Comparative Evaluation of Machine Learning Models for Mobile Phone Price Prediction: Assessing Accuracy, Robustness, and Generalization Performance by Saima Anwar Lashari, Muhammad Muntazir Khan, Abdullah Khan, Sana Salahuddin, Muhammad Noman Ata

“…These days, mobile phones are the most commonly purchased goods. Thousands of new models with improved features, designs, and specifications are released yearly. …”
Get full text

Simulation of mechanical behavior and multi-scale performance analysis of complex microstructural models in composite material layer design by Hailiang Su, Qiulin Qin, Deng An, Tengteng Wei, Zhe Han, Yuyan He, Guogui Huang

Get full text

Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model by Sivaraja M., Swaminathen A. N., Kuttimarks M. S., Rajprasad J., Sakthivel M., Rex J.

“…The RF regression is utilized for feature selection, identifying the most significant factors affecting ACP and optimizing the input features for the LSSVM model. …”
Get full text

Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests. by Carlos Jaime-González, Pablo Acebes, Ana Mateos, Eduardo T Mezquida

“…To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). …”
Get full text

An Improved Transformer-Based Model for Urban Pedestrian Detection by Tianyong Wu, Xiang Li, Qiuxuan Dong

“…First, we incorporate the high-low frequency (HiLo) attention into the encoder, therefore enhancing the model’s detection performance. Furthermore, we present a nonlinear feature fusion module that fuses information from various feature scales and contexts more successfully. …”
Get full text

Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications by Damjan Strnad, Stefan Kohek, Borut Zalik, Libor Vasa, Andrej Nerat

“…A viable solution is to store the data in a compact encoded format, and perform on-the-fly decoding when it is needed for processing. …”
Get full text

1D-Concatenate based channel estimation DNN model optimization method by Min LU, Zehao QIN, Zhihui CHEN, Min ZHANG, Guangxue YUE

“…In order to improve the channel estimation accuracy of DNN model in wireless communication, a DNN model optimization method based on 1D-Concatenate was proposed.In this method, Concatenate performs one-dimensional data transformation, the DNN model was introduced by hopping connection, the gradient disappearance problem was suppressed, and 1D-Concatenate was used to restore the data features lost during network training to improve the accuracy of DNN channel estimation.In order to verify the effectiveness of the optimization method, a typical DNN-based wireless communication channel estimation model was selected for comparative simulation experiments.Experimental results show that the estimated gain of the existing DNN model can be increased by 77.10% by the proposed optimization method, and the channel gain can be increased by up to 3 dB under high signal-to-noise ratio.This optimization method can effectively improve the channel estimation accuracy of DNN model in wireless communication, especially the improvement effect is significant under high signal-to-noise ratio.…”
Get full text

Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model by Beevi Fathima, N Santhi Dr, N Ramasamy Dr

“…Furthermore, the Tandem-MU-Net model is used to extract essential morphological features and categorize stroke types, including Hemorrhagic and Acute strokes, through an investigation of their neutral and ionic forms. …”
Get full text

Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition by WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo

“…To address this issue, this study proposed a miner behavior recognition model based on a lightweight pose estimation network (Lite-HRNet) and a multi-dimensional feature-enhanced spatial-temporal graph convolutional network (MEST-GCN). …”
Get full text

A Comparative Analysis of Machine Learning Models for Predicting EFL Student Language Performance in Smart Learning Environments by Banchakarn Sameephet, Wirapong Chansanam, Mahboubeh Rakhshandehroo, Chawin Srisawat, Kittichai Nilubol, Arnon Jannok, Bhirawit Satthamnuwong, Kornwipa Poonpon

“…Therefore, data preprocessing and feature selection play a significant role in improving model performance. …”
Get full text

Methodology for Feature Selection of Time Domain Vibration Signals for Assessing the Failure Severity Levels in Gearboxes by Antonio Pérez-Torres, René-Vinicio Sánchez, Susana Barceló-Cerdá

“…The most informative subset of CIs is identified and selected through a wrapper-based selection approach and artificial intelligence tools. The selected features are then evaluated based on the classification accuracy and the area under the curve (AUC) in receiver operating characteristic (ROC) achieved using Random Forest (RF) and K-nearest neighbours (K-NN) models, with performance exceeding 98%. …”
Get full text

Performance Comparison Between Deep Learning Models for Fault Classification in Transmission Lines Using Time Series Data by Nadeem Ahmed Tunio, Mohsin Ali Tunio, Muhammad Amir Raza, Muhammad Faheem, Ashfaque Ahmed Hashmani, Rumaisa Nadeem

“…Leveraging a comprehensive data set encompassing diverse fault scenarios like single‐phase to ground fault (AG), double line to ground fault (ABG), three‐phase fault (ABC) from both simulated and real transmission line data, the study provides a rigorous evaluation of these models’ performance under realistic conditions. The results demonstrate that TCN achieves a fault classification accuracy of 99.9%, significantly outperforming BiLSTM (92.31%) and GRU (95.27%), while also excelling in precision, recall, F1 score, and training efficiency. …”
Get full text

Comparative Performance of Machine Learning Models for Landslide Susceptibility Assessment: Impact of Sampling Strategies in Highway Buffer Zone by Zhenyu Tang, Shumao Qiu, Haoying Xia, Daming Lin, Mingzhou Bai

“…This study evaluates the impact of two different non-landslide sampling methods—random sampling and sampling constrained by the Global Landslide Hazard Map (GLHM)—on the performance of various machine learning and deep learning models, including Naïve Bayes (NB), Support Vector Machine (SVM), SVM-Random Forest hybrid (SVM-RF), and XGBoost. …”
Get full text

Towards an intelligent integrated methodology for accurate determination of volume percentages in three-phase flow systems by Abdullah M. Iliyasu, Mohammad Sh. Daoud, Ahmed Sayed Salama, John William Grimaldo Guerrero, Kaoru Hirota

“…This feature selection process optimises the performance of subsequent machine learning models, streamlining the input space for enhanced interpretability and efficiency. …”
Get full text

Random features and polynomial rules by Fabián Aguirre-López, Silvio Franz, Mauro Pastore

“…In this work, we present a thorough analysis of the generalization performance of random features models for generic supervised learning problems with Gaussian data. …”
Get full text

A dual-branch deep learning model based on fNIRS for assessing 3D visual fatigue by Yan Wu, Yan Wu, Yan Wu, TianQi Mu, SongNan Qu, XiuJun Li, XiuJun Li, XiuJun Li, Qi Li, Qi Li, Qi Li

“…Furthermore, to adaptively select fNIRS hemodynamic features, a channel attention mechanism was integrated to provide a weighted representation of multiple features.ResultsThe constructed model achieved an average accuracy of 93.12% within subjects and 84.65% across subjects, demonstrating its superior performance compared to traditional machine learning models and deep learning models.DiscussionThis study successfully constructed a novel deep learning framework for the automatic evaluation of 3D visual fatigue using fNIRS data. …”
Get full text

Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrou... by Tianyu Yang, Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao

“…Incorporating the subjective ‘swirl sign’, identified as the most significant feature in univariate analysis, into the simplified model enhanced its performance. …”
Get full text

A Novel Semantic Driven Meta-Learning Model for Rare Attack Detection by Y. Annie Jerusha, S. P. Syed Ibrahim, Vijay Varadharajan

“…Our approach enhances intrusion detection by integrating an attention-based model for semantic feature extraction and the Simple Neural Attentive Meta-Learner (SNAIL) for rare attack class detection. …”
Get full text

A Decision-Aid Model for Predicting Triple-Negative Breast Cancer ICI Response Based on Tumor Mutation Burden by Houda Bendani, Nasma Boumajdi, Lahcen Belyamani, Azeddine Ibrahimi

“…Then, four machine learning models were trained to classify TNBC patients based on histological features into high and low TMB. …”
Get full text

Enhanced Multi-Class Breast Cancer Classification from Whole-Slide Histopathology Images Using a Proposed Deep Learning Model by Adnan Rafiq, Arfan Jaffar, Ghazanfar Latif, Sohail Masood, Sherif E. Abdelhamid

Get full text