Optimization of distribution networks using quantum annealing for loss reduction and voltage improvement in electrical vehicle parking management by Naser Rashnu, Babak Mozafari, Reza Sharifi

“…Traditional optimization techniques like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) often struggle with the nonlinear, high-dimensional nature of EV-grid interaction problems. …”
Get full text

Improving Sharpness-Aware Minimization Using Label Smoothing and Adaptive Adversarial Cross-Entropy Loss by Tanapat Ratchatorn, Masayuki Tanaka

“…Building on this principle, Sharpness-Aware Minimization (SAM) was introduced to improve model generalization and has achieved state-of-the-art performance through its weight perturbation and updating steps. …”
Get full text

Optimization Strategy of Multi-group Inspection Path of Distribution Equipment with Equipment Information Included by Chaoqiang CHEN, Hanyang GONG, Di ZHANG, Zhidan ZHANG, Jian LE

“…A distribution equipment information model is established, and a multi group distribution equipment routing optimization model is proposed to minimize the total routing cost, which is solved by genetic algorithm. …”
Get full text

Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model by Shengli Wang, Xiaolong Guo, Tianle Sun, Lihui Xu, Jinfeng Zhu, Zhicai Li, Jinjiang Zhang

“…A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). …”
Get full text

Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…A combination of statistical models for feature selection and machine learning algorithms for prediction was used, with Random Forest showing the best performance. …”
Get full text

Improving Acceptance to Sensory Substitution: A Study on the V2A-SS Learning Model Based on Information Processing Learning Theory by Kyeong Deok Moon, Yun Kyung Park, Moo Seop Kim, Chi Yoon Jeong

“…This improvement is significantly higher than the gain of 2.72% achieved by optimizing the V2A-SS algorithm with Mel-Scaled Frequency Mapping. …”
Get full text

A Distribution Network Expansion Project Classification Model Based on Data Augmentation and Dimensionality Reduction Method by Xin ZHOU, Jingxing LIN, Zhiwei XIE, Zheng ZHANG, Ruduo LIANG, Zuhong OU

Get full text

A Novel Two-Stage Adaptive Filtering Model-Based GNSS/INS Tightly Coupled Precise Relative Navigation Algorithm for Autonomous Aerial Refueling by Ling Wu, Yongrong Sun, Xiyu Fu, Rui Xu

Get full text

A novel controllability method on temporal networks based on tree model by Peyman Arebi

“…Evaluation against conventional methods on experimental datasets reveals notable improvements: a 41.8% reduction in the minimum number of control nodes, a 36.37% decrease in time of receiving fully control network, and a 38.5% reduction in control algorithm execution time compared to layered model-based control methods.…”
Get full text

Hybrid-driven modeling using a BiLSTM–AdaBoost algorithm for diameter prediction in the constant diameter stage of Czochralski silicon single crystals by Yu-Yu Liu, Ding Liu, Shi-Hai Wu, Yi-Ming Jing

“…In this paper, a hybrid-driven modeling method integrating Bidirectional Long Short-Term Memory network (BiLSTM) and Adaptive Boosting (AdaBoost) algorithm is proposed, aiming to improve the accuracy and stability of crystal diameter prediction in the medium diameter stage of the SSC growth by the Czochralski (CZ) method. …”
Get full text

An Inverse Modeling Multi-Objective Optimization Technique Based on Incremental Learning and Fuzzy Clustering by Gadallah Mohamed Abd Elaziz, Yasmine Abouelseoud, Sara H. Kamel

“…This paper aims to develop an inverse modeling MOEA based on decomposition that employs an incremental learning-based support vector regression (SVR) model, as an alternative to the Gaussian process model, in order to improve the quality of obtained solutions and speed up convergence of the algorithm. …”
Get full text

Abnormal Sound Detection of Wind Turbine Gearboxes Based on Improved MobileFaceNet and Feature Fusion by Yuelong Liang, Haorui Liu, Yayu Chen

“…To solve problems such as the unstable detection performance of the sound anomaly detection of wind turbine gearboxes when only normal data are used for training, and the poor detection performance caused by the poor classification of samples with high similarity, this paper proposes a self-supervised wind turbine gearbox sound anomaly detection algorithm that fuses time-domain features and Mel spectrograms, improves the MobileFaceNet (MFN) model, and combines the Gaussian Mixture Model (GMM). …”
Get full text

Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition by Faheed A.F. Alrslani, Manal Abdullah Alohali, Mohammed Aljebreen, Hamed Alqahtani, Asma Alshuhail, Menwa Alshammeri, Wafa Sulaiman Almukadi

“…Furthermore, the performance of the IDBN model is improved by the Cetacean Optimization Algorithm (COA)-based hyperparameter tuning process. …”
Get full text

LM-CNN-based Automatic Cost Calculation Model for Power Transmission and Transformation Projects by Xiaolin WU, Ling LUAN, Lianwu PAN, Hailong LI

“…Compared with the BP neural network and GD-CNN, the proposed model with higher prediction accuracy and stability combines the advantages of Levenberg-Marquart algorithm and convolutional neural network model to improve the calculation effect of power transmission and transformation project cost.…”
Get full text

Improving Distribution Prediction by Integrating Expert Range Maps and Opportunistic Occurrences: Evidence From Japanese Sea Cucumber by Bingqing Xiao, Songxi Yuan, Ákos Bede‐Fazekas, Jinxin Zhou, Xingyu Song, Qiang Lin, Lei Cui, Zhixin Zhang

“…We first fitted SDMs for this species based on opportunistic occurrence records via four modeling algorithms, then built two types of ensemble models using stacked generalization: an ensemble model that solely used four model predictions and an expert‐informed ensemble model that further accounted for distance to the IUCN expert range map. …”
Get full text

Predictive modeling for mechanical characteristics of ultra high-performance concrete blended with eggshell powder and nano silica utilizing traditional technique and machine learn... by Yi Zhang, Qizhi Zhang, Ali H. AlAteah, Ahmed Essam, Sahar A. Mostafa

Get full text

Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…Compared to a baseline model from the literature, O-LGB achieved significant improvements in predictive performance. …”
Get full text

A multicenter study on developing a prognostic model for severe fever with thrombocytopenia syndrome using machine learning by Jian-She Xu, Kai Yang, Bin Quan, Jing Xie, Yi-Shan Zheng, Yi-Shan Zheng

“…Twenty-four commonly available clinical features were selected, and the Boruta algorithm identified 12 candidate predictors, ranked by Z-scores, which were progressively incorporated into 10 machine learning models to develop prognostic models. …”
Get full text

A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system by Amna Ikram, Sunnia Ikram, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Adil Hussain, Amal H. Alharbi, Marwa M. Eid, Marwa M. Eid

“…This study proposes a Fuzzy-Optimized Hybrid Ensemble Model (FOHEM), integrating stacked ensemble machine learning algorithms with a fuzzy inference system (FIS) to improve yield prediction. …”
Get full text

Solving the Richards infiltration equation by coupling physics-informed neural networks with Hydrus-1D by Yanling Li, Qianxing Sun, Yuliang Fu, Junfang Wei

“…This approach is designed to handle the intricate boundary conditions and nonlinear water diffusion characteristics in groundwater seepage by integrating data with physical constraints, thereby forming a dual-driven solution framework that leverages both data and physics. The proposed improved algorithm integrates Hydrus data, leveraging a small portion of data to reduce the model’s dependence on parameter initialization. …”
Get full text