The coverage method of unmanned aerial vehicle mounted base station sensor network based on relative distance by Taifei Zhao, Hua Wang, Qianwen Ma

“…The simulation results show that the coverage of the proposed algorithm is 22.4% higher than that of random deployment, and it is 9.9%, 4.7% and 2.1% higher than similar virtual force-oriented node, circular binary segmentation and hybrid local virtual force algorithms.…”
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Modeling the Relationship between Financial Stability and Banking Risks: Artificial Intelligence Approach by Hakeem Faraj Gumar, Parviz Piri, Mehdi Heydari

“…A wide range of artificial neural network approaches and machine learning algorithms have been used for data analysis. These methods include artificial neural network, deep neural network, convolutional neural network, recurrent neural network, self-organizing neural network, gradient boosting, random forest, decision tree, spatial clustering, k-means algorithm, k-nearest neighbor, support vector regression and support vector machine. …”
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Deployment and Operation of Battery Swapping Stations for Electric Two-Wheelers Based on Machine Learning by Yu Feng, Xiaochun Lu

“…Then, on a 3000 m grid scale, a prediction model of BSS quantity with random forest, support vector regression, and gradient-boosting decision tree algorithm was built. …”
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Characteristics and Rapid Prediction of Seismic Subsidence of Saturated Seabed Foundation with Interbedded Soft Clay–Sand by Liuyuan Zhao, Miaojun Sun, Jianhong Ye, Fuqin Yang, Kunpeng He

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Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique by Chao Zhou, Yue Wang, Ying Cao, Ramesh P. Singh, Bayes Ahmed, Mahdi Motagh, Yang Wang, Ling Chen, Guangchao Tan, Shanshan Li

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Improving Attack Detection in IoV with Class Balancing and Feature Selection by Thierry Widyatama, Ifan Rizqa, Fauzi Adi Rafrastara

“…The ensemble algorithms evaluated in this research comprise Random Forest, Gradient Boosting, and XGBoost. …”
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Prediction of Anemia from Multi-Data Attribute Co-Existence by Talal Qadah, Asmaa Munshi

“…Therefore, this study has reevaluated the claims within the domain of detecting and predicting anemia with the best machine learning algorithm. Another research problem, lies with the fact that previous studies on anemia prediction utilized limited machine learning algorithms across a narrow range of datasets, whereas this current study employed numerous machine learning algorithms across a wide range of anemia datasets and tested three hypotheses. …”
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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

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Unveiling shadows: A data-driven insight on depression among Bangladeshi university students by Sanjib Kumar Sen, Md. Shifatul Ahsan Apurba, Anika Priodorshinee Mrittika, Md. Tawhid Anwar, A.B.M. Alim Al Islam, Jannatun Noor

“…After rigorous analysis, Random Forest emerged as the best-performing algorithm, exhibiting remarkable accuracy (87%), precision (78%), recall (95%), and f1-score (86%). …”
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Using machine learning for the assessment of ecological status of unmonitored waters in Poland by Andrzej Martyszunis, Małgorzata Loga, Karol Przeździecki

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AN INTELLIGENT POSTOPERATIVE CHRONIC PAIN PREDICTION SYSTEM (I-POCPP) by Elif Kartal, Fatma Önay Koçoğlu, Zeki Özen, İlkim Ecem Emre, Gürcan Güngör, Pervin Sutaş Bozkurt

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Comparative Analysis of Facial Expression Recognition Methods by Denys - Florin COT

“… This paper aimed to investigate human emotion recognition through the analysis of facial expressions, using both classical machine learning methods and advanced techniques based on deep neural networks. The research compares the performance of classical machine learning algorithms (such as K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree, and Random Forest) with the modern deep learning methods (such as Convolutional Neural Networks, Deep Neural Networks, and Recursive Neural Networks) using standardized datasets. …”
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Wavelet-Based ensembled intelligent technique for a better quality of fault detection and classification in AC microgrids by Nityananda Giri, Pravati Nayak, Ranjan Kumar Mallick, Sairam Mishra, Aymen Flah, Habib Kraiem, Lukas Prokop, Mohammad Kanan

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An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance by Abhijeet Das

“…In addition, the study area's hydro-chemical facies were examined, and machine learning models’ hyperparameters such as Random Forest (RF), Borda Scoring Algorithm (BSA), Decision Tree (DT), Multilayer Perception (MLP), and Naïve Bayes (NB), were executed before, to training and testing the samples of surface water. …”
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Combined L-Band Polarimetric SAR and GPR Data to Develop Models for Leak Detection in the Water Pipeline Networks by Yuyao Zhang, Hongliang Guan, Fuzhou Duan

“…The model features are selected with the Boruta wrapper algorithm based on the SAOCOM-1A images after pre-processing, and the SSRDC values at sampling locations within the research area are calculated with the reflected wave method based on the GPR data. …”
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Residential Building Renovation Considering Energy, Carbon Emissions, and Cost: An Approach Integrating Machine Learning and Evolutionary Generation by Rudai Shan, Wanyu Lai, Huan Tang, Xiangyu Leng, Wei Gu

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Machine Learning for Prediction of Relapses in Multiple Drug Resistant Tuberculosis Patients by A. S. Аlliluev, O. V. Filinyuk, E. E. Shnаyder, S. V. Аksenov

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Multi-Temporal Remote Sensing Satellite Data Analysis for the 2023 Devastating Flood in Derna, Northern Libya by Roman Shults, Ashraf Farahat, Muhammad Usman, Md Masudur Rahman

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Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals by Siddhant Arora, Sridharan Naveen Venkatesh, Vaithiyanathan Sugumaran, Anoop Prabhakaranpillai Sreelatha, Vetri Selvi Mahamuni

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Old Drugs, New Indications (Review) by I. I. Miroshnichenko, E. A. Valdman, I. I. Kuz'min

“…Machine learning (ML) algorithms: Bayes classifier, logistic regression, support vector machine, decision tree, random forest and others are successfully used in biochemical pharmaceutical, toxicological research. …”
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