improving diagnostics and optimizing healthcare resources.…”
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Using machine learning to predict outcomes following transcarotid artery revascularization by Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Leen Al-Omran, Duminda N. Wijeysundera, Mohamad A. Hussain, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Graham Roche-Nagle, Mohammed Al-Omran

“…We developed machine learning (ML) algorithms that predict 1-year outcomes following TCAR. …”
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A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion by Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu

“…The experimental results showed that this paper’s algorithm had better objective indexes on all test sets and outperformed the comparison algorithms. …”
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SS-YOLO: A Lightweight Deep Learning Model Focused on Side-Scan Sonar Target Detection by Na Yang, Guoyu Li, Shengli Wang, Zhengrong Wei, Hu Ren, Xiaobo Zhang, Yanliang Pei

“…Thus, meeting our need to deploy SSS target detection algorithms on unmanned surface vessel (USV) for real-time target detection. …”
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Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model by Merjulah Roby, Juan C. Restrepo, Haehwan Park, Satish C. Muluk, Mark K. Eskandari, Seungik Baek, Ender A. Finol

“…Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. …”
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Distribution of University Admission Quotas: Problems of Competitive Selection Process by L. M. Nurieva, S. G. Kiselev

“…The main reason for the inability of the competitive mechanism to carry out the correct ranking of participants in terms of success is the major methodological and mathematical errors inherent in the algorithms for determining the effectiveness of educational organisations. …”
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Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning by El Arbi Abdellaoui Alaoui, Stéphane Cédric Koumetio Tekouabou, Sri Hartini, Zuherman Rustam, Hassan Silkan, Said Agoujil

“…Therefore, we propose a machine learning-based approach which combines a new technique of preprocessing the data for features transformation, resampling techniques to eliminate the bias and the deviation of instability and performing classifier tests based on the Support Vector Machine (SVM) and Decision Tree (DT) algorithms. The tests carried out on dataset collected in Nur Hidayah Hospital of Yogyakarta in Indonesia show a great improvement compared to previous studies. …”
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Digital Twin-Empowered Green Mobility Management in Next-Gen Transportation Networks by Kubra Duran, Lal Verda Cakir, Achille Fonzone, Trung Q. Duong, Berk Canberk

“…Moreover, our hybrid implementation of intelligent algorithms for emission prediction resulted in higher accuracy when compared to baselines. …”
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Blood Genomics Identifies Three Subtypes of Systemic Lupus Erythematosus: “IFN-High,” “NE-High,” and “Mixed” by Mintian Cui, Taotao Li, Xinwei Yan, Chao Wang, Qi Shen, Hongbiao Ren, Liangshuang Li, Ruijie Zhang

“…Based on disease activity scores and gene expression data, we defined a global SLE signature and merged three clustering algorithms to develop a single-sample subtype classifier (SSC). …”
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EMNet: A Novel Few-Shot Image Classification Model with Enhanced Self-Correlation Attention and Multi-Branch Joint Module by Fufang Li, Weixiang Zhang, Yi Shang

“…EMNet shows its potential for bio-inspired algorithms in optimizing feature extraction and enhancing generalization capabilities. …”
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Robust Next-Day Scheduling of PV Generation Sources Supplying a Standalone DC Microgrid via a Semi-Definite Programming Model by Walter Gil-Gonzalez, Oscar Danilo Montoya, Luis F. Grisales-Norena, Fabio Andrade

“…Evaluating a 27-bus standalone DC microgrid, the SDP model outperforms random-based algorithms by achieving global optima in both objectives. …”
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Use of machine learning in osteoarthritis research: a systematic literature review by Francis Berenbaum, Jérémie Sellam, David Klatzmann, Atul J Butte, Karine Louati, Encarnita Mariotti-Ferrandiz, Marie Binvignat, Valentina Pedoia

“…For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected.Results From 1148 screened articles, 46 were selected and analysed; most were published after 2017. …”
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Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review by Md Mohshin Ali, Md. Arif Hossen, Azrina Abd Aziz

“…Furthermore, the review outlines the commonly employed ML algorithms, presents recent progress, and identifies crucial parameters influencing PC-CO2R efficiency. …”
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Event-Based Slip Estimation Framework for Space Rovers Traversing Soft Terrains by Ruqayya Alhammadi, Yahya Zweiri, Ahmad Abubakar, Mubarak Yakubu, Laith Abuassi, Lakmal Seneviratne

“…Precise estimation of this slip is important for rover navigation algorithms, as it helps to prevent rovers from traversing areas with excessive slippage, thereby avoiding entrapment —an outcome that could lead to mission failure. …”
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Epigenetics of suicidal behavior by R. N. Mustafin, A. V. Kazantseva, R. F. Enikeeva, Yu. D. Davydova, S. B. Malykh, V. V. Viktorov, E. K. Khusnutdinova

“…The role of histone modifications in distinct genes (Cx30, Cx43, TrkB.T1) and the expression of specific long noncoding RNAs and microRNAs in the development of suicidal behavior, which is promising for the development of diagnostic algorithms and target therapy, is discussed.…”
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Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks by Hussein A. M. Hussein, Sharafiz B. Abdul Rahim, Faizal B. Mustapha, Prajindra S. Krishnan, Nawal Aswan B. Abdul Jalil

“…This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. …”
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