A New Numerical Procedure for Vibration Analysis of Beam under Impulse and Multiharmonics Piezoelectric Actuators by Yassin Belkourchia, Lahcen Azrar

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Recommendation System with Biclustering by Jianjun Sun, Yu Zhang

“…Experiment results demonstrate that the proposed method outperforms state-of-the-art models in terms of several aspects on three benchmark datasets.…”
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Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons by K. Jayasree, Malaya Kumar Hota

“…The CNN model generated with AVOA yielded a success rate of 97.95% for the GENSCAN training set and 95.39% for the HMR195 dataset. The proposed approach is compared with the state-of-the-art methods using AUC, F1-score, Recall, and Precision. …”
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Efficient Lane Detection Technique Based on Lightweight Attention Deep Neural Network by Zhiting Yao, Xiyuan Chen

“…The proposed network achieves comparable results with state-of-the-art methods on two popular lane detection benchmarks (TuSimple and CULane), with faster calculation efficiency at 259 frames-per-second (FPS) on CULane dataset, and the total number of model parameters only requires 1.57 M. …”
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Nonlinear functional response parameter estimation in a stochastic predator-prey model by Gianni Gilioli, Sara Pasquali, Fabrizio Ruggeri

“…The model is estimated on a dataset obtained from a field survey. Finally, the estimated model is used to forecast predator-prey dynamics in similar fields, with slightly different initial conditions.…”
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A Comparative Analysis of Support Vector Machine and K-Nearest Neighbors Models for Network Attack Traffic Detection by Han Zhuoxi

“…This research centers on the use of advanced machine learning methods, particularly Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), to improve the detection of network attack traffic. The UNSW-NB15 dataset, which includes various attack types and normal traffic patterns, is used to evaluate the performance of these two models. …”
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A Truncated Waveguide Fed by a Microstrip as a Radiating Element for High-Performance Automotive Anticollision Radars by Giovanni Andrea Casula, Giuseppe Mazzarella, Giorgio Montisci

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A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information by Zhengchao Zhang, Congyuan Ji, Yineng Wang, Yanni Yang

“…Validated by a practical city-wide multimodal traffic dataset in Beijing, our model significantly outperforms the random utility models and simple fully connected neural network in terms of the prediction accuracy. …”
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A Novel Real-Time Autonomous Localization Algorithm Based on Weighted Loosely Coupled Visual–Inertial Data of the Velocity Layer by Cheng Liu, Tao Wang, Zhi Li, Peng Tian

“…Furthermore, by incorporating a weighting mechanism, the algorithm allows for a flexible adjustment of the emphasis placed on IMU data versus visual information, which augments the robustness and adaptability of autonomous motion estimation for robots. The simulation and dataset experiments demonstrate that the ICEKF can provide reliable estimates for robot motion trajectories.…”
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Hyperspectral Image Classification Based on Attentional Residual Networks by Ning Wang, Xin Pan, Xiaoling Luo, Xiaojing Gao

“…This method was tested on the self-collected Herbage (HB) dataset and the public datasets Indian Pines (IP) and Pavia University (PU), and compared with four classical deep learning classification methods. …”
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Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning by Huaitao Shi, Yajun Shang, Xiaochen Zhang, Yinghan Tang

“…The experiments are conducted on the publicly available XJTU-SY dataset. The experimental results show that the proposed method can effectively learn the transferable features and compensate the differences between the source and target domains and has a promising application with higher accuracy and robustness for the prediction of early failures of rolling bearings.…”
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Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features by Eman Magdy, Nourhan Zayed, Mahmoud Fakhr

“…Using 70 different patients’ lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. …”
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Association Between Language Skills and Statistical Learning in Aphasia by Talat BULUT, Emine BAHAR

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Failure Mechanism of Anti-Inclined Karst Slope Induced by Underground Multiseam Mining by Nanyun Wang, Zuliang Zhong, Xinrong Liu, Guofu Gao

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Effectiveness Evaluation of Random Forest, Naive Bayes, and Support Vector Machine Models for KDDCUP99 Anomaly Detection Based on K-means Clustering by Zhang Majun

“…This research utilizes the KDDCUP99 dataset to incorporate K-means clustering with three classifiers: Random Forest (RF). …”
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Enhancing rice seed purity recognition accuracy based on optimal feature selection by Thi-Thu-Hong Phan, Le Huu Bao Nguyen

“…To achieve this, we construct a comprehensive dataset by leveraging diverse types of features encompassing morphological properties, overall image structure, texture information, and color distribution from rice seeds. …”
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Brain Tumor Detection Using a Deep CNN Model by Sonia Ben Brahim, Samia Dardouri, Ridha Bouallegue

“…The proposed model is built upon the state-of-the-art CNN architecture VGG16, employing a data augmentation approach. The dataset utilized in this paper consists of 3000 brain MR images sourced from Kaggle, with 1500 images reported to contain tumors. …”
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Combining ethnographic and clickstream data to identify user browsing strategies by Lillian Clark, I-Hsien Ting, Chris Kimble, Peter Wright, Daniel Kudenko

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Prediction of the Compressive Strength of Concrete Admixed with Metakaolin Using Gene Expression Programming by Oluwatobi O. Akin, Amana Ocholi, Olugbenga S. Abejide, Johnson A. Obari

“…In this work, modelling of the compressive strength of concrete admixed with metakaolin was carried out using the Gene Expression Programming (GEP) algorithm. The dataset from laboratory experimentation was used for the analysis. …”
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Classification of Riau Batik Motifs Using the Convolutional Neural Network (CNN) Algorithm by Dhea Amanda Ramadhan, Dian Ramadhani

“…By leveraging the Convolutional Neural Network (CNN) algorithm, the classification system was trained using a dataset of 1,440 images. The model was fine-tuned through optimization of batch size and epoch parameters to maximize classification accuracy. …”
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