Empirical Evaluation and Scalability Analysis of Proof of Team Sprint (PoTS): Reward Fairness, Energy Efficiency, and System Stability by Naoki Yonezawa

“…This paper presents a comprehensive empirical evaluation of the Proof of Team Sprint (PoTS) consensus algorithm, focusing on its impact on reward fairness, energy efficiency, system stability, and scalability. …”
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Optimizing Class Imbalance in Facial Expression Recognition Using Dynamic Intra-Class Clustering by Qingdu Li, Keting Fu, Jian Liu, Yishan Li, Qinze Ren, Kang Xu, Junxiu Fu, Na Liu, Ye Yuan

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A Multi-Task Based Clustering Personalized Federated Learning Method by Ao Xiong, Han Zhou, Yu Song, Dong Wang, Xu Wei, Da Li, Bo Gao

“…Simulation experiments conducted on carbon emission prediction data demonstrate that the proposed algorithm performs better in various evaluation metrics compared with the Federated Averaging (FedAvg) algorithm and traditional clustering personalized federated learning algorithm. …”
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A Blockchain Network Communication Architecture Based on Information-Centric Networking by Yufei Zhou, Rui Han, Yang Li

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Coalmine image super-resolution reconstruction via fusing multi-dimensional feature and residual attention network by Jian CHENG, Lifei MI, Hao LI, Heping LI, Guangfu WANG, Yongzhuang MA

“…Experimental results indicate that the proposed method significantly outperforms existing mainstream algorithms in both objective evaluation metrics and subjective visual analysis. …”
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LASSO-derived nomogram prediction model for lymph node metastasis in colorectal cancer: a retrospective analysis by Xiyun Quan, Yi Deng, Zhimin Liu, Zhenqin Gao, Huimei Yi, Ming Li

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An adaptive search mechanism with convolutional learning networks for online social media text summarization and classification model by Reema G. AL-anazi, Muhammad Swaileh A. Alzaidi, Majdy M. Eltahir, Hassan Alkhiri, Samah Hazzaa Alajmani, Abdulbasit A. Darem, Mohammed Alshahrani, Nawaf Alhebaishi

“…Removing valued patterns and insights from this vast amount of textual data requires advanced methods to provide information while preserving its essential components successfully. …”
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A Joint Optimization Method for Power and Array of Multi-Point Sources System by Zhihao Cai, Shiqi Xing, Xinyuan Su, Junpeng Wang, Weize Meng, Ziwen Xiao

“…Third, based on the non-dominated sorting whale optimization algorithm (NSWOA), an adaptive inertia weight based on the cosine function and logistic chaotic map is introduced to obtain the optimal arrangement. …”
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A simulation-based network analysis of intervention targets for comorbid symptoms of depression and anxiety in Chinese healthcare workers in the post-dynamic zero-COVID policy era by Chao Zhang, Ruyong Li, Wei Zhang, Yanqiang Tao, Xiangping Liu, Yichao Lv

“…Simulated interventions suggested that alleviating “Anhedonia” can the most reduce the overall severity of the network, while aggravating “guilt” can the most increase the overall severity. …”
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Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and... by Mequannent Sharew Melaku, Lamrot Yohannes, Eliyas Addisu Taye, Nebebe Demis Baykemagn

“…Therefore, this study aimed to predict khat chewing practices and their determinant factors among men aged 15 to 59 years in Ethiopia using a machine learning algorithm.MethodsThis study used data from the 2011 and 2016 Ethiopian Demographic and Health Surveys (EDHS). …”
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Sustainable Self-Training Pig Detection System with Augmented Single Labeled Target Data for Solving Domain Shift Problem by Junhee Lee, Heechan Chae, Seungwook Son, Jongwoong Seo, Yooil Suh, Jonguk Lee, Yongwha Chung, Daihee Park

“…To address the degradation issue, this study proposes a self-training-based domain adaptation method that utilizes a single label on target (SLOT) sample from the target domain, a genetic algorithm (GA)-based data augmentation search (DAS) designed explicitly for SLOT data to optimize the augmentation parameters, and a super-low-threshold strategy to include low-confidence-scored pseudo-labels during self-training. …”
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Interpretable machine learning approaches for predicting prostate cancer by using multiple heavy metal exposures based on the data from NHANES 2003–2018 by Zu-Ming You, Yuan-Sheng Li, Fan-Shuo Meng, Rui-Xiang Zhang, Chen-Xi Xie, Zhijiang Liang, Ji-Yuan Zhou

“…Among the eight ML models evaluated, the random forest (RF) algorithm showed superior performance, achieving an accuracy of 72.835 %, an area under the receiver operating characteristic curve (AUC) of 0.869, an F1 score of 0.145, a G-mean of 0.749, and a Youden index of 0.498 in the test set. …”
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Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method by Er Wang, Tianbao Huang, Zhi Liu, Lei Bao, Binbing Guo, Zhibo Yu, Zihang Feng, Hongbin Luo, Guanglong Ou

“…To address this, the study introduces two Least Absolute Shrinkage and Selection Operator Lasso-based variable selection methods: Least Absolute Shrinkage and Selection Operator Genetic Algorithm (Lasso-GA) and Variance Inflation Factor Least Absolute Shrinkage and Selection Operator (VIF-Lasso). …”
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A mobile application-based decision support system for routing and decision making problems by Oğuz Bedel, Fehmi Özsoydan, Kutay Çetinkurt, Metehan Bolat

“…The mentioned application adopts a Simulated Annealing Algorithm to find promising routes for the users, while multi-criteria decision-making module uses both Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on the choice of the users. …”
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Vertex Coloring and Eulerian and Hamiltonian Paths of Delaunay Graphs Associated with Sensor Networks by Manuel Ceballos, María Millán

“…Sensor networks represent distributed systems of interconnected devices that collect and transmit data, while graph theory provides a robust framework for modeling and analyzing complex networks. …”
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“Optimizing the Optimization”: A Hybrid Evolutionary-Based AI Scheme for Optimal Performance by Agathoklis A. Krimpenis, Loukas Athanasakos

“…This mixed scheme was tested on challenging benchmark functions, demonstrating convergence speeds that were at least three times faster than the best-performing standalone algorithms while maintaining solution quality. These results highlight its potential as an efficient optimization approach for computationally intensive problems, regardless of the included algorithms and their standalone performance.…”
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Aspirational platform governance: How creators legitimise content moderation through accusations of bias by Blake Hallinan, CJ Reynolds, Yehonatan Kuperberg, Omer Rothenstein

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IoT driven healthcare monitoring with evolutionary optimization and game theory by Shitharth Selvarajan, Hariprasath Manoharan, Taher Al-Shehari, Nasser A. Alsadhan, Subav Singh

“…By incorporating two evolutionary algorithms, the proposed approach optimizes the state of action for each participant while reducing energy consumption and processing delay. …”
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A synergic quantum particle swarm optimisation for constrained combinatorial test generation by Xu Guo, Xiaoyu Song, Jian‐tao Zhou

“…Besides, the QPIO method enriches the application of the QPSO algorithm in the context of CT.…”
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Potential of random forest machine learning algorithm for geological mapping using PALSAR and Sentinel-2A remote sensing data: A case study of Tsagaan-uul area, southern Mongolia by Munkhsuren Badrakh, Narantsetseg Tserendash, Erdenejargal Choindonjamts, Gáspár Albert

“…Geological mapping in remote and geologically complex regions can be substantially improved by integrating remote sensing data with machine learning algorithms. This study evaluates the effectiveness of the Random Forest algorithm for geological mapping in the Tsagaan-uul area of the Khatanbulag ancient massif, Mongolia, a region characterized by limited accessibility and sparse field data. …”
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