Design and Optimization of Hybrid CNN-DT Model-Based Network Intrusion Detection Algorithm Using Deep Reinforcement Learning by Lu Qiu, Zhiping Xu, Lixiong Lin, Jiachun Zheng, Jiahui Su

“…The experimental results show that the CNN–decision tree (DT) algorithm optimized by actor–critic (AC) achieves an accuracy of 0.9792 on the KDD dataset, which is 5.63% higher than the unoptimized CNN-DT model.…”
Get full text

Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model by Xiaoai Dai, Junying Cheng, Shouheng Guo, Chengchen Wang, Ge Qu, Wenxin Liu, Weile Li, Heng Lu, Youlin Wang, Binyang Zeng, Yunjie Peng, Shuneng Liang

“…Two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters. …”
Get full text

Artificial intelligence and numerical weather prediction models: A technical survey by Muhammad Waqas, Usa Wannasingha Humphries, Bunthid Chueasa, Angkool Wangwongchai

Get full text

Nearest-neighbors neural network architecture for efficient sampling of statistical physics models by Luca Maria Del Bono, Federico Ricci-Tersenghi, Francesco Zamponi

Get full text

A data-driven cost estimation model for agile development based on Kolmogorov-Arnold Networks and AdamW optimization by Xiaoyan Zhao, Xin Xiong, Zulkefli Mansor, Rozilawati Razali, Mohd Zakree Ahmad Nazri, Liangyu Li

“…Traditional estimation methods and existing machine learning models often fail to adapt effectively to the dynamic agile environment. …”
Get full text

Prediction method of gas emission in working face based on feature selection and BO-GBDT by MA Wenwei

“…The results showed that the optimization algorithm itself had minimal impact on the accuracy and generalization of the GBDT model. …”
Get full text

Perceived MOOC satisfaction: A review mining approach using machine learning and fine-tuned BERTs by Xieling Chen, Haoran Xie, Di Zou, Gary Cheng, Xiaohui Tao, Fu Lee Wang

“…This study investigates the application of machine learning and BERT models to identify topic categories in helpful online course reviews and uncover factors that influence the overall satisfaction of learners in massive open online courses (MOOCs). …”
Get full text

Architecture-Aware Augmentation: A Hybrid Deep Learning and Machine Learning Approach for Enhanced Parkinson’s Disease Detection by Madjda Khedimi, Tao Zhang, Hanine Merzougui, Xin Zhao, Yanzhang Geng, Khamsa Djaroudib, Pascal Lorenz

“…These results highlight that hybrid models respond differently to augmentation, and careful selection of augmentation strategies is necessary for optimizing model performance. …”
Get full text

ProbC: joint modeling of epigenome and transcriptome effects in 3D genome by Emre Sefer

Get full text

Computational optimization of 3D printed bone scaffolds using orthogonal array-driven FEA and neural network modeling by Amulya Shetty, Aamirah Fathima, B Anika, Raviraj Shetty, Vinyas, J.P. Supriya, Adithya Hegde

“…The novelty of this work lies in its integrative, multi-modal approach that synergizes experimental design, machine learning-based predictive modeling, and simulation. …”
Get full text

Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques by Zuleide Ferreira, Bruna Almeida, Ana Cristina Costa, Manoel do Couto Fernandes, Pedro Cabral

“…Although some studies have employed machine learning (ML) algorithms and multi-criteria analysis (MCA) for landslide susceptibility mapping (LSM), comparative evaluations of these methods remain scarce, particularly regarding predictor importance, performance metrics, and hyperparameter optimization. …”
Get full text

GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks by Olusegun Folorunso, Oluwafolake Ojo, Mutiu Busari, Muftau Adebayo, Joshua Adejumobi, Daniel Folorunso, Femi Ayo, Orobosade Alabi, Olusola Olabanjo

“…Digital Soil Mapping (DSM) leverages Machine Learning (ML) to create detailed soil maps, helping mitigate nutrient depletion. …”
Get full text

Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC by Muhammad Ricky Perdana Putra, Ema Utami

“…The use of ensemble techniques to build models can improve performance, but previous research has not reviewed the most optimal ensemble technique for this case study. …”
Get full text

Modelling of Surface Roughness and Tool Wear when Finishing Milling Process of the Circular Bevel Gear by Dong Van PHAM, Thinh Xuan Hoang

Get full text

Research on emulsion concentration detection technology based on interpretable machine learning methods by Jiaxu Kang, Jianwei Li, Meng Wang, Xinwei Guo, Dinghao Liu, Chao Qu, Chengbin Guo

“…SHAP-based interpretability analysis identified electrical conductivity as the primary predictive factor, with temperature exhibiting minimal influence. The optimized RF model was deployed on a cloud platform integrated with a Siemens S7–200SMART PLC via the Aprus-X IoT framework. …”
Get full text

A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function by Tahsin Ali Mohammed Amin, Sabah Robitan Mahmood, Rebar Dara Mohammed, Pshtiwan Jabar Karim

“…When attempting to classify data with a high degree of uncertainty, many researchers have turned to heuristic approaches and machine learning (ML) methods. We propose an entirely new ML method in this paper by fusing the Radial Basis Function (RBF) network based on ant colony optimization (ACO). …”
Get full text

Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China by Xiehui Li, Yuting Liu, Lei Wang

“…This study first analyzed the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), <i>k</i>-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. …”
Get full text

Rolling Bearing Fault Diagnosis Based on Optimized VMD Combining Signal Features and Improved CNN by Yingyong Zou, Xingkui Zhang, Wenzhuo Zhao, Tao Liu

“…The time-domain features of the reconstructed signals are computed, and the feature vectors are constructed, which are used as inputs to the deep learning network; the CNN combined with the support vector machine (SVM) network model is used for the extraction of the features and the classification of the faults. …”
Get full text

Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance by Bala Subramanian C, Bharathi ST, Shanmugapriya S

Get full text

Optimal design strategy of traditional courtyard based on performance and data-driven method—A case study of Yanshen ancient town, China by Zhixin Xu, Xin Zheng, Xiangfeng Li

Get full text