COMPARATIVE ANALYSIS OF COLORING ALGORITHMS FOR ORDINARY WEIGHTED GRAPH by Andrey Sergeyevich Merzlenko, Valery Grigoryevich Kobak

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Hybrid Weighted Random Forests Method for Prediction & Classification of Online Buying Customers by Umesh Kumar Lilhore, Sarita Simaiya, Devendra Prasad, Deepak Kumar Verma

“…This research article mainly proposed an extension of the Random Forest classifier named “Weighted Random Forests” (wRF), which incorporates tree-level weights to provide much more accurate trees throughout the calculation as well as an assessment of vector relevance. …”
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Computerised analysis of non-conjugate spiral bevel gear mesh using an advanced and fast-converging tooth contact model by Maksat Temirkhan, Andas Amrin

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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

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Introducing Primality Testing Algorithm with an Implementation on 64 bits RSA Encryption Using Verilog by Rehan Shams, Fozia Hanif Khan, Umair Jillani, M. Umair

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Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering by Bo Xu, Chunjiang Zhao, Guijun Yang, Yuan Zhang, Changbin Liu, Haikuan Feng, Xiaodong Yang, Hao Yang

“…Subsequently, we harnessed the TreeQSM algorithm, which is custom-designed for extracting tree topological structures, to extract 11 archetypal structural phenotypic parameters of the maize tassels. …”
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Prediction of Optimum Operating Parameters to Enhance the Performance of PEMFC Using Machine Learning Algorithms by Arunadevi M, Karthikeyan B, Anirudh Shrihari, Saravanan S, Sundararaju K, R Palanisamy, Mohamed Awad, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Hany S. Hussein, Mahmoud M. Hussein, Ahmed I. Omar

“…Different MLAs are modelled to explore the PEMFC performance and results proved that gradient boosting regression provides better predictions compared to other algorithms such as decision tree regressor, support vector machine regressor, and random forest regression.…”
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Leveraging mixed-effects regression trees for the analysis of high-dimensional longitudinal data to identify the low and high-risk subgroups: simulation study with application to g... by Mina Jahangiri, Anoshirvan Kazemnejad, Keith S. Goldfeld, Maryam S. Daneshpour, Mehdi Momen, Shayan Mostafaei, Davood Khalili, Mahdi Akbarzadeh

“…Previous studies have shown that this model can be sensitive to parametric assumptions and provides less predictive performance than non-parametric methods such as random effects-expectation maximization (RE-EM) and unbiased RE-EM regression tree algorithms. …”
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Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir by Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya

“…The complexity of the job market requires individuals and organizations to understand the trends and needs of the world of work. One of the main challenges is the right career placement. That is becoming increasingly popular is the use of Machine Learning  algorithms in the decision-making process. …”
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Dye-cleaning prediction with a variant of nature-inspired algorithms coupled with extreme gradient boosting by Tiyasha Tiyasha, Chijioke Elijah Onu, Mohamed A. Ismail, Rama Rao Karri, Abdelfattah Amari, Vinay Kumar, Suraj Kumar Bhagat

“…Hyperparameter tuning via differential evolution (DE), genetic algorithm (GA), random search (RS), and grid search (GS) with the XGBoost model was conducted to achieve more accurate results. …”
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Proposal for Using AI to Assess Clinical Data Integrity and Generate Metadata: Algorithm Development and Validation by Caroline Bönisch, Christian Schmidt, Dorothea Kesztyüs, Hans A Kestler, Tibor Kesztyüs

“…Logistic regression, k-nearest neighbors, a naive bayes classifier, a decision tree classifier, a random forest classifier, extreme gradient boosting (XGB), and support vector machines (SVM) were selected as machine learning algorithms. …”
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K-Means Centroid Optimization with Genetic Algorithm for Clustering Micro, Small, Medium Enterprises in Yogyakarta by Muhammad Faris Akbar, Lisna Zahrotun

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A Novel Toolbox for Generating Realistic Biological Cell Geometries for Electromagnetic Microdosimetry by Mehrdad Saviz, A.H. Buchali Safiee, Elham Sharifi

“…We have designed a free, user-friendly tool in MATLAB that combines several known or new algorithms for easy production of three-dimensional complex cell shapes based on minimum data. …”
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Artificial intelligence survival models for identifying relevant risk factors for incident diabetes in Azar cohort population by Neda Gilani, Mohammadhossein Somi, Farzaneh Hamidi, Pasqualina Santaguida, Elnaz Faramarzi, Reza Arabi Belaghi

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Association between serum hypertriglyceridemia and hematological indices: data mining approaches by Somayeh Ghiasi Hafezi, Amin Mansoori, Alireza Kooshki, Marzieh Hosseini, Sahar Ghoflchi, Mark Ghamsary, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

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Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data by Jiali Wang, Jia Chen

“…Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). Finally, three improved Boosting algorithms were proposed, namely Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting Tree (XGBoost), and Gradient Boosting Decision Tree (CatBoost). …”
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Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task. by ZeSheng Lin

“…Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. …”
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Three-Dimensional Trajectory Tracking Control for Stratospheric Airship Based on Deep Reinforcement Learning by Xixiang Yang, Fangchao Bai, Xiaowei Yang, Yuelong Pan

“…The Boltzmann random distribution of reward value and probability of wind direction angle were taken as the action selection criteria of the Q-learning algorithm, the cerebellar model articulation controller (CMAC) neural network was constructed for the discrete action value, and the optimal action sequence was fast obtained. …”
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Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations by Yong Li, Xin Sun, Yongtao Qu, Shuling Yang, Yueyi Zhai, Yan Qu

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Research and predictive analysis of pyrolysis characteristics of multi-source organic solid wastes by ZHANG Zihang, XING Bo, MA Zhongqing, HU Yanjun, ZHANG Zhixiao, YUAN Shizhen, LU Rufei, CHEN Yingquan, WANG Shurong*

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