A Hybrid Mangrove Identification Method by Combining the Time-Frequency Threshold of the Mangrove Index With a Random Forest Binary Classifier by Zhuokai Jian, Bin Ai, Jiali Zeng, Yuchao Sun

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Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

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Wide Binaries from GAIA DR3 : testing GR vs MOND with realistic triple modelling by Charalambos Pittordis, Will Sutherland, Paul Shepherd

“…We provide an updated test for modifications of gravity from a sample of wide-binary stars from Gaia DR3, and their sky-projected relative velocities. …”
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Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS by Ahmed M. Elshewey, Enas Selem, Amira Hassan Abed

“…The performance of the proposed model is compared with another machine learning models, namely, random forest, decision tree, bagging classifier, adaptive boosting, and k-nearest neighbor, and the performance of the models is evaluated using accuracy, sensitivity, specificity, F-score, and area under the ROC curve. …”
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Depth first traversal algorithm for the back-off tree of distributed queuing by Wennai WANG, Yanhe ZHANG, Wei WU, Chen BAI, Bin WANG

“…An analytic model was provided for the conventional distributed queueing (DQ) and its back-off tree operations, followed by a design of improving algorithm based on depth first traversal.Combing the specific analysis of complete binary tree with generalized extension by random tree reconstruction, the performance of proposed algorithm was evaluated on the throughput in both theory and simulation experiment.A theoretic optimal solution of contention slots of DQ frame and a brief description of simulation extension based on the open source NS-3 were presented.The simulation results show that the maximum stationary throughput by the proposed algorithm reaches 70% of the physical capacity of channel.…”
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Formation of streams of binary sequences with controlled structure of "cellular" automata in homogeneous register environments by P. A. Kadiev, K. K. Nazarov, Z. G. Kardashova

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Controlled random tests with limited Hamming distance by V. N. Yarmolik, V. V. Petrovskaya, D. V. Demenkovets, V. A. Levantsevich

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Article RETRACTED due to manipulation by the authors Enhancing the Probability of Victor for Top Players via Knockout Tournament Fixture by Hegen Dadang Prayoga, Tomoliyus Tomoliyus, Ria Lumintuarso, Yudik prasetyo, Endang Rini Sukamti, Ari Tri Fitrianto, Andi Kasanrawali, Ramadhan Arifin, Ahmad Maulana, Muhammad Habibie

“…Keywords: Single Elimination, Binary Tree, Dummy Players, Election Procedures, Hierarchically. …”
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Statistical Fragility of Findings From Randomized Phase 3 Trials in Pediatric Oncology by Hannah E. Olsen, Pei‐Chi Kao, Caleb Richmond, David S. Shulman, Wendy B. London, Steven G. DuBois

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An Integer Erasure Correction Coding and Its Application for Security Enhancement of Encryption by Miodrag J. Mihaljevic, Aleksandar Radonjic, Nevena Mijajlovic, Lianhai Wang, Shujiang Xu

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Using a 3D Chaotic Dynamic System as a Random Key Generator for Image Steganography by Mohammed Abod Husain, Saad Al-Momen

“…Furthermore, an algorithm is suggested to generate a random binary key, serving as the controller for the embedding process. …”
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IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning by U. A. Vishniakou, Xia YiWei

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Heavy Tailed Distribution of Binary Classification Model by Damilare Oladimeji, Emmanuel Oguntade, Samuel Olanrewaju

“…The proposed research incorporates the utilization of a heavy-tailed skewed distribution referred to as the inverse Weibull as a link function in the context of a binary classification model. This selection is motivated by the need to address the existence of rare or extreme events in random processes. …”
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Using binary classification to evaluate the quality of machine translators by Ran Li, Yihao Yang, Kelin Shen, Mohammad Hijji

“…Second, the dataset is used to train the multiple binary classifiers, e.g., decision tree, random forest, extreme gradient boosting, support vector machines, logistic regression, etc. …”
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Quasars Can Signpost Supermassive Black Hole Binaries by J. Andrew Casey-Clyde, Chiara M. F. Mingarelli, Jenny E. Greene, Andy D. Goulding, Siyuan Chen, Jonathan R. Trump

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Patient, Physician, and Assessor Blinding in Phase III Randomized Trials in Oncology: A Meta‐Epidemiological Analysis by Gabrielle Brown, Pavlos Msaouel, Avital M. Miller, Ramez Kouzy, Timothy A. Lin, Joseph Abi Jaoude, Ethan B. Ludmir, Alexander D. Sherry

“…ABSTRACT Background Blinding mitigates bias in randomized trials and may be especially crucial for surrogate endpoints, such as progression‐free survival (PFS). …”
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Balanced Multi-Class Network Intrusion Detection Using Machine Learning by Faraz Ahmad Khan, Asghar Ali Shah, Nizal Alshammry, Saifullah Saif, Wasim Khan, Muhammad Osama Malik, Zahid Ullah

“…For binary classification, the Decision Tree (DT) model has exhibited the highest test accuracy of 99.96%, followed by Random Forest (99.84%), Adaboost (99.77%), and Xgboost (99.57), with the highest average precision of 100% and ROC-AUC of 99.96%. …”
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Optimal Coherence Length Control in Interferometric Fiber Optic Hydrophones via PRBS Modulation: Theory and Experiment by Wujie Wang, Qihao Hu, Lina Ma, Fan Shang, Hongze Leng, Junqiang Song

“…In this study, we propose a pseudo-random binary sequence (PRBS) phase modulation method for laser coherence length control, establishing the first theoretical model that quantitatively links PRBS parameter to coherence length, elucidating the mechanism underlying its suppression of parasitic interference noise. …”
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Machine Learning Methods for Predicting Cardiovascular Diseases: A Comparative Analysis by Aiym B. Temirbayeva, Arshyn Altybay

“…The research evaluates and compares the performance of five algorithms - Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting - on a dataset containing clinical features of patients. …”
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Fundamental Parameters for Totally Eclipsing Contact Binaries Observed by TESS by Xu Ding, KaiFan Ji, ZhiMing Song, XueFen Tian, JinLiang Wang, ChuanJun Wang, QiYuan Cheng, JianPing Xiong

“…Our analysis identified 96 targets with mass ratios below 0.25, all of which were not listed in any previous catalog, thus signifying the discovery of new LMR system candidates. Assuming all 143 binary systems are affected by a third light during parameter estimation, we train a neural network (NN _l _3 ) model considering the third light. …”
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