Factors influencing the spatial distribution of police stops and their efficacy in crime prevention and control by Zhuoying Fan, Xuewei Zhang, Guangwen Song, Chunxia Zhang

“…Second, by clustering based on different associated factors of police stops, existing police stops can be classified into five categories according to their purposes: (i) composite stops positively associated with “Alarm, Visiting Population, Criminals” (AVC-CPS); (ii) composite stops positively associated with “Alarm, Visiting Population, Bus Station” (AVB-CPS); (iii) random stops with no significant positive association (NA-RPS); (iv) single police stops positively associated with “Alarm” (A-SPS); and (v) single stops positively associated with “Visiting Population” (V-SPS). …”
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Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition by Marjan Kordani, Mohsen Bagheritabar, Iman Ahmadianfar, Arvin Samadi-Koucheksaraee

“…The proposed framework includes a new hybrid machine learning (ML) model based on generalized ridge regression and kernel ridge regression with a regularized locally weighted (GRKR) method. This research developed an optimized multivariate variational mode decomposition (OMVMD) technique, optimized by the Runge-Kutta algorithm (RUN), to decompose the input variables. …”
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An Evaluation of the Sensitivity of a Source Term Estimation Methodology of Sensor Configuration in an Urban-like Environment by Panagiotis Gkirmpas, Fotios Barmpas, George Tsegas, George Efthimiou, Paul Tremper, Till Riedel, Christos Vlachokostas, Nicolas Moussiopoulos

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Leveraging Finite-Precision Errors in Chaotic Systems for Enhanced Image Encryption by B. M. El-Den, Saad Aldosary, Haitham Khaled, Tarek M. Hassan, Walid Raslan

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Effective tweets classification for disaster crisis based on ensemble of classifiers by Christopher Ifeanyi Eke, Kholoud Maswadi, Musa Phiri, Mulenga, Mohammad Imran, Dekera Kwaghtyo, Akeremale Olusola Collins

“…A range of supervised learning algorithms like Decision Trees, Logistic Regression, Support Vector Machines, and Random Forests, were evaluated individually and as part of ensemble methods like AdaBoost, Bagging, and Random Subspace. …”
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Wetland Gain and Loss in the Mississippi River Bird‐Foot Delta by Jiangjie Yang, Zhijun Dai, Xuefei Mei, Yaying Lou, Sergio Fagherazzi

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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

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Data fusion-based improvements in empirical regression and machine learning for global daily ∼ 8 km resolution sea surface nitrate estimation and interpretation by Aifen Zhong, Difeng Wang, Fang Gong, Jingjing Huang, Zhuoqi Zheng, Xianqiang He, Qing Zhang, Qiankun Zhu

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Computer analysis shows differences between mitochondrial miRNAs and other miRNAs by P. S. Vorozheykin, I. I. Titov

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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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Energy-Aware Task Allocation for Multi-Cloud Networks by Sambit Kumar Mishra, Sonali Mishra, Ahmed Alsayat, N Z Jhanjhi, Mamoona Humayun, Kshira Sagar Sahoo, Ashish Kr. Luhach

“…However, the average energy consumption improved through <italic>ETAMCN</italic> is approximately 14&#x0025;, 6.3&#x0025;, and 2.8&#x0025; in opposed to the random allocation algorithm, Cloud Z-Score Normalization (<italic>CZSN</italic>) algorithm, and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS), respectively. …”
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Spatial and temporal distribution patterns and factors influencing hepatitis B in China: a geo-epidemiological study by Kang Fang, Na Cheng, Chuang Nie, Wentao Song, Yunkang Zhao, Jie Pan, Qi Yin, Jiwei Zheng, Qinglin Chen, Tianxin Xiang

“…Spatial autocorrelation analysis and spatiotemporal scanning were used to analyze the spatiotemporal distribution characteristics. The random forest algorithm was used to screen the potential influencing factors. …”
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A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution by Dongfeng Ren, Xin Qiu, Zehua An

“…The proposed model innovatively incorporates texture, geometric, and temporal features of building images, as well as socio-economic characteristics extracted using the distance decay algorithm. The results yield the following conclusions: (1) The proposed method achieves an overall classification accuracy of 0.77, which is 0.12 higher than that of the random forest-based approach. (2) The introduction of time features and the distance decay method further improved the model performance, increasing the accuracy by 0.04 and 0.03, respectively. (3) The correlation between the building functions and population distribution varies significantly across different scales. …”
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Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques by Yadigar Imamverdiyev, Elshan Baghirov, John Chukwu Ikechukwu

“…Utilizing the CIC-MalMem-2022 dataset, the effectiveness of decision trees, gradient-boosted trees, logistic Regression, random forest, and LightGBM in identifying obfuscated malware was evaluated. …”
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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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Survival analysis using machine learning in transplantation: a practical introduction by Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque

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Analyzing Method of the Absorption Capability of New Energy in Distribution Network with Consideration of Regional Self-discipline and Absorption Quality by Yan CHEN, Wei JIN, Wenbin WANG, Huibin LI, Shengfeng HAN, Kai JIA

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Considering the effect of non-landslide sample selection on landslide susceptibility assessment by Youchen Zhu, Deliang Sun, Haijia Wen, Qiang Zhang, Qin Ji, Changming Li, Pinggen Zhou, Jianjun Zhao

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A Survey on Data Mining for Data-Driven Industrial Assets Maintenance by Eduardo Coronel, Benjamín Barán, Pedro Gardel

“…The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. …”
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A Machine Learning Approach to Evaluate the Performance of Rural Bank by Jun Wei, Tao Ye, Zhe Zhang

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