Applying binary mixed model to predict knee osteoarthritis pain. by Helal El-Zaatari, Liubov Arbeeva, Amanda E Nelson

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Short Paper - The Binary Linearization Complexity of Pseudo-Boolean Functions by Walter, Matthias

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Magnetic activities of two contact binaries in quadruple stellar systems by Yuangui Yang, Shuang Wang

“…Given the third light of $$\ell _3\sim 4.0\%$$ for HT Vir, we estimate the mass of the third body to be $$M_3=0.66(2)~\textrm{M}_{\odot }$$ . …”
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Distance-based target tracking algorithm in binary sensor network by SUN Xiao-yan1, LI Jian-dong1, HUANG Peng-yu1, ZHANG Wen-zhu1

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Integrated Imager and 3.22 <italic>&#x03BC;</italic>s/Kernel-Latency All-Digital In-Imager Global-Parallel Binary Convolutional Neural Network Accelerator for Image Processing by Ruizhi Wang, Cheng-Hsuan Wu, Makoto Takamiya

“…This new approach employs a global-parallel processing concept, which enables multiply-and-accumulate operations (MACs) to be executed simultaneously within the imager array in a 2D manner, eliminating the additional latency associated with row-by-row processing and data access from random access memories (RAMs). In this design, convolution and subsampling operations using a <inline-formula> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> 3 kernel are completed within just nine steps of global-parallel processing, regardless of image size. …”
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Chaotic Binary Firefly Spectrum Allocation Strategy Based on Logistic Mapping by TENG Zhi-jun, ZHANG Hua, ZHANG Ai-ling, HAN Zhong-ting, ZHANG Heng-jia

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Efficient Phase Retrieval via Improved Binary Amplitude Modulation Masks by Chao Yang, Cheng Xu, Hui Pang, Jun Lan, Lixin Zhao, Song Hu, Wei Yan, Xianchang Zhu

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Simple definition of error probability at binary signaling over generalized channels by V. P. Tuzlukov

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Cleaning images from impulse noise in a binary symmetrical channel by A. R. Orazaev

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Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning by Ahmad Aldelemy, Raed A. Abd-Alhameed

“…Our methodology includes data analysis, transformation, training, and testing machine learning classifiers such as Naïve Bayes, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Artificial Neural Networks, AdaBoost, and Gradient Descent. …”
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Dissimilarity measures based on the application of Hamming distance to generate controlled probabilistic tests by V. N. Yarmolik, V. V. Petrovskaya, N. A. Shevchenko

“…Accordingly, the computational complexity for all three options is comparable and does not exceed 3n comparison operations. …”
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Machine Learning Predictors for Min-Entropy Estimation by Javier Blanco-Romero, Vicente Lorenzo, Florina Almenares Mendoza, Daniel Díaz-Sánchez

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Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease by Priyadharsini C, Asnath Victy Phamila Y

“…Conclusion: This is the first work to experiment with 144 combinations to identify suitable deep architecture for binary retinal disease classification. The study recommends Xception for feature extraction ensembled with ExtraTreeClassifier, Light gradient boosting machine, Random Forest, AdaBoost classifiers, and meta-learner as Logistic Regression. …”
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Clinical Applicability of Machine Learning Models for Binary and Multi-Class Electrocardiogram Classification by Daniel Nasef, Demarcus Nasef, Kennette James Basco, Alana Singh, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

“…Convolutional neural networks, deep neural networks, and tree-based models, including Gradient Boosting Classifier and Random Forest, were trained and evaluated using standard metrics (accuracy, precision, recall, and F1 score) and learning curve convergence analysis. …”
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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, E. N. Dzhakhnaeva

“…Traditional classification algorithms, including LSVC (Support Vector Machine), GBT (Gradient Boosting Tree), and RF (Random Forest), have been chosen for this study. …”
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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, V. V. Dzhakhnaev

“…Traditional classification algorithms, including LSVC (Support Vector Machine), GBT (Gradient Boosting Tree), and RF (Random Forest), have been chosen for this study. …”
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Flowsheets for hydroxyacetone–phenol binary mixture separation: The use of special distillation methods by I. S. Gaganov, E. V. Rytova, A. K. Frolkova

“…To study the possibility of hydroxyacetone–phenol binary mixture (a constituent of a mixture of phenol production by the cumene method) separation in flowsheets based on the use of distillation special methods. …”
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Asymptotic behavior of the empirical checkerboard copula process for binary data: An educational presentation by Christian Genest, Johanna G. Nešlehová

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Transformer-Based Vulnerability Detection in IoT Firmware Binaries Using Opcode Sequences by M. Nandish, Jalesh Kumar, H. G. Mohan, M. V. Manoj Kumar

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Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities by Paul Iacobescu, Virginia Marina, Catalin Anghel, Aurelian-Dumitrache Anghele

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