A population spatialization method based on the integration of feature selection and an improved random forest model. by Zhen Zhao, Hongmei Guo, Xueli Jiang, Ying Zhang, Changjiang Lu, Can Zhang, Zonghang He

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”
Get full text

FLDQN: Cooperative Multi-Agent Federated Reinforcement Learning for Solving Travel Time Minimization Problems in Dynamic Environments Using SUMO Simulation by Abdul Wahab Mamond, Majid Kundroo, Seong-eun Yoo, Seonghoon Kim, Taehong Kim

“…Addressing these issues requires innovative approaches for optimizing road network utilization. While Deep Reinforcement Learning (DRL)-based methods have shown remarkable effectiveness in dynamic scenarios like traffic management, their primary focus has been on single-agent setups, limiting their applicability to real-world multi-agent systems. …”
Get full text

Association between international normalized ratio-to-albumin ratio and mortality in critically ill patients with gastrointestinal bleeding: a retrospective MIMIC-IV database study by Xingyi Yang, Shasha Ying, Lihong Lv, Yishu Ji, Jiaqian Ying, Huyan Ke

“…X-tile analysis determined an optimal cutoff value of 0.73 for PTAR in predicting 28-day mortality. …”
Get full text

Global trends in machine learning applications for single-cell transcriptomics research by Xinyu Liu, Zhen Zhang, Chao Tan, Yinquan Ai, Hao Liu, Yuan Li, Jin Yang, Yongyan Song

“…Research hotspots concentrated on random forest (RF) and deep learning models, showing transition from algorithm development to clinical applications (e.g., tumor immune microenvironment analysis). …”
Get full text

Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach by Mohammed Aly, Mohamed H. Behiry

“…Abstract Three machine learning algorithms—Logistic Boosting, Random Forest, and Support Vector Machines (SVM)—were evaluated for anomaly detection in IoT-driven industrial environments. …”
Get full text

SLR Systematic Literature Review: Metode Penilaian Kinerja Karyawan Menggunakan Human Performance Technology by Roni Habibi, Artha Glory Romey Manurung

“…Therefore, this systematic literature review is carried out to identify the methods used for employee performance appraisal and mentions that optimization algorithms and fuzzy comprehensive methods are used as evaluation methods to assess performance appraisal methods. …”
Get full text

WaveConstLib: A java library for signal analysis and wavelet construction by Çağla Sarvan Cibil, Nalan Özkurt

“…It provides tools to create wavelet functions tailored to specific signals while adhering to WT conditions. Unlike traditional methods, WaveConstLib employs multi-objective evolutionary algorithms (MOEAs) optimization to construct signal-specific wavelet functions, ensuring superior adaptability and performance. …”
Get full text

IDLEWiSE by Chiara Fusar Bassini, Leonard Hackel, Thorren Kirschbaum

“…This paper outlines an optimization tool using efficient machine-learning algorithms like decision trees to learn optimal shutdown policies online. …”
Get full text

Load balancing for high performance computing using quantum annealing by Omer Rathore, Alastair Basden, Nicholas Chancellor, Halim Kusumaatmaja

“…While the methodology for obtaining real simulation data to partition is application specific, the proposed balancing protocol itself remains completely general. …”
Get full text

Vision transformer-enhanced thermal anomaly detection in building facades through fusion of thermal and visible imagery by Siyu Zheng, Jiaxin Zhang, Rui Zu, Yunqin Li

“…This research employs an optimized Vision Transformer (ViT) technology, enhanced by the Line Acquisition, Filtering, and Revision (LAFR) algorithm. …”
Get full text

An Empirical Configuration Study of a Common Document Clustering Pipeline by Anton Eklund, Mona Forsman, Frank Drewes

“…The results suggest that BERT embeddings combined with UMAP dimension reduction to no less than 15 dimensions provides a good basis for clustering, regardless of the specific clustering algorithm used. Moreover, while UMAP performed better than PCA in our experiments, tuning the UMAP settings showed little impact on the overall performance. …”
Get full text

Accelerating and Compressing Transformer-Based PLMs for Enhanced Comprehension of Computer Terminology by Jian Peng, Kai Zhong

“…Our method involves a pipeline parallelism algorithm designed to accelerate training. It is paired with an innovative mixed compression strategy that combines pruning and knowledge distillation to effectively reduce the model size while preserving its performance. …”
Get full text

Secondary Intraocular Lens Options in Intraocular Lens Dislocation: A Review by Alexander Maloof, Ava James, Nicholas Pavic, Shane Zhang, Stephanie Wu, Christopher Go

“…While there are numerous IOL techniques and lens options for addressing IOL dislocation, there is currently no consensus on the optimal method. …”
Get full text

Design of Asymmetrical Relay Resonators for Maximum Efficiency of Wireless Power Transfer by Bo-Hee Choi, Jeong-Hae Lee

“…The optimum placement is found by scanning the positions of the relays and optimum capacitance can be found by using genetic algorithm (GA). The PTEs are enhanced when capacitance is optimally designed by GA according to the position of relays, respectively, and then maximum efficiency is obtained at the optimum placement of relays. …”
Get full text

Design of nodes importance assessment method for complex network based on neighborhood information by Xing LI, Jie ZHAN, Baoquan REN, Siqi ZHU

“…Accurate identification of influential nodes in complex networks is crucial for network management and network security.The local centrality method is concise and easy to use, but ignores the topological relationship between neighboring nodes and cannot provide globally optimal results.A node importance assessment method was proposed to correlate the node edge relationship and topology, which firstly applied the H-index and information entropy to assess the nodes, then combined the structural holes of the nodes with the structural characteristics of the nodes, and took into account the attribute of “bridging” while focusing on the node’s own quality and the amount of information about the neighboring nodes.The algorithm was validated by simulating the propagation process using the SIR model, and the Kendall correlation coefficient, complementary cumulative distribution function and propagation influence were applied to validate the validity and applicability of the method.Comparison of the experimental results on six real network datasets shows that the proposed method is more accurate than the traditional centrality methods in identifying and ordering the key nodes in the network.…”
Get full text

Adaptive rumor propagation and activity contagion in higher-order networks by Yafang Dong, Liang’an Huo, Matjaž Perc, Stefano Boccaletti

“…We also apply a sequential quadratic programming algorithm to optimize the parameters of our model and validate its accuracy and applicability on real-world data. …”
Get full text

Skip-Connected CNN Exploiting BNN Surrogate for Antenna Modelling by Yubo Tian, Jinlong Sun, Zhiwei Zhu

“…The combination of evolutionary algorithms with full-wave electromagnetic (EM) simulation software is an effective method for antenna optimal design. …”
Get full text

Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment by Qianpiao MA, Qingmin JIA, Jianchun LIU, Hongli XU, Renchao XIE, Tao HUANG

“…., edge heterogeneity, data Non-IID, and communication resource constraints, a grouping asynchronous federated learning (FedGA) mechanism was proposed.Edge nodes were divided into multiple groups, each of which performed global updated asynchronously with the global model, while edge nodes within a group communicate with the parameter server through time-sharing communication.Theoretical analysis established a quantitative relationship between the convergence bound of FedGA and the data distribution among the groups.A time-sharing scheduling magic mirror method (MMM) was proposed to optimize the completion time of a single round of model updating within a group.Based on both the theoretical analysis for FedGA and MMM, an effective grouping algorithm was designed for minimizing the overall training completion time.Experimental results demonstrate that the proposed FedGA and MMM can reduce model training time by 30.1%～87.4% compared to the existing state-of-the-art methods.…”
Get full text

A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment. by Tao Yu, Wei Huang, Xin Tang, Duosi Zheng

“…The effectiveness, efficiency, and robustness of the proposed TSC-SVM model are demonstrated through various real-world applications. The proposed algorithm is anticipated to expand the customer base for financial institutions while reducing economic losses.…”
Get full text

A framework for detecting and predicting highway traffic anomalies via multimodal fusion and heterogeneous graph neural networks. by Shaowei Sun, Mingzhou Liu

“…To address these limitations, we propose a framework based on multimodal deep fusion and heterogeneous graph neural networks (HGNNs), incorporating an Ensemble Contrastive Pessimistic Likelihood Estimation (CPLE) algorithm to optimize performance. The framework integrates static and dynamic traffic data, such as video images, traffic flow, vehicle speed, and tunnel weather conditions. …”
Get full text