Indoor Localization Using Visible Light Via Fusion of Multiple Classifiers by Xiansheng Guo, Sihua Shao, Nirwan Ansari, Abdallah Khreishah

“…Unlike the existing RSSs-based algorithms, several representative machine learning algorithms are adopted to train multiple classifiers based on these RSSs fingerprints. …”
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Analysis of the Effectiveness of Traditional and Ensemble Machine Learning Models for Mushroom Classification by Neny Sulistianingsih, Galih Hendro Martono

“…Overall, this study highlights the importance of algorithm selection tailored to data characteristics and supports the use of ensemble learning to boost predictive reliability. …”
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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…The landscape of machine learning constantly demands innovative approaches to enhance algorithms’ performance across diverse tasks. Meta-learning, known as “learning to learn” is a promising way to overcome these diversity challenges by blending multiple algorithms. …”
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A METHOD FOR INVESTIGATING MACHINE LEARNING ATTACKS ON ARBITER-TYPE PHYSICALLY UNCLONABLE FUNCTIONS by Yuri A. Korotaev

“…The corresponding APUF was implemented on a field-programmable gate array (FPGA). The prediction accuracy after training reached 97.46%.…”
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Reversible data hiding in encrypted domain based on fine-grained access control by ZHANG Minqing, PENG Shen, JIANG Chao, DI Fuqiang, DONG Yufeng

“…Firstly, pixels were classified according to the size of the prediction error value, and different categories of pixels were marked using a parametric binary tree. …”
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Optimization of Pertamax Fuel Distribution Using Clarke-Wright Savings, Nearest Neighbour, and Goal Programming (Case Study: Malang City) by Tharisa Melani, Sobri Abusini, Marjono Marjono

“…The results show that the combination of CWS and NN algorithms reduced the total travel distance by 140 km, or 12.5% reduction. …”
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A review on control systems hardware and software for robots of various scale and purpose. Part 2. Service robotics by Aleksey M. Romanov

“…The following conclusions are made on the basis of the review results: the key technology in service robotics from the point of view of scalability is the Robot Operating System (ROS); service robotics is today the main springboard for testing intelligent algorithms for the tactical and strategic control levels that are integrated into a common system based on ROS; the problem of ensuring fault tolerance in the service robotics is practically neglected, with the exception of the issue of increasing reliability by changing behavioral algorithms; in a number of areas of service robotics, in which the reduction of mass and dimensions is especially important, the robot control systems are implemented on a single computing device, in other cases a multi-level architecture implemented on Linux-based embedded computers with ROS are used.…”
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A Topical Review of Quantum and Classical Machine Learning Approaches to Disaster Escape Routing Problems by A. Vinil, Parameswaran Iyer, Jetain Chetan, Aniket Bembale, Nagendra Singh

“…The pathfinding problem in a graph has been solved using several classical algorithms, notably Dijkstra’s and A* algorithms. …”
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Using big data analytics to improve HIV medical care utilisation in South Carolina: A study protocol by Mohammad Rifat Haider, Bankole Olatosi, Jiajia Zhang, Sharon Weissman, Jianjun Hu, Xiaoming Li

“…In addition, established secure data governance rules, data encryption and encrypted predictive techniques will be deployed. In addition to the data anonymisation as a part of privacy-preserving analytics, encryption schemes that protect running prediction algorithms on encrypted data will also be deployed. …”
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Investigating the Role of Code Smells in Preventive Maintenance by Junaid Ali Reshi, Satwinder Singh

“…Code smells, which are indicators of the software quality have not been put to an extensive study for as to determine their role in the prediction of defects in the software. This study aims to investigate the role of code smells in prediction of non-faulty classes. …”
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Novel Condition Monitoring Method for Wind Turbines Based on the Adaptive Multivariate Control Charts and SCADA Data by Qinkai Han, Zhentang Wang, Tao Hu

“…After comparing the regression accuracy of several popular algorithms in the MRA, the random forest is adopted for feature selection and regression prediction. …”
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Bike-Sharing Static Rebalancing by Considering the Collection of Bicycles in Need of Repair by Sheng Zhang, Guanhua Xiang, Zhongxiang Huang

“…The sum of the initial bicycle number and the minimum net flow value was determined to be the demand for static rebalancing, and this led to the proposal of a bike-sharing demand prediction method based on autoregressive integrated moving average models. …”
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Cost-sensitive regression learning on small dataset through intra-cluster product favoured feature selection by Fangfang Xu, Huan Zhao, Weihua Zhou, Yun Zhou

“…Massive regression and forecasting tasks are generally cost-sensitive regression learning problems with asymmetric costs between over-prediction and under-prediction. However, existing classic methods, such as clustering and feature selection, are subject to difficulties in dealing with small datasets. …”
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Robust Fault Classification in Permanent Magnet Synchronous Machines Under Dynamic and Noisy Conditions by Mikal Laursen, Van-Van Huynh, Duy-Hung Ha, Mahmoud S. Mahmoud, van Khang Huynh

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Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP) by Tim Rahmel, Michael Adamzik, Hartmuth Nowak, Lars Bergmann, Björn Koos, Martin Eisenacher, Barbara Sitek, Britta Marko, Lars Palmowski, Andrea Witowski, Katharina Rump, Julia Bandow, Patrick Günther

“…Our two-way approach involves creating two distinct algorithms: the first focuses on predictive accuracy and generalisability using routine clinical parameters, while the second leverages an extended dataset including a plethora of factors currently insufficiently explored and not available in standard clinical practice but may help to enhance precision. …”
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Field Grading of Longan SSC via Vis-NIR and Improved BP Neural Network by Jun Li, Meiqi Zhang, Kaixuan Wu, Hengxu Chen, Zhe Ma, Juan Xia, Guangwen Huang

“…Initially, nine preprocessing methods were combined with six classification algorithms to develop the longan SSC grading prediction model. …”
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Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. I. Data Set Preparation by Thomas Williams, Christopher B. Prior, David MacTaggart

“…This publicly available living data set will allow users to incorporate these data into their own flare prediction algorithms.…”
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Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…Six machine learning algorithms were used to establish NDVI prediction models: random forest (RF), BP neural networks (BP), support vector machines (SVM), genetic algorithm (GA), radial basis function (RBF), and convolutional neural networks (CNN). …”
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Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma by Feiyu Qi, Guiming Zha, Yanfang Zhang, Sihua Liu, Yuhang Yang, Wanliang Sun, Dongdong Wang, Zhong Liu, Zheng Lu, Dengyong Zhang

“…Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. …”
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Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain by Shangmin Chen, Yongshan Gao, Lin Du, Mengzhen Min, Lei Xie, Liping Li, Xiaodong Chen, Zhigang Zhong

“…This study included 13,074 middle-aged and older adults from the China health and retirement longitudinal study (wave 2011–2015) to separately develop four-year fall risk prediction models for older adults with and without pain, using five machine learning algorithms with 145 input variables as candidate features. …”
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