Lactylation associated biomarkers and immune infiltration in aortic dissection by Jianfeng Ye, Yuntao Fu, Yuanjia Ke, Huanting Liu, Sini Fang, Yumeng Lei, Ying Mao, Liqiu Yan, Youcheng Wang

“…WGCNA and machine learning revealed two optimal feature genes, PGK1 and HMGA1, which were validated in an independent dataset and demonstrated high diagnostic accuracy (AUC: PGK1 = 1, HMGA1 = 0.94). …”
Get full text

On-farm 3D images of beef cattle for the prediction of carcass classification traits and cold carcass weight by H. Nisbet, N. Lambe, G.A. Miller, A. Doeschl-Wilson, D. Barclay, A. Wheaton, C-A. Duthie

“…Data for 762 animals were used to build models for the prediction of EUROP conformation and fat class, and cold carcass weight (CCW), using either traditional statistics (multiple linear regression using stepwise feature selection) or machine learning techniques (random forest models). …”
Get full text

Expanding Multilingual Co-Speech Interaction: The Impact of Enhanced Gesture Units in Text-to-Gesture Synthesis for Digital Humans by Ghazanfar Ali, Woojoo Kim, Muhammad Shahid Anwar, Jae-In Hwang, Ahyoung Choi

Get full text

Intelligent algorithm-based model for predicting mass transfer performance in CO2 absorption within a rotating packed bed by Wei Zhang, Hao Chen, Ke Huang, Xing Shu, Cheng Fu, Bin Huang

“…Using dimensional analysis, key factors are transformed into dimensionless numbers, which are then input into models integrating least squares support vector machine (LSSVM) with genetic algorithm (GA), particle swarm optimization (PSO), and a hybrid GA-PSO (HGAPSO). …”
Get full text

An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records by Hongnian Wang, Mingyang Zhang, Liyi Mai, Xin Li, Abdelouahab Bellou, Lijuan Wu

“…As the number of top-ranking features increased, the models’ accuracy began to stabilize, while feature subset stability (considering sample variations) and inter-method feature similarity reached optimal levels, confirming the validity of the FS framework. …”
Get full text

The impact of online food delivery applications on dietary pattern disruption in the Arab region by Radwan Qasrawi, Radwan Qasrawi, Suliman Thwib, Ghada Issa, Malak Amro, Razan AbuGhoush, Maha Hoteit, Maha Hoteit, Sahar Khairy, Narmeen Jamal Al-Awwad, Narmeen Jamal Al-Awwad, Khlood Bookari, Sabika Allehdan, Dalal Alkazemi, Haleama Al Sabbah, Salima Al Maamari, Asma H. Malkawi, Reema Tayyem

“…Model performance was evaluated using multiple metrics, including sensitivity, precision, F1-score, and AUC. …”
Get full text

Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms by S. Jayanthi, Swathi Sowmya Bavirthi, P. Murali, K. Vijaya Kumar, Hend Khalid Alkahtani, Mohamad Khairi Ishak, Samih M. Mostafa

“…Furthermore, the feature selection process uses the RIME optimization algorithm (ROA) model to select the most relevant features from the data. …”
Get full text

Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants by Qiying Yu, Wenzhong Li, Yungang Bai, Zhenlin Lu, Yingying Xu, Chengshuai Liu, Lu Tian, Chen Shi, Biao Cao, Tianning Xie, Jianghui Zhang, Caihong Hu

“…In response, we propose a hybrid runoff prediction model that combines Dung Beetle Optimization (DBO)'s optimization capabilities, Temporal Convolutional Networks (TCN)’s proficiency in extracting local temporal features, and the Transformer’s ability to capture long-term dependencies. …”
Get full text

Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles by Khaled Tarmissi, Hanan Abdullah Mengash, Noha Negm, Yahia Said, Ali M. Al-Sharafi

“…Nowadays, deep learning (DL) and machine learning (ML) models are critical to enhancing object detection in AVs, particularly in adversarial weather conditions. …”
Get full text

A Hybrid Approach of DenseNet121 with Attention and Bi-LSTM for Yoga Pose Estimation by Aarthy K., Alice Nithya

“…The system is designed to integrate advanced AI techniques, providing an innovative approach to pose recognition that leverages several sophisticated machine learning models and algorithms to enhance performance. …”
Get full text

Optimisation of curing models and ultra-high-precision forming strategy in vat photopolymerization of silica-based ceramic modified by graphene by Yongkang Yang, Xiqing Xu, Boran Wang, Shiyuan Li, Ziqi Jia, Xusen Guo, Shuhuai Wang, Shuxin Niu, Xin Li

Get full text

An efficient trustworthy cyberattack defence mechanism system for self guided federated learning framework using attention induced deep convolution neural networks by Louai A. Maghrabi, Alanoud Subahi, Nouf Atiahallah Alghanmi, Turki Althaqafi, Nahla J. Abid, Nasser N. Albogami, Mahmoud Ragab

“…Federated learning (FL), a decentralized machine learning (ML) model, provides a promising solution by permitting spread objects to train techniques on local data collaboratively without distributing sensitive data. …”
Get full text

Development of ultrasound-based clinical, radiomics and deep learning fusion models for the diagnosis of benign and malignant soft tissue tumors by Xinpeng Dai, Haiyong Lu, Xinying Wang, Bingxin Zhao, Zongjie Liu, Tao Sun, Feng Gao, Peng Xie, Hong Yu, Xin Sui

“…Radiomics features and deep learning features were extracted from the ultrasound images, and the optimal features were selected to construct fusion models using support vector machines. …”
Get full text

Construction of a Deep Learning Model for Unmanned Aerial Vehicle-Assisted Safe Lightweight Industrial Quality Inspection in Complex Environments by Zhongyuan Jing, Ruyan Wang

“…In addition, differential privacy is applied to the uplink data on this basis to further protect data privacy. After the model parameters are aggregated on the pilot UAV, the model is subjected to knowledge distillation to reduce the amount of downlink data without affecting the utility. …”
Get full text

Spatiotemporal forecasting of the edge localized modes in tokamak plasmas using neural networks by Anirban Samaddar, Qian Gong, Sandeep Madireddy, Christopher Hansen, Semin Joung, David R Smith, Yixuan Sun, Fatima Ebrahimi, Prasanna Balapraksh, Andrew Oakleigh Nelson

Get full text

Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy by Jing Xiao, Yuan Zhang, Xin Du, Qiangzi Li, Hongyan Wang, Yueting Wang, Jingyuan Xu, Yong Dong, Yunqi Shen, Sifeng Yan, Shuguang Gong, Haoxuan Hu

“…By identifying critical features for maize density and incorporating machine learning to explore optimal feature combinations, we developed a multi-temporal model that enhances estimation accuracy, particularly during leaf development, stem elongation, and tasseling stages (R² = 0.602, RMSE = 0.094). …”
Get full text

An optimal global biochar application strategy based on matching biochar and soil properties to reduce global cropland greenhouse gas emissions: findings from a global meta-analysi... by Debo He, Zhixin Dong, Bo Zhu

“…In this study, the impact of biochar surface functional groups on soil GHG emissions was investigated using molecular model calculation. Machine learning (ML) technology was applied to predict the responses of soil GHG emissions and crop yields under different biochar feedstocks and application rates, aiming to determine the optimum biochar application strategies based on specific soil properties and environmental conditions on a global scale. …”
Get full text

Advancing EEG-based biometric identification through multi-modal data fusion and deep learning techniques by Touseef Ur Rehman, Madallah Alruwaili, Muhammad Hameed Siddiqi, Yousef Alhwaiti, Sajid Anwar, Zahid Halim, Maaz Alam

Get full text

Two-level feature selection method based on SVM for intrusion detection by Xiao-nian WU, Xiao-jin PENG, Yu-yang YANG, Kun FANG

“…To select optimized features for intrusion detection，a two-level feature selection method based on support vector machine was proposed.This method set an evaluation index named feature evaluation value for feature selection，which was the ratio of the detection rate and false alarm rate.Firstly，this method filtrated noise and irrelevant features to reduce the feature dimension respectively by Fisher score and information gain in the filtration mode.Then，a crossing feature subset was obtained based on the above two filtered feature sets.And combining support vector machine，the sequential backward selection algorithm in the wrapper mode was used to select the optimal feature subset from the crossing feature subset.The simulation test results show that，the better classification performance is obtained according to the selected optimal feature subset，and the modeling time and testing time of the system are reduced effectively.…”
Get full text

Adaptive Remaining Capacity Estimator of Lithium-Ion Battery Using Genetic Algorithm-Tuned Random Forest Regressor Under Dynamic Thermal and Operational Environments by Uzair Khan, Mohd Tariq, Arif I. Sarwat

“…The results demonstrated significant improvements in state of charge (SOC) estimation accuracy. The proposed GA-optimized RF model achieved an MAE of 0.0026 at 25 °C and 0.0102 at −20 °C, showing a 41.37% to 50% reduction in the MAE compared to traditional random forest models without GA optimization. …”
Get full text