Churn prediction for SaaS company with machine learning by Hugo Eduardo Sanches, Ayslan Trevizan Possebom, Linnyer Beatrys Ruiz Aylon

“…Originality/value – By applying machine learning to churn prediction, this study offers valuable insights into the performance and comparative analysis of different algorithms in a real-world SaaS environment. …”
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Degradation-Aware Bi-Level Optimization of Second-Life Battery Energy Storage System Considering Demand Charge Reduction by Ali Hassan, Guilherme Vieira Hollweg, Wencong Su, Xuan Zhou, Mengqi Wang

“…Compared with energy management with no consideration of degradation or demand charge reduction, this algorithm results in 71% less degradation of BESS and 57.3% demand charge reduction for the industrial consumer.…”
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Induction of Convolutional Decision Trees for Semantic Segmentation of Color Images Using Differential Evolution and Time and Memory Reduction Techniques by Adriana-Laura López-Lobato, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes

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Massive Scenario Reduction Based Distribution-Level Power System Planning Considering the Coordination of Source, Network, Load and Storage by Jinsen LIU, Ning LUO, Jie WANG, Chang XU, Yi Cao, Zhiwen Liu

“…In order to solve the problem of coordination between the massive operation data of renewable power generation and the coordinated planning of the source-network-load-storage, this paper proposes a coordinated planning method of the source-network-load-storage based on the massive scenario dimension reduction. Firstly, the dimensionality reduction clustering is carried out on the wind-light-load mass high-dimensional scenarios by the principal component Gaussian mixture clustering algorithm, and the typical scenario set of wind and power loads is obtained; then, a source-network-load-storage coordination planning model of distribution network for massive scenarios is constructed, and the second-order cone relaxation technique is adopted to convert the non-convex constraints to convex ones; finally, the effectiveness of the proposed massive scenario dimension reduction clustering method and distribution network planning model is verified on the Portugal 54-node distribution network.…”
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Hybrid vegetation-seawall coastal systems for wave hazard reduction: analytics for cost-effective design from optimized features by Erfan Amini, Reza Marsooli, Somayeh Moazeni, Bilal M. Ayyub

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Red-KPLS Feature Reduction with 1D-ResNet50: Deep Learning Approach for Multiclass Alzheimer’s Staging by Syrine Neffati, Ameni Filali, Kawther Mekki, Kais Bouzrara

“…The proposed method integrates discrete wavelet transform (DWT) for multi-scale feature extraction, a novel reduced kernel partial least squares (Red-KPLS) algorithm for feature reduction, and ResNet-50 for classification. …”
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An Adaptive Noise Reduction Method Based on Improved Dislocation Superposition Method for Abnormal Noise Fault Component of Automotive Engine by Jiaoyi Hou, Pengwei Guo, Aoyu Xu, Dayong Ning, Shengtao Chen, Zengmeng Zhang, Yongjun Gong, Yinglong Chen, Hao Tian, Hongwei Du

“…This study proposes an adaptive noise reduction method based on the dislocation superposition method (DSM), which can realize the adaptive noise reduction and the extraction of fault a component from the automobile engine abnormal noise signal of low SNR. …”
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Outage reduction in 5G uplink cooperative NOMA using PSO-optimized relay selection and successive interference cancelation by David S. Bhatti, M. Asif Haroon, Haeung Choi, Seungmin Kim, Heung-No Lee

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Health poverty reduction effect of medical insurance in China’s middle-aged and elderly populations: a cross-sectional study by Haijun Yang, Rui Chen, Ye Li, Qunhong Wu, Linghan Shan, Bing Wu, Fang Yin, Yiyun Zhang, Yongqiang Lai, Xinwei Liu, Wanxin Tian, Xiyu Zhang, Yujuan Xu, Wenqing Miao, Baoguo Shi, Feifei Leng

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AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images by Csaba Csikos, Sándor Barna, Ákos Kovács, Péter Czina, Ádám Budai, Melinda Szoliková, Iván Gábor Nagy, Borbála Husztik, Gábor Kiszler, Ildikó Garai

“…This study aimed to assess the performance of an AI-based bone scan noise-reduction filter on noisy, low-count images in a routine clinical environment. …”
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The Influence of Network Structural Preference on Link Prediction by Yongcheng Wang, Yu Wang, Xinye Lin, Wei Wang

“…However, in the social network, link prediction may raise concerns about privacy and security, because, through link prediction algorithms, criminals can predict the friends of an account user and may even further discover private information such as the address and bank accounts. …”
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Comparison of machine learning models for coronavirus prediction by B. K. Amos, I. V. Smirnov, M. M. Hermann

“…It was found that when using AB algorithms, greater accuracy is achieved, but the stability of the LSVM algorithm is higher. …”
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Performance of Machine Learning Classifiers for Diabetes Prediction by Mijala Manandhar, Shaikat Baidya, Babalpreet Kaur, Katia Atoji

“…Future research should focus on integrating multiple datasets and exploring more complex ML algorithms to enhance prediction accuracy and generalization. …”
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Machine Learning and the Conundrum of Stroke Risk Prediction by Yaacoub Chahine, Matthew J Magoon, Bahetihazi Maidu, Juan C del Álamo, Patrick M Boyle, Nazem Akoum

“…The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. …”
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Location Prediction on Trajectory Data: A Review by Ruizhi Wu, Guangchun Luo, Junming Shao, Ling Tian, Chengzong Peng

“…This survey provides a comprehensive overview of location prediction, including basic definitions and concepts, algorithms, and applications. …”
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State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms by Romel Carrera, Leonidas Quiroz, Cesar Guevara, Patricia Acosta-Vargas

“…This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. …”
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Analysis of Traffic Conflicts at Roundabout Entrances and Exits – A Machine Learning Approach for Enhanced Safety by Yuzhou DUAN, Zhipeng LIN, Yulong WANG, Qiaowen BAI

“…It achieved high accuracy in predicting traffic conflict areas at the entrances and exits of a roundabout, with a prediction accuracy of 0.86 and an AUC (area under the receiver operating characteristic curve) of 0.88. …”
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Resolution of the consensus of the Russian Federation experts on the diagnosis and treatment of osteoarthritis, 2022 by A. M. Lila, V. I. Mazurov, A. I. Martynov, N. V. Zagorodny, L. I. Alekseeva, N. V. Chichasova, A. E. Karateev, A. V. Naumov, E. V. Zonova, A. P. Rachin, E. A. Taskina

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Introducing an Evolutionary Method to Create the Bounds of Artificial Neural Networks by Ioannis G. Tsoulos, Vasileios Charilogis, Dimitrios Tsalikakis

“…From the conducted experiments, it appears that the proposed method reduced the average classification error by 30%, compared to the genetic algorithm, and the average regression error by 45%, as compared to the genetic algorithm.…”
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Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007–2018 by Efrain Riveros Perez, Bibiana Avella-Molano

“…The performance of five machine learning algorithms (logistic regression, support vector machine, random forest, XGBoost and CatBoost) was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (AUC). …”
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