LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization by Ilayda Yaman, Guoda Tian, Erik Tegler, Jens Gulin, Nikhil Challa, Fredrik Tufvesson, Ove Edfors, Kalle Astrom, Steffen Malkowsky, Liang Liu

“…Each sensor is paired with a current state-of-the-art localization algorithm and evaluated for different aspects: localization accuracy, reliability and sensitivity to environment changes, calibration requirements, and potential system complexity. …”
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Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities by Yang Chen, Xiaolin Wang, Chang Zhang

“…The algorithm extracts mixed feature information of local long path and local short path based on the information retention module, and decomposes the information by combining wavelet transform, inputs the different components obtained from the decomposition into the network for training, and removes the noise by subsequent feature screening of the network structure. …”
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Global progress in competitive co-evolution: a systematic comparison of alternative methods by Stefano Nolfi, Paolo Pagliuca

“…Finally, we compare the efficacy of four qualitatively different algorithms, including two newly introduced methods. …”
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The Macroalgae Ecological Quality Index (MEQI) in the Basin of Mexico: a proposal of aquatic bioindicators for peri-urban rivers by Javier Carmona-Jiménez, Victor Hugo Salinas-Camarillo, Angela Caro-Borrero

“…By assigning an indicator value to each group of species, we calculated the MEQI using the formula adapted from Pantle and Buck, indicating five different ecological quality classes. …”
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Impact of dimensionality reduction techniques on student performance prediction using machine learning by Koushik Roy, Huu-Hoa Nguyen, Dewan Md. Farid

“…This reduction in dimensionality enhances the performance of predictive models and provides valuable insights into the key factors influencing student performance. The study evaluates ADRA using four different student performance datasets and six machine learning algorithms, comparing it to three existing dimensionality reduction methods. …”
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Prediction of Moisture Content in Kiwi (Actinidia deliciosa) Dried Using Machine Learning Approaches by Halil Nusret BULUS, Soner CELEN

“…In this study, kiwi slices of three different thicknesses were dried using 90 W microwave power. …”
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Two-Timescale Cross-Layer Design for URLLC Over Parallel Fading Channels With Imperfect CSI by Hongsen Peng, Meixia Tao

“…Based on the small timescale optimization method, we adopt the ternary search algorithm to optimize the pilot length and pilot power through Monte Carlo evaluations in the large timescale. …”
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Parallel Simulation Multi-Sample Task Scheduling Approach Based on Deep Reinforcement Learning in Cloud Computing Environment by Yuhao Xiao, Yping Yao, Feng Zhu

“…Complex scenario analysis and evaluation simulations often involve multiple sets of simulation applications with different combinations of parameters, thus resulting in high computing power consumption, which is one of the factors that limits the efficiency of multi-sample parallel simulations. …”
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Predicting Landslide Deposit Zones: Insights from Advanced Sampling Strategies in the Ilopango Caldera, El Salvador by Laura Paola Calderon-Cucunuba, Abel Alexei Argueta-Platero, Tomás Fernández, Claudio Mercurio, Chiara Martinello, Edoardo Rotigliano, Christian Conoscenti

“…Additionally, the Multivariate Adaptive Regression Splines (MARS) algorithm was used for modeling. …”
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Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks by Vikas, Charu Wahi, Bharat Bhushan Sagar, Manisha Manjul

“…The mechanism in TEAHR overcomes these challenges by placing an adaptive trust assessment mechanism that adapts to the background network conditions and real-time activities of the nodes. …”
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Impact of dimensionality reduction techniques on student performance prediction using machine learning by Koushik Roy, Huu-Hoa Nguyen, Dewan Md. Farid

“…This reduction in dimensionality enhances the performance of predictive models and provides valuable insights into the key factors influencing student performance. The study evaluates ADRA using four different student performance datasets and six machine learning algorithms, comparing it to three existing dimensionality reduction methods. …”
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Research on Hyperspectral Inversion of Soil Organic Carbon in Agricultural Fields of the Southern Shaanxi Mountain Area by Yunhao Han, Bin Wang, Jingyi Yang, Fang Yin, Linsen He

“…For feature band selection, three methods were utilized: Variable Importance Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Stepwise Projection Algorithm (SPA). …”
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End-Edge Collaborative Lightweight Secure Federated Learning for Anomaly Detection of Wireless Industrial Control Systems by Chi Xu, Xinyi Du, Lin Li, Xinchun Li, Haibin Yu

“…Furthermore, to ensure the parameter security during wireless transmission in the open radio environment, we propose an adaptive key generation algorithm for parameter encryption. …”
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A Study on the Impact of Obstacle Size on Training Models Based on DQN and DDQN by Lu Siyu, Tao Ye, Zeng Junwei, Zuo Qihuan

“…Various parameters such as obstacle size and complexity influence the agent's performance, promoting efficient learning and policy optimization using both DQN and DDQN algorithms under different configurations. After our experiment, comparing with DQN, we found that DDQN could be a better algorithm in car racing scenarios as in some extreme environments, DQN would not very accurately estimate future reward, therefore, agent could not make better decisions because of the misguided evaluation. …”
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Symbiotic Learning Grey Wolf Optimizer for Engineering and Power Flow Optimization Problems by Aala Kalananda Vamsi Krishna Reddy, Komanapalli Venkata Lakshmi Narayana

“…SL-GWO’s symbiotic hunting strategies provide a major overhaul to the exiting hierarchical hunting through population sub-grouping into attacking hunters and experienced hunters with individually crafted dynamic adaptive tuning. The hunting mechanisms are implemented through the inclusion of random omega wolves from the wolfpack thereby reducing the algorithm’s excessive dependence on the three dominant wolves and enhancing the population diversity. …”
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An echo state network based on enhanced intersecting cortical model for discrete chaotic system prediction by Xubin Wang, Pei Ma, Jing Lian, Jizhao Liu, Yide Ma

“…The performance of the ESN-EICM was compared to that of a standard ESN and a Long Short-Term Memory (LSTM) network. The evaluation used data from three discrete chaotic systems (Logistic, Sine, and Ricker) for both one-step and multi-step prediction tasks.ResultsThe experimental results indicate that the ESN-EICM produced lower error metrics (MSE, RMSE, MAE) compared to the standard ESN and LSTM models across the tested systems, with the performance difference being more pronounced in multi-step forecasting scenarios. …”
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Angle detection method for surface mount components based on weighted clustering by Chunfu ZHANG, Zhiqiang SONG

“…To validate the effectiveness of the proposed method, we conducted experimental tests on eight different types of chip packages and compared the results with those from two state-of-the-art line segment detection algorithms currently in use. …”
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Enhancing MANET Security Through Federated Learning and Multiobjective Optimization: A Trust-Aware Routing Framework by Saad M. Hassan, Mohd Murtadha Mohamad, Farkhana Binti Muchtar, Firoz Bin Yusuf Patel Dawoodi

“…We evaluate this framework against existing protocols across various scenarios, including different network densities, mobility patterns, and malicious node percentages. …”
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Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis by Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, Sungwon Kim, Kaywan Othman Ahmed, Salim Heddam

“…In this study, the predictive accuracy of six different machine learning models, including Natural Gradient-based Boosting (NGBoost), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Support vector regression (SVR), Gaussian Process Regression (GPR), and Extremely Randomized Tree (ERT) was evaluated for modelling the parameter of permeate flow as a key element in system efficiency, energy consumption, and water quality using six various input combinations of feed water salt concentration, condenser inlet temperature, feed flow rate, and evaporator inlet temperature. …”
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Rapid Lactic Acid Content Detection in Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging by Xiaoyu Xue, Haiqing Tian, Kai Zhao, Yang Yu, Ziqing Xiao, Chunxiang Zhuo, Jianying Sun

“…To minimize model redundancy, three algorithms, such as competitive adaptive reweighted sampling (CARS), were used to extract the characteristic wavelengths of the three dyes, and the combination of the characteristic wavelengths obtained by each algorithm was used as an input variable to build an analytical model for quantitative prediction of the lactic acid content by support vector regression (SVR). …”
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