Transformer Fault Diagnosis Based on Multi-Strategy Enhanced Dung Beetle Algorithm and Optimized SVM by Shuming Zhang, Hong Zhou

“…To address the challenge of low accuracy in transformer fault diagnosis using support vector machines (SVMs), an enhanced fault diagnosis model is proposed, which utilizes an improved dung beetle optimization algorithm (IDBO) to optimize an SVM. …”
Get full text

Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers by Rocco D’Ingillo, Alberto Castronovo, Stefano Straullu, Vittorio Curri

“…Future work will explore hybrid modeling approaches, integrating physics-based regression with Machine Learning (ML) to enhance performance in high-variance spectral regions. …”
Get full text

A Combined‐Mode Machine Learning Model for Predicting Stroke Recurrence During Hospitalization in Patients with Acute Minor Ischemic Stroke by Wanxing Ye, Jin Gan, Meng Wang, Ziyang Liu, Hongqiu Gu, Xin Yang, Chunjuan Wang, Xia Meng, Yong Jiang, Hao Li, Liping Liu, Yongjun Wang, Zixiao Li

“…This study evaluated the effectiveness of machine learning models in predicting in‐hospital recurrence. …”
Get full text

Enhanced Hyperspectral Forest Soil Organic Matter Prediction Using a Black-Winged Kite Algorithm-Optimized Convolutional Neural Network and Support Vector Machine by Yun Deng, Lifan Xiao, Yuanyuan Shi

Get full text

Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events by Patrícia Cristina Steffen, Júlio Gomes, Eloy Kaviski, Daniel Henrique Marco Detzel

“…Combined with Machine Learning techniques, river flow simulation is optimized through increased data similarity within clusters. …”
Get full text

A Hybrid Learnable Fusion of ConvNeXt and Swin Transformer for Optimized Image Classification by Jaber Qezelbash-Chamak, Karen Hicklin

Get full text

Data-driven intelligent productivity prediction model for horizontal fracture stimulation by Qian Li, Yiyong Sui, Mengying Luo, Bin Guan, Lu Liu, Yuan Zhao

“…Finally, during fracturing design, the optimal productivity prediction model was matched to each interval based on its characteristics to predict post-fracturing productivity. …”
Get full text

Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm by Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu

“…Next, to tackle the challenge of parameter selection for DHKELM, an optimal point set strategy, the Gompertz growth model, and a Levy flight strategy are employed to optimize the parameters of DHKELM using IBKA before model training. …”
Get full text

Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis by Mostafa Alizade-Harakiyan, Amin Khodaei, Ali Yousefi, Hamed Zamani, Asghar Mesbahi

Get full text

Inverse System Decoupling Control of Composite Cage Rotor Bearingless Induction Motor Based on Support Vector Machine Optimized by Improved Simulated Annealing-Genetic Algorithm by Chengling Lu, Junhui Cheng, Qifeng Ding, Gang Zhang, Jie Fang, Lei Zhang, Chengtao Du, Yanxue Zhang

“…Subsequently, an SVM regression equation is established, and the SVM kernel function parameters are optimized using the ISA-GA to train a high-precision inverse system decoupling control model. …”
Get full text

Optimizing photovoltaic integration in grid management via a deep learning-based scenario analysis by Zhiming Gu, Bo Li, Guipeng Zhang, Bo Li

Get full text

Application of machine learning for predicting the incubation period of water droplet erosion in metals by Khaled AlHammad, Mamoun Medraj, Moussa Tembely

“…This work bridges the gap between data-driven modeling and physical understanding of WDE, providing a valuable tool for engineers to optimize material selection and maintenance strategies in erosion-prone applications.…”
Get full text

Android collusion attack detection model by Hongyu YANG, Zaiming WANG

“…In order to solve the problem of poor efficiency and low accuracy of Android collusion detection,an Android collusion attack model based on component communication was proposed.Firstly,the feature vector set was extracted from the known applications and the feature vector set was generated.Secondly,the security policy rule set was generated through training and classifying the privilege feature set.Then,the component communication finite state machine according to the component and communication mode feature vector set was generated,and security policy rule set was optimized.Finally,a new state machine was generated by extracting the unknown application’s feature vector set,and the optimized security policy rule set was matched to detect privilege collusion attacks.The experimental results show that the proposed model has better detective efficiency and higher accuracy.…”
Get full text

Investigation of comparative machine learning models in effluent dephenolization process onto H3PO4-anchored corn cob by Chijioke Elijah Onu, Joseph Tagbo Nwabanne, Ositadimma Chamberline Iheanocho, Paschal Enyinnaya Ohale, Chiamaka Peace Onu, Marcel Ikenna Ejimofor, Suraj Kumar Bhagat, Christian O. Asadu, Christopher C. Obi, Chidiogo Ezekwem

Get full text

Pioneering machine learning techniques to estimate thermal conductivity of carbon-based phase change materials: A comprehensive modeling framework by Raouf Hassan, Alireza Baghban

“…Extensive machine learning algorithms were explored; however, CatBoost, XGBoost, ANN, Random Forest, and Gradient Boosting emerged as the most accurate models. …”
Get full text

Intelligent System for Reducing Waste and Enhancing Efficiency in Copper Production Using Machine Learning by Bagdaulet Kenzhaliyev, Timur Imankulov, Aksultan Mukhanbet, Sergey Kvyatkovskiy, Maral Dyussebekova, Nurdaulet Tasmurzayev

“…By integrating ML models with a systematic hyperparameter optimization approach, this work advances the potential for sustainable and precise metallurgical processes. …”
Get full text

Extreme high accuracy prediction and design of Fe-C-Cr-Mn-Si steel using machine learning by Hao Wu, Jianyuan Zhang, Jintao Zhang, Chengjie Ge, Lu Ren, Xinkun Suo

“…In this study, a data-driven model combining machine learning (ML), firefly optimization algorithm (FA) and conditional generative adversarial networks (CGANs) were proposed to predict solid solution strengthening theory of Fe-C-Cr-Mn-Si steel. …”
Get full text

Synergizing Machine Learning and Physical Models for Enhanced Gas Production Forecasting: A Comparative Study of Short- and Long-Term Feasibility by Bafren K. Raoof, Ali Rabia, Usama Alameedy, Pshtiwan Shakor, Moses Karakouzian

“…Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. …”
Get full text

Unveiling the role of coagulation-related genes in acute myeloid leukemia prognosis and immune microenvironment through machine learning by Liyun Ji, Yanxia Yang, Siyue Ma

“…Additionally, a prognostic model was constructed using machine learning techniques, and its prognostic ability was validated. …”
Get full text

Prediction and Analysis of Borosilicate Glass Surface Deformation Induced by Flame Jet by Biling Wang, Yutang Zhai, Xinming Zhang, Xinxin Qi, Weijie Fu

Get full text