Novel delayed binary time-series pattern based machine learning techniques for stock market forecasting by Zeqiye Zhan, Song-Kyoo Kim

“…This study proposes an innovative machine learning technique for stock market forecasting that leverages delayed binary time-series patterns to enhance prediction accuracy. …”
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Improved Quantum Query Upper Bounds Based on Classical Decision Trees by Arjan Cornelissen, Nikhil S. Mande, Subhasree Patro

“…We also show a polynomial separation between rank and randomized rank for the complete binary AND-OR tree. …”
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A comparative study of machine learning and deep learning models in binary and multiclass classification for intrusion detection systems by Ayesha Alharthi, Meera Alaryani, Sanaa Kaddoura

“…Preprocessing steps included normalization, label encoding, and a 70:10:20 train-validation-test split. Seven models, Random Forest, Decision Tree, K-Nearest Neighbors, XGBoost, Convolutional Neural Network, Gated Recurrent Unit, and Long Short-Term Memory, were trained and evaluated using precision, recall, and F1-score. …”
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IMPACT OF BRASSINOLIDE, NANO-SILICON, AND MYCORRHIZAE ON THE NUTRIENT COMPOSITION AND PRODUCTIVITY OF OLIVE TREES BASHIKA CV by Esraa AL-Jaleely, Ayad Alalaf, Suliman Kako

“…The third factor involved the addition of a bio-fertilizer (mycorrhizal fungus) at three concentrations (0, 50, and 100 g tree⁻¹). The experiment used a Randomized Complete Block Design (R.C.B.D) with three factors, three replicates, and one tree per experimental unit. …”
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Perbandingan Kerja Binomial GLMM Tree dan BIMM Forest untuk Memodelkan Status Bekerja Penduduk by Dwi Agustin Nuriani Sirodj, Khairil Anwar Notodiputro, Bagus Sartono

“…Selanjutnya metode alternatif lainnya adalah Binary Mixed Model (BiMM) Forest yang menggabungkan prinsip kerja Bayesian GLMM dan Random Forest. …”
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Biosorption of Acid Dye in Single and Multidye Systems onto Sawdust of Locust Bean (Parkia biglobosa) Tree by Abdur-Rahim Adebisi Giwa, Khadijat Ayanpeju Abdulsalam, Francois Wewers, Mary Adelaide Oladipo

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Gender roles in agroforestry value chains: evidence from fruit tree-based agroforestry in Dodota district, Ethiopia by Kidist Kassaye, Getahun Kassa, Getahun Kassa, Abera Tilahun Abdi, Abera Tilahun Abdi

“…Despite the economic significance of fruit tree-based agroforestry in Ethiopia, gender considerations throughout the value chain activities are still underexplored. …”
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Understanding overfitting in random forest for probability estimation: a visualization and simulation study by Lasai Barreñada, Paula Dhiman, Dirk Timmerman, Anne-Laure Boulesteix, Ben Van Calster

“…When the aim is probability estimation, the simulation results go against the common recommendation to use fully grown trees in random forest models.…”
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Response of Olive Trees (Olea europaea L.) cv. Zaity to Bio health and Foliar Spray of Tecamin max and Boron by Araz Siyar Faris, Shaymaa Mahfodh Abdulqader

“…A factorial experiment with three replications was carried out in a randomized complete block design (RCBD), using one tree for each experimental unit. …”
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COMPARATIVE STUDY OF SURVIVAL SUPPORT VECTOR MACHINE AND RANDOM SURVIVAL FOREST IN SURVIVAL DATA by Ni Gusti Ayu Putu Puteri Suantari, Anwar Fitrianto, Bagus Sartono

“…Random Survival Forest is tree based method that using boostrapping algorithm, and Survival Support Vector Machine using hybrid approaches between regression and ranking constrain. …”
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Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

“…While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. …”
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Bernoulli trials and permutation statistics by Don Rawlings

“…The distributions of a general class of random variables known as binary tree statistics are also given.…”
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Improving Early Detection of Dementia: Extra Trees-Based Classification Model Using Inter-Relation-Based Features and K-Means Synthetic Minority Oversampling Technique by Yanawut Chaiyo, Worasak Rueangsirarak, Georgi Hristov, Punnarumol Temdee

“…For data balancing, the K-Means Synthetic Minority Oversampling Technique (K-Means SMOTE) was applied to generate synthetic samples in under-represented regions of the feature space, addressing class imbalance. Extra Trees (ET) was used for model construction due to its noise resilience and ability to manage multicollinearity. …”
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Intelligent intrusion detection system based on crowd search optimization for attack classification in network security by Chetan Gupta, Amit Kumar, Neelesh Kumar Jain

“…We have developed the hybrid model based on crow search optimization (CSO) and random forest (RF) algorithm for identifying threats and irregularities in a computer network. …”
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Enhancing predictive maintenance in automotive industry: addressing class imbalance using advanced machine learning techniques by Yashashree Mahale, Shrikrishna Kolhar, Anjali S. More

“…Six machine learning models, including logistic regression, support vector machine, decision tree, and random forest, along with gradient boosting algorithms using extreme gradient boost (XGBoost) and light gradient boosting machine frameworks, were implemented. …”
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A comparison of modeling approaches for static and dynamic prediction of central line-associated bloodstream infections using electronic health records (part 2): random forest mode... by Elena Albu, Shan Gao, Pieter Stijnen, Frank E Rademakers, Christel Janssens, Veerle Cossey, Yves Debaveye, Laure Wynants, Ben Van Calster

“…Models including multiple outcome events (multinomial and competing risks) display a different internal structure compared to binary and survival models, choosing different variables for early splits in trees. …”
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Efficient diagnosis of diabetes mellitus using an improved ensemble method by Blessing Oluwatobi Olorunfemi, Adewale Opeoluwa Ogunde, Ahmad Almogren, Abidemi Emmanuel Adeniyi, Sunday Adeola Ajagbe, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Asif Mehmood, Habib Hamam

“…The first phase utilized J48, Classification and Regression Tree (CART), and Decision Stump (DS) to create a random forest model. …”
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Developing a hybrid feature selection method to detect botnet attacks in IoT devices by Alshaeaa H.Y., Ghadhban Z.M., Ministry of Education, Iraq

“…Several classification models including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and bagging are utilized for the classification purpose. …”
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Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods by Shiyang Cheng, Qihang Zhang, Hao Min, Wenhui Jiang, Jueting Liu, Chunsheng Liu, Zehua Wang

“…Then, we applied four machine learning methods—random forest, gradient boosting decision tree, extreme gradient boosting, and multi-layer perceptron—to develop binary classification models for N-dealkylation. …”
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Using an ensemble approach to predict habitat of Dusky Grouse ( Dendragapus obscurus ) in Montana, USA by Elizabeth A Leipold, Claire N Gower, Lance McNew

“…We converted both models to binary values and used an ensemble (frequency histogram) approach to combine the models into a final predictive map. …”
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